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Artificial Intelligence (AI) has revolutionized various industries, and one of the sectors where its impact is particularly profound is manufacturing. The integration of AI technologies in manufacturing processes has brought about a multitude of benefits, leading to increased efficiency, productivity, and innovation.

AI in manufacturing

In this comprehensive guide, we will delve into the role of AI in Manufacturing, uncovering the key benefits and the most common applications that are revolutionizing the industry. You’ll learn about the essential steps to implement AI into your manufacturing processes, how to choose the right AI tools and technologies, and strategies to overcome the challenges associated with AI implementation. Additionally, through case studies, this guide will showcase real-world successes of AI in manufacturing, offering insights into future trends and emerging technologies that could shape the next generation of manufacturing.

Understanding the Role of AI in Manufacturing

AI in Manufacturing has profoundly transformed the manufacturing industry, introducing capabilities that mimic human intelligence, automate decision-making, and enhance operational efficiency. The integration of AI is not just transforming the way manufacturing processes are conducted; it’s also redefining the potential for productivity and precision in ways previously unimaginable. The significance of AI in manufacturing lies in its ability to analyze vast amounts of data, optimize operations, and predict potential failures before they occur, thus ensuring smoother, more efficient production lines and higher-quality products.

Implementing AI in Manufacturing

The integration of AI in Manufacturing processes has led to numerous positive effects on the industry including improved efficiency, enhanced quality control, predictive maintenance, supply chain optimization, customization, and safety improvements. By leveraging AI technology, manufacturers can achieve greater competitiveness, innovation, and success in the rapidly evolving manufacturing landscape.

• Improved Efficiency: AI in manufacturing can analyze large amounts of data quickly and accurately, leading to more efficient production processes. This can result in reduced downtime, increased productivity, and lower operational costs.
• Enhanced Quality Control: AI in manufacturing can be used to monitor and control production processes in real-time, ensuring that products meet quality standards. This can help manufacturers reduce defects and improve overall product quality.
• Predictive Maintenance: AI can analyze data from sensors and other sources to predict when equipment is likely to fail. This allows manufacturers to perform maintenance proactively, reducing downtime and extending the lifespan of machinery.
• Supply Chain Optimization: AI can optimize supply chain operations by analyzing data on inventory levels, demand forecasts, and production schedules. This can help manufacturers reduce lead times, lower costs, and improve overall supply chain efficiency.
• Customization and Personalization: AI in manufacturing can help manufacturers customize products to meet individual customer needs and preferences. Following such an approach can encourage a sense of loyalty in the customer and help increase customer satisfaction.
• Safety Improvements: AI can be used to monitor and analyze data from sensors to identify potential safety hazards in the manufacturing environment. This feature in artificial intelligence can play an effective role in preventing accidents and improving overall safety.
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Steps to Implement AI in Manufacturing:

1) Assessing Current Systems for Implementing AI in Manufacturing

To successfully implement AI in Manufacturing processes, the first crucial step is to assess your current systems comprehensively. Understand that the effectiveness of AI adoption heavily relies on the digital growth of each organization. Companies that are further along in digitizing their core business processes are significantly more likely to succeed in integrating AI effectively. Evaluate your existing technology infrastructure to ensure it is capable of supporting AI applications. This includes having a robust data architecture that makes data accessible, reliable, and secure. Additionally, your applications should be scalable, integrated, and preferably cloud-based to facilitate the seamless adoption of AI technologies.

2) Identifying Applications of AI in Manufacturing

Once desired systems are assessed, the next step is identifying the most beneficial AI applications for specific manufacturing needs. Begin by clearly defining goals for AI in Manufacturing. It doesn’t matter if you’re aiming to automate repetitive tasks or transform the nature of work through collaboration between humans and machines. Each AI initiative should start with a solid business case that aligns with your strategic objectives. Consider various levels of automation and AI, from systems where humans remain in the loop to fully autonomous processes. This step-by-step approach not only builds trust in the technology but also enhances the acceptance and integration of AI across your organization.

For instance, predictive maintenance can be a starting point where AI uses machine learning algorithms to predict equipment failures before they happen, thus reducing downtime and maintenance costs. Furthermore, leverage AI to optimize the entire value chain, from production to customer service. Do not limit the use of AI to isolated projects or departments; instead, integrate it across business units to maximize its impact. This holistic approach ensures that AI contributes to continuous improvement and innovation within your company.

By following these steps and continuously experimenting and learning from outcomes, you can effectively implement AI in manufacturing processes, leading to enhanced efficiency, reduced costs, and improved product quality.

Common Applications of AI in Manufacturing:

A) Predictive Maintenance

Predictive maintenance stands out as a transformative application of AI in Manufacturing. By leveraging machine learning algorithms, predictive maintenance systems analyze historical and real-time data to foresee potential equipment failures before they occur. This proactive approach not only reduces downtime but also extends the lifespan of machinery, thereby enhancing overall productivity and reducing operational costs. For example, AI frameworks connected through the Industrial Internet of Things (IIoT) enable “smart factories” to utilize vast datasets to anticipate and mitigate equipment failures, significantly improving overall equipment effectiveness (OEE).

By integrating human insight into the learning process, AI-based systems continually refine their predictive capabilities, making them more adept at anticipating machinery failures. This application previously operated on rule-based systems, which had limitations in solving machine health issues comprehensively. Key elements in Predictive Maintenance include machine analysis, human inspection, and anomaly detection. The machine analysis process uses unsupervised learning to analyze sensor data and discern patterns indicating normal machinery operation. Human inspection investigates anomalies, labeling them with root causes, thereby enabling semi-supervised learning for future anomaly detection. This iterative process enhances prediction accuracy over time. Flags deviations from established patterns as anomalies, signaling potential issues.

Complex machinery operates under varying conditions and generates extensive data encompassing parameters like temperature, pressure, and vibrations. AI-based systems proactively identify potential issues rather than reacting to anomalies. Predictive Maintenance generally has progressed from rule-based to AI-based systems leveraging advanced analytics and machine learning.

In rule-based predictive maintenance, maintenance actions hinge on fixed thresholds coded into the system. Alerts are generated if measurements exceed these thresholds. For instance, a pump might trigger an alarm when its vibration signal RMS exceeds 7.1 mm/s. This approach relies heavily on established ISO standards and conventional methods. For example, consider a temperature sensor in critical equipment that alerts when temperatures surpass 60 – 70°C (according to the type of device and its settings), signaling potential equipment failure.  While simple to implement, this method proves inadequate for larger, more intricate machinery.

Machine Mode Analysis in AI-Based Predictive Maintenance:

Many machines operate under varying cycles, power levels, and processing conditions influenced by factors like fluctuating production speeds and raw materials. In rule-based predictive maintenance systems, undesired anomaly alarms may trigger due to expected changes in these parameters, leading to numerous false positives. To address this issue, each process parameter such as speed, and power along with representative signal samples should be incorporated into the training of an effective AI-based predictive maintenance model. Subsequently, this model enables the identification of distinct machine operating modes and deviations from these modes.

Importance of Anomaly Detection in Rotating Machinery

Anomaly detection in rotating machinery leverages advanced data analytics to enhance predictive maintenance strategies. By detecting anomalies early and accurately, organizations can mitigate risks, optimize maintenance efforts, and extend the operational life of critical assets. The use of machine learning techniques continues to advance the field, enabling more precise and effective anomaly detection in industrial applications. Rotating machinery such as pumps, turbines, motors, and generators are critical assets in industrial settings. They operate under varying conditions and are subject to wear, fatigue, and other degradation mechanisms. Anomaly detection helps in:

• Early Fault Detection: Identifying anomalies early can prevent catastrophic failures and minimize downtime.
• Condition Monitoring: Continuous monitoring of machinery health provides insights into its operational status.
• Optimized Maintenance: Predictive maintenance schedules repairs or replacements based on actual machine condition, optimizing maintenance resources.

Techniques Used in Anomaly Detection:

1. Statistical Methods: These include methods like mean and standard deviation analysis, Z-score analysis, and cumulative sum (CUSUM) algorithms. These methods detect anomalies based on statistical deviations from the expected behavior of the machinery parameters.
2. Machine Learning Techniques: Supervised, unsupervised, and semi-supervised machine learning algorithms are used to detect anomalies:

• Supervised Learning: Requires labeled data where anomalies are explicitly identified. Algorithms like Support Vector Machines (SVM) or Random Forests can be trained to classify anomalies.
• Unsupervised Learning: Detects anomalies without labeled data by learning patterns in normal data. Techniques like clustering (e.g., K-means) or density-based methods (e.g., Local Outlier Factor) are commonly used.
• Semi-supervised Learning: Uses a combination of labeled and unlabeled data to improve anomaly detection accuracy over time.

 Example of Anomaly Detection in Rotating Machinery

Let’s consider a centrifugal pump used in an industrial process:

• Data Collection: Sensors measure parameters such as vibration, temperature, pressure, and flow rate.
• Data Preprocessing: Raw sensor data undergoes preprocessing steps like normalization, filtering, and feature extraction to prepare it for anomaly detection.
• Anomaly Detection Algorithm: An unsupervised learning algorithm like Isolation Forest is applied to the preprocessed data. This algorithm identifies instances where the data points are significantly different from normal patterns.
• Result Interpretation: Detected anomalies are flagged and presented to maintenance personnel for further investigation. For example, a sudden increase in vibration amplitude beyond normal operational limits could indicate bearing wear or misalignment.
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B) Quality Control by AI in Manufacturing

AI in Manufacturing significantly enhances quality control (QC) processes, ensuring products meet stringent standards while reducing waste and human error. AI-powered systems, particularly those utilizing machine vision, are pivotal in defect detection and product inspection. These systems analyze images captured during production to identify any defects such as misalignments or inconsistencies that might not be visible to the human eye. Moreover, AI-driven QC systems contribute to maintaining high standards by ensuring consistency across production batches, reducing the incidence of defective outputs, and thereby safeguarding brand reputation. They also play a crucial role in regulatory compliance by automatically detecting non-conformities and potential safety issues before products reach the market. AI applications in QC are not limited to defect detection; they also include automated measurement and real-time monitoring of production environments. This ensures that products are not only manufactured to precision but are also stored and transported under optimal conditions, thus maintaining their quality until they reach consumers.

In summary, the integration of AI in Manufacturing through predictive maintenance and quality control not only streamlines operations but also ensures high quality and compliance, positioning manufacturers at the forefront of industry innovation and efficiency.

AI Inspection in Manufacturing

Inspection in AI in Manufacturing refers to the use of artificial intelligence (AI) technologies to automate and enhance the inspection processes in manufacturing industries. It involves the application of computer vision, machine learning, and other AI techniques to analyze and interpret visual data, such as images or videos, to detect defects, anomalies, or quality issues in manufactured products.

One example of inspection AI in Manufacturing is the inspection of electronic circuit boards. Traditional methods of inspection involve manual visual inspection, which can be time-consuming and prone to human error. With AI inspection, computer vision algorithms can be trained to analyze images of circuit boards and identify any defects or abnormalities, such as missing components, soldering issues, or incorrect placements. This helps in improving the accuracy and efficiency of the inspection process.

Another example is the inspection of automotive parts. AI inspection systems can be used to analyze images or 3D scans of car components, such as engine parts or body panels, to detect any manufacturing defects, surface imperfections, or dimensional variations. By automating this process, manufacturers can ensure the quality of their products and reduce the chances of faulty parts reaching the market.

In terms of a table, here’s a simplified example to illustrate the potential benefits of AI inspection in manufacturing:

It’s important to note that the specific applications and benefits of Overall Equipment Effectiveness inspection can vary depending on the industry, product, and manufacturing process involved. Nonetheless, the overall goal is to leverage AI technologies to enhance the inspection process, improve product quality, and increase efficiency in manufacturing.

Key Benefits of Integrating AI in Manufacturing

Increased Efficiency and Productivity

One of the most significant advantages of integrating AI in Manufacturing is the substantial increase in efficiency and productivity. By automating repetitive and time-consuming tasks such as data entry, invoicing, and scheduling, AI technology frees up employee time, allowing them to focus on more critical and strategic aspects of their work. Furthermore, AI-driven data analysis and annotation play a crucial role in enhancing productivity by enabling businesses to make informed decisions on resource allocation and investment. Predictive analytics, a powerful application of AI, allows companies to analyze large datasets to identify patterns and trends. This capability not only guides decision-making processes but also helps in identifying potential bottlenecks and inefficiencies within workflows. By leveraging AI tools, businesses can optimize processes and significantly improve productivity. Additionally, AI-enabled systems can forecast demand and supply dynamics, enabling strategic decisions regarding inventory management, staffing levels, and pricing strategies.

Cost Savings and Reduced Waste

AI technologies also contribute significantly to cost savings and waste reduction in manufacturing. Predictive maintenance, enabled by AI in Manufacturing, analyzes sensor data from machinery to predict potential breakdowns before they occur. This proactive approach prevents expensive downtime, extends equipment life, and enhances overall productivity. Moreover, AI-driven quality control automates inspection tasks, ensuring products meet stringent quality standards and reducing the likelihood of defective products reaching customers. AI algorithms optimize various manufacturing processes by analyzing data from production lines, supply chain systems, and inventory management. This optimization not only reduces waste but also improves operational efficiency by identifying inefficiencies and suggesting actionable improvements. In terms of supply chain management, AI helps maintain optimal inventory levels and improves relationships with suppliers, which in turn reduces costs and accelerates delivery times.

Furthermore, AI in Manufacturing’s role in energy management contributes to more sustainable manufacturing practices. By analyzing energy usage patterns and identifying waste, AI provides strategies for optimizing energy consumption, which not only reduces costs but also minimizes environmental impact. The integration of AI in manufacturing underscores the transformative impact of AI technologies in redefining manufacturing processes.

Historical Context and Evolution of AI in Manufacturing

The role of AI in Manufacturing is pivotal and ever-evolving. It not only enhances current processes but also paves the way for future innovations that continue to push the boundaries of what is possible in manufacturing. Maybe it can be said the journey of AI in manufacturing dates back over a century, with notable advancements during significant historical periods. For instance, during World War II, Ford implemented electro-mechanical vision systems to inspect aircraft parts, which dramatically reduced production cycle times by 60% at the Willow Run plant. This period marked the beginning of using AI to enhance manufacturing efficiency and output. The evolution continued through the 1950s and 60s with the introduction of robotics and more complex AI-driven processes. Technologies like Failure Modes and Effects Analysis (FMEA) and Statistical Process Control (SPC) became integral in monitoring and enhancing production health.

Today, the role of AI in Manufacturing has expanded into the realms of smart factories and Industry, where interconnected devices and systems communicate and operate semi-autonomously. These advancements not only streamline operations but also open new avenues for innovation and efficiency in manufacturing processes. As AI technology continues to advance, its applications in manufacturing are becoming increasingly sophisticated. Manufacturers are now employing AI to optimize supply chains, improve production schedules, and even manage energy consumption more efficiently, all of which contribute to reduced waste and improved delivery times.

AI vs. Machine Learning in Manufacturing

AI and Machine Learning (ML) are often used interchangeably, but it is crucial to distinguish between the two. AI refers to the broader capability of machines to perform tasks that typically require human intelligence, including reasoning, learning, and self-correction. Machine Learning, a subset of AI, involves algorithms that enable machines to improve at tasks with experience, primarily through the analysis of large volumes of data.

Machine Learning: Algorithms to incorporate intelligence into machines by automatically learning from data.

Artificial Intelligence: Ability of machine to imitate human intelligence

AI in Manufacturing encompasses a variety of applications, but Machine Learning specifically drives innovations in predictive maintenance and quality control. Predictive maintenance uses ML to foresee equipment failures before they occur, significantly reducing downtime and maintenance costs. Similarly, ML enhances quality control processes by enabling the precise detection of defects that might elude human inspectors, thus ensuring higher product quality and consistency.

Comparison Table between AI and ML:

Production Planning and Scheduling with AI in Manufacturing

Production planning is a strategic process that includes planning and managing activities and managing resources related to the production of products or services in a company. The main purpose of production planning is to optimize production processes, manage resources, and improve production efficiency and quality. Artificial intelligence helps in production planning with the following:

• Artificial intelligence can be used in plan optimization of the production process, inventory management, setting machine parameters, resource allocation, time planning, and supply chain management with the help of commonly used algorithms such as machine learning algorithms and optimization algorithms.
• With the presence of artificial intelligence in the production industry, strategic and management decisions in this field will be more accurate and optimal; such as more accurate market forecasting and more comprehensive analysis of performance data.
• By using smart algorithms, energy consumption can be improved and the optimization of energy resources can be advanced to an acceptable level.
• Smart algorithms can determine the best solutions for production planning and inventory management by identifying market demand. Also, by using market data analysis, it is possible to identify current trends in the market, which can be used to design optimal strategies to compete with other companies and performed
• Artificial intelligence helps to increase productivity in production processes and reduce waste, such as optimizing the use of materials and resources, reducing setup time and changing devices, and improving operational processes.

Choosing the Right AI Tools and Technologies

AI Platforms and Solutions

When selecting tools and technologies for AI in Manufacturing, it’s essential to consider platforms that integrate seamlessly with your existing systems and can scale according to your needs. These platforms provide tools for data analytics, AI-based predictive maintenance, and process optimization, enabling you to leverage advanced analytics and machine learning to enhance operational efficiency.  Some platforms offer comprehensive AI solutions tailored for the manufacturing sector such as:

AWS: This pre-trained platform provides a suite of AI services including SageMaker and Lex, which are designed to integrate with the broader AWS ecosystem, allowing for a flexible and scalable adoption of AI technologies. AWS provides manufacturers with cloud infrastructure and AI solutions that prioritize flexibility, scalability, and advanced analytics. AWS offers a suite of AI services, including Rekognition, Lex, SageMaker, Polly, and Comprehend that integrate seamlessly with existing infrastructure. This makes AWS a flexible solution for businesses of all sizes. One of the standout features of AWS is its strong predictive maintenance capabilities, which alert teams to potential issues before they become major problems. Overall, AWS offers manufacturers a comprehensive suite of tools to optimize their operations and drive productivity.

Azure: Similarly, Azure’s AI solutions automate the rollout of AI across various manufacturing processes. Azure AI seamlessly integrates with the full suite of Azure Cloud tools, streamlining the deployment of AI in manufacturing processes without requiring specialized ML engineering expertise. Manufacturers can leverage AI across their machinery for tasks such as inspection, inventory planning, and demand forecasting. The Azure AI Platform boasts a diverse array of services, including natural language processing, computer vision, conversational AI, and a fully managed machine learning service. These services empower developers to create sophisticated AI applications without deep knowledge of AI algorithms or infrastructure.

Google Cloud AI: This platform extends its capabilities to offer holistic data analysis and optimization of manufacturing processes.

IBM Watson and Oracle: These platforms focus on combining AI with IoT to deliver real-time insights and predictive analytics, enhancing decision-making and operational efficiency in manufacturing. These platforms support a wide range of applications from quality control to supply chain optimization, providing a robust foundation for integrating AI into your manufacturing operations.

GE Predix: General Electric (GE) offers AI solutions designed to improve manufacturing processes. The AI platform from GE predicts equipment maintenance needs, reducing downtime and enhancing operational efficiency. It also provides quality control by inspecting materials used in product construction. These tools seamlessly integrate with GE’s Industrial Internet of Things (IIoT) platform, enabling organizations to optimize performance, enhance efficiency, and lower costs. GE’s Digital Plant tools facilitate code-free development, empowering operators to make informed decisions quickly. GE enables businesses to maximize value through lean manufacturing, improved execution, and enhanced asset and process performance. For instance, a wind turbine using Predix can gather sensor analytics to identify anomalies and use predictive maintenance during planned downtime.

IoT & SAP Leonardo: This innovative platform AI in Manufacturing enables manufacturing businesses to integrate these technologies for a comprehensive operational overhaul. By harnessing the capabilities of AI and machine learning, SAP Leonardo automates various processes within the manufacturing environment. This empowers businesses to optimize their operations, foster collaboration, and enhance overall productivity.

Hardware Requirements

Choosing the right hardware is crucial for the effective implementation of AI in Manufacturing. By carefully selecting AI platforms and ensuring the hardware meets the specific requirements of your AI applications, you can optimize the integration of AI technologies in your manufacturing processes, it helps to reduce the relative cost, produce high-quality products, and improve efficiency. The hardware requirements vary significantly based on the AI applications you plan to run.

For high-performance needs, selecting CPUs with a balance between clock speed and core count is vital to handling complex computations efficiently. Additionally, the cache size and PCIe lanes in CPUs play a significant role in determining the computational efficiency and the ability to connect multiple peripherals and GPUs. Servers and storage also play a critical role, especially when dealing with large datasets. The choice of server can impact the speed and efficiency of AI model training, with newer models offering better performance and more extensive data handling capabilities. Additionally, considering the network speed is crucial as it can be a bottleneck in AI training, affecting the overall training time and efficiency.

AI-Based Product Development

AI-based product development refers to the use of AI technologies and techniques in the process of creating new products or improving existing ones. This approach leverages AI’s capabilities to analyze data, automate tasks, and make predictions to enhance various aspects of the product development lifecycle.

Here are some key components and benefits of AI-based product development:

• Market Research and Insights: AI can analyze large volumes of data from various sources such as social media, customer feedback, and market trends to provide valuable insights into customer preferences, market demands, and competitive landscapes. This helps in identifying potential product opportunities and understanding customer needs more effectively.
• Design and Prototyping: AI in manufacturing can assist in the design phase by generating innovative concepts, optimizing designs based on performance criteria, and even creating virtual prototypes. This can significantly speed up the product development process and improve the quality of designs.
• Predictive Analytics for Demand Forecasting: AI algorithms can analyze historical sales data, market trends, and other relevant factors to predict future demand for a product. This helps in optimizing production schedules, inventory management, and resource allocation.
• Quality Control and Testing: AI in manufacturing can be used to automate quality control processes by analyzing production data in real time, detecting defects, and ensuring that products meet quality standards. This can result in improved product quality and reduced defects.
• Enhanced Customer Service: AI-powered chatbots and virtual assistants can be integrated into products to provide proactive customer support, troubleshoot issues, and gather feedback for continuous improvement.

Overcoming Challenges in Implementing AI in Manufacturing

Data Quality and Availability

One of the primary challenges in Implementing AI in Manufacturing is ensuring the quality and availability of data. As manufacturers collect data from a broad network of sources, including physical sensors and manual reports, maintaining high data quality is crucial for effective AI applications. However, legacy system architectures and weak data governance can lead to poor data quality, with data at different plants being manipulated in various ways, making them ungovernable. To address these issues, it’s essential to modernize industrial data management and incorporate data quality as a central component of your data infrastructure. Adopting proper frameworks can improve data quality in targeted, iterative ways, focusing on the most critical business issues.

Workforce Training and Adaptation

Another significant challenge is preparing your workforce to adapt to and utilize AI technologies effectively. AI readiness to enhance AI-related competencies involves:

• Assessing the Current Capabilities
• Technological Proficiency of Employees
• Designing Tailored Training Programs

These programs should include a range of learning modalities such as in-person workshops, online courses, and hands-on exercises. Additionally, fostering a culture of continuous learning and collaboration is crucial. Establishing cross-functional AI teams and implementing mentorship programs can facilitate knowledge sharing and skill development, empowering employees to leverage AI tools confidently. To support these initiatives, organizations must invest in the necessary tools and infrastructure and integrate principle training to ensure employees understand the main implications of AI technologies. Moreover, emphasizing the collaborative nature of human-AI interaction helps employees see AI as a tool to augment their capabilities rather than a replacement, fostering a more accepting and innovative working environment. By addressing these challenges through strategic planning and continuous improvement, manufacturers can effectively overcome the hurdles of AI implementation, leading to enhanced productivity and innovation in their operations.

Case Studies of Successful Implementing AI in Manufacturing

Examples from Industry Leaders

In the realm of small and medium-sized manufacturers (SMMs), AI implementation has proven to be a game-changer. Some companies like Precision Global and Metromont have leveraged AI to enhance equipment uptime, increase quality and throughput, and reduce scrap, resulting in healthier bottom lines and increased profits. For instance, Rolls-Royce and JTEKT have implemented AI solutions that significantly improved their manufacturing processes. Delta Bravo, a third-party AI solutions provider, has been instrumental in these transformations, working on approximately 90 projects since 2017 and demonstrating substantial returns on investment. Another notable example is Vistra, a major U.S. power producer, which utilized an AI-powered tool to optimize heat rates. This implementation led to a 1% increase in efficiency, translating into millions in savings and reduced greenhouse gas emissions. Similarly, Wayfair optimized container ship logistics during the COVID-19 pandemic through AI, achieving a remarkable 7.5% reduction in inbound logistics costs.

Here’s an overview of how some of the top companies are using AI in Manufacturing:

1. Augury:

Augury is a predictive maintenance company that utilizes AI and machine learning to monitor the performance of industrial equipment. By analyzing data from sensors and IoT devices, Augury’s AI algorithms can predict potential equipment failures, enabling manufacturers to perform proactive maintenance and avoid costly downtime. For example, Augury’s AI technology can analyze vibration patterns in machinery to detect early signs of wear and tear.

2. Siemens:

Siemens, a global leader in industrial automation and digitalization, leverages AI in Manufacturing through its MindSphere platform. MindSphere uses AI and analytics to optimize production processes, improve product quality, and enable predictive maintenance. Siemens also offers AI-powered solutions for digital twins, which are virtual replicas of physical assets used for simulation and optimization.

3. IBM:

IBM provides AI solutions for manufacturing through its Watson IoT platform. This platform integrates AI, analytics, and IoT to help manufacturers gain insights from their operational data. IBM’s AI capabilities enable manufacturers to enhance production efficiency, quality control, and supply chain management. For example, IBM’s AI algorithms can analyze sensor data from production lines to identify patterns and anomalies that may affect product quality.

4. Intel Corporation:

Intel Corporation offers AI-powered solutions for manufacturing, including computer vision systems for quality inspection, predictive maintenance algorithms, and AI-based process optimization tools. Intel’s AI technology can be used to analyze visual data from cameras on the production line to detect defects or deviations from quality standards.

5. Rockwell Automation:

Rockwell Automation uses AI and machine learning in its manufacturing solutions to enable predictive maintenance, optimize production processes, and improve overall equipment effectiveness. By integrating AI into its control systems and analytics platforms, Rockwell Automation helps manufacturers reduce downtime, minimize waste, and enhance productivity.

6. GE:

GE employs AI in Manufacturing through its Predix platform, which provides industrial IoT and analytics capabilities. GE’s AI-powered solutions enable manufacturers to monitor equipment performance, predict maintenance needs, and optimize energy usage. For instance, GE’s AI algorithms can analyze data from sensors in power plants to optimize energy generation and reduce operational costs.

7. NVIDIA:

NVIDIA offers AI technologies for manufacturing, including deep learning-based solutions for robotics, computer vision, and autonomous systems. NVIDIA’s AI platforms enable manufacturers to develop advanced robotics for tasks such as material handling, assembly, and inspection. For example, NVIDIA’s AI-powered robots can adapt to changing production environments and perform complex tasks with high precision.

8. Uptake:

Uptake provides AI in Manufacturing and predictive analytics solutions for industrial equipment monitoring and maintenance. Uptake’s AI algorithms analyze sensor data from machinery to identify potential failures and maintenance needs. By leveraging AI-driven insights, manufacturers can optimize their maintenance schedules and reduce unplanned downtime.

9. Veo Robotics:

Veo Robotics develops AI-powered software and hardware solutions for collaborative robotics in manufacturing environments. Veo’s AI technology enables robots to work safely alongside human workers, performing tasks that require precision and flexibility. For example, Veo’s AI-powered robots can assist with intricate assembly processes or material handling tasks.

10. Automation Anywhere:

Automation Anywhere offers AI-powered robotic process automation (RPA) solutions for streamlining manufacturing operations. By integrating AI into RPA workflows, Automation Anywhere enables manufacturers to automate repetitive tasks, data entry processes, and workflow optimizations. For instance, Automation Anywhere’s AI-driven RPA bots can analyze data from production systems to make real-time decisions and trigger automated actions.

11. Machina Labs, Inc.:

Machina Labs develops AI-driven solutions for industrial automation and predictive maintenance. Machina Labs’ AI technology uses sensor data and machine learning algorithms to predict equipment failures, optimize production processes, and improve operational efficiency. For example, Machina Labs’ AI platform can analyze historical data to forecast potential issues in manufacturing equipment and recommend proactive maintenance actions.

These companies are at the forefront of leveraging AI technologies to drive innovation and efficiency in the manufacturing industry, demonstrating the diverse applications of AI in areas such as predictive maintenance, quality control, process optimization, robotics, and industrial automation.

Lessons Learned

Implementing AI in Manufacturing is not without its challenges and learning curves. One of the critical lessons learned is the importance of starting with a clear problem definition and ensuring the right team composition, including leadership and technical experts, to foster successful adoption. For example, piloting AI on one production line before scaling it incrementally has proven to be a cost-effective strategy for smaller manufacturers. Moreover, understanding the economics of AI deployment is crucial. The hidden costs associated with computing resources in cloud environments can impact the overall cost-effectiveness of AI solutions. Manufacturers should be wary of technical lock-in with single cloud providers and consider the most cost-effective scaling options.

The industry has also learned that there is no one-size-fits-all solution in AI. Customizing AI applications to fit specific manufacturing processes and continuously tweaking them based on shifting priorities are essential for achieving desired outcomes. Additionally, the integration of MLOps (Machine Learning Operations) is crucial for translating AI projects into successful business cases, optimizing resource use, and ensuring scalability without prohibitive costs. These case studies and lessons underscore the transformative potential of AI in manufacturing, highlighting both the successes and the complexities involved in integrating advanced technologies into traditional manufacturing environments.

Future Trends and Emerging Technologies by AI in Manufacturing

Digital Twins

The adoption of factory digital twins is rapidly increasing across various industries due to their significant benefits in optimizing manufacturing processes. They are expected to incorporate generative AI technologies, potentially revolutionizing factory management by providing real-time recommendations and adjusting to supply chain disruptions or changes in market demands. Moreover, advanced models can integrate seamlessly with existing manufacturing execution systems, IoT devices, and inventory databases. This integration enables optimal sequencing of product lines, minimizing downtime and enhancing efficiency within the confines of customer delivery requirements and physical storage capacities.

Collaborative Robots and Automation

Collaborative robots, or cobots, are transforming the industrial landscape by working safely alongside human operators without the need for physical barriers. This integration enhances safety, as cobots are equipped with advanced sensors to detect and avoid collisions, and can halt operations immediately if necessary. The annual growth rate indicates cobots’ ability to increase operational efficiency, improve quality control, and reduce production costs. Cobots are now increasingly equipped with AI, which simplifies their integration and operation. AI enables these robots to learn from human actions, predict maintenance needs, and even respond to voice commands, making them more intuitive and effective as team members on the production floor. Furthermore, the use of virtual reality (VR) in training allows workers to safely and effectively learn to operate and interact with cobots, further reducing the time and cost associated with their deployment.

The ongoing advancements in collaborative robots and digital twins signify a shift towards more interconnected and intelligent manufacturing environments. These technologies not only enhance current manufacturing capabilities but also pave the way for future innovations that could redefine industry standards and operational efficiencies.

Conclusion

The exploration of AI’s integration within manufacturing has laid bare the technology’s transformative power, from augmenting efficiency and precision to fostering substantial cost savings and sustainability. By harnessing AI, companies unlock a myriad of potentials, including predictive maintenance, optimized production schedules, and enhanced quality control, showcasing a significant leap toward achieving operational excellence. Importantly, the seamless fusion of AI with manufacturing processes emerges not just as an ambitious vision but as a practical step toward future-proofing industries against evolving challenges.

As the manufacturing landscape continues to evolve, the integration of digital twins and collaborative robots signifies the next frontier in industrial innovation. These advancements not only promise to elevate the efficiency and adaptability of manufacturing processes but also to redefine the relationship between human workers and machines, fostering safer, more collaborative environments. The journey ahead for AI in manufacturing is paved with opportunities for growth and innovation, urging continuous exploration, learning, and adaptation. Embracing these technological advancements equips manufacturers to navigate the complexities of modern industry dynamics, ultimately steering toward a future where efficiency, sustainability, and human ingenuity converge.

Efficiency reigns supreme in manufacturing, where every step, every adjustment, and every motion is meticulously orchestrated to yield maximum output with minimal input. At the heart of this pursuit lies the art of process optimization – a strategic endeavor that harmonizes human efforts, machine capabilities, and data-driven insights to streamline operations and propel productivity to unprecedented heights.

Plan Optimization is a critical process that involves analyzing and refining production schedules, resource allocations, and other key factors to ensure that your manufacturing operations are running at their full potential. In today’s highly competitive and rapidly evolving manufacturing landscape, the ability to effectively optimize your production plans is a critical competitive advantage. In the realm of modern industry, the optimization of the manufacturing process emerges as a pivotal factor in enhancing efficiency, reducing costs, and maintaining competitive advantage.

This article delves into the intricacies of Plan Optimization in the manufacturing sector, starting with an overview of the concept and its key elements. It further explores various approaches to plan optimization, highlighting the tools and technologies that play a crucial role in achieving operational excellence. Through case studies, readers will gain insights into real-world applications and successes of plan optimization strategies.

The article also addresses common challenges organizations face in optimizing their production processes and outlines the tangible benefits of effective plan optimization. Finally, it looks ahead to future trends in the field, providing a comprehensive guide for professionals aiming to harness the power of plan optimization to drive their manufacturing processes forward.

Whether you’re a seasoned manufacturing veteran or just starting out, this article will provide you with the insights and practical advice you need to unlock the potential of plan optimization in your business.

What is Plan Optimization?

Plan Optimization in the manufacturing process involves a systematic approach aimed at enhancing efficiency and productivity. This concept encompasses various methodologies and technologies designed to refine every aspect of production. Key techniques include product design optimization, which focuses on the shape, size, and functionality of products to meet market demands effectively. Moreover, plan optimization incorporates advanced strategies such as Planning Optimization for master planning, which significantly reduces the time required for material requirements planning (MRP), allowing for more frequent and efficient planning cycles.

The Nexus of Process Optimization and Efficiency

In the manufacturing arena, the pursuit of efficiency is a relentless quest, fueled by the desire to extract maximum value from every resource at hand. Process optimization emerges as a critical enabler, meticulously fine-tuning the intricate dance between human operators, sophisticated machinery, and the myriad tasks that culminate in the creation of finished goods.

Contrary to the notion of merely churning out products en masse, true efficiency lies in the harmonious interplay of various elements – from the precise orchestration of checks and adjustments to the seamless flow of materials and information. By optimizing these processes, manufacturers can unlock a realm where every input is strategically leveraged, every motion is purposeful, and every output is a testament to the synergy between human ingenuity and technological prowess.

Understanding the Importance of Plan Optimization

In the fast-paced and highly competitive manufacturing world, the ability to quickly adapt to changing market conditions and customer demands is essential. Plan Optimization plays a crucial role in this process by enabling you to:

• Improve resource utilization: By optimizing your production plans, you can ensure that your equipment, labor, and other resources are being used as efficiently as possible, reducing waste and maximizing output.
• Enhance production flexibility: Optimized plans allow you to respond more quickly to changes in demand, enabling you to adjust your production schedules and allocate resources accordingly.
• Reduce inventory and storage costs: Improved planning can help you minimize the amount of work-in-progress and finished goods inventory, reducing the costs associated with storage and handling.
• Boost profitability: The cumulative effects of improved resource utilization, production flexibility, and cost savings can result in a significant boost to your bottom line, making plan optimization a critical competitive advantage.

The Difference between Process and Production Optimization

Process optimization and production optimization are two key concepts in the field of operations management that focus on improving efficiency, reducing costs, and maximizing output in a business or manufacturing setting. While both concepts aim to enhance overall performance, they have distinct approaches and objectives.

While process optimization and production optimization share the common goal of improving performance and efficiency in a business or manufacturing setting, they differ in scope, objectives, and approach. Understanding the distinctions between these two concepts can help organizations implement targeted strategies to optimize their operations effectively. Here is a detailed explanation of the key differences between process and production optimization:

Production Optimization:

• Production optimization focuses on improving the overall output, capacity, and performance of a production system or manufacturing facility.
• The primary objective of production optimization is to maximize output, minimize downtime, reduce costs, and improve the overall efficiency of the production process.
• Production optimization typically involves optimizing equipment utilization, minimizing setup times, improving scheduling and planning, and enhancing resource allocation to maximize production capacity.
• Examples of production optimization include implementing predictive maintenance strategies, optimizing production schedules, adopting advanced manufacturing technologies, and using data analytics to improve decision-making.

Key Differences:

1. Scope: Process optimization focuses on improving individual processes or workflows within an organization, while production optimization looks at optimizing the overall production system or manufacturing operations.
2. Objectives: Process optimization aims to enhance efficiency, quality, and productivity at each stage of a process, while production optimization focuses on maximizing output, minimizing costs, and improving overall production performance.
3. Approach: Process optimization involves analyzing and improving specific processes through reengineering and redesign, while production optimization focuses on optimizing production systems, equipment, and resources to maximize output and efficiency.

Decoding the Distinction: Process vs. Production Optimization

While the terms “process optimization” and “production optimization” are often used interchangeably, they represent distinct yet complementary facets of the manufacturing equation. Process optimization zeroes in on the intricate steps within a specific production stage, meticulously refining and streamlining each phase to maximize efficiency and minimize waste.

On the other hand, production optimization takes a holistic view, encompassing the entire manufacturing ecosystem – from equipment layout and inventory protocols to material flow and facility design. By harnessing real-time data, advanced analytics, and sophisticated modeling techniques, production optimization aims to enhance the overall system performance, ensuring that resources are optimally allocated and bottlenecks are proactively identified and mitigated.

Key Factors to Consider for Successful Plan Optimization

Achieving successful Plan Optimization in manufacturing requires a comprehensive understanding of the various factors that can impact your production operations.

Some of the key factors to consider include:

• Production Capacity: Accurately assessing your production capacity, including the capabilities of your equipment, labor, and other resources, is essential for developing effective production plans.
• Demand Forecasting: Accurate demand forecasting is crucial for ensuring that your production plans are aligned with customer needs and market trends.
• Process Complexity: Analyzing the complexity of your manufacturing processes, including the interdependencies between different production stages, can reveal opportunities for optimization.
• Data Availability and Quality: Ensuring that you have access to accurate, up-to-date data on your production operations is critical for effective plan optimization.
• Organizational Alignment: Securing buy-in and cooperation from across your organization, from the shop floor to the executive suite, is essential for successful plan optimization implementation.

Strategy development in plan optimization focuses on defining clear objectives that need to be maximized or minimized within the manufacturing process.

Approaches to Plan Optimization

Lean Manufacturing

Lean Manufacturing focuses on maximizing customer value while minimizing waste. This approach identifies and eliminates non-value-added activities, enhancing efficiency and quality. Key principles include Value Stream Mapping, which analyzes the flow of materials and information to pinpoint waste, and Just-in-Time production, which reduces inventory costs by producing only what is needed when it is needed.

Six Sigma

Six Sigma aims to improve manufacturing quality by reducing defects and variability in processes. The methodology is highly data-driven, utilizing statistical tools to identify and eliminate errors. The DMAIC framework—Define, Measure, Analyze, Improve, and Control—is central to Six Sigma, providing a structured approach to problem-solving that ensures sustainable quality improvements.

Total Quality Management (TQM)

Key components include process quality and fitness for use, ensuring that products not only meet customer needs but are also free from defects and inefficiencies.

Strategies for Plan Optimization in Manufacturing

Effective Plan Optimization in manufacturing requires a multifaceted approach that combines various strategies and techniques.

Demand-Driven Planning:

Aligning your production plans with actual customer demand, rather than relying on historical sales data or forecasts, can help you minimize inventory and improve responsiveness.

Capacity-Constrained Scheduling:

Identifying and addressing production bottlenecks by optimizing your scheduling and resource allocation can lead to significant improvements in throughput and efficiency.

Lean Manufacturing Principles:

Applying lean manufacturing principles, such as waste reduction, just-in-time production, and continuous improvement, can help you streamline your manufacturing processes and optimize your production plans.

Advanced Analytics and Simulation:

Leveraging data-driven analytics and simulation tools can provide valuable insights into your production operations, enabling you to identify optimization opportunities and test different scenarios.

Integrated Planning and Execution:

Aligning your planning and execution processes, and ensuring seamless communication and coordination between different departments, can help you respond more effectively to changes and disruptions.

Collaborative Optimization:

Engaging with suppliers, customers, and other stakeholders to optimize the entire supply chain, rather than just individual production processes, can lead to more holistic and effective plan optimization.

Collecting Real-Time Machine Data in Plan Optimization

Collecting real-time machine data in production optimization refers to the process of gathering and analyzing data from machines and equipment in real time to optimize production planning and operations. This data collection is done using various sensors, monitoring systems, and software tools that capture and transmit information about machine performance, productivity, and other relevant metrics.

Here is a detailed explanation of collecting real-time machine data in production optimization:

Data Collection:

• Sensors and monitoring systems are installed on machines and equipment to collect data on various parameters such as temperature, pressure, speed, vibration, energy consumption, and production output.
• These sensors are connected to a data acquisition system or an Industrial Internet of Things (IIoT) platform that collects stores, and processes the data in real time.

Real-Time Monitoring:

• The collected data is continuously monitored in real-time to track the performance of machines and equipment.
• Real-time monitoring allows operators and production managers to have immediate visibility into machine status, identify any deviations or anomalies, and take prompt action to address issues.

Data Analysis:

• The collected machine data is analyzed using advanced analytics techniques to gain insights into machine performance, efficiency, and potential areas for improvement.
• Data analysis can involve various techniques such as statistical analysis, predictive modeling, machine learning algorithms, and data visualization.

Production Optimization:

• The insights obtained from the analysis of real-time machine data are used to optimize production planning and operations.
• By understanding machine performance patterns, operators can identify opportunities for improving efficiency, reducing downtime, minimizing waste, and optimizing resource allocation.
• Real-time machine data can also be used for predictive maintenance, where potential equipment failures or maintenance needs are detected early, allowing for proactive maintenance actions to be taken.

Continuous Improvement:

• Collecting real-time machine data enables continuous improvement efforts by providing ongoing feedback on the effectiveness of production processes and optimization strategies.
• By monitoring key performance indicators (KPIs) derived from machine data, organizations can track their progress over time and make data-driven decisions to further optimize production operations.

Benefits of Collecting Real-Time Machine Data in Production Optimization:

1. Improved Efficiency: Real-time machine data allows for quick identification of bottlenecks, inefficiencies, and performance issues, leading to targeted improvements in production processes.
2. Reduced Downtime: Early detection of machine failures or maintenance needs through real-time monitoring helps minimize unplanned downtime and optimize maintenance schedules.
3. Enhanced Decision-Making: Data-driven insights from real-time machine data enable better decision-making regarding production planning, resource allocation, and optimization strategies.
4. Increased Productivity: Optimizing production operations based on real-time machine data leads to increased productivity, reduced cycle times, and improved overall equipment effectiveness (OEE).

Implementing Workflows & Notifications Based on Machine Sensors in Plan Optimization

Implementing workflows and notifications based on machine sensors in Plan Optimization involves utilizing real-time data from machine sensors to automate and optimize production workflows and processes. This approach enables organizations to proactively respond to changes in machine performance, production status, and other relevant parameters, leading to improved efficiency, reduced downtime, and enhanced decision-making. Here’s a detailed explanation of implementing workflows and notifications based on machine sensors in plan optimization:

Sensor-Based Data Collection:

• Machine sensors are installed to collect real-time data on various parameters such as temperature, pressure, speed, vibration, energy consumption, and production output.
• The collected sensor data is transmitted to a central system or an Industrial Internet of Things (IIoT) platform for processing and analysis.

Workflow Automation:

• Based on the real-time data from machine sensors, automated workflows can be implemented to streamline production processes and optimize resource utilization.
• Workflow automation involves setting up rules and triggers that automatically initiate specific actions or tasks based on predefined conditions or thresholds derived from sensor data.

Notification Systems:

• Notifications are configured to alert relevant personnel or systems when certain conditions or events occur based on the sensor data.
• Notifications can be sent via email, SMS, mobile apps, or integrated with existing communication and collaboration tools to ensure timely awareness of critical production-related information.

Plan Optimization:

• The real-time data from machine sensors is used to optimize production planning and scheduling by dynamically adjusting workflows and resource allocation based on current machine performance and production status.
• By integrating sensor data into the planning process, organizations can make more informed decisions regarding production priorities, job sequencing, and resource utilization.

Proactive Maintenance:

• Sensor data-driven workflows and notifications can be used to implement proactive maintenance strategies by triggering maintenance tasks or alerts based on predictive analytics or early detection of equipment issues.
• Proactive maintenance helps minimize downtime, reduce the risk of unexpected failures, and extend the lifespan of machinery and equipment.

Continuous Improvement:

• Implementing workflows and notifications based on machine sensors supports continuous improvement efforts by providing real-time feedback on production processes and enabling rapid adjustments to optimize operations.
• By analyzing historical sensor data and performance trends, organizations can identify areas for improvement and refine their workflows and notification systems to further enhance production efficiency.

Benefits of Implementing Workflows & Notifications Based on Machine Sensors in Plan Optimization:

1. Enhanced Efficiency: Automated workflows and notifications based on sensor data enable streamlined production processes, reducing manual intervention and optimizing resource allocation.
2. Reduced Downtime: Proactive notifications for maintenance or performance issues help minimize unplanned downtime and support predictive maintenance strategies.
3. Improved Responsiveness: Real-time alerts and notifications enable quick response to changes in machine performance or production status, leading to more agile decision-making.
4. Data-Driven Decision-Making: Sensor-based workflows provide actionable insights derived from real-time data, supporting informed decision-making for plan optimization.

In summary, implementing workflows and notifications based on machine sensors in Plan Optimization leverages real-time data to automate workflows, proactively manage production processes, optimize resource utilization, and support continuous improvement efforts. This data-driven approach enhances efficiency, reduces downtime, improves responsiveness, and enables informed decision-making for optimized production planning and operations.

Challenges and Solutions in Plan Optimization

While the benefits of Plan Optimization are well-documented, implementing and maintaining an effective plan optimization strategy can present a range of challenges. By anticipating and addressing these challenges proactively, you can develop a more robust and sustainable plan optimization strategy that delivers lasting benefits to your manufacturing operations:

• Data Availability and Quality: Ensuring that you have access to accurate, up-to-date data on your production operations is essential for effective plan optimization. Solutions may include implementing robust data collection and management systems, as well as integrating data from multiple sources.
• Organizational Resistance to Change: Plan optimization often requires significant changes to existing processes and workflows, which can meet resistance from employees. Addressing this challenge may involve effective change management strategies, such as clear communication, training, and incentivizing adoption.
• Complexity of Manufacturing Processes: The inherent complexity of many manufacturing processes can make it challenging to develop and implement effective optimization strategies. Solutions may involve the use of advanced analytics, simulation tools, and cross-functional collaboration to better understand and address these complexities.
• Supply Chain Disruptions: Unexpected disruptions in the supply chain, such as supplier delays or material shortages, can throw off even the most carefully optimized production plans. Strategies to address this may include building more flexibility and resilience into your supply chain, as well as developing contingency plans.
• Lack of Skilled Personnel: Effective plan optimization requires a combination of technical expertise, data analysis skills, and manufacturing domain knowledge. Addressing this challenge may involve investing in training and development, as well as recruiting and retaining talented individuals with the right skill sets.

Bottleneck Analysis in Plan Optimization

Bottleneck analysis in plan optimization involves identifying and addressing the key constraints or bottlenecks in a production process that limit overall throughput or efficiency. By analyzing the flow of work, resources, and information within a production system, organizations can pinpoint areas where capacity is constrained, leading to delays, inefficiencies, or reduced productivity. Here’s a detailed explanation of bottleneck analysis in plan optimization:

1. Identification of Bottlenecks:

• Bottlenecks are typically found in processes, machines, or resources that have the lowest capacity or highest utilization rate, causing them to slow down the overall production flow.
• Through data collection, observation, and performance metrics analysis, organizations can identify bottlenecks by comparing actual production rates with the maximum capacity of individual components in the production system.

2. Analysis of Impact:

• Bottlenecks can have a significant impact on production efficiency, lead times, and overall performance metrics such as cycle time, throughput, and resource utilization.
• By quantifying the impact of bottlenecks on key performance indicators, organizations can prioritize improvement efforts and allocate resources effectively to address the most critical constraints.

3. Optimization Strategies:

• Once bottlenecks are identified, organizations can implement various strategies to optimize production processes and mitigate the impact of constraints.
• Strategies may include reallocating resources, balancing workloads, improving process flows, upgrading equipment, implementing automation solutions, or redesigning workflows to streamline operations and increase throughput.

4. Continuous Monitoring and Improvement:

• Bottleneck analysis is an ongoing process that requires continuous production performance monitoring and regular evaluation of potential constraints.
• Organizations can proactively identify new bottlenecks, adjust strategies, and continuously improve plan optimization efforts by establishing a feedback loop for monitoring and analyzing production data.

Downtime Analysis in Plan Optimization

Downtime analysis in plan optimization focuses on identifying and reducing unplanned or planned downtime events that disrupt production processes and impact overall operational efficiency. By analyzing downtime causes, durations, and frequency, organizations can implement strategies to minimize downtime, improve equipment reliability, and enhance production uptime. Here’s a detailed explanation of downtime analysis in plan optimization:

1. Downtime Classification:

• Downtime events can be classified such as equipment failures, maintenance activities, changeovers, material shortages, operator errors, or external factors like power outages or supply chain disruptions.
• By categorizing downtime events, organizations can gain insights into the root causes and patterns of downtime occurrences within the production system.

2. Downtime Tracking and Analysis:

• Organizations track downtime events using real-time data from machine sensors, production logs, maintenance records, and other sources to analyze the frequency, duration, and impact of downtime on production performance.
• By conducting root cause analysis and Pareto analysis, organizations can identify the most common causes of downtime and prioritize improvement efforts based on their impact on production uptime.

3. Downtime Reduction Strategies:

• Based on the analysis of downtime data and root causes, organizations can implement strategies to reduce downtime and improve equipment reliability.
• Strategies may include preventive maintenance programs, predictive maintenance techniques, operator training, spare parts management, process optimization, automation solutions, or scheduling adjustments to minimize downtime events.

4. Performance Monitoring and Reporting:

• Continuous monitoring of downtime metrics and performance indicators enables organizations to track progress in reducing downtime and evaluate the effectiveness of implemented improvement initiatives.
• By generating reports and dashboards that provide visibility into downtime trends, organizations can make data-driven decisions to optimize production planning and mitigate the impact of unplanned disruptions.

5. Collaboration and Communication:

• Effective communication and collaboration among cross-functional teams are essential for addressing downtime issues and implementing sustainable solutions.
• By involving maintenance personnel, operators, engineers, planners, and other stakeholders in downtime analysis and improvement initiatives, organizations can leverage collective expertise to identify opportunities for enhancing plan optimization.

Benefits of Bottleneck Analysis and Downtime Analysis in Plan Optimization:

• Improved Efficiency: Identifying and addressing bottlenecks and downtime events leads to streamlined processes, increased throughput, and enhanced operational efficiency.
• Enhanced Reliability: By reducing unplanned downtime and improving equipment reliability, organizations can minimize disruptions and maintain consistent production output.
• Cost Savings: Optimizing production processes through bottleneck analysis and downtime reduction strategies helps reduce operational costs associated with inefficiencies, maintenance expenses, and lost production time.
• Data-Driven Decision-Making: Analyzing bottleneck and downtime data provides valuable insights for informed decision-making in plan optimization, enabling organizations to prioritize improvement efforts based on empirical evidence.

In summary, bottleneck analysis in plan optimization focuses on identifying constraints that limit production capacity, while downtime analysis aims to reduce disruptions that impact operational efficiency. By implementing strategies to address bottlenecks and minimize downtime events through data-driven analysis and continuous improvement efforts, organizations can enhance productivity, reliability, and cost-effectiveness in their production processes.

Tools and Technologies for Plan Optimization in Manufacturing

Leveraging the right tools and technologies is essential for successful Plan Optimization in manufacturing. By integrating these tools and technologies into your manufacturing operations, you can unlock the full potential of plan optimization, driving increased efficiency, cost savings, and overall operational performance. Some of the key tools and technologies that can support your efforts include:

Advanced Planning and Scheduling (APS) Software: APS software uses sophisticated algorithms and optimization techniques to generate detailed production schedules that take into account factors such as resource constraints, production priorities, and delivery deadlines.

Manufacturing Execution Systems (MES): MES solutions provide real-time visibility into your production processes, allowing you to monitor performance, identify bottlenecks, and make more informed planning decisions.

Simulation and Modeling Tools: Specialized simulation and modeling software can help you analyze the impact of different planning scenarios, test optimization strategies, and validate the feasibility of your production plans.

Predictive Analytics and Machine Learning: By leveraging predictive analytics and machine learning algorithms, you can develop more accurate demand forecasts, identify potential supply chain disruptions, and optimize your production plans accordingly.

Collaborative Planning Platforms: Cloud-based collaborative planning platforms enable you to share information, coordinate with suppliers and customers, and jointly optimize your production plans across the entire supply chain.

Best Practices for Implementing Plan Optimization in Manufacturing

To ensure the successful implementation of Plan Optimization in your manufacturing operations, consider the following best practices:

Establish Clear Objectives and Key Performance Indicators (KPIs): Define specific, measurable goals for your Plan Optimization efforts, and identify the KPIs that will be used to track and measure success.

Ensure Cross-Functional Collaboration: Involve stakeholders from across your organization, including production, supply chain, finance, and IT, to ensure that your plan optimization efforts are aligned with the broader business objectives.

Adopt a Continuous Improvement Mindset: Treat Plan Optimization as an ongoing process, not a one-time project. Continuously monitor and analyze your performance, and be willing to adjust your strategies and tactics as needed.

Leverage Data-Driven Insights: Utilize advanced analytics, simulation tools, and other data-driven technologies to gain a deeper understanding of your manufacturing processes and identify opportunities for optimization.

Foster a Culture of Experimentation: Encourage a culture of experimentation and innovation, where employees are empowered to test and implement new ideas for improving production plans and processes.

Invest in Employee Training and Development: Ensure that your employees have the necessary skills and knowledge to effectively implement and maintain your plan optimization strategies.

Integrate Plan Optimization with Other Operational Initiatives: Align your Plan Optimization efforts with other operational initiatives, such as lean manufacturing, Six Sigma, and Industry 4.0, to create a more holistic and effective approach to improving manufacturing performance.

By following these best practices, you can increase the chances of successfully implementing and sustaining your plan optimization efforts, ultimately driving long-term improvements in your manufacturing operations.

Future Trends in Plan Optimization

As the manufacturing industry continues to evolve, we can expect to see several exciting trends and advancements in the field of plan optimization. Some of the key trends to watch out for include:

Increased Adoption of Artificial Intelligence and Machine Learning:

AI in manufacturing and machine learning algorithms will play an increasingly important role in Plan Optimization, enabling more accurate demand forecasting, predictive maintenance, and real-time decision-making.

Greater Integration of IoT and Edge Computing:

The integration of IoT devices and edge computing technologies will provide manufacturers with unprecedented visibility into their production processes, allowing for more granular and responsive plan optimization.

Emphasis on Sustainability and Environmental Responsibility:

Plan Optimization strategies will need to consider the environmental impact of manufacturing operations, driving the development of more sustainable production practices and the optimization of energy and resource usage.

Collaborative Planning Across the Supply Chain:

Manufacturers will increasingly collaborate with suppliers, customers, and other stakeholders to optimize production plans and supply chain operations, leveraging cloud-based platforms and advanced data-sharing technologies.

Personalization and Mass Customization:

As consumer demand for personalized products continues to grow, Plan Optimization will need to adapt to support more flexible and agile production processes that can accommodate customization and rapid changeovers.

Increased Adoption of Digital Twins:

The use of digital twins, or virtual representations of physical manufacturing systems, will enable more accurate simulation and optimization of production plans, leading to improved decision-making and operational performance.

By staying attuned to these emerging trends and proactively adapting your Plan Optimization strategies, you can position your manufacturing business for long-term success in the years to come.

Conclusion

Throughout this exploration of advanced Plan Optimization techniques in manufacturing, we have delved into the key strategies, tools, and approaches that underscore the importance of efficient and effective production processes. From the integration of lean manufacturing principles and the implementation of Six Sigma to the adoption of cutting-edge tools like Data Analytics and Simulation Software, these methodologies collectively enhance productivity, reduce costs, and streamline operations.

Looking forward, the manufacturing industry stands on the brink of a technological revolution, with AI and machine learning, digital twins, and a heightened focus on sustainability poised to redefine traditional practices. These future trends not only promise greater operational efficiencies but also hold the potential for a more sustainable, responsive manufacturing ecosystem. As manufacturers continue to navigate the challenges and opportunities presented by an increasingly competitive global market, the strategic application of these advanced optimization techniques and technologies will be crucial in achieving lasting success and innovation in manufacturing processes.

What is the Smart Factory?

The Smart Factory is a suite of modern digital applications that deliver solutions for reliability, efficiency, and profitability. The Smart Factory method aims to connect people, processes, and assets within the organization much better than what has been done with traditional digital solutions. The Smart Factory becomes a reality because of advent of new technology available, which include IIoT, cloud/edge computing, data analytics, virtual reality, and digitalization for integrated workflows.

The Smart Factory intends to address a critical issue with existing practice: key activities and data are in silos and thus unable to convert data into actions in a systematic and timely manner.  The key activities include supply chain management, production monitoring and optimization, distributed control system (DCS), asset management and maintenance. In other words, all of these systems generate a lot of data, making it practically impossible for site engineers to gather, correlate, and analyze data in a timely manner to predict operating issues and equipment failures to come up effective actions for prevention and optimization.

The Smart Factory is more than just data collection and software tools. It is about integrating new digital solutions with existing data infrastructure. It is about better management of asset, workflows, and knowledge. The Smart Factory creates value through real‐time data monitoring, models, and optimization to reveal new insights for opportunities, leading to turn‐key solutions. It also applies advanced analytics and machine learning (ML) techniques to develop new heuristics and enable the solving of issues that we were previously unable to.

It leverages the long‐standing experience, paired with new digital technologies to resolve operating issues better, faster consistently. Use of the Smart Factory method can result in faster detection and resolution of operating problems as well as more effective best knowledge sharing and management.

Picture1. Graphical status of the Smart Factory

The history of the Smart Factory

The Smart Factory is a part of the technological transformation known as Industry 4.0 or the Fourth Industrial Revolution. Each of the first three industrial revolutions was born out of an innovative new technology that completely changed the way we worked and manufactured goods; Namely, the steam engine, the assembly line, and the power of the computer. Today, the fourth revolution is driven by digital transformation and intelligent automation.

Industry 4.0 has reinvented how businesses design, manufacture, and distribute their products. Technologies such as Industrial Internet  of Things (IIoT), cloud connectivity, AI, and machine learning are now deeply woven into the manufacturing process. This unified and integrated approach to manufacturing results in products, factories, and assets that are connected and intelligent. Today’s Industry 4.0 initiatives also look to develop symbiotic and rewarding collaborations between people and technology. When the accuracy and speed of 4.0 tools comes together with the creativity, talent, and innovation of your people, you get a win/win for both your human resources and your bottom line. Your manufacturing operations become more efficient and productive, and your teams are relieved of a lot of repetitive tasks – giving them the opportunity to collaborate with smart technologies and better equip themselves for the evolving technological landscape and the AI-powered future of work.

Since the 1800s, we have experienced three industrial revolutions. They were called “revolutions” because the innovation that drove them didn’t just slightly improve productivity and efficiency – it completely revolutionized how goods were produced and how work was done. We are now in the Fourth Industrial Revolution (Industry 4.0).

First industrial revolution

By the early 1800s, the First Industrial Revolution was underway. The invention of the steam engine reduced industrial reliance on animal and human labor, ushering in a new age of manufacturing and precision engineering.

Second industrial revolution

A century later, the growing use of petroleum and electric power meant that machinery could be leaner and less cumbersome. The Second Industrial Revolution was driven by the assembly line and mass production processes, many of which are still in use today.

Third industrial revolution

Around the middle of the 20th century, computers hit the scene. The Third Industrial Revolution saw the early development of factory automation and robotics. This era also saw the first use of computerized business systems that were built to manage and analyze data.

Fourth industrial revolution

Today, manufacturing is increasingly powered by information. Vast amounts of data come from across the business and around the world, in real time, around the clock. AI is at the heart of the Fourth Industrial Revolution, allowing manufacturers to not only gather all that data but use it – to analyze, predict, understand, and report. Industry 4.0 is not characterized by a single technology. It is defined by the seamless integration of a number of systems, tools, and innovations.

Picture2. Graphical display of the history of the smart factory

Major targets of the Smart Factory

In order to identify and prioritize opportunities along the digital thread, the Smart Factory method focuses on optimality, reliability, and human capital. For each of these value drivers, the Smart Factory connects the factory operations to the key financial parameters, resulting in a framework that companies can leverage to systematically identify weaknesses and take actions to resolve them and make more benefits.

Reliability enhancement

The Smart Factory offers reliability monitoring that includes process models and fault models. This solution provides early detection of process and equipment issues and includes troubleshooting recommendations to resolve these issues before they become costly. Through the dashboard, recommendations are shown in a clear and timely manner to deliver improved learning to customers’ organizations. For example, tracking solutions for root causes of low efficiency in mass production lines.

Picture3. The Smart Factory dashboard

Integrated Energy and Process Optimization

The process optimization service, as an important part of the Smart Factory, rigorously simulates an entire complex of process units to determine the maximum profitability of the complex, given current constraints and market conditions. Process optimization is about improving a process in terms of material consumption, energy use, yield optimization and finding best strategy for planning product lines is called planning optimization.

Asset Performance Management

The Smart Factory includes asset performance management (APM), which goes beyond traditional machine monitoring and data gathering. By merging together decades of machine modeling experience with modern analytics, the APM digital predict machinery availability, drill to the root causes of inefficient machine operation, and bring order to reliability and maintenance.

Profit Performance Optimization for the Total Site

The Smart Factory includes site‐wide performance monitoring. It provides visibility into the economic consequences of process performance as cost of lost opportunity and ensures sustainable benefits over varying operating conditions, visualizing real‐time unit profitability.

Challenges & opportunities for the Smart Factory

Environmental, quality, and safety regulations are becoming ever stricter. In today’s competitive environment, process plants are under more pressure than ever to deliver improved operating performance. Making the things even more challenging, the workforce is evolving, as an information technology (IT) savvy, but less experienced generation moves into roles once held by industry veterans, who have retired or are looking to retire. These trends are making the industrial norms to be rewritten, affecting profitability, reliability, and safety. Thus, the industry is at a turning point. The answer lies in the digital transformation. The experience demonstrates that digitalization can turn a distracted organization which is bogged down in reacting to day‐to‐day issues into an agile, well‐oiled machine that proactively anticipates issues and organizes to prioritize and solve them before they escalate.

Thus, industrial companies need digital solutions to integrate different data sources with advanced analytics to make decisions in near real time to run their production processes more efficiently. More importantly, digitalization aims to create new value beyond digitization by enhancing companies’ business capabilities and achieving operation excellence across the entire operation value chains. For most industrial plants, mere Industrial Internet of Things (IIoT) deployment is no longer a competitive advantage without understanding the implications of these insights. These advantages could be anything from improving safety to productivity to operational efficiency to workflow integration and automation. This is the part where true business outcome is delivered.

To achieve the best digitalization outcome, it requires optimal integration of IT, operation technology (OT), engineering technology (ET), and data technology (DT). IT consists of computers, networking devices, and other devices to generate, process, store, secure, and exchange all forms of electronic data. OT mainly includes industrial sensors and control systems, while ET comprises process models simulation and optimization and best practices for operation. DT consists of data analytics and machine learning (ML) technology. This integration of IT/OT/ET/DT creates significant value for companies and will lead to new business opportunities and growth.

Operation activities in a process plant

Process operation activities can be represented as a hierarchy (Figure 1.1). The foundation of the structure is composed of field devices (Level 1) such as sensors, transmitters, actuators, and valves that measure process variables such as flows, compositions, temperatures, and pressures, which are required for any industrial control system. The next level (Level 2) consists of regulatory controllers, which maintain measured values within specified limits. Process control plays a key role in ensuring process safety and protecting personnel, equipment, and the environment (Level 3). While regulatory controllers tackle each variable individually, the advanced process control such as multivariable and constraint controllers (Level 4) evaluates multiple variables simultaneously and enables an entire process unit close to a set of limiting constraints. To determine the optimum set points for a set of controllers, real‐time optimization (Level 5) is employed, which also consider advanced control strategies beyond control modules, process supervision and coordination, shutdowns and start‐ups, reliability and safety management, maintenance scheduling, etc. At Level 6, enterprise management involves organizing and coordinating the operations of multiple plants. This level is the highest in the plant hierarchy and handles various functions including feed purchasing, production planning, scheduling, optimization procedures relevant to general production planning, coordination between different plants, feed movement and product delivery, etc.

The successful implementation of these six‐level activities is a critical factor in making plant operation as profitable and reliable as possible. Levels 1, 2, and 3 are required for all process plants while the activities in Levels 4, 5, and 6 are designed for maximizing profit. The frequency of execution is much lower for higher‐level activities, and the time scale of each activity increases up the hierarchy from less than a second to days and months. This is due to the need of production change cycle as well as the increase in computational requirements and analysis time from the lowest level to the highest. These activities are related and should be carefully coordinated. Levels 4 and 5, which focus on the control functions of advanced process control and real‐time optimization for a process plant, are especially beneficial for a plant’s operations. In the above six levels, each layer depends on the previous layer in the hierarchy, and the control performance of an individual layer directly affects the stability of the process, the quality of the product, and the costs associated with making the product. However, running a single unit at its local optimum is not necessarily the best strategy for achieving overall profitability of the facility. This is where the production management layer steps in to manage the individual units’ APCs in concert to accomplish plant‐wide optimization. Furthermore, enterprise management coordinates multiple plant operations via optimizing feed selection and product generation and delivery to the market. That is where global optimum can be achieved for a company as a whole. In general, SFP (Smart Factory Planning) will enable you to manage  most of above levels in an integrated way.

FIGURE1. Six‐level hierarchy for process control and optimization

• Major Challenges
• Supply Chain Management

In today’s business environment, a lack of agility can hinder an organization’s ability to be competitive. With high capital costs and ever‐increasing energy expenditures, an organization can become locked into patterns where it is implementing shortsighted, cost‐cutting measures that can adversely affect operational productivity—decreasing overall market responsiveness. The bottom line is: maintaining a big picture view of key metrics—such as selecting and purchasing cheap raw materials and increasing manufacturing throughput, as well as transportations and logistics accuracy—helps minimize overall costs. This is done by optimizing the whole supply chain including business and production planning and operation scheduling of production processes, which plays key roles in the search for better profits. Consider the oil refining sectors as an example. The supply chain of an integrated oil company stretches from the production and purchasing of crude oil to customers buying petrochemical products or fuel for heating or transport. Industrial supply chain consists of feed purchasing, delivery, feed mixing and scheduling, operation, and control.

The management of such enterprise involves making decisions on

• What feed to buy and where and when?
• What processes to operate and how?
• What products to make?
• When each product is blended to make on‐spec product to sell?
• What maintenance schedule for key equipment?
• How to integrate refining and petrochemical production?

These decisions will be made repeatedly when demands and supplies change, production upsets occur, and transportation logistics change. A typical medium refinery may make products worth around $10million/day. The difference in profit margin between best and worst supply chain management could be at least around $1/barrel, which is amount to $100,000/day for a refinery of 100,000 barrel/day capacity. The value for the gap is very significant. Thus, refineries strive to optimize operation effectively via digital systems in order to close the gap and capture the opportunity. SFP let you manage and optimize your supply chain system with MRP main feature which are located on the SFP, for implementing in any factory need to be Customized.

Process Efficiency Improvement

The best process efficiency consists of three components: consumption rate, design efficiency, and operation efficiency.

Consumption rate is the efficient use of raw materials. To get the best consumption rate, the key is to select the most cost‐effective raw materials.

Design efficiency is about achieving the best technology selection and process configurations to obtain optimal system designs featuring low capital and operating costs. Process integration and intensification is the key enabler to achieve high design efficiency.

Operation efficiency is how the teams operate the processes with high economic profit and minimum energy consumption via optimizing process conditions and better control. Integrated process optimization and control is the key enabler to achieve high operation efficiency.

SFP strived to use the newest and the most academic approaches to enhance the level of efficiency with OEE and MRP Indexes which are the best KPIs to increase efficiency.

Asset Management

In manufacturing, what is more crucial than the equipment itself? It is downtime, which damages business and costly for manufacturers. That is the reason why asset managers try their best to keep their equipment performing, despite constrained budgets and aging infrastructure. That is why equipment availability is one of the most important metrics for asset management. Yet many organizations lack the insight and visibility they need to proactively refine their operations in a cost‐effective way. In fact, many organizations struggle to establish a benchmark from which to build an asset management strategy based on the basic information. Such information might include:

• Do we have a register of assets down to a significant level?
• Do we know the physical location of these assets?
• Do we know how many?
• Do we know the condition of the assets?
• Do we know, or are we able to report in the future, the life cycle costs?
• Have we assessed and determined a risk profile for these assets?

In industrial and production settings, Total Preventative Maintenance (TPM) is a critical strategy for maintaining equipment and machines. By following a TPM program, organizations can reduce downtime, increase efficiency, and improve safety.

Digital solutions for Total Preventative Maintenance can reduce costs even as they reduce risk. Total preventative maintenance can analyze patterns, provide early warning signs for equipment failure, and respond with greater agility. This strategy can also perform at higher levels of reliability, quality, and safety without needing more resource.  By using SFP (Smart Factory Planning), you will be able to ensure that your equipment and machines are at the highest level of efficiency and performance, because with SFP, you will be able to implement Total Preventive Maintenance (TPM) strategy and calculate the OEE index.

Picture4. Asset management

Workflows Integration and Automation

The missing ingredient in order to achieve business outcomes is an organizational culture of excellence implying operating workflows and best practices—the more that these tasks are automated, the more repeatable the outcome. If a start‐up or shutdown procedure can be automated, experience shows that the number of unsafe and costly incidents is reduced. Our digitalization philosophy is for our customers to wisely invest in automation to mitigate the risks through good digital tool design practices and supervision. SFP helps companies to visualize their workflows and to build their organizational culture among their staff.

Methodology of the Smart Factory (Connected Plant)

To overcome the above challenges, the Smart Factory methodology was developed (Edwards, 2016). This methodology aims to connect people, processes, and assets within the organization much better than what has been doing with traditional digital solutions. The Smart Factory wants to achieve the following outcome for customers:

• Every day is your best day of production.
• Everyone in your organization is a leading expert.
• Can maximize the overall profit based on the market conditions and asset capability.
• Can maximize asset utilization via predicting and correcting abnormal situations.
• Can make the company be the best in the industry.

The Smart Factory is more than just data collection and software tools. It is about applying intelligent models to reveal insights. It is about integrating new digital solutions with existing data infrastructure. It is about better management of asset, workflows, and knowledge via digital solutions. Industrial plants can benefit from being connected since they typically operate below their optimum output capacity due to process variations, unplanned downtime, and inconsistent workforce competency. Reaching and sustaining optimum levels of plant performance requires the transformation of real‐time data into actionable insights. This will allow processes to operate at optimum levels for longer periods of time, identify and resolve asset issues before unplanned outages, and help every worker to operate as an industry leading expert. A Smart Factory is a single pane of glass software solution that collects real‐time data and runs it through a digital plant model to predict and control deviations leading to optimized plant performance. By using our Smart Factory software, in addition to data collection and visual display of workflows, your organization can optimize the efficiency of your machines and find the most ideal time points for ordering and reduce the costs of your company drastically.

Process Data Overview

Process data often include process variables, quality data, and measured disturbances. These variables are often connected by the process flows and controller feedback, making them highly interactive and dynamic. Process data are measured at rather fast sampling rates, which can go from seconds to minutes for online measurement and control‐related variables.

Numerical Data

This is the most common data type. Basically, it represents some quantifiable thing that can be measured. Four major measurements are flow rate, temperature, pressure,  and level:

• Temperature measurement devices such as thermocouples and resistant temperature detectors are the most common instruments found in industrial plans.
• 

Picture5. Thermocouples

• Pressure transmitters are another common instrument in the form of differential pressure (DP) between one process connection and a reference pressure (e.g., atmosphere) or another process connection. DP transmitters are used to infer other process variables, such as flow and level.
• 

Picture6. differential pressure (DP)

• Flow measurements. Flow rates can be calculated from DP measurement. Volumetric flow can be calculated from the velocity. Mass flow rate can be calculated from the density of the material. Accurate flow measurements are essential for regulatory and APC (Advanced Process Control systems) applications.
• 

Picture7. Some kinds of flowmeter

• Level measurements. The level or height of materials in a vessel can be measured directly in some cases, but DP transmitters are commonly used to infer level as a function of hydrostatic pressure and density of the material.
• 

Picture8. Hydrostatic level measurement

Beyond single‐point measurements, time series measurement data is another common data type in process industry. Usually, this kind of data is acquired on a regular time interval.

Analyzer Data

Process analyzers are integral parts of many APC (Advanced Process Control systems) applications. They come in a variety of types and process specific configurations. For example, an analyzer may provide continuous results or update intermittently and measure a property or composition of a stream.

Picture9. Process Analyzer

Engineering Data

In chemical process issues, inferential or soft sensors are used to calculate. Once the data is converted to desired format, soft sensors, engineering calculations, and process simulation become analytical building blocks for assisting plant operation, which helps to identify patterns of operation and predict optimal conditions and failure operating modes.

It is the analytical features rather than the raw data itself that become the starting point for statistic modeling and/or ML algorithms. In many cases, well‐crafted and well‐engineered features may well become the data quality benchmarking basis for data analytics applications.

Connected Measurement

Process control requires accurate, responsive, and maintainable measurements. Within industry, the top four measurements are flow, pressure, temperature, and level. Each of these individual measurements may be connected in a digital transformation project that has the aim to bring new insight to the end user, realizing a valuable outcome not previously available. However, for this chapter, the focus is on flow measurement.

The industrial processes and plants must operate not only at desired capacity but also at optimal efficiency and safety; Safety that enables a system to predict undesirable process or plant conditions and equipment failures before they occur and efficiency that systematically addresses the metering as part of the continuous improvement process. A growing number of metering asset owners seek to demonstrate the power of the Industrial Internet of Things (“IIoT”) to improve overall operational and business performance.

This chapter describes how the Smart Factory, often referred to as the IIoT, can support continuous improvement and solve previously unsolved problems to increase meter availability and its operational performance, safety, and reliability while maintaining its desired accuracy. By taking advantage of streaming data from sensors to rapidly assess operational status, a digital system can identify potential warning signs, deliver alerts, and automatically trigger actions.

The History of Metering: Metering Supervisory Systems

As the flow meter technologies developed, the approach “Supervisory Systems” also developed where Supervisory is defined as “observing and directing an activity or a person.”

Prior to the evolution of the electronic computer, most of the installed meters’ data was connected to chart recorders with local data collection by human operators. These paper charts and subsequent operatives’ notes were dispatched to a central facility where the information was processed in terms of “measurement custody transfer” and “operations” reports. The main challenge here was that the centralized facility had to process reports based on inputs. Most of the early remote terminal unit (“RTU”) and supervisory control and data acquisition (“SCADA”) systems had a limited capability in terms of signal input/output (“I/O”) and subsequent processing.

Picture10. Chart Recorder

The early 1970s saw what was to become the microprocessor. By 1975 the first dedicated flow computer (“FC”) was commercially. The essential inputs are flow (volume or mass), pressure, temperature, and often density or fluid composition as seen in the following figure. The critical outputs are bulk flow rate and the integrated totalization over predefined periods of time from 1 to 24 hour, recording any notified alarms and any mismeasurement events.

Figure2: Early single‐stream flow computer

Through the 1980s and 1990s, the universal multifunction controllers began to appear where there could be said to have been an overlap between the early Programmable Logic Controller (“PLC”) capabilities and the traditional Flow Computer (FC), but the main issue was that the measurement engineers were not control systems engineers working with PLC. At this point in time, a demarcation was created between the dedicated measurement systems engineers and the control systems engineers. This demarcation has continued to exist within the industry to this present day.

The metering supervisory computer (“MSC”) came into commercial operation as the number of FCs and flow meters grew to see a typical metering system. The MSC grew from the requirement to enable a greater communications network as well as computing power with its associated memory to implement advanced features across the total measurements system; ironically, these solutions have evolved into ever more powerful traditional SCADA (supervisory control and data acquisition) control systems!

Picture11. SCADA (supervisory control and data acquisition) control systems

A typical overview of an MSC system is provided in the following figure.

Figure3: Traditionally connected flow meter and the metering supervisory computer

In summary, the metering supervisory computer has existed for decades and may be regarded as a dedicated SCADA system for the metering engineers. The key change being highlighted in this chapter is in the connected digital transformation of these established technologies and not the fundamental metering technology itself. It is about the inherent digitization of a meter technology and its ability to relate to a larger system context to bring about new insightful outcomes.

Picture12. Programmable Logic Controller (PLC)

The History of Metering Diagnostics and Condition‐Based Monitoring (CBM)

As the flow meters developed, so too did the ability to diagnose operational faults being largely based on the commercialization of the personal computer and ever‐evolving generic software such as mathematical‐based spreadsheets. As the software developed and the computing power grew exponentially, more complex analyses were performed on operational metering assets. Complexity encompassed not only the meter itself but also other associated devices that comprised the larger measurement ecosystem. Developments continued over several decades with a huge digital transformation enabler being the dual technologies of cloud computing and open‐source frameworks for performing analysis on near‐real‐time collected, streamed data.

Meter diagnostics and condition‐based monitoring is digitization using physical computers generating analytical data to improve our knowledge and the maintenance of meter assets while better engaging people through the “automation of knowledge.”

A meter’s diagnostics is a method that is used for reporting the original design tolerances against an operational meter’s fault or failure enabling the end user to identify why it has failed.

A meter’s CBM system is the process of monitoring parameters of condition within a meter, its associated devices (e.g. pressure transmitter, temperature transmitter, quality analyzer, etc.) and the local in situ environment for the meter in order to identify a significant change that is indicative of a developing fault. It is a major component of predictive maintenance and is, therefore, enabling an end user to identify what is going to fail at a future and predetermined time. Predictive maintenance is time series data that includes a timestamp, a set of device readings collected at the same time as timestamps, and device identifiers. The goal of predictive maintenance is to predict at the time “t,” using the data up to that time, whether the equipment will fail in some specified period.

Why connected metering?

Connectivity in this setting is that enabled by the Smart Factory. The traditional installed‐base meters are great potentials for the automation‐of‐maintenance agenda. Automation through online self‐diagnostics brings about “CBM” analytics that have been utilized to extend the traditional recalibration frequency of ultrasonic meters, a unified approach toward maintaining meter accuracy and reliability. Such an approach has been actively pursued within the industry for the past two decades and pursued with great success. With the advancement in technologies, it is now possible to connect differential‐pressure devices, Coriolis meters, and turbine meters to highlight only three other technologies and even analytical devices such as the gas chromatograph. The ever‐advancing Smart Factory is the IIoT that is simply the “Intelligent Interconnection for Operational Transformation” that starts with the “BIG WHY’S” and not the “What.”

Firstly, know why there is a clear need to connect and, subsequently, why is that connectivity going to transform and improve operations! Once the “Why’s” are identified, the “How’s” become a little more intuitive or, at least, more readily definable when it is clear “What” already exists. The Smart Factory toward connected metering is not a rip‐and‐replace strategy but one based on ubiquitous connectivity within an evolving digital ecosystem. The first why is to connect the installed base of various metering types to leverage the technical advances historically seen with ultrasonic meters toward diagnostics and CBM. Advances that have reduced the traditional recalibration frequency while providing an online CBM facility to highlight when the meter is likely to drift outside its stated design performance or is trending toward failure. A strategic choice that moves away from the manual preventative maintenance “calendar” schedules toward online self‐monitoring 24/7/365. The IIoT, the Smart Factory, must provide a positive‐financial impact to enable its rollout, and it must deliver capability that was not previously possible. The greatest saving shall arise from subsequent data analytics that are only enabled by the Smart Factory.

Secondly, why is to enable data analytics not previously possible to bring about new capability to yield a significant positive‐financial saving, the enabler being the IIoT’s complex mesh framework with multiple data paths where subsequent advanced analytics shall be performed in multiple places in providing a technically auditable measurement. To further go beyond the traditional CBM approach, the advanced analytics will enable a near‐real‐time uncertainty analysis of a meter to determine its performance with respect to its uncertainty and the combined uncertainty of the overall metering system itself. The greatest negative‐financial impact for any metering asset is its lack of online CBM with its associated near‐real time uncertainty and not its extended recalibration frequency! To be able to connect the meter and its associated CBM with a near‐real‐time uncertainty is a challenge for the industry for which the author proposes the solution to be the Smart Factory by way of the IIOT.

Unique attributes of process data analytics

There are four important and unique attributes of process data attributes discussed:

• Working compatibly with first principles or process knowledge;
• Applying mathematical programming models for profit optimization;
• Dealing effectively with uncertainties that are usually time varying;
• Generating interpretable solutions.

Chemical processes are designed via applying first principles based on chemistry and physics. First principal models are largely represented by differential and algebraic equations (DAEs). These models become the basis for process simulations, optimization, and control. To achieve economic targets while observing reliable and safe operation, mathematical programming methods such as linear (LP), nonlinear (NLP), and mixed integer linear programing (MILP) have been applied widely. These methods use an objective function for profit maximization and constraints for reliability and safety. Industrial process operations are like a coin with two sides.

On one side are the first principle and optimization models, which are so‐called white models because the modeling equations used can clearly describe technical and economic relationships. Uncertainties are the other side of the coin; they are everywhere in industrial operations and control and are time varying and dynamically changing in mean and variance. Data‐driven models based on statistics and ML can be applied to model uncertainty. Since no fundamental relationships are employed in these models, they are termed as black‐box models.

One of attributes to make ML and analytics applicable in process industries is that they need to be interpretable or understandable by operation personnel. Even if certain operations can be automated, human operators and decision makers must be in the loop and understand the prediction results for more confident implementation. If the people do not understand what the ML is doing, they will not take the risk to act upon it.

Personnel must be convinced that the new solutions offer more values than risks to be adopted. Comparing first principal models with ML models, industrial personnel tend to trust first principal models more than data‐driven models because they are more interpretable. Therefore, sophisticated ML methods including deep learning (DL) and deep reinforcement learning (RL) have to pass the interpretability test before they can be widely adopted in industrial practice.

In summary, process data analytics should have the following desirable features:

• Interpretable and scalable (up to thousands of variables);
• Making use of all kinds of data (g., process data, spectra, vibration, and image data);
• Relatively easy to apply to real processes comparing to other methods;
• Online use for real‐time monitoring and decision making;
• Offline troubleshooting as a valuable tool for continuous improvement.

What is the value proposition of SFP?

Finally, we must say that with the software designed by our company, you don’t need to worry about any aspect of your production process. The data that were introduced in the previous chapters, which were generated from various tools (sensors, transmitters, actuators, and valves), will never be lost, and be unified and be integrated in the SFP software cycle that our company has created. They will become useful information which is created by statistics, fundamentals, Artificial Intelligence, Industrial Internet of Things (IIoT), Cloud connectivity, Machine Learning and Mathematics Programming, that will be reported to managers in dashboards. With this information, you can also implement knowledge management, because you have turned data into information, and this information will become knowledge during the software.

You can easily calculate the productivity of human capital and machinery and predict production planning based on this information for future periods. Naturally, the number of financial damages caused by improper working of operators and machines will decrease and positive financial effects will be created in the factory.

The SFP software shows the entire production process graphically (FPC). When you have the entire process of the production line schematically, you will be able to easily identify production bottlenecks and design suitable solutions. Bottlenecks can have a significant impact on your production flow, greatly increasing production time and cost. For example, suppose a furniture manufacturer inputs metal and other raw materials into the production process and then incurs labor and machinery costs to produce and assemble the furniture. After the production is completed, the finished goods (FGs) are stored in the warehouse inventory.

When the furniture is sold to the customer, the cost of storage is transferred to cost of goods sold. If there is a bottleneck at the beginning of the production, the furniture manufacturer will not be able to enter enough raw materials into the production process, as a result, the machinery will remain unused and the paid workers will not perform productively, and the situation of non-optimal use of resources will be created. This increases the cost of production and also presents a large potential opportunity cost, and may mean that finished goods are not delivered to customers on time.

The bottleneck affects the production capacity that the company can achieve each month. Company managers may decide to reduce production targets to match the company’s production capacity, or they may seek solutions that simultaneously prevent bottlenecks and increase production. Companies often use Capacity Requirements Planning (CRP) tools and methods to determine and achieve production goals. But currently, with SFP that we prepared, you can use the solutions proposed by SFP  in addition to identifying the bottlenecks.

In the material requirements planning (MRP) section, several different scenarios for ordering raw materials have been placed for companies to choose the best method according to their policies. Also, for the limitations that may occur in the production line, for example, the lack of raw materials in a product tree, SFP is able to design an alternative production plan in several seconds to prevent the idleness of the production line.

With the intelligentization that has been done in SFP you will be able to check the wastes rate of finished goods (FGs) and know the causes and roots of the occurrence of wastes and adopt the proposed solutions according to those reasons.

Identifying and rooting the reasons for not matching the actual production with the nominal capacity of the devices is one of the other capabilities of this software. For example, when the operators are having breakfast and they don’t start the machines on time, or the machines break down, or the production plan was not the right one. All these reasons can be clearly tracked in the software.

SFP has the ability to provide various reports of the product production process and the location of each part in the production process (Flow Process Diagram – FPC), supply chain with material requirements planning (MRP) and human capital productivity and efficiency of machinery (OEE).

Efficiency, Effectiveness and Productivity

The first step to get familiar with the Overall Equipment Effectiveness (OEE) index is to know its constituent words. In this section, we will get to know the meaning of effectiveness and the difference between it and efficiency and productivity.

At the first glance, the above words may seem the same, but in management science, there is a fundamental difference between the above words, which we will explain below.

1- Efficiency

Efficiency is determined by the amount of time, cost and energy, or in other words, by the resources needed to achieve certain results. Therefore, to calculate this index, we can calculate the ratio of planned resources to the actual number of consumed resources. For example, in a production system according to previous planning, we use a specific device that requires a predetermined amount of labor, energy and raw materials. If we are able to perform our daily production with less energy or fewer operators or less costs, we have done more efficiently with fewer resources.

2- Effectiveness

Effectiveness shows the degree of access to predetermined goals. For example, production effectiveness is determined by comparing production capacity with what is actually produced. In other words, the Effectiveness of a device can be calculated by dividing the actual production amount by the target or planned amount.

Therefore, Effectiveness does not provide us with information about the efficiency and number of resources that should be available for the production of the desired product.

3- Productivity

By considering the amount of production achieved (Effectiveness) compared to the efforts made to obtain the desired result (Efficiency) is determined. In other words, if we can produce more products with fewer resources, productivity will increase.

The purposeful definition of productivity is as follows: “Expanding a company to produce more wealth! The goal of a manufacturing company is not only to reduce its consumption costs, but also to produce wealth as much as possible. Usually, to calculate the productivity, we divide the output by the amount of input, which in a production system is equal to: output with the manufactured product and input is the same as the resources consumed for production.

According to Figure 1, if we consider the goal of reaching the money, the method of reaching it is not important, that is, if we reach the money through any of the ways A1, A2, A3, we have acted effectively (Effectiveness), but in terms of the efficiency of reaching the destination with the least resources, regardless It is desired from its correctness, for example, the selected goal may be incorrect, in the above figure, ways B1, B2, B3 are wrong ways while in efficiency, we choose the closest path which is B1 but it leads to a wrong way while it is the most efficient way among all the ways.

In productivity, in addition to choosing the right destination, the closest way is also chosen to reach the desired destination. In other words, path A2 not only brings us to the goal, which is also the closest way to the money. So, with selecting this way, we have reached the target, and this way is more effective because of the shortest route (cost).

Productivity also emphasizes on Effectiveness and efficiency at the same time with minimum cost. Also with productivity, we reaches our target. According to the above definitions, the Overall Equipment Effectiveness (OEE) seeks a goal, and reaching it means being effective, but what is this goal?

The goal is to use the maximum capacity of the equipment. In other words, with the help of this the index, the capacity of using equipment is measured.

2- TPM & TQM

As mentioned in the previous section, the first step to get to know the Overall Equipment Effectiveness (OEE) index is to know the words that make it up. In this part, we will describe Overall and related words.

What is the concept of being overall or total?

Today, we are faced with words such as total quality management, total productive maintenance, overall equipment effectiveness and concepts of this kind, in all of which comprehensiveness means a holistic and systematic look at the mentioned categories that we are going to describe.

What is Total Quality Management (TQM)?

Perhaps the term total quality management is one of the most common terms in the field of quality. Total quality management is to improve in work methods for survival in the current competitive environment.

The components of TQM are:

Total is a sign of its inclusiveness and comprehensiveness.

Quality refers to the degree of conformity of the finished goods or services with the customer’s needs.

Therefore, total quality management is the art of managing the entire collection to get the best outputs.

TQM is both a culture and a philosophy and a set of guiding principles to show the foundations of continuous improvement in the organization. Total quality management is a smart, calm and continuous action that has a significant impact on meeting the goals of the organization and finally, the level of customer satisfaction, efficiency and competition in the market are increases by TQM.

In total quality management, it is possible to point out the necessity of employee participation and also the system approach which is one of the principles of the quality management system.

this comprehensive approach means a systemic view of the organization and considering the role of each of the components (departments) to achieve quality. in other words, in the total quality management, all departments and organizational units participate in the Organization’s achievement of quality and customer satisfaction.

Implementation techniques of total quality management:

In total quality management, quality control groups will be formed voluntarily and with a specific purpose in each department of the organization, and they must be fully familiar with the current situation in the organization, work problems in various fields, including the quality of the operation process and product manufacturing, and use from practical tools and techniques to plan and solve these issues.

Among the tools that are used practically are:

• Check Lists
• Brain storming
• Histogram
• Cause and effect diagram
• Parato diagram
• Deming cycle (plan, do, check, act)
• Scatter plot
• Control chart
• 5S
• ISO 9001 Standard
• QFD (Quality Function Deployment)
• Suggestion system

Comparison of old and new quality culture

You can see differences between old and new quality culture in the following table:

What is Total Productive Maintenance (TPM)?

To maintain production at the highest level, in fact to survive production, manufacturers are increasingly required to shift from random maintenance thinking to productive maintenance theory. The method that is defined based on the proper training of human resources through a detailed program and is organized with the comprehensive participation of all the employees of the organization in the maintenance of the equipment. Total productive maintenance is a move towards a comprehensive teamwork approach to preventive maintenance and total quality management.

TPM is to improve productivity and without it, the survival of the company will be jeopardized. The most important part is a new partnership between manufacturing, maintenance, engineering and technical service manpower to improve what is called Overall Equipment Effectiveness (OEE), a program with the goal of zero downtime (caused by sudden equipment failure) and zero waste. It is through improving or reducing six types of common losses that may exist in production workshops. (This is explained in the following sections.)

TPM Principles:

1. Improving equipment efficiency.
2. Participation of operators in daily maintenance of equipment.
3. Improving efficient maintenance.
4. Education and training.
5. Design and management of equipment with the purpose of preventive maintenance.

Comparison of TQM & TPM

In the table below, the evolution of quality and total maintenance is shown:

As it is clear from the comparison table, nowadays a comprehensive approach prevails in various areas from quality to maintenance and repairs. The Overall Equipment Effectiveness (OEE) index also includes all the factors influencing the effectiveness of the equipment and in the calculation of this index all the factors that can It is considered to affect the effectiveness of the equipment, such as stoppages, reducing speed and quality.

3- What is the history of OEE (Overall Equipment Effectiveness)?

Overall Equipment Effectiveness (OEE) tries to measure all the influencing factors on the capacity of the equipment in order to check if the equipment meets the set goals? for example, if a machine is purchased that has a daily production capacity of 1000 pieces, does this machine actually have the capacity to produce 1000 pieces? Also, identifying all the factors that are involved in not using the capacity and the impact of each of them will help us in measuring and analyzing the effectiveness of the entire equipment. This index is expressed as a percentage. If this index is 70% for a device, it means that 30% of the capacity of the device has not been used.

The history of OEE

Equipment effectiveness was first used in the 1960s by Seiichi Nakajiama at Nippon Denso. Mr. Nakajiama used this index as a critical key in Total Productive Maintenance (TPM). According to Nakajiama, TPM is a factory improvement methodology that enables rapid and continuous improvement of the production process. They do this by involving employees and workers, empowering them and measuring the results continuously and related to each other. In the mid-1990s, Sematech company, whose industry is in the field of semi-conductive fiber production, agreed to use the OEE to improve the productivity of their factories. After that, manufacturers of other industries all over the world embraced the methodology of OEE and applied it to improve production efficiency and also improve the efficiency of their assets.

OEE is the benchmark used for TPM programs. Determining the level of Overall Equipment Effectiveness (OEE) is obtained by measuring the efficiency of the equipment. Measuring the equipment effectiveness should be done beyond the available time or the machine’s activity time and should include all the items with the efficiency of the equipment.

The formula for equipment effectiveness should consist of available time, performance and quality. In this way, it is possible for all units of the organization to participate in determining the effectiveness of the equipment. This formula is shown as follows:

Equipment Effectiveness = Available time × Performance × Quality

4- Six major losses

One of the most important goals of OEE and TPM is reducing or removing that thing which is called six major losses. These losses are major reasons for creating waste in Overall Equipment Effectiveness (OEE). These six losses are divided into 3 parts that each category of them is assigned to one of these factors: Availability, Performance and Quality.

Equipment failure

Failure of equipment is one of the important factors affecting the effectiveness of equipment. Some related factors are:

• Not considering maintenance plans
• Total failure of production line

Waste of time due to commissioning and setting up devices

The settings and time required to set up the equipment have a direct impact on the effectiveness of the equipment. Some of the factors influencing the setting and setup time are:

• Setting or change
• Shortage of raw material
• Shortage of human resource
• Time to prepare the device

Short stops

During the time of working with the device, there are usually many short stops that, despite its short duration, have a significant impact on the equipment effectiveness due to repeated repetitions. Examples of these stops are:

• Slowing down the flow of production
• Jamming of components
• Inappropriate feeding
• Failure of sensors
• Non-delivery of the product
• Time to prepare the device

Work at low speed

The speed lower than the power of the equipment is one of the factors that is usually not considered and becomes a chronic problem. Some of its factors are:

• Improper initialization.
• Determination of low nominal capacity.
• Low design capacity.
• Wear and tear of equipment.
• Inefficiency of operators.
• Not knowing the capabilities of the equipment and how to work with them.

Wastes and rework

In many cases, a lot of production time is wasted due to wastes and rework. Examples of it are:

• Production of wastes parts
• Production of parts that need to be reworked
• Incorrect assembly of parts

Wastes caused by starting equipment

The time spent on the production of waste material that occurs during the preparation of the device, its start-up or the initial stage of production, which is significant in many cases. Examples of which are:

• Startup wastes
• Production of defective parts at the beginning of the line

5- Equipment Availability Index

What is availability?

The first step in calculating the effectiveness of the equipment is the time when the equipment is ready for production, that is, when the equipment is not stopped and available for production. These stops include planned and unplanned stops. We use the following relationship to calculate the availability index.

For a better understanding of the availability index, the following figure can help. As you can see in the figure, we have a total time, which can be a day, a week, a month, or a year. Settings for labor absence, raw material failure, power outage, etc. are stopped, some of them are planned stops and some are unplanned stops. If we subtract the total time of these stops from the total time, the available time is obtained.

For example, if we have a 6-hour stoppage in a working day, when there are 24 hours for production, the equipment availability index is 75%, or in other words, we have not used 25% of the equipment capacity, and we can check the causes of the stoppages. And to solve them, add up to 25% more to the capacity of our equipment.

Stoppage fees

A lot of costs are imposed on the organization due to stoppages, which are mentioned in a few cases.

• The cost of idle manpower when the machine stops.
• Late fees or customer penalties due to late delivery.
• The cost of customer dissatisfaction.
• Interest rate cost of capital, for example, if we have a device that is stopped 67% of a year, and this machine costs 100,000$, it is like we have kept 100,000$ of the company’s capital in the company’s fund for 120 days without using it, which if the capital interest rate is 20%, 6,666$ will be the cost of capital stagnation.
• In accounting, the cost of depreciation of equipment and buildings are calculated annually, and it is included in the final price. As mentioned in the case of fixed costs, in case of stopping and non-production, the share of this fixed cost in the cost price of the product unit will increase.

Types of Stoppages

In the classification of stops, we divide them into two groups: planned stops and unplanned stops, examples of which are mentioned below.

Planned stops:

• PM
• Setup
• Rest times
• Planned holidays

Unplanned stops:

• Incidental repairs
• Absence of manpower
• Deficit of raw materials
• Power outage
• Quality problems

Important points when buying equipment

• Investigating product life cycle costs
• The time required to setup the equipment
• Maintenance capability

Should the total time of 24 hours be taken into account when calculating the availability index?

Scheduled times do not include planned stoppages such as closed work shifts in the total time. that’s mean:

For example, if a company works in two 12-hour work shifts and another company only works in one 12-hour shift, and the stoppages of each work shift are on average 2 hours, then the equipment availability index of these two companies will be:

As can be seen, in the planned method, the equipment availability index of the two companies will be equal, while the company that works in two shifts works more effectively, but in the 24-hour method, this difference is clearly visible. Therefore, using the planned method eliminates the possibility of comparing companies with each other. Also, the management of an organization will never be informed of the importance of lost economic opportunities and the fact that a significant part of the capacity is empty, and will be misled by high index numbers. Considering the above argument, the intensification of competition at the international level and its non-negligible effect on the cost price, despite the existence of numerous sources, we still strongly recommend the 24-hour method.

Connection between Six major Losses and Availability

As mentioned in the previous section, the failure of the equipment and the loss of time due to the setup and adjustment of the devices were mentioned as 2 of the 6 major losses, and these two major losses are factors affecting the availability index and the duration the stop time caused by them is included in the calculations.

6- Equipment Performance Index

Performance

The performance index compares the amount of production in the available time with the capacity and capability designed in the device. To calculate the performance index, we use the division of the actual production by the amount of standard production in the available time, that is, if the actual production in the available time is 900 pieces, but the amount Standard expected production is 1000 pieces, so the performance index will be 90%.

As explained about the availability index, the factor that decreases this index is the stops, but the main factor in the decrease of the performance index is unrecordable small stops or working at a low speed. In other words, there may be small stops periodically during the working time which is not recorded anywhere due to the high frequency, they may have a significant contribution to the reduction of the performance index. For example, due to improper placement, the production operator may have to take a short time to get a tool periodically during spending a shift that this time is not recorded anywhere, also working at a speed lower than the capacity of the machine is another factor of reducing this index. Performance index has the following formula:

Reasons of low performance:

• Lack of trained operators
• Inability of operators
• Lack of proper methods and instructions
• Wrong way
• Not knowing the capabilities of the device
• Wear and tear of devices

How to improve performance index:

The important point here is to identify the anticipated capacity of the device in its design or the maximum capability of the device, which can be accessed through its documentation, catalogs, or previous records. After determining the ideal time, you should modify the method of doing work or training operators to bring the real time closer to the ideal time.

Try to discover the factors that make you unable to make the most of the device’s power with comprehensive reviews. For this purpose, it can be useful to carefully observe the work process and use work and time study methods.

What is work study?

Work study is a general term for all the techniques that are used in order to examine human work in all fields and systematically make all the effective factors in the efficiency and economicness of the desired situation to be examined and in the result of improvement will be created. Method study and work measurement are two work study techniques. According to the above definition, work study is directly related to productivity. In most cases, this technique is welcomed in order to increase the amount of the product, according to the certain amount of resources, without causing a large investment cost.

Increase productivity with work study

There are many effective factors in productivity, and they are different depending on the type of organization. Obviously, productivity can be increased by investing more and buying more modern equipment and machines, but this requires a lot of investment. If we want to increase productivity by using available resources, the use of work study will be very effective.

Why is work study valuable?

Some aspects of the nature of work study and why it is such a valuable tool for management are summarized as follows:

• Work study increases the efficiency of production (or the productivity of the factory or operating units) by reorganizing work, and it is a method that needn’t naturally invest on equipment and machinery a lot.
• It is a systematic
• It is the most accurate method that has been created so far to determine performance standards and these standards are necessary for effective production planning and control.
• The benefits resulting from the correct application of the work study started immediately and these benefits will continue until the operation continues in an improved form.
• Work study is a tool that can be used everywhere. This technique is applicable in all places where manual work is done, such as factories, offices, stores, laboratories, service industries, such as wholesale and retail distributors, restaurants and farms.
• It is one of the most penetrating inspection tools available to the management, and this makes it a valuable weapon for starting an attack on ineffective points in an organization.

The main stages of work study:

There are eight steps to fully implement the work study, which are:

• Select the job or process to be studied.
• Record all the events that happen using direct observation. For this purpose, the most appropriate recording techniques are used, and therefore, the information in the most appropriate form will be prepared for analysis.
• Examining the recorded events with a critical view of everything that is done and paying attention to things such as the purpose, the activity, the place where the activity is carried out, the sequence of doing it, the person who does the activity and the person’s intention to do that activity.
• Providing the most economical method considering all environmental conditions. (Develop)
• Measuring the amount of work done by the chosen method and calculating its standard time.
• Defining the new method and its related time.
• Implementation (Install) of a new method that is consistent with standard work and time allowed.
• Maintenance of new standard work by appropriate control procedures.

Connection between Six major Losses and Performance

Earlier, short stops and working at low speed were mentioned as 2 out of 6 major losses, and these two major losses are factors affecting the performance index and cause the machine to not be able to have the maximum production rate.

7- Quality index

If there is a lot of wastes and rework in an organization, it means that we have wasted the organization’s time and resources.

As can be seen in the above figure, considerable time may be spent on the production of low-quality products that in addition to the costs mentioned in previous parts, the cost of the damaged and unusable product should also be added. The quality index is calculated by the following formula:

What measures are necessary to reduce quality problems?

Low-quality raw materials can cause problems in all stages of production or the FGs. In order to solve this problem, in addition to partnering with suppliers, it is necessary to stop choosing suppliers based only on price. To solve quality problems in other stages of production, after identifying the amount and causes of wastes, using problem solving methods, putting the processes under control and using error-proof methods.

Effective tools in improving quality: (Tools for controlling processes)

Check Lists

Checklists are collected and used in a specific format to form data and events related to the operational process or product. Checklists are generally used in two ways:

• Checklists related to process distribution
• Checklists for recording issues and causes of errors

Brain Storming

Brainstorming is one of the important and simple methods used to objectify potential talents and create new ideas. In this method, the people who come together each propose an idea, and the proposal of each idea causes the association of new ideas in the minds of other members of the group.

In this method, one member of the group is responsible for recording all the ideas presented. Then, the agreed ideas are categorized and determined as the agenda of the group meeting based on priority, and the problem is analyzed and the solution is presented. It is important to note that in order to get the desired result from this method, first of all, the meeting space must be prepared for presenting the points of view of all members, secondly, the fear of mocking others among the group members has disappeared, and thirdly, the group leader or the person in charge of the meeting has the power to summarize and lead the meeting as well.

Histogram

A histogram is a type of bar chart that can be used to describe continuous data. In a histogram chart, the frequency of the variables related to the subject or the investigated problem is classified in a certain domain, so that they can be understood and analyzed more easily. To prepare a histogram, it is necessary to observe the following principles:

• Collect and record data
• Domain determination
• Determining the distance between floors
• Determining the boundaries of the classes
• Frequency table setup
• Charts
• Record the data in the chart
• Register histogram properties

Cause and effect diagram (Fish bone diagram)

It is used to identify problem symptoms in all production and service processes.

Parato Diagram

The Parato chart or the 80-20 rule was first proposed by the Italian economist Wilfrid Parato. He believed that 80% of the wealth of a society is owned by 20% of the people of that society. Therefore, if the income of 20% of the people in the society is paid enough attention, 80% of the money circulation of the society can be calculated.

This law can be easily used in the issues and problems of the production and service unit and to prioritize and determine the importance of each problem. For example, most of the breakdowns of production equipment are related to a part of the machinery that is repeated frequently.

Now, if instead of basic repair and inspection of all parts of the equipment, we pay more attention to the part that is constantly damaged, in addition to fixing the defect and preventing the repetition of the error, the time and cost of the repair will be saved. In relation to human resources, it can be claimed that in a production or service unit, generally 80% of errors are made by 20% of the organization’s employees, so if enough time is devoted to this category, an important part of the organization’s problems will be solved.

PDCA Cycle

The cycle of Planning, Doing, Checking and Act is a practical method for the success of quality control groups. Its purpose is continuous improvement of operation processes and prevention of defects.

Scatter Diagram

There are many factors that affect each other in a production or service environment, such as the effect of staff satisfaction on increasing productivity or production technology and production efficiency rate. A scatter diagram is one of the simplest diagrams that easily depicts the relationship between two variables.

This chart is used in the following three ways:

• Relationship between variables
• The relationship between cause and effect
• Correlation of two variables

Control Chart

A control chart is a visual chart that shows the possibility of analyzing the data obtained from the performance of the operation process or the product. This chart can show the changes that happen in the production process with using statistical methods.

Connection between Six major Losses and Quality

Earlier, wastes and rework were mentioned as 2 out of 6 major losses, and these two major losses are factors affecting the quality index. Defective parts not only waste time and reduce effectiveness, but also lead to other high costs such as rework costs.

8- OEE (Overall Equipment Effectiveness) index?

The Overall Equipment Effectiveness (OEE) index is the product of three indicators: availability, performance and quality. In other words, any factor that reduces the effectiveness of equipment can be classified in these three categories.

OEE = Availability × Performance × Quality

One of the advantages of this index is its comprehensiveness, and the activities of all organizational units play a role in this index. For example, if the sales unit cannot receive enough orders from customers, the production will be stopped, and as a result, the Overall Equipment Effectiveness (OEE) index will decrease. Or consider that the procurement unit cannot provide raw materials or spare parts on time, the Overall Equipment Effectiveness (OEE) index will decrease. Also, this index has created coordination between organizational units, and its increase can be a common organizational goal. For example, in many cases, the production unit, without considering the quality, only thinks about increasing the production statistics, and the reason is that production statistics have always been the criterion for measuring this unit, now by replacing the Overall Equipment Effectiveness (OEE) index, this problem is solved It will also become important for the production unit, because the production of defective products will reduce this index. Also, the quality unit will give more value to the production statistics and pay attention to the quantity in addition to the quality.

Reaching the world level in Overall Equipment Effectiveness (OEE)

Overall Equipment Effectiveness (OEE) index is a strict test. For example, if each of the basic factors has a value of 90%, the value of 72.9% will be obtained for Overall Equipment Effectiveness (OEE) index. That is, to reach the high value of Overall Equipment Effectiveness (OEE) index, all the factors must have their highest value. Practically, in order to achieve the goal of reaching the global level in this index, each of the factors must reach a value completely different from each other, and these values are given in the table below.

These values have a different value for each manufacturing plant. For example, if a factory has implemented a six-sigma program to improve quality, obtaining a value of 99.9% for the quality of manufactured products is not so satisfactory. By reaching this number, it is placed in an acceptable and standard position. Extensive studies conducted worldwide indicate that the average level of Overall Equipment Effectiveness (OEE) index for manufacturing plants is about 60%, while by considering the above table, we realize that the percentage of this index to be in the world class is 85% or more.

9- Quality management and OEE standards?

• According to the fourth item of the quality management standard in relation to the organization’s processes:
• Every organization should identify the processes required by the quality management system and their application in the entire company.
• Every organization should determine the sequence and mutual effects of these processes.
• Every organization should determine criteria and methods to ensure the effectiveness of implementation and control of these processes.
• Every organization should ensure the availability of resources and information needed to support the implementation and monitoring of these processes.
• Every organization should monitor, measure and analyze these processes.
• Every organization takes the necessary measures to achieve the planned results as well as continuous improvement of these processes.

According to the standard, the processes of the organization must be monitored, measured and analyzed, and OEE index or its components (Availability, Performance, Quality) can be suitable indicators for monitoring related processes.

• According to the standard of the organization, you must determine the quality goals.

Top management must ensure that quality objectives, including those required to meet product requirements, are established for relevant functions and levels of the organization. Quality objectives should be measurable and consistent with the quality policy.

The OEE index or its components can be determined and improved as quality goals. For example, consider the following quality goal.

10% increase in the overall equipment effectiveness index in the next 1 year.

• Data analysis is mentioned in item 8-4 of the standard.

The organization must determine, collect and analyze appropriate data to show the effectiveness of the quality management system and evaluate in which places continuous improvement of the effectiveness of the quality system can take place. Data analysis should provide information on:

• Customer satisfaction
• Compliance with product requirements
• Characteristics and orientation of processes and products

The OEE index or its components can provide useful information for data analysis.

• According to item 8-5 of the quality management system standard, continuous improvement is a requirement.

The organization should continuously improve the effectiveness of the quality management system through the use of quality policy, quality objectives, audit results, data analysis, corrective and preventive measures and management review. Improvement opportunities can be identified by using the information of OEE index. For example, by using availability index trends, preventive measures can be determined to prevent stoppages.

• In paragraph 3-6 related to the infrastructure in the ISOTS16949 technical specification, there is a reference to the principles of lean production, and the OEE index is one of the tools used in lean production.

Lean production is actually a philosophy and attitude that aims to remove and eliminate any additional process from raw material preparation stage to production and finally sale that creates added value.

10- Six-Sigma and OEE (Overall Equipment Effectiveness)

Sigma is one of the letters of the Greek alphabet and in the science of statistics, it is one of the important indicators of scattering called standard deviation, and it is actually a scale for measuring deviation; Sigma indicates how much a process has deviated from its desired state, so it is actually a metaphor for extreme accuracy in reducing quality costs, which emphasizes the importance of accurate calculations in the production and service delivery process. to give Six-Sigma means reaching a level of product quality and service delivery that reduces work process errors by 3.4 in a million situations.

Six-Sigma is an organizational transformation strategy. The transformational strategy of six-sigma is a system that leads to the development and expansion of management, statistical methods and ultimately solves problems and allows the company to make leaps and transformations.

Six-Sigma is a philosophy of continuous improvement and moves towards excellence in everything. Six-Sigma is a system that determines where we are, where we want to be, how we will get there and how we will improve along the way.

In the six-sigma program, everything that is contrary to the satisfaction of customers and their intended usefulness is called waste. The six-sigma strategy includes the use of statistical tools in the form of a structured method to achieve the knowledge required in today’s competitive world, to produce products and provide better services, faster and at a lower cost.

Six-Sigma is a systematic and dynamic approach to guide information and a methodology to eliminate waste; With the aim that six-sigma is in our distance between the lower and upper characteristic limits of the target value. Six-Sigma programs are used for any process, from manufacturing to any operational process and even for service services.

Six-Sigma is a comprehensive methodology of effective organizational improvement that has a structure, program and powerful quality management tools. The six-sigma approach is to clearly reduce all errors of the organization and reach the level of six sigma in errors. In this regard, we need to pay attention to the following points:

• To reach six-sigma, we have to go through the previous stages and levels of sigma.
• The purpose of reducing errors in the organization is to reduce errors in processes, not in people.
• The Six-Sigma approach is used to identify and eliminate errors in processes in the DMAIC cycle. This cycle can be considered as an effective improvement cycle in Six- Conceptually, this cycle can be considered the same as PDCA (Plan, Do, Check, Act) and PSDA (Plan, Study, Do, Act) improvement cycles, but in DMAIC cycle, a very operational error reduction cycle based on accurate monitoring is implemented. This cycle consists of the first letter of the word’s Definition, Measurement, Analysis, Improvement and Control.

In describing this cycle, it should be said that any error reduction starts from the correct definition. Measurement in the methodology of the DMAIC cycle after defining the issue shows that the emphasis of six sigma to explain the existing situation in order to improve it. Measurement leads to the formation and detection of information flow in the process and can determine the level of errors and trends and at a higher level and their priority. Using the information from the previous section, the analysis will find the root causes of errors in the process. One of the common errors in organizations is failure to recognize the main factors and roots of errors. Improvement is the stage in which the necessary answers to the questions raised in the previous stages are formed. Reducing the error towards the formulated goals and measuring the initial results after implementation is one of the improvement methods related to this stage. Control can be seen as a stage in the continuity and consolidation of improvement.

In the following, we will explain the role of the Overall equipment effectiveness (OEE) index in the different stages of six sigma:

• Identification

Availability, quality and performance indexes can clearly show system issues and problems and can be a good start for an improvement project.

• Measurement

The mentioned indexes are calculative and can be a suitable criterion for measurement. For example, waste values or a specific stop or … can be achieved in the OEE index.

• Analysis

The information obtained from the calculation of the OEE index can help a lot to analyze the root of the problems.

• Optimization

The OEE index provides the possibility to see the impact of improvement in the system, and we can see the improved indicators with calculation scales.

• Control

The OEE index provides the possibility of observing, tracking and directing the process to control it.

• OEE (Overall Equipment Effectiveness) calculation

Example1:

Machine A produced 1000 pieces in 5 days. If the total stoppages are 30 hours and the standard production time is 5 minutes, and 100 production pieces are defective. What is the OEE index of the device?

Answer:

Example2:

Machine 1, which has a standard production time of 10 minutes for one piece, has the following stops during the day, and its production statistics are according to the table below. Now calculate the index of this device.

Answer:

Think you want to cook your favorite dinner, when you are cooking, you understand that you’re missing one of the major ingredients. Annoying, right?

if it happens in the production process of your business, it will be more and more difficult. Running out of a specific raw material from your production line not only halts production, but can lead to back orders,  sales loss, and a disrupt your supply chain workflows.

Material requirements planning (or MRPs) can prevent these types of issues. A lot of companies count on MRP software to keep – inventory management at optimal levels, minimize fixed Capital, optimize production planning, and finally simplify their supply chain management process.

What is Material Requirements Planning (MRP)?

Material Requirements Planning (MRP) is a business process strategy for integrating information that is incremental. MRPs are implemented from modular hardware and software programs linked to central databases that store and transmit business data and information.

Paper information systems and non-integrated computer systems that have paper outputs lead to many information errors such as missing data, redundant data, numerical errors that originate from their incorrect registration in the system, wrong calculations that originate from numerical errors, and Inappropriate decisions are caused by incorrect or outdated data. In addition, because in non-integrated systems, similar data are grouped differently in separate databases used in different functional areas, some data are unreliable. So, companies use MRP to determine raw and pack materials requirements for the manufacturing process integrally.

MRP systems support businesses to find out:

• Which raw/ pack materials it needs to be built.
• How much of those materials are required to produce the quantity of finished goods that will meet demand.
• When raw/ pack materials are needed.

With this knowledge, a business can set an optimize and efficient stock policy, and reach the supply chain up for success.

What is the history of MRP?

MRP evolved from the first commercial database management packages developed by Jane Thomas at IBM in the 1960s. The original structure was called BOMP (Board of Material Processor), which evolved in the next generation into a more general tool called DBOMP (Database Organization and Maintenance Program). These were implemented on mainframes such as the IBM/360.

The vision of MRP was to centralize and integrate business information in a way that would facilitate decision making for planning managers and increase overall – supply chain process effectiveness including Warehouse, Production line and also decrease a sales loss, fixed cost, in parallel improve the procurement process. In the 1980s, manufacturers developed systems to calculate resource requirements for a single  Finished good (FG) run based on sales forecasts.

In order to calculate the raw materials needed to produce products and schedule the purchase of those materials along with the time required for equipment and labor, production managers realized that they would need computer and software technology to manage information.

Originally, for manufacturing operations, they built custom software programs that ran on mainframes.

Like ERP systems today, MRP was designed to give us a lot of information through a centralized database. However, the hardware, software, and relational database technology of the 1980s was not advanced enough to provide the speed and capacity needed to run these systems simultaneously, and the cost of these systems was too high for most commercial enterprises. However, the vision that was established, and changes in fundamental business processes along with successive advances in technology, have led to more cost-effective enterprise and application integration systems that large businesses and many small and medium-sized businesses use. They use them.

Why is material requirements planning important?

In order to produce products on-time, you’ll need to have the accurate amount of production inventory — but -calculating the volume of it is too difficult. If the volume of production is too much, the costs of your business increases unnecessarily, and if it is too little, you cannot meet customer demand because you confront a shortage of goods.

MRP systems make it easier to achieve this balance. MRP enables manufacturers to:

• Better plan inventory procurement.
• Always have sufficient stock on hand to create enough finished goods.
• Smooth Planning and overseeing the production process
• Simplify the overall supply chain
• Decrease inventory fixed capital / or increase working capital/ cash flow.
• Set the optimize Stock policy.
• Building optimum budget forecast.

What are the main features of an MRP system?

There are a few major features that every MRP system has to include.

1- MPS (Master production schedule) or sales forecast

MRP process starts with demand forecasting, and works backwards to determine how much FG stock is needed.
A good MRP system should support you build precise demand forecasts, so you can quickly calculate what required resource such as available time, Blue colors, you’ll need to manufacture them.

2- Data structure

MRPs store and reorganize your company’s unique production inventory data to make it easier for you to manage. The final result is clean, visible data that your business can use to simplify the manufacturing process.

3- Schedule planning

Timing procurement and scheduling production are very difficult, but an MRP system has to perform it easier. An appropriate MRP system let you know the best reorder point and time for each part of material , and can help you factor in lead times and current inventory levels to time replenishment perfectly.

MRP systems help you organize master production schedules (MPS), giving you an accurate view of labor, capacity , and activity requirements for every part of finished goods you manufacture.

4- Purchase planning

One of the most crucial functions of a material requirements planning system (or MRP) is helping you plan your procurement.

Your MRP system should provide you with a detailed list of materials (and accompanying amounts) that you need to maintain optimal inventory levels. You can then plan your purchases accordingly by considering other factors like production lead times and minimum order quantities.

5-Inventory management

Material requirements planning systems (or MRP) also help you keep track of all stocks/inventories you keep in the warehouses/ your organization. With software to monitor details such as varying shelf lives and inventory turnover, it is much easier for a business to manage their raw materials inventory over time and improve inventory management.

MRP process explained in 5 steps

Here are the five key steps that make up the material requirements planning process.

• Step 1: Create your Product Tree (PT) and Bill of Materials (BOM)

The Product Tree is a fun, visual, and useful tool that gamifies product management. It helps product managers (PMs) organize, prioritize, and tame the barrage of product feature inputs from customers and internal stakeholders.

Before you can even get your MRP system up and running, you’ll need to create your company’s bill of materials (or BOM).

A bill of materials (or BOM) is just a hierarchical list of all the final products (or independent demand items) and the raw materials, components, and  semi-finished goods needed to create them (or dependent demand items), as well as the quantities required.

The PT and BOM provides the data that an MRP needs to function. Thus, to get your MRP system to work, you’ll first need to input your BOM data correctly into your MRP.

From there, as long as your PT and BOM hierarchy is accurate, the MRP will take care of tracking material and component levels, identifying which products are dependent on other components or materials, and calculating what items in what quantities are needed by what date for the manufacturing process to go smoothly.

Examples of Product Tree:

Examples of BOM:

Note: You must know that planning is from up to down and procurement is from down to up.

• Step 2: Set goals and estimate demand

The MRP process works backwards from MPS This means it takes the amount of a product that you expect to sell in a given time period considering your stock policy, and breaks that number down to calculate how much of each raw material or component you’ll need to create that amount of FG.

For instance, imagine you must deliver 100 Chairs ( MPS) with above PT and BOM on the last day of a month ???(time period). = Yes, I mean it as time period

To Calculate the MPS, start by looking at your sales forecasts and customer orders.

• Step 3: Check current inventory

In your MPS calculation, you should also factor in how many units of  FGsyou already have. Tracking your inventory and checking demand against the inventory you currently have in stock prevents you from ending up with excess material that doesn’t get sold.

Your MRP system should give you visibility into inventory levels across multiple channels and locations, and help you identify which resources are available to use, which are already assigned to a manufacturing process, and how best to allocate production inventory overall.

For example, you need 100 Chairs on the last day of this month, and the chair’s consumption factor and current inventory are according to the above table. Please calculate the demand of Screw, Wheel and Truck?

The demand of Wheel, Chair Base, Task Chair Control, Seat, Arm Pad and Back on the last day of this month are 445, 440, 165, 80, 175 and 85.

• Step 4: Check lead time

Now you have to check lead time and determine the time that you should order the requirement of your product. For illustration the point, I want to calculate the day that we must order for above sample.

Note: Time that you must order the requirement of your product is the eighteenth day of the month.

• Step 5: Determining the amount of batch order

There are six different ways to order a product or parts of a product. In this section, I want to describe these ways:

• Fixed batch
• L4L (lot 4 lot)
• LUC (labor unit cost)
• LTC (labor toral cost)
• PPB (part period balancing)
• POQ (production order quantity)

Fixed Batch:

If a seller says that “I just sell my products as a dozen (fixed batches)”, We have to order our requirements as fixed batches. Therefore, if we need 14 parts, we must order 2 dozen. It’s mean we will have 24 parts instead of 14 parts because of the condition of seller and we cannot do anything.

Example:

Question: Imagine the demand for a product is according to the below table. If the lead time is equal to 1 week, and the seller has said that his products will be sold in 8 pieces. Place orders using fixed batches as the approach for ordering.

Answer with fixed batch:

Calculation:

Lot for lot (L4L):

In the method of ordering, the volume of the order is equal to the amount of our demand. As a example, we solve previous instance with L4L approach.

Answer with L4L:

Note: In this way, as you can see in the above table, we do not have any inventory in our periods.

labor unit cost (LUC):

In the way of ordering, we use the minimum of cost per each unit, and we have to do these items step by step:

• We start with the first period and calculate the total cost of maintenance and ordering for each batch to that period.
  
• The output of the first step is divided into total ordered products for counting the cost for each product.
• Until UC decreases, we continue our calculation. When UC is broken and starts to increase, we stop our calculation.
• Batch order is equal to the minimum of
• We repeat our steps according to above guideline until meet our demand.

Question: Imagine the demand for a product is according to the below table. If the lead time is equal to 2 weeks, and holding cost is 0.05 $ per week per product and the cost of ordering is 5.75 $. Place orders using LUC as the approach for ordering.

Answer with LUC:

 Calculation:

Labor toral cost (LTC):

In this method the cost of ??? (holding cost=HC)  is calculated cumulative as long as this calculation is equal or close to ordering cost, we can stop and determine batch order. We want to solve previous sample with this way.

Answer with LTC:

Part period balancing (PPB):

In the way of ordering, we have to do these steps:

• First of all, calculate EPP:
  
• In each period we have to multiply the demand of the period by the number of period which product must be kept.
• Cumulatively calculate the value of previous part to approach to EPP.
• When the value of item 2 approaches to item 3, you must stop this work and determine batch order.

Now, we can solve previous example with PPB way.

Answer with PPB:

Production order quantity (POQ):

• • Calculate EOQ (economic order quantity)
    
    
  • Calculate
  • Calculate POQ
    
    
  • Order based on the value of POQ.

Question: Imagine the demand for a product is according to the below table in 1 year. If the lead time is equal to 2 weeks, the yearly demand is 1440, the cost of ordering is 60$, (holding cost) is 30% yearly, the cost of product is 90$ and periods of planning is 52 weeks. Place orders using POQ as the approach for ordering.

Answer with POQ:

Note: In every approach which has the (holding cost), the unit of this scale has to be according to the unit of period. For instance, in all our examples, we calculate based on week.

Right now, I want to conclude bullet 5 with a sensible example that include all topics which has to be followed by employees.

Question: Imagine a product with the below tree and demand according to the below table.

Ordering cost: 5.75 $ (Except 082) – Ordering cost: 5 $ for 082

(holding cost): 0.05 $ per week (Except 082) – (holding cost): 0.33 per month for 082

• 
  Answers:

 Note: Safety Stock means that the inventory has to be equal to safety stock in the minimum gesture. For instance, the minimum inventory must be 10 instead of 0 in 457.

Note: If we have initial inventory, we must consider it into our order. For example, we must order 70 instead of 90 in 082, because initial inventory is equal to 90

 Note: Repetitive product: 129 and 118; Question: which we can plan for production? why?

Answer: Certainly 118, because we could plan each product that is above 118 but we have not calculated orders for 315 which is above 129 yet, so it’s clear we cannot order 129 as long as doing plan for 315.

Is an MRP system essential?

it’s worthy weighing carefully advantages and disadvantages to better assess the value of an MRP system.

Advantages with an MRP system

An MRP system ensures that you have all the essential raw materials for production, which yield the subsequent benefits:

• enhanced efficiency in the manufacturing system.
• Decreased risk of errors as a result of automating manual tasks.
• Reducing supplying lead times
• Increasing customer satisfaction.
• Optimized inventory levels
• Decreasing stockouts

Disadvantages with an MRP system

There are a fewrisks :

• Input data must be precise to present appropriate result
• MRPs can be sophisticated and expensive to develop

MRP vs ERP explained

While both material requirements planning (MRP) and enterprise resource planning (ERP) play a critical role in helping you plan your resources, their scopes are different.

ERP systems plan and manage resources for multiple organizational functions, including finance, accounting, payroll, sales, operations, manufacturing, supply chain, suppliers management, and much more. Businesses use ERP systems to maintain resource visibility across departments, and to simplify the resource management process for the whole company.

MRP systems, on the other hand, focus particularly on production resource planning — that is, planning and managing the resources involved in the manufacturing process. MRP systems are mostly used by manufacturing companies.

Material requirements planning FAQs

Here are answers to some of the top questions about material requirements planning.

1. What is the importance of material requirement planning?

MRP is a major section of building a simplified, efficient manufacturing process. MRP systems help you make sure you have all the raw materials and essential parts for manufacturing products.

Also, MRPs allows a producer to keep its production schedule, simplify its production process, decrease customer lead times, and improve customer satisfaction.

2. What are the types of MRP?

MRP is just one type of resource planning system.

3. What comes after MRP is completed?

After MRP is completed, a business must control the production process, receive raw materials at their warehouse, and store thestock.