Enterprise Manufacturing Intelligence Tools and Use Cases

What is Manufacturing Intelligence?

The history of Enterprise Manufacturing Intelligence (EMI) solutions can be traced back to the late 1990s when the first manufacturing execution systems (MIS) were developed. This early system gave manufacturers real-time visibility into their operations' performance, allowing them to identify and address inefficiencies, improve quality, and reduce costs. However, as technology progressed, the Internet of Things (IoT) and Industry 4.0 became more prevalent, and the amount of data available from the manufacturing industry.

Enterprise manufacturing intelligence has come a long way since its early beginnings in the manufacturing industry. Through advancements in automation, computer systems, data collection, and artificial intelligence, EMI has become a critical tool for manufacturers looking to improve their operations and stay ahead of the competition.

Manufacturing Process Automation uses robotic tools to operate a manufacturing process when making any physical product. Taken From Article, Manufacturing Process Automation

Why do Manufacturers need Business Intelligence?

As previously mentioned, manufacturing processes are becoming more nuanced than ever before calling for intelligent decision-making that is grounded on reliable information and the ability to extract meaningful information from data is key, which is why business intelligence solutions are gaining popularity within the sector.

BI tools are those that facilitate the collection, integration, and analysis of data from disparate sources. Usually, Intelligence software includes the following features:

• Dashboards displaying charts and graphs for quick visualization of most important statistics and trends
• Self- service data editing for staff to access, transform, and store digital information without the aid of an IT department.
• Advanced analytics that leverage data science and artificial intelligence for predictive modeling
• Secure user administration with role-based access permissions and activity logs to ensure data safety.

Aggregating Industrial Data Gathering

Enterprise Manufacturing Intelligence (EMI) is software that aggregates data from industrial IT systems, enabling them to be viewed, processed, and analyzed to guide and optimize operations. The goal is to combine data from

1. SCADA systems (PLC, DCS, Historian, etc.) particularly including parameters of control, measurement, operating cycles, and line analysis systems.
2. Production Execution Information Systems (MES: manufacturing execution systems).
3. Quality Information Systems (ERP) with raw material purchase, standard production costs, etc.
4. Financial Information Systems (ERP) with raw material purchase, standard production costs, etc.
5. IIoT (Industrial Internet of things) including equipment monitoring sensors and geolocation systems.

Aggregating and combining means collecting data in one place to make it easier to manipulate and it requires storage. But the challenge is finding the best method for intended uses while staying flexible. Data Warehouses or Data Lakes provide this service.

Any Enterprise Manufacturing Intelligence solution must provide a Data Lake optimized for storing and manipulating industrial data. For example, the ability to efficiently process time series as well as traceability and relational data. The existence of a pre-established data model adapted to industrial use makes implementing a chosen solution quicker and easier.

Business users use a set of services provided by it to create reports and achieve a goal. Taken From Article, Data Visualization with Microsoft Power BI

Developing Manufacturing System

Technology that became commercial within the past five years is probably still new to many the other hand technologies expected to be available within the next five years need attention. The focus of the technologies appropriate for manufacturing is on mechanical products and their components. For uniformity batch manufacturing technology features that the individual operation is divided into one primary and three secondary processes. The principle of the process is supported by preprocessing finishing operations, and the generally grouped secondary processes. In medium-sized manufacturing companies, the focal point of manufacturing technology can be production, sales engineering, quality control, development, research, or other functions. Large industries are more likely to have an organized plan to innovate manufacturing processes. Whatever the actual name of the technology department - Development R&D justification is an issue. The stimulus can come from technological arguments or market situation.

Intelligent Manufacturing System

An intelligent Manufacturing process has the ability to self-regulate and self-control to manufacture the product within the design specifications. An integrated concept with factories of the future, where products are produced in an artificial life environment adds value to this. Researchers working on the implementation of the EXPERT SYSTEM, have come out with this concept of adding intelligence. Recent developments in feature-based solid modeling techniques for design representation and AI applications have shown a potential to substitute EXPERT SYSTEMS for some decisions currently handled by a designer's knowledge of design rules and practices. The first effort in this endeavor was through Knowledge-Based Systems, and expert systems were not sufficient enough to generate the required level of intelligence owing to their dependence on symbolic representation of the knowledge and the human expertise required to encode them efficiently. However, the advent of ANN in the ’80s as self-organizing dynamic systems and model-free estimators, the emergence of Fuzzy Logic Systems for modeling uncertainty and human reasoning, and the acceptance of GA as a global optimization technique for hard problems have led to the realization of Intelligent Manufacturing system as a feasible proposition in the current decade.

Technology-driven approach that uses Internet-connected machinery to observe the production process. Taken From Article, Internet of Things Solutions for Smart Manufacturing

Intelligent manufacturing can be achieved in three basic ways.

• Existing manufacturing processes can become intelligent by monitoring and controlling the state of the manufacturing machine.
• Existing processes can be made intelligent by adding sensors to monitor and control the state of the product being processed.
• New processes can be intelligently designed to produce parts of desired quality without the need for sensing and control of the process.

Intelligent Manufacturing system:

1. Uses technology that can minimize the use of the human Brain
2. Regulation for product mix and priority production, self-regulated.
3. Self-controlled operations with an automatic feedback mechanism.
4. Monitoring and control of the manufacturing machine.
5. Monitoring and controlling the state of the product being processed.
6. New processes with intelligence can be made to produce parts of desired quality without the need for sensing and control of the process.

Stages of development of subsystems of EMI Use Cases: 

What are the Use Cases of Enterprise Manufacturing Intelligence?

Enterprise Manufacturing Intelligence (EMI) is a solution that provides real-time visibility, analysis, and insights into production processes. The following are some of the use cases for EMI in the manufacturing industry:

Production Monitoring

Real-time monitoring and control is a key use case for Enterprise Manufacturing Intelligence (EMI) systems. EMI systems provide real-time visibility into production processes, enabling manufacturers to identify and resolve problems as they occur. This can lead to improved production efficiency, reduced downtime, and higher quality products.

In a typical EMI system, data from sensors, machines, and other sources is collected and analyzed in real-time. The system can then provide real-time notifications and alerts if any problems occur, such as machinery failure, process deviations, or quality issues. This enables manufacturers to respond quickly and resolve problems before they escalate, reducing downtime and improving the efficiency of production processes.

EMI systems also provide real-time dashboards and reports, enabling manufacturers to monitor the performance of their operations and make data-driven decisions. For example, they can track production metrics such as throughput, cycle time, and scrap rate, and use this information to optimize production processes and identify areas for improvement.

Overall, real-time monitoring and control is a key benefit of EMI systems, enabling manufacturers to improve production efficiency, reduce downtime, and increase the quality of their products.

Predictive Maintenance

Predictive maintenance is a key use case for Enterprise manufacturing Intelligence (EMI) systems. Predictive maintenance involves using data and analytics to predict when equipment is likely to fail, enabling manufacturers to plan maintenance and avoid unplanned downtime.

EMI systems can also provide real-time notifications and alerts if any problems occur, such as machinery failure, process deviations, or quality issues. This enables manufacturers to respond quickly and resolve problems before they escalate, reducing downtime and improving the efficiency of production processes.

Overall, predictive maintenance is a key benefit of EMI systems, enabling manufacturers to minimize unplanned downtime, reduce maintenance costs, and improve the efficiency of their production processes.

Helping enterprises to cost savings, greater predictability, and improved availability of the systems. Taken From Article, Predictive Maintenance using ML

Quality Control and Improvement

Quality control and improvement is another key use case for Enterprise Manufacturing Intelligence (EMI) systems. EMI systems can help manufacturers monitor and control the quality of their products by providing real-time data and analytics on production processes, enabling manufacturers to identify and resolve quality issues quickly.

In a typical EMI system, data from sensors, machines, and other sources is collected and analyzed in real-time. The system can then use this data to monitor the quality of production processes and identify deviations from established quality standards. For example, the system may detect an increase in scrap rate or an abnormal pattern in the production process that could indicate a quality problem.

EMI systems can also provide real-time notifications and alerts if any quality problems occur, enabling manufacturers to respond quickly and resolve the problem before it escalates. This can help manufacturers to improve the quality of their products and reduce the number of defects.

The quality control and improvement is a key benefit of EMI systems, enabling manufacturers to improve the quality of their products, reduce defects, and increase customer satisfaction.

Supply Chain Optimization

Supply chain management help manufacturers optimize their supply chain by providing real-time data and analytics on production processes, inventory levels, and supplier performance.

In a typical EMI system, data from sensors, machines, and other sources is collected and analyzed in real-time. The system can use this data to monitor production processes, inventory levels, and supplier performance, enabling manufacturers to make data-driven decisions about their supply chain. For example, the system may detect low inventory levels of a critical component and automatically trigger a recorder, reducing the risk of stockouts and improving the efficiency of production processes.

EMI systems can also provide real-time dashboards and reports, enabling manufacturers to monitor the performance of their supply chain and identity areas for improvement. For example, they can track supplier delivery times and accuracy, enabling manufacturers to identify and address any performance issues and improve supplier relationships.

The supply chain optimisation is a key benefit of EMI systems, enabling manufacturers to improve the efficiency of their supply chain, reduce costs, and increase customer satisfaction.

Technical faults in machinery will decrease turbines' productivity, disrupt deliverables, and ultimately decrease credibility. Taken From Article, IoT Applications for Analyzing Manufacturing Industries

Energy Management

Enterprise manufacturing intelligence systems can help manufacturers monitor and optimize their energy usage by collecting and analyzing data from energy-related equipment and systems.

In a typical EMI system, data from energy meters, sensors, and other sources is collected and analyzed in real-time. The system can use data to monitor energy usage across the manufacturing facility and identity opportunities for energy efficiency and cost savings. For example, the system may detect an increase in energy usage during certain times of day or detect an inefficient use of energy by a particular machine, enabling manufacturers to take action to reduce energy usage and improve efficiency. EMI also provide real time dashboards and reports, enabling manufacturers to monitor energy usage and track progress towards energy efficiency goals. In addition, EMI systems can be integrated with other systems, such as building management systems (BMS) and enterprise resource planning (ERP) systems, to provide a comprehensive view of energy usage and support decision-making. Energy management is a key benefit of EMI systems, enabling manufacturers to reduce energy costs, improve energy efficiency, and reduce their environmental impact.

Production Planning and Scheduling

EMI systems can support manufacturers improve the efficiency of their production processes by providing real-time data and analytics on production processes, inventory levels, and resource utilization.

In a typical EMI system, data from sensors, machines, and other sources is collected and analyzed in real-time. The system can then use this data to monitor production processes, inventory levels, and resource utilization, enabling manufacturers to make data-driven decisions about their production processes. For example, the system may detect that a particular machine is operating at less than optimal efficiency and schedule maintenance to improve its performance.

EMI systems can also support production planning and scheduling by providing realtime dashboards and reports, enabling manufacturers to monitor the status of production processes and make informed decisions about production schedules. For example, the system may detect that inventory levels of a critical component are low, enabling manufacturers to adjust production schedules to ensure that adequate supplies are available.

In addition, EMI systems can be integrated with other systems, such as manufacturing execution systems (MES) and enterprise resource planning (ERP) systems, to provide a comprehensive view of production processes and support decision-making. Overall, production planning and scheduling is a key benefit of EMI systems, enabling manufacturers to improve the efficiency of their production processes, reduce costs, and increase customer satisfaction.

Process Optimization

EMI helps manufacturers improve the efficiency of their production processes by collecting and analyzing data from sensors, machines, and other sources, and using this data to identify opportunities for improvement.

In a typical EMI system, data from sensors, machines, and other sources is collected and analyzed in real-time. The system can then use this data to monitor production processes, identify deviations from established process standards, and identify opportunities for improvement. For example, the system may detect that a particular machine is operating at less than optimal efficiency, enabling manufacturers to adjust the process to improve its performance. EMI systems can also support process optimization by providing real-time dashboards and reports, enabling manufacturers to monitor the performance of production processes and make informed decisions about process improvement. For example, the system may detect that a particular production step is causing frequent quality defects, enabling manufacturers to adjust the process to improve product quality.

In addition, EMI systems can be integrated with other systems, such as manufacturing execution systems (MES) and enterprise resource planning (ERP) systems, to provide a comprehensive view of production processes and support decision-making. Process optimization is a key benefit of EMI systems, enabling manufacturers to improve the efficiency of their production processes, reduce costs, and increase customer satisfaction.

Employee Training and Development

EMI systems can support employee training and development by providing real-time data and analytics on employee performance and skill sets, enabling organizations to identify training and development needs and develop customized training programs to meet these needs.

In a typical EMI system, data from sensors, machines, and other sources is collected and analyzed in real-time to monitor employee performance and skill sets. The system can then use this data to identify areas where employees may need additional training and development, such as in the operation of specific machines or in specific processes. EMI systems can also support employee training and development by providing realtime dashboards and reports, enabling organizations to monitor the progress of employee training and development programs and make informed decisions about training and development initiatives.  In addition, EMI systems can be integrated with other systems, such as human resources management systems (HRMS) and learning management systems (LMS), to provide a comprehensive view of employee training and development and support decision-making.

Employee training and development is a key benefit of EMI systems, enabling organizations to improve employee performance and skills, increase employee satisfaction, and support long-term organizational success.

A technology that works within the software and eases human efforts and hence called Software Automation. Download to explore potential of RPA for Businesses

What are the Tools used for Intelligent Manufacturing?

The tools are generally used in intelligent manufacturing:

Fuzzy logic

Fuzzy logic system provides a means of expressing the linguistic variable in a suitable form for processing using a Computer Fuzzy logic Control of processes offer flexibility by which process states and control actions can be described directly from the experience and advice of the human operators, thus making it possible to apply practical operational experience in the computerized control of the complex multivariant process. It provides a mathematical framework to capture the uncertainties associated with human cognitive systems such as thinking and reasoning. The control rule is formulated as linguistic expressions involving every word like High medium, low, etc.

Genetic Algorithms

GA is the powerful probabilistic heuristic procedure for global search and optimization in multi-parameter search spaces, based on the mechanic's natural genetics. It is to exploit historical information to locate new points in the search space with expected improved performance. This is also used as a tool for optimal assembly planning. Genetic algorithms referred to as ADAPTIVE COMPUTATION are based on the evolutionary concept of natural selection and survival of the fittest. In simple terms, a genetic algorithm generates new rules to replace the least useful rules already in place. These software tools allow the user to solve complex problems, such as scheduling large number of conflicting tasks, finding the shortest route that connects a number of locations, or streamlining the communications network. The genetic algorithms are used to optimize the search routine used in assembly planning with the goal of improving the assembly process of the mechanical product thereby minimizing time and cost.

Scheduling with Neural Network

Artificial Neural Networks aimed towards the modeling of networks of real neurons motivated by their robustness, fault tolerance, flexibility, and the learning ability of the biological brains, the building blocks of the ANN variously called neurons, processing elements, etc. do have striking similarities with their biological counterpart. An interactive tool for short-term production scheduling with system features and a graphical interface that allows operators to interactively control the schedule generation and see the influence on key parameters. A constraint management subsystem checks that the current schedule agrees with the restriction imposed from the production environments and if not, the reason for conflict is identified and presented to the planner for rectification. This knowledge base system is able to effectively react to unexpected events or delays. Neural network techniques come into play, repairing an inconsistent schedule towards a consistent or optimal schedule.

Case-Based Reasoning

This is extensively used as a tool to interpret the process parameters. Human experts need to be consulted to interpret the analytical results of the process, Providing an opportunity to improve the procedure with an expert system. Case-based reasoning represents knowledge as ‘cases’ i.e. examples of past problems and solutions. Intelligent Design and Analysis Software: The expert system produces optimal design parameters without violating any material or machine constraints. This is actually an extension of CBR (case-based reasoning).

Artificial Intelligence

This tool is an attempt to increase the number of human characteristics, computer, and computer-controlled systems. It is basically an ability to imitate human intelligence. The sub-tools used are

• An algorithm is a computer program that solves the selected problems within the given time frame.
• Early vision Computer calculations that allow systems to see by providing lowlevel data.
• Higher level vision Computer calculation that allows systems to accomplish higher levels such as smart improvement within an environment object recognition and reasoning about objects.
• Knowledge Engineering. A process by which knowledge is collected from experts

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Conclusion

Enterprise Manufacturing Intelligence (EMI) systems are a critical component of modern manufacturing operations, providing real-time data and analytics to support decision-making and improve the efficiency of production processes. EMI systems support a wide range of use cases, including real-time monitoring and control, predictive maintenance, quality control and improvement, supply chain optimization, energy management, production planning and scheduling, process optimization, and employee training and development. EMI systems can help manufacturers reduce costs, increase customer satisfaction, and support long-term organizational success by providing real-time data and analytics to support decision-making. 

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