by Don Lindsey and Khoa To
In today’s fast-paced industrial landscape, manufacturing is undergoing significant transformation driven by changing market demands and cutting-edge technology such as AI, robotics, IoT, quantum computing, and global internet connectivity. Many companies already use some technologies, while others plan to integrate them to become more efficient, resilient, and responsive to changing market challenges.
However, along with these, or any technologies, companies need a foundation—a set of principles and methodologies that guide how these tools should be applied to achieve optimal results. That’s where Factory Physics comes in.
Factory Physics is based on universal principles of operations and manufacturing, such as the laws of variability, throughput, and inventory dynamics. Factory Physics provides a framework for understanding how your company’s technologies interact and ensures they work harmoniously within the broader system.
Factory Physics emphasizes integrating various production system elements and ensures that improvements in one area do not create problems in another.
This article will look at five leading-edge technologies that will—and, in many companies, already do—shape the future of manufacturing: AI, robotics, IoT, quantum computing, and global internet connectivity and storage. At the same time, we’ll explore how employing Factory Physics principles and methods will help you focus on systemic harmony and harness the full potential of these advanced technologies, driving innovation and achieving superior performance in the modern industrial environment.
AI (Artificial Intelligence)
Artificial Intelligence is today’s most prevalent and potentially impactful technology. The use and understanding of artificial Intelligence will play a significant part in the execution of our ERP systems in the future and our ability to understand and control the factory.
AI fundamentally changes supply chain management by enhancing decision-making processes and operational efficiency. Its impact is felt across various aspects of the supply chain, like Demand Forecasting. AI uses historical data, market trends, and external factors to predict market demand more accurately. This will help optimize inventory levels and reduce the risk of stockouts or overstocking.
AI can also assist with logistics by optimizing transportation routes, traffic patterns, weather conditions, and vehicle capacity. This means faster deliveries and reduced transportation costs.
Through Predictive Maintenance, AI can analyze sensor information from equipment and machinery to predict potential failures before they occur, preventing costly downtime and production disruptions.
A keyword in the supply chain manager’s vocabulary is always “Customer.” Through hyper-personalization, AI enables personalized inventory management and delivery processes based on individual customer preferences, improving customer satisfaction.
With Risk Management, AI-powered systems can identify and anticipate potential risks like natural disasters or geopolitical events, helping companies prepare for and mitigate disruptions.
Companies implementing AI in their supply chains have reported significant benefits, including reduced logistics costs, improved inventory, and enhanced service levels.
Robotics
Many of us have had the opportunity to deal with implementing and integrating a barcode system into our operational functionality. RFID tags, biometrics, eye tracking, and other technologies can mechanize our operational inventory and production infrastructure. Robotics is transforming warehouse operations and logistics through warehouse automation. Autonomous vehicles, self-driving trucks, drones, and quality control can work with robots equipped with advanced sensors. Artificial Intelligence can perform quality checks more accurately and consistently than human workers.
Effective data management is crucial for modern supply chains, primarily when it drives the physical movement of inventory around the shop. Robotics provides enhanced visibility, integrating data from various sources to provide a comprehensive view of the entire supply chain and enable management.
Predictive Analytics is improved by collecting and analyzing large amounts of data to develop more accurate predictive models for demand forecasting and risk assessment.
The supplier’s ability to deliver quality production on time is the key to our success. Artificial Intelligence aids supplier performance monitoring through integrated data systems, allowing for real-time tracking of supplier performance and enabling proactive management of supplier relationships.
AI and robotics often work hand in hand to enhance manufacturing processes. AI algorithms can optimize robotic operations by analyzing real-time data and improving precision, efficiency, and adaptability. Factory Physics helps organizations model these interactions, ensuring that AI-driven insights are effectively applied to robotic systems. By doing so, organizations can achieve seamless automation and reduced downtime, leading to increased productivity.
IoT (Internet of Things)
As IoT technology grows and our ability to track individual things at more granular levels, we can see an increased use of the Internet to assist and guide our organizational manufacturing operations.
IoT devices provide unprecedented visibility into supply chain operations with Real-time Tracking, Condition Monitoring, and Predictive Maintenance. Gartner predicts, “The rise of IoT will allow supply chains to provide more differentiated services to customers more efficiently.”
One of the key developments in the world of IoT is what is being called a digital twin: virtual replicas of physical assets or processes. Digital twins can allow companies to test and perfect supply chain processes in a virtual environment before implementing changes in the real world. They can provide predictive maintenance assistance by simulating the equipment performance of items in the field. Digital twins can predict maintenance needs and optimize maintenance schedules. This technology lets companies virtually test and validate new systems or processes, reducing the time and cost of physical implementation if done cautiously.
The IoT connects machines, sensors, and systems, enabling real-time data collection and communication. Factory Physics emphasizes the importance of integrating IoT data into production models to enhance decision-making and process optimization. Understanding how IoT data flows through the system helps organizations anticipate potential issues, manage resources more effectively, and maintain optimal performance across interconnected devices.
Quantum Computing
Quantum computing uses quantum mechanics, computer science, and mathematics to solve complex problems faster than traditional computers. We now see considerable improvements in hardware, software, and computing storage. Just recently, we had to invent a new unit of storage, the Yottabyte (YB): 1,024 zettabytes [1,000,000,000,000,000,000,000 (one sextillion) kilobytes are in a yottabyte, a Yottabyte (YB): 1,024 zettabytes. There will become a point where all knowledge will be available to each individual or entity at the speed of light. What will that mean in the Age of Aquarius is anyone’s guess.
Quantum computing holds significant promise for supply chain optimization by solving complex supply chain problems and enhancing forecasting. Quantum algorithms can improve the accuracy of demand forecasting and risk assessment models. Quantum computing could enhance supply chain data security and transactions for all.
Quantum computing will transform cloud-based solutions for supply chain analytics, with real-time insights available on demand. As computers grow to what is after a Yottabyte, quantum computing, with its infinite scalability, can easily scale to handle vast volumes of data from various sources across the supply chain.
Quantum computing promises to revolutionize complex problem-solving with its ability to process vast amounts of data at unprecedented speeds. Factory Physics aids organizations in identifying which aspects of their operations could benefit most from quantum computing. Businesses can better integrate quantum solutions into their existing processes and systems by understanding the potential impacts on simulation, optimization, and decision-making.
Global Connectivity and Storage Capacity
As supply chain managers become increasingly international and worldly, they must learn to exploit global networks through improved internet connectivity, software, and storage capacity. Global collaboration matches big data analytics across supply chains supported by cloud-based solutions. This will upgrade and improve the internet infrastructure that will support adopting cloud-based supply chain management solutions, providing flexibility and scalability.
Global connectivity and cloud storage have become essential for managing distributed operations and large-scale data. Factory Physics highlights the need to balance these elements within the overall system. Effective use of worldwide internet connectivity ensures that data flows smoothly between different locations while adequate storage capacity supports the growing volume of data generated by advanced technologies. Harmonizing these components allows for robust data analysis, better resource management, and enhanced collaboration across global operations.
Factory Physics
The core of Factory Physics lies in understanding the interdependence among these technologies. For example, AI-driven analytics might rely on data collected via IoT sensors, while robotics systems must be adjusted based on insights from quantum computing simulations. Worldwide connectivity ensures that data and control signals can travel globally, facilitating coordinated operations and real-time adjustments.
Factory Physics principles apply not just to the emerging technologies discussed here but regardless of the specific technology being used.
Our Call to Action
To optimally employ any new or existing technologies and automation tools, we need a deep understanding of them. Factory Physics provides an organized approach to this understanding that guides how our tools should be applied to achieve the best results.
Our call is to learn how to use our existing ERP and any new technologies through Factory Physics principles. By applying Factory Physics principles, we can design systems where each technology complements and enhances the others rather than working in isolation. This holistic approach ensures the synchronization of all components, leading to more efficient, agile, and resilient operations.
Don Lindsey, CFPIM, CIRM, is a knowledgeable Implementation Project Manager, Trainer, and Business Analyst. He has been an implementation manager on several large, complex ERP projects and has worked with ERP systems since 2007 in Manufacturing, Systems Management, Service & Support, and Finance. Don has a diversified background in various manufacturing industries, from Medical to Electronics to Industrial to Consumer Products. He has spoken for many years at the APICS Conferences, having taught in the APIC Certification program at California State University at Fullerton for over 20 years.
Khoa To is the Associate Director of RxSight Analytics, leveraging over 24 years of experience in data analytics and process engineering to drive successful New Product Introduction (NPI) projects. He is a seasoned expert in transforming complex data into actionable insights, utilizing tools such as Power BI, advanced statistical analysis, and flow simulation. Khoa’s deep understanding of Factory Physics principles allows him to optimize manufacturing processes, ensuring efficiency and scalability. By integrating data-driven strategies with Factory Physics, he has consistently delivered robust NPI outcomes, improving supply chain resilience and ensuring compliance with regulatory standards while enhancing overall operational performance
