AI in predictive manufacturing
Written by Jens Eriksvik & Rickard Holmkvist
Manufacturing companies face increasing pressure to optimize operations, reduce costs, and enhance competitiveness. To meet these challenges, manufacturing and supply chain companies are turning to predictive manufacturing. This approach leverages advanced analytics and AI algorithms to anticipate disruptions, optimize production processes, and enhance overall efficiency.
Moving effectively towards predictive manufacturing requires a strategic approach and the right tech solutions. In this article, we'll explore the role of AI in predictive manufacturing, its key use cases, best practices, and steps for businesses to get started.
Industry 4.0 and predictive manufacturing
Industry 4.0 is the ongoing automation of traditional manufacturing and industrial practices using smart tech. It is a fusion of technologies that blur the lines between the physical and digital domains. Six primary technologies are driving Industry 4.0; internet of things, cloud computing, artificial intelligence, federated AI, cybersecurity and digital twins.
Convergence of technologies enabling predictive manufacturing
The convergence of these technologies creates a more intelligent and connected manufacturing environment, offering real-time analytics, increased flexibility and efficiency and customer-centric manufacturing.
Enabled by these tech developments, predictive manufacturing is a data-driven approach that uses historical and real-time data to forecast future events and outcomes in the manufacturing process. It involves leveraging advanced analytics techniques, such as machine learning and predictive modeling, to analyze data from various sources, including sensors, equipment, and production systems. By identifying patterns, trends, and anomalies in the data, predictive manufacturing enables companies to make informed decisions, optimize processes, and mitigate risks.
When manufacturing machinery are fully integrated with digital systems, real-time data collection and analysis becomes possible, and this data can then be used to optimize production processes, predict maintenance needs, and improve overall quality control. Automation enables process efficiency and predictive algorithms enable supply chain flexibility, ultimately where the entire supply chain becomes more agile to support customer-centric manufacturing.
Building a resilient, sustainable and efficient supply chains through digital twins
There are many applications and use-cases to be explored with these technologies, but the ability to build resilient, sustainable and efficient supply chains is perhaps one of the most interesting. Imagine a supply chain fully modeled as a ‘digital twin’, including the manufacturing process, where the physical equipment is fully integrated with digital systems, offering a real-time view of your supply chain flows. While this is a daunting task, value can be realized in a stepwise manner. For example, benefits can already be created with an isolated view of 1) the manufacturing process, or 2) with a limited extension down- or up-stream.
Digital twins leverage the data and connectivity of Industry 4.0 to create a dynamic digital counterpart that reflects the real world. This allows for better decision making, improved efficiency, and overall optimization across the entire manufacturing process. A digital twin is essentially a virtual representation of a physical object, process, or system. It's constantly updated with data from the physical counterpart using IoT sensors. This creates a bridge between the physical world and the digital world, allowing for better monitoring and analysis. This can also be extended with concepts like federated AI/machine learning to further enhance dynamic adaptability, privacy and data security. In essence, the combination of federated AI and digital twins creates a dynamic and collaborative manufacturing ecosystem. Factories can leverage the power of AI and real-time data, while still maintaining data privacy, to optimize processes, improve efficiency, and drive innovation across the industry.
“Digital twins are revolutionizing manufacturing by bridging the physical and digital worlds. They offer unprecedented insights into how machines are performing, allowing us to predict maintenance needs, optimize processes, and ultimately build a more sustainable and efficient future for manufacturing”
Jens Eriksvik, CEO @ Algorithma
Creating value from digital twins
There are four pockets of distinct value to be explored: efficiency gains, innovation, supply chain resilience and sustainability. This value can be illustrated through a set of straight-forward use-cases; how to sharpen foresight, optimize production, securing delivery and reduce environmental impact.
Sharpening foresight: AI for predictive maintenance
The factory utilizes digital twins for equipment health monitoring. Historical maintenance data is analyzed to identify common failure patterns and lead times for specific equipment types. This allows for:
Developing predictive maintenance schedules based on equipment usage and sensor data from the digital twin.
Proactive stocking of spare parts most likely to fail, minimizing downtime.
Optimizing technician training by focusing on the most common maintenance tasks identified through historical data analysis.
Optimizing production: AI for efficiency
The factory leverages digital twins to track production processes in real-time. Data analysis from the digital twin identifies bottlenecks and inefficiencies. This allows for:
Simulating production scenarios using the digital twin to test potential solutions for bottlenecks.
Investing in targeted automation solutions to address specific bottlenecks identified by the digital twin.
Optimizing production line layouts based on data insights to improve material flow and reduce processing times.
Securing delivery: AI for supply chain resilience
The digital twin tracks inventory levels and production capacity. Advanced data analytics are implemented on historical supplier and logistics data to predict potential disruptions. This allows for:
Identifying alternative suppliers and negotiating backup agreements with the orchestrating party to ensure parts availability.
Maintaining buffer stock of critical materials based on historical usage patterns and lead times.
Developing contingency plans to adjust production schedules or source materials quickly in case of disruptions.
Reduce environmental impact: AI for sustainability
The factory's digital twin monitors energy consumption. Data analysis identifies areas for improvement. This allows for:
Investing in energy-efficient machinery based on recommendations from the digital twin's analysis of energy usage patterns.
Optimizing production scheduling to minimize peak energy consumption periods.
Exploring alternative energy sources like solar or wind power based on data on the factory's location and energy needs
Now imagine the potential if this factory operates within a supply chain ecosystem together with other parties represented by similar digital twins. Each factory/party leverages a digital twin, a virtual representation of its physical machinery and processes. By orchestrating these parties, and leverage federated data and insights, this can strengthen the overall resilience and efficiency of the supply chain in a significant way. A collaborative approach would foster continuous improvement and pave the way for a more sustainable future for all participants.
“The true power of digital twins lies in their ability to transform data into actionable insights. By leveraging these insights, companies can optimize their operations, reduce costs, improve product quality, and gain a significant competitive advantage."
Rickard Holmkvist, COO @ Opticos
Taking the first steps towards predictive manufacturing
The potential of AI in predictive manufacturing is undeniable. Transitioning to this data-driven approach requires careful planning and execution. AI and new tech is not a silver bullet and the necessary groundwork will increase chances of success:
Assess readiness: Conduct a thorough evaluation of your current manufacturing processes, data collection capabilities, and infrastructure. Identify areas where data collection can be improved and invest in necessary sensors and IT systems.
Set target KPIs: Clearly define your goals for implementing predictive manufacturing. What areas do you want to improve - efficiency, maintenance, sustainability? Establish key performance indicators (KPIs) to track progress towards these goals.
Craft data strategy: Plan how you will collect, store, and analyze data. Ensure data security and establish a governance framework for responsible data use.
Build capabilities: Consider hiring data scientists or partnering with AI specialists who can help develop and implement AI models for predictive maintenance, process optimization, and supply chain management.
Start small and scale: Begin with a pilot project focused on a specific area like predictive maintenance for a single machine or production line. This allows you to test the technology, refine your approach, and demonstrate the value proposition before scaling up across your entire operation.
Continuous improvement: Predictive manufacturing is an ongoing journey. Regularly evaluate your AI models and data analysis processes to ensure they remain accurate and effective. Be prepared to adapt and refine your approach as your needs and the manufacturing landscape evolve.
In addition, look beyond the boundaries of your own organization. The magic happens when connecting a series of federated model to create a dynamic, data-driven supply-chain.
PS. Check out the digital twin we made for shipping.