The Autonomous Factory: the future of manufacturing
Autonomous Production Twin: enabling autonomous manufacturing
Autonomous manufacturing has arrived. Just as industry 2.0 electrified factories and industry 3.0 introduced robotics, within a matter of years, fully autonomous processes will be the norm in many sectors – the essence of industry 4.0. Demand-driven MRP will enable factories to efficiently fulfill orders without the need of human control on an operational level.
Autonomous production will enable companies to create self-controlled, self-regulated and optimized material flows and shop floors. Intelligent, decentralized robots will communicate and adapt to one another, each performing a role, but all aligned to a common goal. Imperfect human intervention will be eliminated, allowing talent to be channeled towards more meaningful, creative tasks.
Achieving autonomous production systems requires data to be identified, accessed and contextualized, relevant use cases to be found, and the overall business value to be defined. It is a step-by-step process, and businesses that embrace the transition will gain a distinct competitive advantage. The only question remains: what are the steps required to make autonomy a reality?
Autonomous production systems are reliant on accurate, consistent, process-wide data collection, and connectivity. Without data, autonomous systems are informed by only a partial view of the factory, leading to imperfect decision-making. Without connectivity, devices cannot be instantly controlled, causing inefficiencies.
Smart devices are the first step to capturing and using this data. By integrating sensors in all stages of manufacturing and networking these devices, data can begin to be used to inform decision-making. A human operator is still required to view, analyze and action, but since all aspects of production are represented, decisions are grounded in a more precise representation of reality. On top of this, devices will also be able to be controlled remotely, whether they are stationary machines or robots moving within a facility.
Once devices are made smart, Digital Device Twins can be implemented. These are digital replicas of smart devices that allow operators to plan, optimize, strategize and innovate their manufacturing without influencing reality and causing issues such as downtime.
The next step to the autonomy of smart devices, BearingPoint’s new Autonomous Production Twin builds on smart devices and digital device twins. Truly intelligent robots must be able to individually and constantly carry out complex tasks such as image and object recognition, and require powerful, decentralized computing capabilities.
Autonomous Production Twin is the steppingstone to full autonomy and intelligence. A real-time bridge between all robots, it contains information on their states in real time, allowing them to communicate, negotiate tasks, determine priorities and much more. This allows them to work alongside one another as if they were perfectly aligned by a supercomputer.
Combining digital twins with ERP and MES data, Autonomous Production Twin forms a complete view that provides the means to simulate and optimize the entirety of production. This not only dramatically reduces costs and improves efficiency, but allows operational improvements to be tested before implementation, removing the possibility of expensive errors created by testing on the shop-floor.
Factory processes, material flows, order fulfillment: if parts are moving, Autonomous Production Twin can track them, and the benefits for engineering and business are significant:
By using predictive quality, condition monitoring, horizontal integration, ERP and machine data to monitor assets and contextualize failures, Autonomous Production Twin tackles issues such as line stoppages, material shortages, congestion and missed deliveries by itself. This reduces costs, ensures the most efficient use of resources and guarantees perfect products.
By aligning production and material flow processes, the risk of planning-dependent process disruptions and the occurrence of wasted stock are also both reduced, all while equipment efficiency is improved by orders of magnitude. On top of this, operational planning will require less human input, drastically improving productivity.
But how do these features manifest in the real business world?
For a leading automotive OEM, BearingPoint implemented an Autonomous Production Twin for a warehousing operation, visualizing warehouse movements in 3D and in real time. With the real-time layer in place, the client can analyze the entire operation to identify machine issues and stock levels to enable intervention.
In addition, we have also helped transform manufacturing and logistics processes for automotive businesses. Factory Navigator is an online Autonomous Production Twin platform for intra-logistics processes that integrates IoT, ERP and MES data. It has allowed some of Europe’s most well-known carmakers create risk-free process optimization scenarios, optimize their facilities with advanced analytic algorithms and set up early warning alerts for logistics material flows.
And using AI-based parameter optimization, we have revolutionized material planning for businesses by using Autonomous Production Twin real-time data. By placing data relating to active materials, planning parameters, demand patterns, unexpected events, delivery windows, stock reduction and service level requirements, a significant amount of labor can be avoided, all while gaining better-quality optimizations than with traditional, human-led material planning processes.
Complete autonomy can be a reality with Autonomous Production Twin, and adopting these technologies early could put you at a distinct advantage as the rush to autonomy begins.
Get in touch with our experts today to learn how we can help introduce autonomy to your operational processes.