As customers demand more personalised, sustainable products, manufacturers must re-examine their process controls and measures, and bring in machine learning to automate manufacturing process optimisation.
While advancing technologies are disrupting manufacturing, big brands struggle to innovate legacy processes, particularly when they've undergone years of optimisation. That leaves traditional market giants vulnerable to disruption from nimbler young businesses.
It's a challenge that clients have come to us with before, and we know the answer lies in smarter automation. Automated manufacturing process optimisation can adapt a legacy production line for modern demands at a much lower cost than a complete redesign. To create such a system, manufacturers need to look closely at what controls they have throughout the process and what they measure across the line.
By understanding all the controls for each step of the process and taking measurements at every stage, it's possible to build a small-scale representation of the process powered by real data – a digital twin. And by programming that digital twin with machine learning algorithms, you'll have a powerful tool for automated optimisation and testing that won't put your core operations at risk.
While automating manufacturing process optimisation is a complex task, it becomes easier to manage when you break it down into three steps. When we helped an FMCG company automate its manufacturing process optimisation, we worked with their team to really understand the controls and measures they had at their disposal and built a process digital twin (a smaller representation of their production line powered by machine learning and real data) to test ideas before finally integrating it into the live production line.
As consumer expectations evolve ever faster, manufacturers need to find new ways to keep up. Automated manufacturing process optimisation, powered by AI and real-time data, can transform legacy production lines for minimal cost, but doing so won't be easy. You need to understand every step of your manufacturing process and build a data-informed digital representation of those steps before using machine learning to give that digital twin the power to automate your manufacturing process optimisation.