How 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.

Creating an automated system for manufacturing process optimisation

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.

1. Break down your manufacturing process

The first step is to break down your process into key transformation phases, such as cooking or mixing. You can then look closely at the variables in each, building an understanding of what you can adjust and how changes might interact with each other and impact your product. Crucial to this will be actual data about key process steps from a live production line. Such data will ensure you're basing ideas on what's currently happening, rather than using received wisdom to make assumptions.

2. Build an artificially intelligent digital twin

With hypotheses about what you could change in mind, you'll be able to decide whether your digital twin needs to mimic a transform phase or the end-to-end process. You'll also be able to decide whether you need to build a new small-scale model of your line, adapt an existing pilot line or work on a purely digital representation of you process.

Whatever your digital twin looks like, you'll need to feed it with real-time data. This data will let a machine learning algorithm minimise downtime by recognising issues as soon as they happen and predicting maintenance requirements. It will also let you test possible optimisations without risking ongoing production.

3. Automate your manufacturing process optimisation

Finally, give the digital twin control. When you're confident you've adequately validated the machine learning algorithm on the small-scale version of your production line, integrate it into the live production line. By giving it control of all the levers in your production process, the artificial intelligence (AI) will automatically optimise every step based on the goals you set it. So, if you want to change the colour of your final product, for example, you just need to set that as the output measure. The algorithm will then automatically make the necessary changes in the process. And if you assign costs to different measures, such as the amount of input material used or the energy it takes to run a step, the AI could even automatically find the most cost-efficient way to manufacture your product.

AI-powered manufacturing process optimisation is essential to long-term success

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.