Your factory is bursting with AI optimization opportunities

Implementing AI technologies is nothing like planning an expedition to Mars: it’s familiar territory by now. Plenty of firms are already leveraging AI solutions to drive production, supply chain, quality and other cost savings that translate directly to the bottom line. The moment your competition begins making exponential gains using AI, you’ve lost the race. Don’t hesitate!

It’s not ‘winner takes all’ for those fast-movers in operations that get a head start with AI – but AI can give you the boost you need to increase efficiency, quality and service levels. Getting started now means guaranteed gains in the near future. However, you can expect a learning curve. While the advantages of AI bring benefits that increase exponentially, you have to start small to ensure success. That means starting from specific challenges and pain points.

Operations: a broad domain with specific challenges

Fortunately, the specific pain points associated with operations are already well-known, and the machine learning team at delaware has already developed use cases, packages and references to address them. Here are just a few examples in five core operations domains.

1. Quality and efficiency

AI-driven quality inspection is mainly based on pictures or videos of the end product or the supplied raw materials. We have used deep neural networks to closely examine the final products in a variety of industries, from semiconductors to building components, to help our customers increase the quality of their products.

However, there are key advantages in combining quality inspection with quality optimization. The result of quality optimized in function of efficiency is a reduction in the amount of scrap produced (or the reuse of the ideal amount of scrap in subsequent processes), lower consumption of resources and more efficient processes – AKA, the ideal balance of the highest possible quality at the highest possible efficiency.

It’s important to note that in sectors like food and chemicals, which both rely on continuous production, a company may have to lay significant groundwork before getting started on a quality optimization project. This is due to difficulties in tracing the route of the final project through the various processes and machines in the factory. The solution? Start small, start now.

2. Planning and routing

AI-driven planning and production solutions involve both humans and machines. ERP systems can suggest a production plan, but it’s generally far from optimal, since ERP systems aren’t intelligent. It’s then up to the factory planner to consider which products can be manufactured subsequently, and the many rules, conditions and constraints involved in the production process.

Machine learning algorithms for planning and routing don’t replace your factory planner. However, they can reduce the time he or she spends on low value-added activities and manual or repetitive tasks and suggest an optimal plan that takes the correct limitations and rules into account.

3. Demand and supply forecasting

For sectors like food that are highly dependent on seasonality and outside factors like weather, demand forecasting can be hugely advantageous. For example, we’ve developed a machine-learning algorithm that uses current and historical data to predict the quality and quantity of the yield of a specific crop, the insights of which are used to optimize production planning.

However, it’s impossible to optimize the entire supply chain all in one go. For the crop yield case, we enabled demand forecasting, then supply forecasting, and then production optimization in function of both. These are three separate tasks relying on three different machine learning models that support three different organizational roles.

4. Warehousing

Starting small is especially important in this domain of operations. Take, for example, our machine learning solution that tackles the challenge of aging pallets in a customer’s warehouse. In detecting when a pallet will no longer be fit for use, our customer can sell them through another channel or throw them away directly without risking the destruction of the goods on the pallet.

These types of solutions predict risks involved in warehousing processes, allowing a company to take action before any negative consequences occur.

5. Maintenance

The best moment to repair or replace machinery components is a key target in operations – especially in continuous production cycles. Rather than replacing a component at a fixed interval or when it breaks down, these machine-learning algorithms accurately predict when it will fail. As a result, a company knows exactly what to replace and when – before efficiency starts to decrease or the production process is interrupted.

The machine-learning team at delaware developed an algorithm that predicts engine outages on large ships, as well as the time required to perform maintenance. As a result, the customer can easily optimize the planning of engine maintenance processes, making best use of time and resources while avoiding risk.

Your factory is full of opportunity

As you can see, AI applications in operations isn’t just about machines – it enables the people working in tandem with machines to do their jobs and make production process decisions with richer insights. No matter where you are on your AI journey, there are still plenty of opportunities to grasp to optimize processes.

Can’t see the forest for the trees? Get in touch with our experts – we offer an AI discovery and ideation workshop that can help you find your way.

Need some help identifying the best applications of Industry 4.0 technologies in your company? Get in touch with one of our experts.

Our expert

Wouter Labeeuw

Wouter Labeeuw

Wouter Labeeuw works as data science and machine learning consultant, adding intelligence to applications. He is a computer scientist with a PhD in engineering, where the research was focused on applying machine learning in the context of electrical demand response. In January 2016, he started at Delaware, still focusing on machine learning but in a broader context. Within his current role, he is responsible for the data science team within delaware.

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