- Client: Griffith Foods
- Services Provided: Data Science & Machine Learning
Griffith Foods is an American product developer of customized food ingredients. Since the location of their production plant in Herentals doesn’t allow expansion, they asked us to develop an intelligent production sequencing tool to maximize productivity of the factory lines.
The objective of this project was to minimize the cleaning time of the manufacturing equipment between production of different additives. If cleaning takes less time, then there is more time for production, and this way the company can increase their revenue. So far, planning the products on a production line was still quite some manual work, because planning is different every day. So a second objective was also to help the planners in doing their job in a more efficient way.
High-level problem statement
Not every product is the same. They differ in color, odor, texture and allergens. It’s important to take these differences into account during production because Griffith Foods wants to avoid that products get contaminated by other products in terms of colour, allergens, … Since the beginning of production, Griffith Foods ensured the quality through a thorough wet cleaning between each product. This takes approximately 30 minutes, each time. And it’s not always necessary. If products are compatible in terms of allergens, color, texture, … then a dry cleaning is enough. Therefore, Griffith Foods asked Arinti to write an algorithm and create a tool that minimizes the number of wet cleanings based on the differences and equalities between the products. This can save a significant amount of time and effort per day!
Technical solution – Sorting optimization
An algorithm was written according to a logic provided by Griffith Foods. Basically, we matched products together based on their equalities. For example: if two products are yellow, contain small flakes and contain fish as an allergen, then they are compatible and can be produced after each other with a dry wash in between. However, if the next product doesn’t contain fish, then we have to schedule a wet wash between the product that contains fish, and the product that doesn’t contain fish. Basically, the algorithm looks at the combinations possible and starts building sequences of products that only need dry washes in between. Of course these sequences need to be glued together with wet washes. And that’s how we come to a daily plan for production on the different machines in the plant.