AI scheduling tool wins back 17 production days per year
The most expensive cleaning is the one you didn't need to do. Griffith Foods produces customised food ingredients at 21 sites worldwide. At its Herentals facility in Belgium, rising demand was outpacing available capacity — not because of equipment, but because of cleaning schedules. We built an AI scheduling tool that optimises cleaning sequences based on ingredient compatibility, winning back 17 production days per year at a single site.
165 lost production days, hiding in plain sight
Griffith Foods is an American developer and producer of customised food ingredients, operating 21 production sites worldwide. At its Herentals facility in Belgium, rising customer demand was outpacing available production capacity. The answer sat in the cleaning schedule. Between every production batch, lines underwent a full wet cleaning — water, detergent, 30 to 90 minutes of downtime. Across all lines at the Herentals site, that added up to roughly 165 lost production days per year.
The manual planning burden
The production planners knew that some product sequences could safely run with a dry clean in between. Two yellow batches with the same allergen profile don't need a full wet wash. But calculating which sequences were safe, factoring in colour, allergens, texture, odour, and ingredient composition, took hours of manual work. And the planning changed every day.
“Arinti did not just dump a lot of technology into our organisation. They listened to our demands and developed a tool that works for us. Our planners find the tool easy to use.”
Why the sequencing problem was harder than it looked
Griffith Foods produces hundreds of distinct recipes, each differing in colour, odour, texture, and allergen content. The rules governing when a dry clean suffices and when a wet clean is mandatory depend on the specific pair of products running back-to-back.
First attempt: recipe attributes
Our first approach used a model built on recipe-level attributes: allergens, odour, taste, and colour classifications per product. Several of these parameters resisted consistent quantification. Odour and taste, in particular, could not be encoded cleanly enough for an algorithm to act on.
The pivot to ingredient data
We switched to the ingredient lists stored in Griffith Foods' ERP system. Ingredients are factual, granular, and already maintained as structured data. Two products sharing the same base ingredients are likely compatible. The ERP data gave the algorithm a foundation it could work with reliably.
Building the scheduling algorithm
The algorithm works by matching products based on shared characteristics derived from their ingredient lists. Products that overlap in allergen profile, colour, and composition can follow each other with a dry clean. Products that diverge on any critical parameter require a wet clean. From these pairwise compatibility scores, the algorithm builds sequences: chains of products that can run consecutively with only dry cleans between them. The sequences are then stitched together with wet cleans at the boundaries.
A tool the planners actually use
The algorithm runs behind a purpose-built interface designed for the production planners at Herentals. Where the previous process took hours of manual calculation each day, the tool generates an optimised schedule in minutes. Planners can also simulate what happens when an urgent order needs to be inserted mid-schedule. A companion app gives floor operators visibility into which batches run on which machines.
What changed on the factory floor
The Herentals site saw results soon after deployment. The production team reduced wet cleanings by 5%, with a target of 10% or more. That translated into 17 production days won back per year at a single site. Water consumption fell by roughly 1,000 cubic metres per year, with a corresponding drop in detergent use. The project reached payback in 10 months. For the planners, a daily planning task that had consumed hours became a few minutes of review and adjustment.
From one factory to twenty-one
The results at Herentals triggered a decision to roll the tool out globally. Two additional European factories came next, with the explicit goal of using their operational data to refine the algorithm further. The architecture supports this: the algorithm's logic is configuration-driven. Ultimately, all 21 Griffith Foods production sites worldwide are scheduled for deployment.
