OKEvery multipack, before it leaves
The camera counts the packs in each multipack after the shrink wrapper; any that does not have 6 is rejected with its photo.
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Case study · Juices · 6-pack
Zero incomplete multipacks, with a single recipe for every reference
At a juice plant, the shrink wrapper occasionally let through shrink-wrapped 6-packs containing only 5 packs. The distributor was complaining. The challenge was not counting: it was doing so in a plant with so many references that maintaining a recipe for each one was almost impossible.
- Complaints about incomplete multipacks
- Eliminated
- Recipes for all references
- 1
- Tool
- Inference (deep learning)
The challenge
Too many references for one recipe per format
The usual approach is one recipe per format: what to look at, with which tool and with what tolerance. With this customer's number of references, across sizes, flavours and designs, replicating and maintaining a recipe for each one was not viable, and every product changeover would have been an opportunity for error.
- Shrink-wrapped 6-packs at the shrink wrapper outlet
- One missing pack cannot be seen with the naked eye in a shrink-wrapped multipack
- Complaints from the distributor about multipacks with missing units
The solution
One recipe, one inference tool
Instead of one recipe per reference, a single recipe for all packs with a single deep learning inference tool, trained to recognise a pack whatever its design and to count how many there are in each multipack.
No recipe per product
A new reference or a change of flavour does not require creating or loading another recipe.
Photo of every reject
Every rejected multipack is stored with its image, so you can answer the customer with data.
The result
No complaints about incomplete multipacks
Since commissioning and to this day, the distributor's complaints about multipacks with missing units have disappeared completely.
More on this check: counting in cases and multipacks, deep learning inspection and machine vision for beverages.
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