Case Study. Bakery. B2C. COGS control

 Case Study. A B2C bakery chain was looking for an AI/ML solution for cost savings, to reduce COGS.

Discussion : 

Case Study. A B2C bakery chain was looking for an AI/ML solution for cost savings, to reduce COGS.

1.

The retail bakery business has the structural concern of needing display cases to look 75-90% full at all times.

This is solvable via (a) constant refilling of cases which is expensive and wasteful or (b) agile addition and removal of display cases, based on stock available for display ( the tiny shop approach ).

In summary at the retail merchandising / interior design / process design layer - the big shop should try to behave like a small shop. Because that is what is cost effective, per unit of revenue.

2.

The AI/ML bit for performance marketing is always possible - but it doesn't solve the cost problem, if there are too many cases to fill anyway.

With regards to the performance marketing aspect - it's just A/B testing on what people want to buy. There's an interpolated variable ... some products, with inelastic demand will sell even if their case looks mostly empty. Other products with elastic demand may be more sensitive to the empty case.

Main concern :

AI/ML solutions which are implemented to forecast sales and traffic based on external factors ... will simply not help as much. They can help, if other links in the value-chain are already optimised.