Ramamurthy Raghavendra

Barilla spA case analysis

Barilla SpA, an Italian pasta manufacturer, is experiencing amplified levels of inefficiencies and rising costs due to variability in demand from its distributors. The main problem addressed in this case is how to effectively implement JITD system suggested by Giorgio Magialli, the Director of Logistics by resolving the issue of gaining control over the fluctuating demand.

Barilla has a very complex distribution network including independent third party distributors and due to such a multi-echlon network, Barilla has been experiencing large amounts of variability in demand which are resulting in operational inefficiency and increased manufacturing, inventory and distribution costs.

The proposed JITD system required the distributors to share their sales data with Barilla, who would then forecast and deliver appropriate amounts of products to the distributors at the right time in order to effectively meet demand. This was a radical change from the current and more traditional supply-chain setup where the distributors were not sharing any data and could place orders at will. Vitali’s proposal came under severe criticism from not only the distributors but also Barilla’s own Sales and Marketing department for an array of reasons.

Main reasons for fluctuating demand:

Promotions: The use of promotions in the form of price, transportation, and volume discounts was the main strategy to sell more products to the distriutors.

Sales Reps: The compensation system in place at Barilla for Sales reps, made them to push more products into the pipeline during promotional periods and not able to sell sufficient quantities during non promotional periods created wide variation in demand patterns.

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SKU’s: The huge range of SkU’s in each product line led to greater complexity

Gaming bevavior and wrong forecasting practices: The distributors were having full control of their orders to Barilla and used gaming during stock outs periods. The distributors did not use any sophisticated forecasting models or systems to calculate the order quantities without any threshold minimum order quantities but rather just followed a replenishment ordering.

Long lead times: Barilla was dealing with perishable items and the lead times of between 8 and 14 days after it received their orders, the average lead-time being 10 days was high ...

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