Barilla Spa (a) Case Study

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Barilla’s pasta supply chain suffers from classic bullwhip-effect problems: High inventory levels maintained at each level of the supply chain; frequent stockouts at the distribution level; demand variability exaggeration up the chain, and aggravated by constant sales promotions, Full Truck Load(FTL) and other volume incentives; and a lack of information on which to forecast demand. In addition, the increasing variability of Barilla’s dry pasta product (about 800 SKUs) intensifies all the problems mentioned-above.

The growing pain caused by bullwhip–effect in Barilla’s dry pasta supply chain can not be ignored. High inventory levels in Barilla’s Central Distribution Centers (CDCs) and in its distributors’ distribution centers represent increasing financial burdens on both Barilla and its distribution partners. The tight heat and humidity specifications in the pasta dry process makes it difficult for Barilla to quickly respond to the huge fluctuation and uncertainty of demand form distributors. As a result, Barilla’s customer order fill rate is suffering and so does its manufacturing and distribution operation cost. Despite the excess stocks held in the distributors’ warehouses, stockout happens all the time and the order fill rate of the distributors are suffering as well. Further down the chain, end-consumers’ needs will not be fully satisfied if the dysfunction of Barilla’s dry pasta supply chain continues.

Barilla’s customers are divided into three primary segments: small retail shops, large independent supermarkets, and large supermarket chains. Distribution to small retail shops is done by Barilla-Run Depots. Distribution to supermarkets goes through intermediate distribution centers, either owned by the chain, or operated by a third party representing multiple independent supermarkets.

The retailers send their orders to their distributor on a daily basis. The distributor, however, place their orders to Barilla once a week. Even though all of the distributors have computer-supported ordering system, few of them have sophisticated forecasting system or analytical tools for determining order quantities. The distributor’s poor handling of determining order quantities based on inventory levels is vividly demonstrated by Exhibit 12 -14. For example, the inventory level at Cortese DC was 450 quintals in week 29 which was very low comparing with that in other weeks during the year. However, this DC’s order quantities in week 29 were less than 200 quintals which were 50% less than its Mean order quantity (300 Quintals). The result, it incurred the highest stockout rate (about 8.5%) in the next week (week 30) for the year of 1989. The situation of Cortese DC is not an isolated case. Barilla’s other distributors were not effective with regard to their ability to determine order quantities when placing orders with Barilla.

In order to combat the demand fluctuation from their retailers, Barilla’s distributors use safety stock as the cushion to address the demand uncertainty. This strategy, however, makes their overall inventory level higher than it should be and tends to cover the inefficiencies of their demand forecast. To make things even worse, Barilla’s sales and marketing promotion programs and varies volume incentives encourage distributors to place large order in batches further magnifying the demand variability toward Barilla itself.

On Barilla’s side, increase safety stock level and eventually overall inventory level is the natural response to the huge demand variations come from the distributors. The fact that the heat control issues in its production process makes it difficult for Barilla to respond to product shortages. Therefore, further increase the need to hold huge inventory to offset this inflexibility of its manufacturing operations. Without knowing the sales data of its distributors, Barilla encounters great difficulties to forecast pasta demand and plan production accordingly. In addition,...
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