Barilla Case Study

Concerning Barilla?s supply chain that shows in the Figure 1, Barilla?s CDCs and the production factory locate at the locations at the positions with the most variability in the supply chain. The distributors such as GD, DO and BD receives orders from the supermarkets and place orders to Barilla CDCs who then pass the productions orders to the factory (pull supply of products). GD, DO and BD merely used simple periodic review inventory systems to forecast the supermarket?s demand, which may inaccurately decide the order quantities. Having limited information for customers? demand data, they need to base on orders placed by the supermarkets to perform their forecast which fluctuated widely indeed---causing the ?bullwhip effect?. The same effect

Such fluctuating demand add great burden on its manufacturing and logistics operation and forced it to have a huge inventory for the products that seemed to have more demand. Moreover, the production line of Barilla can?t afford the frequent changes in terms of order quantities due to the long and redundant setup process. It comes out that once the unreliable forecast exists, Barilla will wither need to accumulate huge inventory or lose the consumer fill rate which both of them are not favorable. Besides, with such extreme demand, the consumers will easily face the problem of stockout because of the low fill rate of Barilla and the ?Big Order? policy which makes GD/DO stockouts by ?purchasing the wrong products a lot? but ?having no more extra area or money to purchase the correct ones to meet the demand?.

The inefficiencies in Barilla?s distribution system are mainly caused by huge fluctuations in demand and inaccurate forecast. The reasons for making the distributor?s order pattern look like this?increase in variability in its supply chain -- are illustrated below:

LONG LEAD TIMES: it takes approximately 8-14 days for the receipt of items at the distributors end from the date placing the order and the average lead time is 10 days. Such long lead time contributes to variability and reduced service levels.

LACK OF CENTRALIZED DATA: it does not keep a centralized

could be eliminated and so as to reduce the demand fluctuations. Hence, the distributors could release more inventories space and cost and they will be more competitive to react to the variability like after season stock, new products etc.

Improvement in customer fill rate for both Barilla and the distributors: the system puts emphasis on quick response. Since Barilla controls the inventory data and delivery pattern, the production and deliveries are then easier to be scheduled by the distributors to satisfy the consumers? needs. Therefore, with lowered inventory level, the flexibility and reaction speed from the distributors have been enhanced and both the customer fill rate and stockout rate could be improved as well.

With less hurry orders interruption, both distribution and manufacturing costs could be reduced: Barilla has long changeover time to setup another product as mentioned in the case. The less disturbance, the more cost-efficient in the whole supply chain process. But it is only applicable for the bottleneck machines and it is true for the distribution cost too. With less fluctuation caused from unknown situations in the past, a better delivery schedule could be achieved by using integrated data from the