Barilla Case Study: The Underlying Causes And Difficulties

The supply chains of L.L. Bean should be able to respond on the large fluctuations of orders (e.g. during Christmas the amount of orders will be

If the operation have not reordered all the goods they need, this can lead to irritated customers and lower revenue for the operation.

For instance (ceteris paribus), if the transportation of the HP products were air shipped, and the lead time passed from four and a half weeks to three days, logically stock levels would decrease. The same would happen if we reduce the time between orders or the level of satisfaction that we want to assurance to our customers.

These spikes and troughs could be the result of price discounts that the company is offering due to which customers are buying in bulk and stocking up on products. This could lead to variability in demand, leading to excess demand or no demand that would lead to non-efficient production by the company. Root cause analysis should be performed, and if it is established that volume discounts are leading to this phenomenon, it is recommended that the company minimize discounting and work with the customers using a partnership model to ensure a steady demand.

One of the underlying causes of the difficulties that the JITD program was created to solve was the effects of inconsistent demand that came from Barilla’s distributors. The extreme demand variation strained Barillas manufacturing and logistics, and made very hard for Barilla to meet that demand. For example, as noted on the case “the specific sequence of pasta production necessitated by the tight heat and humidity specifications in the tunnel kiln made it difficult to quickly produce a particular pasta that had been sold out due

Lead time varies significantly, which affects the inventory cycle. The plant has to set a safety stock that will protect the production against stock outs, vehicle breakdowns, weather related delays and the shortage of supply from farmers and hogs barns.

The amplification in order variation may cause irrational decision making.(LPW 1997). The variation in demand between different stages in supply chain while moving upside. These variations called as ‘Bullwhip Effect’. They also called as ‘Whip-Saw’ or ‘Whip-lash’ It causes dramatic effect on firm resulting in excessive inventory, poor product forecasts, insufficient or excessive capacities, poor customer service due to unavailable products or long backlogs, uncertain production planning and high cost correction like high shipments.(LPW 1997). There is no bullwhip effect in ideal supply chain where as its assumed that supply is stationary, fix lead time, constant purchase cost overtime, no fixed cost of ordering, and forecasting is not based on past demand.( LPW 1997)

Barilla SpA (Barilla), is an Italian manufacturer that sells pasta to retailers largely through third-party distributors. Barilla has been experiencing widely fluctuating demand patterns from these distributors. Such unpredictable patterns are problematic because a specific sequence of pasta production is used that minimizes the incremental changes in kiln temperature in order to keep the changeover costs low and the product quality high. This process makes manufacturing unfortunately unresponsive to changes in anticipated demand.

The lack of inventory and sales forecast information across the entire supply chain of Barilla increases the bullwhip effect. As very little data is observed, the estimates of the mean and standard deviation of customer demand are not regularly modified. Since safety stock and base stock levels are dependent on the estimates of the mean and standard deviation of customer demand, sales orders have to be changed regularly which increases variability.

The coordination between the supplies and the customers demand is an important aspect in the success of any supply chain. Poor coordination may result in poor supply chain and high cost. One of the example of this is the videocassette retailer. New arrive movies has high demand in the first weeks of the release. After a while this demand start to decrees. These retailers face the problem in the first weeks because the demand is high but it will shortly decline. At that time supplier sold videocassette to retailer for 65$ and the retailer rent it for 3$ so they hit break even after 22 rentals. For that reason retailer cannot order enough videocassette to meet initial demand or they will lose money. To overcome this problem retailer has to come with a way to meet the initial demand without losing money. Blockbuster and supplier reach a deal that will benefit both of them. The supplier agreed to sell the videocassette for less than the production cost in exchange for a portion of the rental income. In this deal blockbuster got the videocassette from the supplier for only 8$ and the breakeven dropped to 6 rentals from 22 rentals. That allow blockbuster to meet initial demand and to share the revenue with the supplier. This case inspired Martin Lariviere and Gérard Cachon to write their article “Supply Chain Coordination with Revenue-Sharing Contracts: Strengths and Limitations,” In their article showed that revenue sharing contracts is not only limited to videocassette

However, the discount associated with advance payment makes the replenishment cycle larger. Zhou et al. [14] developed EPQ (economic production quantity) models to evaluate the optimal make-or-buy decisions when a buyer faces a one-time-only discount offered by a vendor. They showed that their decision depends on the discounted price as well as its timing. Shu et al. [15] assumed that transportation lead time in a vendor buyer supply chain is exponentially distributed and the transportation cost is a function of lot size. They showed that these assumptions lead to noticeable cost reductions when compared with the traditional coordination models.

Postponement Strategies: Large number of product variation makes it difficult to forecast the demand for each variety and this will affect the supply chain

Inventory cycle as optimal demand divided by 2 for predictable product demand, increases with trade promotions because of the increase of order Q size. However, total consumer demand between retail and final consumers may increase or not depending on product elasticity demand and customers preferences (IBM, 2011). IBM propose solution is enabling several degrees of integration and collaboration among participants in the supply chain for evaluation effectiveness of trade promotions price

To remain profitable, consumer products manufacturers must find ways to optimize the performance of their supply chains. They need to support marketing promotions better and avoid frustrating consumers with out-of-stock situations in the store.

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