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Barilla SpA, one of the world’s top manufacturers of pasta based in Italy, is facing severe demand variability that is resulting in additional costs and higher operational inefficiency within its supply chain; as we go up the chain, the uncertainty of having an accurate demand escalates. Maggiali, director of logistics, was actually aware of this growing burden that was imposed on the company’s manufacturing and distribution system. To tackle this issue, the adequate key formulation observed at the time was to implement the “Just-In-Time Distribution” (JITD) that would mitigate the consequences of demand variability. A relatively new system, JITD disputes the current scheme used by Barilla by centralizing a common database between Barilla and its distributors. Such data transparency would benefit in providing accurate forecasts of demand that would be beneficial to both entities. However, the internal resistance that Maggiali encountered made it difficult for him to work on implementing the just-in-time distribution system. Therefore, as a start to our analysis of the problem, we will focus on providing the appropriate solutions such as avoiding multiple demand forecast updates, breaking order batches, stabilizing prices and eliminating gaming in shortage situations. We will subsequently discuss the feasibility of the program in our environment and finally the methodology behind the acquisition of the targeted customers that we aim to onboard in the recommendations.
Barilla SpA is an Italian food company that was established in 1875 in Parma, Italy by Pietro Barilla. It is currently known as the biggest pasta manufacturer in the world and owns 25 plants throughout Italy. Each plant cores on a specific product segment such as pasta, flour mill, and other products that the organization specializes in like cakes, croissants, and bread. The manufacturing company also has two distribution facilities which take the responsibility for delivering their products.
Currently in the system, after the products are produced, they are sorted into two different categories, the fresh and the dry, which are then delivered to the central distribution centers (CDCs). The fresh products are stored for only three days, whereas the dry ones are either taken to the central distribution centers or the Barilla-run depots. The depots deliver the dry products to the small independent shops, and the central distribution centers oversee the dry products to two types of distributers: Grande Distribuzione, which is a distribution organization responsible for distributing to supermarket chains, and Distribuzione organizzata, which is composed of many other distributors.
Giorgio Maggiali is the director of logistics for Barilla SpA and he is facing a lot of resistance when he tries to implement a new manufacturing concept called Just-in-Time Distribution (JITD). In the beginning, this strategy was suggested by the previous director, Brando Vitali. However, Maggiali tightly supports this idea. Due to the current arrangement of the company, variations in demand at the customer level provoke the whole system to operate adversely. The consequence is a surplus of inventory at all levels of the supply chain, which generates additional costs. This result is commonly referred to as the bullwhip effect. To solve this issue, Maggiali must come up with a decision on whether he should proceed with the JITD strategy or not, and what is the best way to implement it in for Barilla SpA.
From the issues described that Barilla was facing, it is evident that the company is facing the Bullwhip effect. At each stage of the supply chain Barilla has high inventory levels and recurrent stock out at the distribution level. Also, they are facing: exaggeration in demand variability up the chain, are being aggravated by sales promotions that give volume incentives like Full Truck Load(FTL), and a shortness of data on which to predict demand. In addition, there is another factor causes the problem to be more severe and it is that Barilla has a large variability of dry products (about 800 stocks keeping units).
Large inventory levels in the company’s central distribution centers and in its distributers’ distribution centers lead to increased costs on both sides, Barilla’s and its corresponding distributers. Barilla has difficulty in responding to big variations and uncertainty of demand. Thus, manufacturing and distribution costs are increasing, and the company is being agonized by the customer order fill rate. Although there is a surplus of stock in distributor warehouses, stock out still occurs frequently, and the order fill rate of the distributers is weak. Furthermore, the needs of end consumers won’t be met if the dysfunction in the supply chain pursues.
The company’s customers are segmented into three main categories that are small retail shops, large independent supermarkets, and large supermarket chains. Deliveries to the small retail shops is done by the organization’s depots, whereas the deliveries to supermarket go through intermediate distribution centers which are run by a third-party organization that represents several different supermarkets or owned by a chain. The retailers send their orders to the distributor daily, however, the distributor places them once a week. Although all the distributors have a computer-supported system, a complex forecasting system or analytical tools for indicating order amounts is only present in a few. In exhibit 12 and 14 (refer to original case for graphs), weak effort in specifying order quantities based on inventory levels is observed. For instance, at Cortese DC in week 29, the inventory level was 500 quintals which is considered low compared to the other orders during the year. Also, this distribution center’s order quantities in the same week were less than 200 quintals which is less than the mean order quantity which is 300 quintals. This leads to a very high stock out rate in the week after at about 8.5% which is shown in exhibit 13 (refer to original case for graphs). The case of Cortese DC is not an isolated situation. Barilla’s other distributors were not efficient regarding their ability to forecast order quantities when arranging orders with Barilla.
To fight the demand variations from their retailers, safety stock is used to solve the issue with demand uncertainty. However, this technique leads to a total inventory level that is much higher than it should be and tends to cover the weakness of their demand forecast. What makes the situation even worse; Barilla’s sales and marketing promotion programs and various volume incitements motivate distributors to place huge orders in batches further increasing the demand fluctuations toward Barilla. On Barilla’s side, to answer issues concerning demand uncertainty from distributors, Barilla increases the safety stock level which eventually leads to higher overall inventory levels. With the sales information of its distributers unknown, Barilla faces many conflicts to forecast the demand for its products and plan accordingly. Also, Barilla’s wide range of pasta products makes the demand forecast and inventory management more complex which leads to a more severe bull-whip effect.
Thus, the main reason for the Bullwhip effect for being present is because of using safety stock as the main wait to address the demand variability at each level of the supply chain, the shortage in demand information sharing between the distributors and Barilla, and the traditional sales and marketing promotion strategies to increase demand volume at the cost of manufacturing planning and inventory control.
As previously stated, the main identified problem that Barilla was facing is a phenomenon referred to as the Bullwhip effect. The symptoms of such an effect are numerous but are identified as follows in the Barilla infrastructure. The lack of accuracy in the information shared among the members of the supply chain (from one end to the other) and their independent decision-making processes regarding demand forecast updates, order batching, price fluctuation and rationing and shortage gaming are the main reasons behind the observed inefficiencies within the supply chain of the company.
It’s so common for every member of the supply chain to forecast its products’ demand in order to accommodate to its production schedule, its capacity plan and its inventory control. Forecast is usually done by relying on previous orders history. Therefore, the order that would be sent to the upstream site of the supply chain, to the supplier would be based on this forecast while taking into consideration the safety stock that each dealer would want to keep in order to avoid stock-outs. Keeping that in mind, each dealer would be contributing to the bullwhip effect by wanting to keep its own level of safety stock. Additionally, when the lead time between replenishments is high, the bullwhip effect would be exacerbated. This is caused by the dealer’s desire to account for this great lead time of inventory and would thus increase its products forecast in order to account for any surges in demand by its customers. The variability in demand when each member of the supply chain exercises the same process to account for its own safety stock causes a colossal increase in levels of inventory stock at each level and thereby consequently incurs higher costs.
1. Avoid Multiple Demand Forecast Updates – To avoid this demand forecast updates from one site to another and to bypass the repetitive processing of data, both sides of the supply chain should be unify their efforts in using the same raw data in the same system. This could be done by implementing the electronic data interchange (EDI) system to facilitate the flow of information. However, this is not enough. In fact, the different methods used to forecast when using the same raw data would also lead to demand variability. Therefore, it’s required to have the upstream site of the supply chain responsible of updating the inventory level and of forecasting the demand of its own downstream site. The downstream site would thus be turned into a passive member of the supply chain. This is referred to as a continuous replenishment program (CRP) or vendor-managed inventory (VMI).
Another remedy is to directly connect to the customers and get the demand information by detouring the downstream site. This turns out to be beneficial, not only in terms of having accurate demand forecasts and low stocks levels but also in recognizing the demand pattern for the products of the company. Plus, if the just-in-time distribution and replenishment could be implemented, it could reduce the bullwhip effect to a minimum and thus, execute operational improvements.
2. Break Order Batches – An additional concern faced by Barilla is the batched orders within the supply chain. This means that the company accumulates demands before issuing an order from its suppliers. In other words, suppliers receive orders periodically or once a month, they witness an erratic flow of demands that is unbearable on a one-time basis, and a null demand for the rest of the month. In addition, this technique is used when the supplier is not able to account for small and frequent orders due the time-consuming processes and to the high costs encountered. Unless a company is using the EDI system to reduce costs, it will always find this frequent ordering method unfeasible not only due to its high costs of placing an order and replenishing it, but also due to transportation expenses.
Companies don’t issue an order unless it would need a full truck-load (FTL) and less-than truck load rates in an attempt to reduce their cost of transportation and would even be given incentivized discounts from the suppliers. When waiting to fill a truck, companies would be having long order cycles within the supply chain and thus, inefficiencies in the supply chain.
However, having frequent orders when using the EDI system and long order cycles are not compatible unless the company would acquire a third-party logistics company that would make small replenishments feasible, while saving costs on full truckloads. In fact, these third-party companies would not only be responsible of the inventory of one company but of many of these in order to realize full truckloads economies. This way, the company would have acquired an effective approach that would again, be mitigating the bullwhip effect.
3. Stabilize Prices – One of the simplest and most cost-effective methods at taming the bullwhip effect would be to “reduce both the frequency and the level of wholesale price discounting.” In the past, Barilla’s sales strategy relied heavily on the use of trade promotions, as a means of penetrating their products into the grocery distribution network. This strategy played a pivotal role in the company’s sales; little did they know they were feeding straight into the Bullwhip effect.
Barilla utilized two strategies that were part of their “Trade Promotions”. Both were implemented using a “canvass” system whereby they would divide the year in ten-twelve periods, which typically lasted four to five weeks. The end purpose of such a divided timetable was for incremental achievement of sales target. The first strategy aimed at setting a specific set of promotions on a specific variety of products that would last for the length of that period or just long enough to reach target sales per canvass period. Such promotions also depended on the margin structure of the category, ranging from 1.4% for semolina pasta, 4% for egg pasta, 4% for biscuits, 8% for sauces and 10% breadsticks.
The second approach is offering their customers “volume discounts”. Such discounts would include Barilla paying for the transportation to distributors and offering deals for purchases by the “Truck-load’. Barilla prioritized and more importantly misinterpreted these methods as profitable in the long run, however they were only aggravating the Bullwhip effect.
4. Eliminate Gaming in Shortage Situations – The best way to combat gaming would be to firmly rely on past sales records, as opposed to immediately responding to drastic instantaneous demand. Although this isn’t a full proof method, as there exists cases where real shortage will be met, it is the first step in eliminating or at least decreasing the fluctuations in demand that arise from customer demands. Aggravating the gaming case is the flexible and forgiving return policies that some companies exercise. It should be imperative of Barilla to impose rigorous cancellation and amendment policies. That way, Barilla as a manufacturer and distributor can protect itself from the uncertain demand.
In their journey to recovering from the spin-offs caused by the Bullwhip effect, Barilla SpA needs to carefully address and cater to the remedies suggested, in order to begin reversing their downward trajectory.
Barilla SpA needs to implement an Electronic Data Interchange system (EDI), that way they can avoid the repetitive processing of data, which is leading to variability in demand. But alongside such sharing of information, should come a continuous replenishment program (CRP) or vendor-managed inventory (VMI) that will unceasingly update the inventory levels from the upstream site as well as forecast the demand of its own downstream site. Additionally, they should directly connect with their customers and demand information by detouring the downstream site. This will serve beneficial in terms of having accurate demand forecasts, low stocks levels and also in recognizing the demand pattern for the products of the company. That’s when, with proper implementation of Just-In-Time-Distribution, the Bullwhip effect would be reduced to a minimum, leading to operational improvements.
The “Batch Ordering” is another issue that Barilla needs to immediately tackle. The company accumulates demands before issuing an order from its supplier. The demand on behalf of the suppliers is erratic, however the deliveries are executed on a periodical basis, which doesn’t meet the needs of nor the end customer or the suppliers. Unless a company is using the EDI system to reduce costs, it will always find this frequent ordering method unfeasible not only due to its high costs of placing an order and replenishing it, but also due to transportation expenses. Barilla SpA also needs to move away from incentivizing and encouraging (using discounts & promotions) full truck-load (FTL) orders. This method was initially used in an attempt to reduce costs; however, it is serving contrary. Such long awaited periods to “fill-up” a truck load, creates an increase in costs, inefficiencies in the supply chain and possibly driving customers away. In order for Barilla to onboard frequent order deliveries using their EDI, they should definitely acquire a third-party logistics company that would make small replenishments feasible while saving costs on full truckloads. This way, the company would have acquired an effective approach that would again, be mitigating the bullwhip effect.
As for the stability of prices, as Patrick Campbell said “Pricing is the exchange rate you put on all the tangible and intangible aspects of your business. Value for cash.” And unfortunately, Barilla SpA has been implementing a strategy that also been serving them otherwise. There should be an all-inclusive discontinuation of the promotions and volume discounts, effective immediately. Such methods cause an incredible increase in costs and only feed into the Bullwhip effect. Establishing a long term fixed cost policy will help control diversion and the fluctuations in demand. Alongside such methods should be an implementation of an Activity-based Costing (ABC) system that would very easily enable Barilla to identify the excessive costs of both sales promotions and volume sales. And lastly, for the control of “Gaming”, Barilla SpA should carefully use the newly implemented EDI system and always rely on past sales records when considering incoming orders from customers, especially in scenarios where the demand is either ends of the extreme.
As for the questions provided at the end of case, we suggest the following to Barilla SpA’s upper management and head of Operations:
The Just-In-Time Distribution system is feasible for Barilla SpA but upon condition. The aforementioned symptoms of the Bullwhip effects are both present and active in the Barilla’s ecosystem and require immediate attention, many of which Barilla’s is dynamically encouraging. Once the above are tackled and addressed, only then can the Just-In-Time Distribution serve to be effective. As for the customer segment targeted, Barilla should aim at onboarding companies that are capable enough to adopt and implement the information systems and have the necessary infrastructure to sustain such a supply chain distribution.
Acquiring their full commitment will require Barilla SpA to clearly highlight the benefits of the Just-In-Time Distribution system. Such a distribution scheme is based on a long-term vision of reduced costs, enhanced supply chain communication and accurate forecast of demand, regardless of erratic fluctuations from both end customer and distributor.
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