IndexIntroductionProblem identificationAnalysisRecommendationsBarilla SpA, one of the world's leading pasta producers based in Italy, is facing serious variability in demand which translates into costs additional costs and greater operational inefficiency within its supply chain; as we move up the chain, the uncertainty of having a precise question increases. Maggiali, director of logistics, was in fact aware of this growing burden weighing on the company's production and distribution system. To address this problem, the appropriate key observed at the time was to implement “Just-In-Time Distribution” (JITD) which would mitigate the consequences of demand variability. A relatively new system, JITD challenges the current scheme used by Barilla by centralizing a common database between Barilla and its distributors. Such data transparency would benefit in providing accurate demand forecasts which would be beneficial to both entities. However, the internal resistance Maggiali encountered made it difficult for him to work on implementing the just-in-time distribution system. Therefore, as the beginning of our analysis of the problem, we will focus on providing suitable solutions, such as avoiding multiple updates of demand forecasts, stopping order batches, stabilizing prices, and eliminating games in shortage situations. We will next discuss the feasibility of the program in our environment and finally the methodology behind the acquisition of target customers which we aim to integrate into the recommendations. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay IntroductionBarilla SpA is an Italian food company founded in 1875 in Parma, Italy, by Pietro Barilla. It is currently known as the largest pasta producer in the world and has 25 factories throughout Italy. Each establishment focuses on a specific product segment such as pasta, mill and other products in which the organization specializes such as cakes, croissants and bread. The manufacturing company also has two distribution facilities that take responsibility for the delivery of its products. Currently in the system, after the products are manufactured, they are divided into two different categories, fresh and dried, which are then delivered to central distribution centers (CDCs). Fresh products are only stored for three days, while dry products are taken to central distribution centers or warehouses managed by Barilla. Depots deliver dry goods to small independent stores, and central distribution centers oversee dry goods to two types of distributors: Large-Scale Retail, which is a distribution organization responsible for distribution to supermarket chains, and Organized Distribution, which is made up of many other distributors. Giorgio Maggiali is the director of logistics at Barilla SpA and faces a lot of resistance when he tries to implement a new production concept called Just-in-Time Distribution (JITD). Initially this strategy was suggested by the previous director, Brando Vitali. However, Maggiali firmly supports this idea. Due to the current structure of the company, changes in demand at the customer level cause the entire system to function negatively. 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 problem, Maggiali must decide whether or not to proceed with the JITD strategy and what is the best way toimplement it for Barilla SpA. Problem Identification From the described problems 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 recurring stocks at the distribution level. Additionally, they face: exaggerated demand variability across the chain, exacerbated by sales promotions that offer volume incentives such as full truck load (FTL), and a shortage of data on which to predict demand. Furthermore, there is another factor that makes the problem more serious and that is that Barilla has a large variability of dry products (around 800 storage units). High levels of inventory in the company's central distribution centers and in the distribution centers of its distributors lead to increased costs on both sides, on the part of Barilla and its corresponding distributors. Barilla has difficulty responding to large variations and uncertainty in demand. Therefore, production and distribution costs are rising, and the company is plagued by customer order fulfillment rates. Although there is a surplus of inventory in distributors' warehouses, stockouts still occur frequently, and distributors' order fulfillment rate is weak. Furthermore, the needs of end consumers will not be met if dysfunction in the supply chain continues. The company's customers are divided into three main categories: small retail stores, large independent supermarkets and large supermarket chains. Deliveries to small retail stores are made from the organization's depots, while deliveries to supermarkets pass through intermediate distribution centers operated by a third-party organization representing several supermarkets or owned by a chain. Retailers send their orders to the distributor daily, however the distributor places them once a week. Although all distributors have a computerized system, only some have a complex forecasting system or analytical tools to indicate order amounts. In Tables 12 and 14 (refer to the original case for graphs), little effort is observed in specifying order quantities based on inventory levels. For example, in Cortese DC in week 29, the inventory level was 500 quintals, considered low compared to other orders for the year. Furthermore, the order quantities of this distribution center in the same week were less than 200 quintals, which is less than the average order quantity of 300 quintals. This leads to a very high stock-out rate in the following week of around 8.5%, as shown in Table 13 (refer to the original case for graphs). The Cortese DC case is not an isolated situation. The other Barilla distributors were not efficient in their ability to predict order quantities when arranging orders with Barilla. To combat variations in demand from their retailers, safety stocks are used to solve the problem of demand uncertainty. However, this technique leads to a much higher total inventory level than it should be and tends to cover up weakness in demand forecasts. What makes the situation even worse; Barilla's sales and marketing promotion programs and various volume incentives motivate distributors to place huge batch orders, further increasing demand fluctuations towards Barilla. For Barilla's part, to respond to issues relating to the uncertainty of demand from distributors, Barilla increases the levelof safety stock which ultimately leads to higher overall inventory levels. Since the sales information of its distributors is not known, Barilla faces many conflicts to predict the demand for its products and plan accordingly. Furthermore, Barilla's wide range of pasta products makes demand forecasting and inventory management more complex, which leads to a more serious bull-whip effect. Therefore, the main reason why the Bullwhip effect is present is due to the use of safety stocks as the main hold. to address demand variability at each level of the supply chain, the lack of demand information sharing between distributors and Barilla, and traditional sales promotion and marketing strategies to increase demand volume at the expense of production planning and inventory control. Analysis As stated previously, the main identified problem that Barilla was facing is a phenomenon called the Bullwhip effect. The symptoms of this effect are numerous but are thus identified in Barilla infrastructures. The lack of accuracy in the information shared between members of the supply chain (from one end to the other) and their independent decision-making processes regarding demand forecast updates, order bundling, price fluctuation, rationing and Shortage play are the main reasons behind the inefficiencies observed within the company's supply chain. It is so common for each member of the supply chain to predict the demand for their products to fit their production schedule, capacity plan and inventory control. The forecast is usually made based on the history of previous orders. Therefore, the order that would be sent to the site up the supply chain, to the supplier, would be based on this forecast, taking into account the safety stocks that each retailer would like to maintain to avoid stockouts. With this in mind, each retailer would be contributing to the bullwhip effect by wanting to maintain its own safety stock level. Furthermore, when the waiting time between refueling is long, the bullwhip effect would be exacerbated. This is caused by the retailer's desire to account for this long inventory lead time and would therefore like to increase their product forecasts to account for any increases in demand from their customers. The variability in demand when each member of the supply chain exercises the same process to account for their safety stocks causes a colossal increase in inventory stock levels at each level and therefore consequently leads to higher costs.1. Avoid multiple updates of demand forecasts – To avoid updates of demand forecasts from site to site and to circumvent repetitive data processing, both sides of the supply chain should unify their efforts in using the same raw data in the same system. This could be achieved by implementing electronic data interchange (EDI) system to facilitate the flow of information. However, this is not enough. In fact, even different methods used to make forecasts using the same raw data would lead to variability in demand. Therefore, the upstream site in the supply chain needs to be responsible for updating the inventory level and forecasting demand of its downstream site. The downstream site would thus be transformed into a passive member of the supply chain. This is called a continuous replenishment program (CRP) orVendor Managed Inventory (VMI). Another remedy is to connect directly to customers and get demand information by diverting the site downstream. This proves beneficial, not only in terms of accurate demand forecasts and low inventory levels, but also in recognizing the demand pattern for the company's products. Furthermore, if just-in-time distribution and replenishment could be implemented, the bullwhip effect could be minimized and thus operational improvements could be made. 2. Discontinuous order batches – An additional concern addressed by Barilla involves orders being grouped together within the supply chain. This means that the company accumulates requests before issuing an order from its suppliers. In other words, suppliers receive orders periodically or once a month, witness an unbearable one-off irregular flow of requests and zero demand for the rest of the month. Furthermore, this technique is used when the supplier is unable to account for small and frequent orders due to the time-consuming processes and high costs encountered. Unless a company uses EDI to reduce costs, it will always find this method of frequent ordering impractical not only because of the high costs of placing an order and replenishing it, but also because of transportation costs. Companies do not place an order unless full truckload (FTL) and less than truckload loading speeds are needed in an attempt to reduce transportation costs and would even receive incentivized discounts from suppliers. While waiting to fill a truck, companies would have long order cycles within the supply chain and therefore inefficiencies in the supply chain. However, having frequent orders when using the EDI system and long order cycles are not compatible unless the company acquires a third-party logistics company which would make small replenishments possible, saving on the costs of full loads. Indeed, these third-party companies would not only be responsible for the inventory of one company but of many of them in order to realize full truckload economies. In this way, the company would have acquired an effective approach which, once again, would mitigate the bullwhip effect.3. Stabilize Prices – One of the simplest and cheapest methods to tame the bullwhip effect would be to “reduce both the frequency and level of wholesale price discounting.” In the past, Barilla's sales strategy relied heavily on the use of commercial promotions as a means of penetrating its products into the food distribution network. This strategy has played a vital role in the company's sales; they didn't know they were directly fueling the Bullwhip Effect. Barilla used two strategies that were part of their “commercial promotions”. Both were implemented using a “canvas” system whereby they divided the year into ten to twelve periods, which typically lasted four to five weeks. The ultimate goal of such a split calendar was to incrementally achieve the sales goal. The first strategy aimed to set a specific series of promotions on a specific variety of products that would last for the duration of that period or just long enough to reach the sales goal per canvas period. These promotions also depended on the margin structure of the category, which varied from 1.4% for semolina pasta, to 4% for egg pasta, to 4% for biscuits, to 8% for sauces and 10% for breadsticks. The second approach is to offer your customers “quantity discounts”. Such discounts would include Barilla paying freight to distributors and offering deals on purchases to..