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The Underlying Causes And Difficulties medical school essay help Python Programming

Executive Summary Barilla, the leading pasta manufacturer in Italy, faces increasing problems related to demand fluctuation. Their distributors also suffer from high inventory holding costs and low service levels on the other hand. This report explains, why the company and their distributors are troubled with this situation and how Barilla intends to solve it. The problem Barilla experiences is called the “Bullwhip Effect”, i. e. that demand variability increases when moving up the supply chain.

Several factors enforce this Bullwhip Effect, e. g. high lead times, poor demand forecasting, and batch ordering. In this report we will point out, that exactly those aspects can be identified as the underlying reasons for Barilla’s problems. In a next step, we will analyze the cost dimensions of the JITD concept Barilla developed in order to reduce demand variability. We identified five cost categories where the JITD approach would lead to a decrease in costs, e. g. inventory holding costs, penalty costs and transportation costs.

However, cost increases, especially implementation costs, have to be taken into consideration as well. In the last part of the report, we will address the issue of implementation hurdles of the JITD program with regard to distributor resistance, sales force refusal and the problematic handling of trade promotions. The resistance Barilla faces from their distributors can be lessened by convincing them of their competence to forecast so pre-cisely, that inventory levels will decrease and out of stock situations will be damped down as well.

Therefore, Barilla might conduct a test phase or offer a warranty. Furthermore, the reluctance from within Barilla, especially from the sales force, has to be taken down by changing the incentive system and clearly defining new tasks for the sales representatives. Concerning trade promotions, the possibility of abolishing promotions and quantity discounts has to be discussed. As conclusion, Barilla needs to improve the overall supply chain cooperation in order share information, reduce the Bullwhip Effect and realize cost savings.

Exercise 1 – Underlying causes of difficulties The Barilla company faced increasing problems of demand fluctuations. As you can see in figure 1, the orders of one of their distributors (Cortese’s Northeast Distribution Center), var-ied widely with high peaks in one week, followed by very low levels of shipments in the next week. These de-mand fluctuations resulted in high inventory costs for Barilla’s Central Distribution Centers as well as for the dis-tributors in their warehouses. Furthermore, stock out levels at the distributor were quite high (see figure 2).

This implies, that the service level towards the retailer suffered, as products were not always available and iretailers had to wait for replenishment. What are the reasons for these demand fluctuations? Pasta in general has a very stable demand, except from some seasonal peaks at Easter and Christmas. That means, consumers buy about the same amount, so that demand patterns should be without high fluctuations. But why does Barilla then face problems of demand variability? This problem is referred to as the “Bullwhip Effect”.

On each level in the supply chain, meaning retailer, distributor and manufacturer, order quantities increase. The retailer will order at his dis-tributor the amount he forecasted to be the consumers demand plus an additional amount of safety stock. The distributor then receives a biased order quantity from the retailer (a higher quantity than the actual forecast). The distributor on the other hand places his order with the manufacturer for the quantity the retailer ordered plus safety stock for his warehouse. This results in an even higher order quantity than the actual demand forecast on retail level has been.

There are several factors influencing the demand variability over the whole supply chain. First of all, the lead time aspect contributes enormously to that problem. Lead time refers to the time between placing an order and receiving a shipment. Barilla had an average lead time of 10 days for shipments to their distributors. Lead times act as a multiplier with regard to demand fluctuations. Even small changes in demand result in either high stock outs or high holding costs due to the time delay in delivery, since the order quantity cannot be adjusted during the lead time.

Another factor is demand forecasting. Retailer, distributor and manu-facturer use different forecasting methods (in most cases not very sophisticated), leading to different results and biasing the demand forecast at the end of the supply chain. A third factor contributing to the Bullwhip Effect is the specific production process of pasta. Barilla cannot re-act to demand changes quickly enough, due to production restrictions as e. g. heat and humidity specifications. That means, if a product is out of stock it cannot be produced and delivered immediately.

Please use 150 words to answer the following question: The phasedown of lead in gasoline began in 1974 when under

Please use 150 words to answer the following question: The phasedown of lead in gasoline began in 1974 when under the Clean Air act Amendments of 1970, the U.S. EPA introduced rules requiring the use of unleaded gasoline in new cars equipped with catalytic converters. To further promote the production of unleaded gasoline, EPA also scheduled performance standards requiring refineries to decrease the average lead content of all gasoline. The table below summarizes the incremental benefits and costs of tightening the U.S. lead standard from 1.1 gram to 0.1 gram per leaded gallon. If the decision were yours to change the lead standard, using the table, how would you justify a change in the lead standard? In your answer, be as specific as you can. You can use the overall costs and benefits or you can focus on a particular aspect of the table below (in the file). There’s also a relative reading you can read which is a discussion paper by the resources of the future in 2003 by Newell and Rogers (2003):