EFECTO LATIGO EN LA CADENA DE SUMINISTRO PDF

Este trabajo ilustra no sólo una innovadora forma de estudiar el efecto látigo, o una forma distinta de modelar las cadenas de suministro usando los principios. Se debe a un desajuste en la cadena de suministro entre las Relación entre precio-demanda pueden incrementar o mitigar el efecto látigo. Efecto Latigo Solución CPFR Planeación agregada. Es la sincronización de la estrategia de la cadena de suministro y de competitiva. Causas.

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The decision making happens at the beginning of every week, when managers look at the information systems and decide how much to order upstream. This can be caused because: However, due latiggo its simplicity, the model is limited in different ways. Suminiistro kind of artificial shortage is caused by the structure of heuristic policies defined by the supply chain managers.

System Dynamics Review14, 1, pp. Determining the Vector of Change. Demand is clearly seasonal during the year, with peaks during the summer between weeks 15 and The following is an extract from the interviews with the purchase manager: This oscillatory distortion is explained next.

However, during the first week the distribution manager orders finished goods from the DC upstream to return to the planned stock levels and cover expected future product demand. Sloan Management ReviewSpring, pp. Better coordination of the supply chain by managers can be promoted once managers are conscious of the global effects of their heuristic policies in the system.

A possible problem to define such a seasonal policy is the uncertainty. In effect, oscillations are particularly suministrl in purchase orders, and they are influenced by previous orders downstream in the supply chain. Due to the inventory policies, the safety stock is defined as days of coverage times the forecast. We have shown that is possible to build such a model and to capture with relative rfecto but high degree of abstraction the complexities of a Supply Chain.

Changes in purchase orders As we have said, the purchase policy rule for raw materials implies dramatic amounts of amplification, phase lag and oscillation in the purchase orders.

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During the first week we have no arrivals but sales of 13, units, resulting in a closing inventory of 6, units.

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But during that same week, the purchase manager receives more than 80, units due to a purchase order launched during the middle of the summer. We produced suministgo forecast for 4 or 5 months directly, creating the forecast from our sales estimations based on the “last month sales” lativo we multiplied it by a factor month by month This paper illustrate not only an innovative form to study the Bullwhip Effect nor only a different way to model supply chains using System Dynamics, but also it establishes a relationship between information structures, decisions rules, and demand distortion in supply chains.

Efectl variables represent decision makers managers and include the use of information inputs into a function that ends with a numerical decision e. An optimal policy will manage an equilibrium point where the variation of order quantities will be economical and equivalent to variations in inventories. University of Manchester, UK. Everything goes together with sales Management Science35, 3, pp.

The continuous line represents sales: Particularly, a model of this nature does not need to detail multiple plants or DCs and products to analyze the information use and decision making process of managers. Model description Given the nature of the System Dynamics cdaena Sterman ; Lane ; Doyle and Fordthe model will not emphasise the detail of the Supply Chain network.

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The model’s validity is based on the consensus and acceptance from the managers rather than in the statistical proves. There is a minimum amount of sugar to buy on a monthly basis of Ton. They have more thansales points. International Journal of Production Economics22, pp.

In principle, the forecast is produced by operational managers using econometric standards, and the sales managers are responsible of fine tune it with expected demand volumes per zone and by product.

Purchase managers generate a supply plan once every month and at least one month in advance. Changes in forecast Now suppose that we could develop a forecast system that provides information for two weeks in advance, in such a way that the purchase manager can order raw materials in advance to receive them the week when they are needed. They purchase sugar based on price. We make explicit the use of information flows and their sources.

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Pepsi Twist, which is Pepsi with a hint of lemon, helped the growth in their cola business. And additional volume growth came from products under the Dole and SoBe brands. We have to take into account maintenance, and order in advance when needed. When a production shortage happens, they use past sales as a guide to assign available products to fulfil demand orders from RDCs. Sugar price varies according to market.

This phase lag it is not caused by the delivering time, which is less than a week, but by the demand which is first served from the RDC before the RDC manager sends an order to the DCs. Systems Dynamics Review18, 4, pp. At each echelon, operation managers receive orders from a downstream echelon and try to fulfil them by taking two decisions: Due to a shortage of raw material, it is not possible to produce the full requirement coming from the production manager.

They recognise that their main business is distribution since advertising depends on PepsiCo Headquarters. Measuring and Avoiding the Bullwhip Effect: During the first DelayTime time units of the simulation, the values specified by Initial are returned Initial is a vector with one element per time step for a period equal to DelayTime.

System Dynamics Review17, 2, pp.

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In the consumer goods industry, and in particular the food industry, it is known that the customer never waits for backorders. The value of Input at DelayTime time units caddena in the simulation. How much is my excess or shortage?

As can be seen, in the model we have defined four stock positions in the model: