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One important mechanism for coordination in a supply chain is the information flows among members of the supply chain. These information flows have a direct impact on the production scheduling, inventory control and delivery plans of individual members in the supply chain. However, the distortion demand information, the retailer¡¦s orders do not coincide with the actual retail sales, tend to have larger variance orders to the supplier than sales to the buyer. This distortion of the demand in upstream activities is known as ¡§bullwhip effect¡¨(Lee et al. 17). Viewed more fundamentally, bullwhip refers to the amplification of end-customer order signals, whereby upstream replenishment demand and physical shipments exceed the original order quantity. The bullwhip effect, as defined by Lee et al.(17), is a phenomenon whereby
Information transferred in the form of orders tens to be distorted and can misguide upstream members in their inventory and production decisions¡K the variance of [replenishment] orders may be larger than that of sales [to end customers], and the distortion tends to increase as one moves upstream.
This concept paper is trying to apply the Value-at-risk (VaR) model in the supply chain management (SCM) and measure the bullwhip effect. VaR is being used in financial risk management as a measure of risk taken with a given confidence level over a specific time interval. VaR can be adopted to measure losses both on short term (1 day) and on long term (1 year) time horizon. In the financial applications only short term VaR are currently used, but there is no logical difficulty in using it also over period lager than the year in supply chain risk management. The purpose of applying VaR provides an approach to measure the supply chain risk under the uncertainty. Not only minimizes the cost and bullwhip effect, but also helps manager to make decision and adjust productivity base on an expected maximum loss under uncertainty demand.
Help with essay on Risk mapping to the Bullwhip Effect: VaR as a risk measure for supply chain
Use VaR to evaluate supply chain¡¦s performance by providing a risk quantification, expressed in monetary units, can helps supply chain managers to determine the most suitable risk management strategy for optimization supply chain.
Literature review
The bullwhip effect has been noted and assigned various causes across a range of academic disciplines. The first academic description of the bullwhip phenomenon is usually ascribed to Forrester(161). Forrester indicates that it is empirically common for the variance of perceived demand to the manufacturer to far exceed the variance of consumer demand and for seasonality to be larger for manufacturers than for retailers. Furthermore, he notes that the effect is amplified at each stage in the supply chain. He states that the principle cause of this is difficulties involving the information feedback loop between companies and systems are too complex for managerial intuition alone to ameliorate. Consequently, Forrester¡¦s remedy lies in understanding the system as a whole, and modeling that system with specific ¡§system dynamics¡¨ simulation models, so that managers can determine appropriate action.
Lee et al (17) have documented the effect in a number of specific businesses and have posited both causes and cures. In agreement with the economists, the bullwhip effect is purported to stem from rational, profit maximizing managers. Two specific sources are claimed for the effects that are relevant for this research
1. Forward buying practices for seasonal items by downstream wholesalers and retailers amplifies the seasonality seen by the manufacturers. As a general practice in the dry grocery industry, wholesales level buyers often induce larger seasonality for manufacturers by purchasing overly large quantities of product during the peak demand season for that product in an attempt to get reduced prices per unit.
. Batching of orders by downstream participants. Demand may be relatively continuous by consumers, but due to ordering costs or periodic ordering system runs it is batched early in the supply chain. This batching of orders induces demand variance up the supply chain that is not present at lower levels.
Towill (11) and Towill et al (1) use simulation techniques to evaluate the effects of various supply chain strategies on demand amplification. The strategies investigated are as follow
1. Eliminating the distribution echelon of the supply chain, by including the distribution function in the manufacturing echelon.
. Integrating the flow of information throughout the chain.
. Implementing a just-in-time (JIT) inventory policy to reduce time delays.
4. Improving the movement of intermediate products and materials by modifying the order quantity procedures.
5. Modifying the parameters of the existing order quantity procedures.
The objective of the simulation model is to determine which strategies are the most effective in smoothing the variations in the demand pattern.,
Research Question and Hypothesis
Uncertainty and distort demand are the major sources of bullwhip effect. For managers who wish to minimize unnecessary and costly variation in their supply chain, it is important to understand the specific causes of bullwhip. Uncertainty can broadly be identified as continuous changes in the echelon time-series for demand, distribution, production and inventory. Traditionally, researchers have limited their analyses and scope to minimum the cost within the effective supply chain. This paper will focus on the cause and effect between uncertainty demand and inventory by using the VaR model to calculate the maximum loss (cost) with distort or uncertainty demand. The key variables of this model are uncertainty demand order, distort order information upstream and lead-time.
The research hypotheses for this paper are
H1 The cost of bullwhip effect can be measured and expressed in a function with monetary units.
H The total cost of bullwhip effect would be the sum of bullwhip-related inventory swing costs, shortage cost, and profit margin lost due to poor availability.
H VaR model can optimize supply chain strategy under acceptable and expectable maximum loss over a certain period.
Methodology
The methodology for this study is based on the Value-at-risk (VaR) model to measure the risk of bullwhip effect in the supply chain management. This design focuses on uncertainty demand and the cost (loss) cause by bullwhip effect. Base on the supply chain ¡§Loss Function¡¨ model from Budiman (00). The minimum loss function expressed mathematically as below. Apply VaR to the loss function to calculate the maximum tolerable risk (loss). Use the result, acceptable risk, as the benchmark to adjust an optimization supply chain.
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