The Bullwhip Effect on Inventory Management Introduction This study will study three main problems related to inventory control. The first problem is about eliminating the bullwhip effect in the whole process of the supply chain. This type of effect is mostly caused by the policies on controlling inventory, which was originally formulated to enhance the smooth production of goods when there is variation in demand in the supply management chain. In addressing these issues, an estimation method based on the control technique will be used. This kind of approach leads to the stability of the policy controlling inventory. However, in most cases deriving policies on inventory demands that one knows precise demand distribution. The second problem is driven by practical application where there is not enough information on the goods’ demand. The study will develop estimations of the first two periods of demand through the use of theoretical theories. Regression-based estimations will be used in order to perfect the quality of the estimates. Several numerical investigations will be used to assess the accuracy of the estimations and evaluate the impact the estimations have on the effectiveness of the policies on inventory. Based on all the above assumptions, the study will formulate a strong and dynamic model of programming and prove the effectiveness of the policies that are dependent on the state for both the regular and emergency orders. Problem description
Inventory is one of the major components in the logistical behavior o the manufacturing sector. The traditional sector’s logistical behavior results are important to the contemporary techniques used in managing the supply chain, such as planning for the material requirements and the time-based competition. Inventory comprises materials and goods in the production and distribution processes such as the component parts, raw materials goods, subassemblies and finished goods.Uncertainty is the main reaUncertaintyetailers store inventory as they wait to see what will happen. One of the major factors causing uncertainty is the external demand, variation of the time for delivering goods, unpredictable production schedules, and price fluctuations. Others hold their inventories to take advantage of the discounts of big volumes that pay back the setup cost over a large number of goods. Using empirical studies and mathematical models, several factors have been identified that can be used to control inventory. Demand, which is is one of the essences, which rolling demand inventory systems determine how complex the resultant models will formulated. The demand process can be classified as determinist versus random, independent versus dependent, and known versus the unknown distribution. Another important factor in the inventory system’s management is the inventory system’s management of an order is place till it arrives. The bullwhip effect may result in huge inefficiency in the supply chain management.For instance, the producers in the supply chain will meet extra costs of production due to high fluctuations in demand, such as layoffs, accession demand on inventory, additional transportation, hiring, and the revenue lost. The reputation of the organization might be injured because of the inability to meet the customers’ demands due to amplifying customers’ demands; the most important question in supply chain management is how to manage and control the effects of the bullwhip. The supply chain’s performance can be the supply chain’s performance of the bullwhip effect, including lead times, forecasting on demand, shortages of supply, batch borders, and changes in the prices of goods. This study will propose various methods of reducing the study to reduce the effect. Such methods include reducing the demand variability, creating a centralized information database, having more strategic partnerships, and shortening the lead time. Most of the commonly used policies on the inventory of investment are the enlargement of the variation when one goes up the supply chain. Thus the inventory stabilization is not realized as expected. Some studies have also proved that this method does not solve the intended problems but magnifies them. For instance, if all retailers use the policies simultaneously, the order’s average variance will exceed the sales variance. In most supply chain models where there are several retailers and one supplier, the demand variation the supplier will face will only be reduced by increasing the retailers’ order intervals. Additionally, when different forecasting strategies are used, the order variance is higher than the variance on demand. There are effective techniques that reduce the variability for demand in the context of the supply chain. Having a policy where specifications on the inventory level ranges within which an order is made on the fixed control volume will reduce the order quantity variability. The excessive premium will also go down. General Methodology: The recent research on reducing the bullwhip effect on inventory management has provided important insights for companies’ managerial use. The studies also show practical solutions to such problems that affect the whole supply chain. The techniques employed and their respective models use various optimization approaches. The experts in supply chain management can formulate models and solve complex mathematical problems that will eventually be used to mitigate the problem. The techniques have to be applied to various situations and models. This is difficult for most organizations unless they seek experts who will assist in the practical situations and the implementation of the models. The bullwhip effect’s consequences are mostly not factored in the policies on the control of inventories that are not difficult to implement and are commonly used in the practical work situations for the supply chain managers. A control variable method will reduce the bullwhip effect or the variability of demand in the supply chain.This technique is easy to use and is effective in solving practical problems in the supply chain sector. Model formulation Recent studies on reducing the bullwhip effect due to the policies on the control of inventories have given important insight into this issue. They are important in providing practical solutions to the bullwhip issue that sometimes brings serious problems in the supply chain. The Control Variable Technique This technique has, for some time, been used to reduce the effects of the bullwhip effect. When the technique is effectively applied, it can change an expensive simulation project and turn it into a viable one. For instance, if one wants to estimate the measure of performance φ = E[Z] of a particular system using output M, which is derived from the system’s simulation. In most cases, when simulating the stable-state anticipated length, Z, Var(Z) ‘s estimation variance would enlarge. The queue, the evolution of the queuing system that is heavily loaded, would become large, making it difficult to get a reasonably decent estimate for some changes on the original estimation Z would be required to create a balance. For this study’s purpose, the output V generated from the simulation with the E[V ] = µv known expectation can be used. The generated variation would be Z + α(µv − V ). In this case, α is a constant which can be regarded as the alternative for φ estimation for a variance. Var (Z + α(µv − V )) = Var(Z) + α
2Var (V ) − 2αCov(Z, V ). Variable V will be used for control purposes when using simulation Z. To understand how the control variable assists in estimation with less variance, the following example should be considered: An assumption should be made that V and Z are correlated and highly positive such that α is a positive. Thus, when significant values of the previous estimator Z (or Z > φ) are observed from simulation, V’s value is going to appear larger than its average of µv. As α∗ is therefore positive adding the termα
(µv − V ) ∗ The estimator Z.A similar observation will be applicable if V and Z are negatively correlated, which will help eradicate the variation Z from its average φ.
Mathematical models effectively solve the bullwhip effect in supply chain management because they are tested and proved to be reliable. If the supply chain is not balanced through such models, then the whole chain from the manufacturers to the consumers will be affected due to price fluctuations and demand changes. Therefore, the mitigation measures must be instituted proactively and practically to reduce the whole supply chain’s effects.
References
Dudin, A., Nazarov, A., & Kirpichnikov, A. (2017). Information technologies and mathematical modeling. Queueing theory and applications: 16th International Conference, ITMM 2017, named after A.F. Terpugov, Kazan, Russia, September 29 – October 3, 2017, proceedings. Springer.
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Ravindran, A. R. (2016). Operations research and management science handbook. CRC Press.