The IUP Journal of Operations Management
Supply Chain Impact from End Customer Demand Volatility: A Computer-Based Simulation Approach

Article Details
Pub. Date :Feb, 2022
Product Name : The IUP Journal of Operations Management
Product Type : Article
Product Code : IJOM20222
Author Name : Martin Lockstrom
Availability : YES
Subject/Domain : Management
Download Format : PDF Format
No. of Pages : 18



The bullwhip effect is a well-known phenomenon in supply chain management. Whereas past research has focused on factors like supply rationing, batch-size, lead time, etc., mainly from an analytical perspective, this paper broadens the research by specifically examining the impact from end customer demand volatility not only on demand (order size) but also on inventory levels and supply chain cost. The results confirm the findings of past research and bring new insights on the impact of component and compound bullwhip effect behavior across supply chain echelons by showing that end customer demand volatility acts as an amplifier.


Supply chains are complex, adaptive systems whose behavior depends on a broad range of factors (Choi et al., 2001; Pathak et al., 2007; and Carter et al., 2015). A commonly observed supply chain phenomenon is the so-called "bullwhip effect", also referred to as "Forrester effect" after its discoverer (Lane and Sterman, 2011). The bullwhip effect refers to the phenomenon that occurs in a supply chain when orders submitted to suppliers have a greater variability than those received from customers. Prior research has identified the influence of such factors, but mainly from a qualitative and analytical perspective. Interestingly, also an "inverse" bullwhip effect has also been discovered and investigated in the past (Li et al., 2017).

Lee (1997) identified demand signal processing (i.e., incorrect demand forecasting), rationing game and lead time, order batching and price variations as the main causes of the bullwhip effect. Lee also discovered that the most effective way of reducing it was by reducing the uncertainty of end customer demand and order lead time along the supply chain by providing each stage with more accurate and timely information, for instance, through Electronic Data Interchange (EDI).