The bullwhip effect on inventory under different information sharing settings based on price-sensitive demand

Information sharing (IS) is proved to be a valid method to counter demand variability amplification along the supply chain, or bullwhip effect (BWE). Different from the traditional way of measuring the BWE based on order quantity, we measure the BWE on inventory in different IS settings and try to find the best IS approach. In this paper, the retailer will face the market demand which is price-sensitive, and the price follows a first-order autoregressive process. This demand model includes some indexes that can provide more useful managerial insights than previously studied parameters. Our study identifies the best IS setting under any conditions, and clarifies that the benefits of IS will be evident when the overall market product pricing process is highly correlated over time, the demand shocks to the retailer are high, the price sensitivity coefficient is small, the overall market shocks are low, the retailer’s lead-time is long and the manufacturer’s lead-time is short.     

The bullwhip effect on product orders and inventory: a perspective of demand forecasting techniques

Demand forecasting is one of the key causes of the bullwhip effect on product orders. Although this aspect of order oscillation is not ignored, the current study focuses on another critical aspect of oscillation: the bullwhip effect on inventory, i.e. the net inventory variance amplification. In particular, this paper studies a two-level supply chain in which the demand is price sensitive, while the price follows a first-order autoregressive pricing process. We derive the analytical expressions of the bullwhip effect on product orders and inventory using minimum mean-squared error, moving average and exponential smoothing forecasting techniques. We also propose the conditions under which the three forecasting techniques would be chosen by the retailer to minimise the sum of the bullwhip effect on product orders and inventory under different weightings. These observations are used to develop managerial insights regarding choosing an appropriate forecasting technique after considering certain distinct characteristics of the product.     

The bullwhip effect in supply chains: a study of contingent and incomplete literature

The bullwhip effect (BWE) describes a phenomenon that involves the increasing amplification of demand variability along a supply chain (SC). The BWE has been a subject that has received continuous attention from researchers over the past 15 years and is a concern for SC managers because it is a major cause of efficiency and effectiveness loss in SCs. Information sharing between actors in an SC is usually considered to be one of the primary means to minimise the BWE. Approximately 50 articles published in major journals on these topics are studied in this article. An analytical framework is used to highlight the contingent character of the conclusions proposed by the authors. In this review, we identify the existence of significant gaps in the literature, especially concerning the BWE when it occurs in the productive part of the SC.     

The impact of consumer price forecasting behaviour on the bullwhip effect

As prices fluctuate over time, a strategic consumer may buy more in advance to reduce his or her future needs in anticipation of higher prices in the future, or may choose to postpone a purchase in anticipation of lower prices in the future. We investigate the bullwhip effect from a consumer price forecasting behavioural perspective in the context of a simple two-level supply chain composed of a supplier and a retailer. We consider two different forms for the demand function – linear and iso-elastic demand functions, both depending on the prices in multiple periods. Assuming that the retailer employs an order-up-to inventory policy with exponential smoothing forecasting technology, we derive analytical expressions for the bullwhip effect under the two demand functions, and extend the results to the multiple-retailer case. We find that consumer forecasting behaviour can reduce the bullwhip effect, most significantly when the consumer sensitivity to price changes is medium (approximately 0.5) for both the demand forms. In addition, for iso-elastic demand, the mitigation of the bullwhip effect induced by consumer price forecasting behaviour becomes more significant as the product price sensitivity coefficient and standard deviation of the price decrease. These findings are applicable to the development of managerial strategies by supply chain members that are conducive to bullwhip effect reduction through customer behaviour.