Information Sharing and Order Variability Control Under a Generalized Demand Model

Abstract

T he value of information sharing and how it could address the bullwhip effect have been the subject of studies in the literature. Most of these studies used different forms of demand models, assuming that no order smoothing was used by the retailer and that the supplier has full knowledge of the retailer's demand model and order policy. In this paper, we contribute to the literature by starting with a most general demand model, coupled with a smoothing policy for order variability control. In addition, we do not require that the supplier has full knowledge of the retailer's demand model and order policy, but instead let the retailer share its projected future orders (and freely revise them as the retailer sees fit). Under such a setting, we first obtain a unifying formula for the magnitude of the bullwhip effect. The formula indicates that it is the forecast correlation over the exposure period as a whole that determines the magnitude of the bullwhip effect. We then quantify the value of information sharing and generalize the existing results in the literature. Finally, we explore the optimal smoothing parameters that could benefit the total supply chain. The resulting optimal policy resembles the postponement strategy. We find that information sharing together with order postponement improves the supply chain performance, even though the order variability may amplify in some cases.

Behavioral causes of the bullwhip effect: An analysis using linear control theory

“…Research on the field typically uses different demand assumptions based upon whether they are centered on structural or behavioral causes of the bullwhip effect. The former assume stationary demand streams (Chen and Lee, 2009), whereas the latter are based on experiments exploiting demand shocks (Croson et al, 2014).…”

A Multiordering Newsvendor Model with Dynamic Forecast Evolution

“…We note that similar forms of linear state dependence has been assumed by a number of papers in the multiperiod inventory models literature. For instance, Chen and Lee (2009) define an affine and stationary base-stock policy for their infinite horizon problem; Schoenmeyr and Graves (2009) assume linearity when constructing the forecast-based ordering policy, and Toktay and Wein (2001) restrict their analysis to linear policies motivated by material requirements planning logic. Proposition 2, however, not only proves the linearity of the base-stock level for our setting with a finite horizon and increasing costs, but also explicitly characterizes the slope and intercept.…”

Modeling Stock-out Loss and Overstocking Loss Generated by Bullwhip Effect

“…Nonetheless, Miragliotta [43] has provided a more elaborate literature review regarding bullwhip effect. An extensive effort has been exerted by a few researchers such as Metters [44] and Chen et al [7,45], Kim et al [28], Frangoo [10], Chen and Lee [46], Isaksson and Seifert [47], George and Pillai [48], Sodhi et al [49], and Ma and Bao [50] to quantify the bullwhip effect. Metters [44] highlights how demand seasonality and forecast error can negatively affect the supply chain profitability under different levels of demand distortion.…”