Impact of information exchange on supplier forecasting performance

Abstract

Forecasts of demand are crucial to drive supply chains and enterprise resource planning systems. Usually, well-known univariate methods that work automatically such as exponential smoothing are employed to accomplish such forecasts. The traditional Supply Chain relies on a decentralised system where each member feeds its own Forecasting Support System (FSS) with incoming orders from direct customers. Nevertheless, other collaboration schemes are also possible, for instance, the Information Exchange framework allows demand information to be shared between the supplier and the retailer. Current theoretical models have shown the limited circumstances where retailer information is valuable to the supplier. However, there has been very little empirical work carried out. This works assesses the role of sharing market sales information obtained by the retailer on the supplier forecasting accuracy. Data have been collected from a manufacturer of domestic cleaning products and a major UK grocery retailer to show the circumstances where information sharing leads to improved accuracy. We find significant evidence of benefits through information sharing.

<b>The importance of trust and collaboration between companies to mitigate the bullwhip effect in supply chain management

“…Bray & Mendelson (2012), Trapero, Korentzes, & Fildes (2012), Li (2013), Sari (2015), Barkataki & Zeineddine (2015) 2. Improved demand forecasting Wright & Yuan (2008), Ali, Boylan, & Syntetos (2012), Trapero et al (2012), Dong et al (2014), Sari (2015), Xu, Dong, &Xia (2015), Nagashima et al (2015) 3. Replenishment policy Lee & Wu (2006), Jaksic & Rusjan (2008), Su & Wong (2008), Kelepouris, Miliots, & Pramatari (2008), Dong et al(2014) VMI Disney & Towill (2003) Relationships between trust, collaboration, and BWE Some studies consider trust as a defining characteristic of the presence of collaboration among members of a SC (Vieira et al, 2009).…”

A Review of the Wood Pellet Value Chain, Modern Value/Supply Chain Management Approaches, and Value/Supply Chain Models

“…Vinterbäck [86], and Hosoda, and Disney [87] discuss some of these approaches, including exponential smoothing, the naïve approach, moving average, autoregressive (AR), autoregressive integrated moving average (ARIMA), autoregressive extra (ARX), vector autoregressive (VAR), neural networks and the quantile regression method. However, these methods have not been proven to be overly effective and still allow for inaccurate demand prediction at each level throughout the supply chain, resulting in the bullwhip effect, which is amplification in demand variability when moving upstream through a value chain or supply chain [88]. Therefore, we are considering the new approaches currently being researched to increase the forecasting accuracy.…”

“…Therefore, we are considering the new approaches currently being researched to increase the forecasting accuracy. Multilayer perception (MLP) is an approach that generalizes either linear or nonlinear functional relationships between inputs and outputs [88]. Yousefi et al [82] designed a comprehensive demand response (CDR) model for a Retail Energy Provider agent in an agent-based retail environment to offer real-time energy prices to customers.…”

“…Yousefi et al [82] found that the CDR model gave a better representation of customers' historical behavior for future demand prediction. We only found four papers in our review that addressed demand forecasting [50,79,87,88], while another three papers, which we categorized as demand-driven management papers, also addressed demand forecasting approaches [49,75,82].…”

Analysis of the Complexity Entropy and Chaos Control of the Bullwhip Effect Considering Price of Evolutionary Game between Two Retailers

“…Furthermore, they conducted research of the bullwhip effect in closed-loop supply chains. Not only the information sharing was discussed, but the information exchange was also studied by Trapero et al [15].…”