Mohammad M. Ali

Feasibility principles for Downstream Demand Inference in supply chains

Many companies are adopting strategies that enable Demand Information Sharing (DIS) between the supply chain links. Recently, a steady stream of research has identified mathematical relationships between demands and orders at any link in the supply chain. Based on these relationships and strict model assumptions, it has been suggested that the upstream member can infer the demand at the downstream member from their orders. If this is so, DIS will be of no value. In this paper, we argue that real-world modelling requires less restrictive assumptions. We present Feasibility Principles to show that it is not possible for an upstream member to accurately infer consumer demand under more realistic model assumptions. Thus, we conclude that DIS has value in supply chains. We then move our focus to the supply chain model assumptions in the papers arguing that there is value in sharing demand information. Using a simulation experiment, we show that the value of sharing demand information in terms of inventory reductions will increase under more realistic supply chain model assumptions.

Supply chain forecasting when information is not shared

The operations management literature is abundant in discussions on the benefits of information sharing in supply chains. However, there are many supply chains where information may not be shared due to constraints such as compatibility of information systems, information quality, trust and confidentiality. Furthermore, a steady stream of papers has explored a phenomenon known as Downstream Demand Inference (DDI) where the upstream member in a supply chain can infer the downstream demand without the need for a formal information sharing mechanism. Recent research has shown that, under more realistic circumstances, DDI is not possible with optimal forecasting methods or Single Exponential Smoothing but is possible when supply chains use a Simple Moving Average (SMA) method. In this paper, we evaluate a simple DDI strategy based on SMA for supply chains where information cannot be shared. This strategy allows the upstream member in the supply chain to infer the consumer demand mathematically rather than it being shared. We compare the DDI strategy with the No Information Sharing (NIS) strategy and an optimal Forecast Information Sharing (FIS) strategy in the supply chain. The comparison is made analytically and by experimentation on real sales data from a major European supermarket located in Germany. We show that using the DDI strategy improves on NIS by reducing the Mean Square Error (MSE) of the forecasts, and cutting inventory costs in the supply chain.

The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes

Various approaches have been considered in the literature to improve demand forecasting in supply chains. Among these approaches, non-overlapping temporal aggregation has been shown to be an effective approach that can improve forecast accuracy. However, the benefit of this approach has been shown only under single exponential smoothing (when it is a non-optimal method) and no theoretical analysis has been conducted to look at the impact of this approach under optimal forecasting. This paper aims to bridge this gap by analysing the impact of temporal aggregation on supply chain demand and orders when optimal forecasting is used. To do so, we consider a two-stage supply chain (e.g. a retailer and a manufacturer) where the retailer faces an autoregressive moving average demand process of order (1,1) -ARMA(1,1)- that is forecasted by using the optimal Minimum Mean Squared Error (MMSE) forecasting method. We derive the analytical expressions of the mean squared forecast error (MSE) at the retailer and the manufacturer levels as well as the bullwhip ratio when the aggregation approach is used. We numerically show that, although the aggregation approach leads to an accuracy loss at the retailers level, it may result in a reduction of the MSE at the manufacturer level up to 90% and a reduction of the bullwhip effect in the supply chain that can reach up to 84% for high lead-times.