Exploring a Downstream Demand Inference Strategy in a Decentralized Two-Level Supply Chain

Abstract

A coordination approach for forecast operations, known as downstream demand inference, enables an upstream actor to infer the demand information at his formal downstream actor without the need for information sharing. This approach was validated if the downstream actor uses the simple moving average (SMA) forecasting method. To answer an investigative question through other forecasting methods, the authors use the weighted moving average (WMA) method, whose weights are determined in this work thanks to the Newton's optimization of the upstream average inventory level. Starting from a two-level supply chain, the simulation results confirm the ability of the approach to reduce the mean squared error and the average inventory level, compared to a decentralized approach. However, the bullwhip effect is only improved after a certain threshold of the parameter of the forecasting method. Still within the framework of the investigation, they carry out a comparison study between the adoption of the SMA method and the WMA method. Finally, they generalize their results for a multi-level supply chain.

Selection of Suppliers for Speech Recognition Products in IT Projects by Combining Techniques with an Integrated Fuzzy MCDM

“…Although the majority of studies employed AHP, ANP, TOPSIS, and their combinations with the fuzzy set to choose the best supplier, VIKOR, DEMATEL, and ANOVA were also used often. VIKOR has been employed in over 750 supply chain management (SCM) studies over the last two decades, and there are over 500 researches on the intuitionistic fuzzy approach to SCM challenges [5,6]. According to research, VIKOR is typically used in conjunction with fuzzy AHP, fuzzy ANP linguistic information, fuzzy DEMATEL, ELECTRE, and BWM, and other fuzzy techniques, whereas intuitionistic fuzzy is typically used in conjunction with MULTIMOORA, AHP, PROMETHEE, UTASTAR, ELECTRE, and ANP.…”