Fuzzy Decision-Making Model for the inventory leveling under uncertainty condition

Abstract

The option to create inventory is not always the optimal choice due to the associated expenses and space requirements. Nevertheless, there are instances where a shortage of materials on customer lines can result in substantial financial penalties. This constant contradiction places supply chain managers in a perplexing predicament, especially when considering the amplification of inventory through the bullwhip effect as it moves across different stages. Moreover, the uncertain backdrop created by unforeseen events intensifies this already critical situation, compelling managers to seek novel decision-making approaches. These approaches should enable the simulation of risks and the selection of suitable scenarios, particularly within the intricate domain of stochastic and dynamically evolving supply chains. In this study, we introduce a new decision-making model rooted in the fuzzy logic concept introduced by Loutfi Zadeh in 1965. This model is applied to criteria assessed by experts, representing the most pertinent parameters for guiding inventory optimization. The chosen criteria encompass Lead Time, Equipment Production Reliability, and Warehousing Costs. This model exhibits the potential to unearth intricate patterns and associations among variables that conventional statistical methods struggle to reveal. Notably, the integration of fuzzy logic for inventory prediction yields promising outcomes, extendable to the realm of artificial intelligence, where comprehensive inference rules facilitate effective decision-making.