A Novel Hybrid IBA-OSCD Approach for Multi-Criteria Inventory Classification

Abstract

Research background: Inventory management plays a crucial role in manufacturing organizations as it enables continuity of production, adequate response to customer demand, financial optimization of inventory and overall adequate organizational liquidity. Inventory management is highly dependent on inventory classification as it allows simplification of the inventory management process by assigning inventory management policies based on categories rather than individual items. Purpose of the article: In this article, a novel hybrid approach for multi-criteria inventory classification is proposed. The approach considers two groups of logically related criteria: One group focuses on inventory ordering dynamics, i.e., lead time, average demand, current inventory quantity, and coefficient of variation, while the other focuses on inventory quantity and value, including average demand, unit price, and dollar usage value. The method follows the ABC classification convention and is used to categorize 290 inventory items into three classes. Methods: The classification process is conducted in two steps. First, each set of criteria is evaluated and aggregated using logical aggregation based on Interpolative Boolean Algebra, allowing for the integration of both logic-based and statistical relationships within the data during aggregation. The resulting values are then further aggregated using ordinal sums of conjunctive and disjunctive functions to perform upward or downward reinforcement , depending on the input values. Findings & Value added: This study demonstrates the novel logic-based approach to multi-criteria inventory classification, which aims to explore a different perspective on inventory management by considering multiple sets of logically related criteria.