Fuzzy c-means clustering-based key performance indicator design for warehouse loading operations

Performance measurements are important motivators in evaluating a company's strategy. The perfor-mance improvement process starts with the measurement of the current situation. Therefore, companies use various metric quantities for the efficiency and productivity of warehouse management. Recently, many studies have been conducted on key performance indicators. In this study, an artificial intelligence-aided key performance indicator is intended for the loading performance of a warehouse, and the analysis is performed based on various scenarios. In the pre-processing phase, five inputs are taken as the unit price, monthly demand quantities, the number of products loaded from the warehouse, the demand that cannot be loaded on time, and the average delay times of the products that cannot be loaded on time. The outputs of the pre-processing phase are clustered using a fuzzy c-means clustering algorithm. Then a key performance indicator for the warehouse loading operations is proposed using the fuzzy c-means clustering result. Researchers and engineers can easily use the proposed scheme to achieve efficiency in warehouse loading management. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Performance evaluations are crucial in assessing a company's strategy, especially in terms of warehouse efficiency and productivity. This study explores the use of artificial intelligence-assisted key performance indicators to enhance warehouse loading performance and analyze different scenarios.