IMPLEMENTATION OF HYBRID ARTIFICIAL NEURAL NETWORK AND MULTI-CRITERIA DECISION MODEL FOR THE RANKING OF CRITERIA THAT AFFECT PRODUCTIVITY – A CASE STUDY.

Abstract

This study delves into the often-overlooked factors influencing industrial productivity, focusing on labour and machine maintenance as key drivers. Extensive research was undertaken in a core shop producing automotive components to identify and assess these factors. Using multi-criteria decision models (MCDM) such as analytic hierarchy process (AHP), fuzzy analytic hierarchy process (FAHP), technique for order of preference by similarity to ideal solution (TOPSIS), viekriterijumsko kompromisno rangiranje (VIKOR) method, enterprise distributed application service (EDAS), and Entropy TOPSIS, the study ranked various productivity criteria. Artificial neural networks were then employed to validate these rankings. The research emphasised the significance of manufacturing equipment and raw materials, following the prioritisation of the workforce. Implementing material handling systems aimed at reducing errors and enhancing productivity proved pivotal. As a result of these strategies, non-value-added activities (NVA) decreased by 65.56%, process time improved by 61.03%, waiting time reduced significantly by 66.66%, manpower decreased by 35%, and costs decreased by 45%. These outcomes translated into a notable 23% increase in production levels in the core shop. The study underscores the efficacy of innovative work methods and standardised operating procedures in maximising productivity.