Reducing Wastage In Manufacturing Through Digitalization: An Adaptive Solution Approach For Process Efficiency

Abstract

The transformation to digital manufacturing has become increasingly critical for companies to remain competitive and achieve efficient manufacturing processes. However, manufacturing operations are often plagued by suboptimal allocation of resources, which can lead to higher costs and lower productivity. Digitalization has the potential to address these challenges by enabling real-time data monitoring, reducing quality costs, and improving product quality. Previous studies have shown that digital manufacturing can improve the efficiency of manufacturing processes and lead to productivity increases in organizations. However, despite these advantages, many digital innovation projects in manufacturing fall short of their initial ambitions, often resulting in incremental improvements to an existing manufacturing system. This is partly due to the challenges faced by manufacturing companies in quantifying the added value versus the costs of digitization technologies. Therefore, the objective of this paper is to propose an adaptive solution approach that addresses the need of aiding the decision process in selecting and assessing digital technologies to reduce wastage in manufacturing processes. The approach combines the 'Makigami' methodology, an 'Activity Diagram' (AD) modelling methodology, and a simplified 'Flow Chart', representing an aggregated view of the more detailed AD via a custom modelling schema, into one coherent framework. We further introduce the 'Methods-Misallocation-Measure' (3M-Graph) framework, which maps methods onto elements of wastage and misallocation, and subsequently assigns potential countermeasures. This tripartite mapping facilitates the identification of wastage during process analysis, the allocation of digital optimization measures and eases the assessment of cost effectiveness. The proposed approach aims to improve process efficiency and reduce wastage in manufacturing through digitalization. We conduct a case study of the approach and its application to an industrial assembly station, comparing the initial and then optimized processes. Future work includes the identification of further improvements and extending the framework by methodologies for estimating cost effectiveness more concisely.