A novel data-driven approach to support decision-making during production scale-up of assembly systems

Kaniappan Chinnathai, M., Alkan, B. and Harrison, R. 2021. A novel data-driven approach to support decision-making during production scale-up of assembly systems. Journal of Manufacturing Systems. 59, pp. 577-595. https://doi.org/10.1016/j.jmsy.2021.03.018

TitleA novel data-driven approach to support decision-making during production scale-up of assembly systems
TypeJournal article
AuthorsKaniappan Chinnathai, M., Alkan, B. and Harrison, R.
Abstract

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.

KeywordsManufacturing systems
Production planning
Genetic Algorithm
Kinematic modelling
JournalJournal of Manufacturing Systems
Journal citation59, pp. 577-595
ISSN1878-6642
0278-6125
Year2021
PublisherElsevier
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jmsy.2021.03.018
Publication dates
Published15 Apr 2021

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