A novel data-driven approach to support decision-making during production scale-up of assembly systems. (April 2021)

• Record Type: Journal Article
• Authors: Kaniappan Chinnathai, Malarvizhi; Alkan, Bugra; Harrison, Robert
• Is Part Of: Journal of manufacturing systems. Volume 59(2021)
• Page Start: 577
• Page End: 595
• Publication Date: 2021-04
• Subjects: Manufacturing systems -- Production planning -- Scale-up -- Demand amplification -- Demand uncertainty -- Data-driven method -- Discrete-Event Simulation -- DES -- Multi-objective optimisation -- Evolutionary optimisation algorithm -- Genetic Algorithm -- GA -- Kinematic modelling; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.03.018
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Novel decision support framework that provides graphical and tabulated comparison of scale-up design solutions. Incorporates simulation optimisation and knowledge-based virtual modelling of assembly systems to increase the accuracy and applicability to industrial settings. A bottom-up approach for screening the design space to eliminate infeasible solutions and select valid designs. Demonstration using real-world industrial battery module assembly system. 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.