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A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations
Purpose: The purpose of this paper is to present the model-driven decision support system (DSS) for small and medium manufacturing enterprises (SMMEs) that actively participates in collaborative activities and manages the planned obsolescence in production. In dealing with the complexity of such demand and supply scenario, the optimisation models are also developed to evaluate the performance of operations practices. Design/methodology/approach: The model-driven DSS for SMMEs, which uses the optimisation models for managing and coordinating planned obsolescence, is developed to determine the optimal manufacturing plan and minimise operating costs. A case application with the planned obsolescence and production scenario is also provided to demonstrate the approach and practical insights of DSS. Findings: Assessing planned obsolescence in production is a challenge for manufacturing managers. A DSS for SMMEs can enable the computerised support in decision making and understand the planned obsolescence scenarios. The causal relationship of different time-varying component obsolescence and availability in production are also examined, which may have an impact on the overall operating costs for producing manufactured products. Research limitations/implications: DSS can resolve and handle the complexity of production and planned obsolescence scenarios in manufacturing industry. The optimisation models used in the DSS excludes the variability in component wear-out life and technology cycle. In the future study, the optimisation models in DSS will be extended by taking into the uncertainty of different component wear-out life and technology cycle considerations. Originality/value: This paper demonstrates the flexibility of DSS that facilitates the optimisation models for collaborative manufacturing in planned obsolescence and achieves cost effectiveness. © 2019, Emerald Publishing Limited.
History
Volume
119Issue
9Start Page
1926End Page
1946Number of Pages
21eISSN
1758-5783ISSN
0263-5577Publisher
Emerald Publishing, UKPublisher DOI
Peer Reviewed
- Yes
Open Access
- No
Acceptance Date
2019-08-27External Author Affiliations
University of Southern QueenslandEra Eligible
- Yes
Journal
Industrial Management and Data SystemsUsage metrics
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