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A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations

journal contribution
posted on 14.01.2020, 00:00 by Swee Kuik, L Diong
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

119

Issue

9

Start Page

1926

End Page

1946

Number of Pages

21

eISSN

1758-5783

ISSN

0263-5577

Publisher

Emerald Publishing, UK

Peer Reviewed

Yes

Open Access

No

Acceptance Date

27/08/2019

External Author Affiliations

University of Southern Queensland

Era Eligible

Yes

Journal

Industrial Management and Data Systems

Usage metrics

CQUniversity

Exports