Abstract
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.
Subject
Industrial and Manufacturing Engineering,Strategy and Management,Computer Science Applications,Industrial relations,Management Information Systems
Reference45 articles.
1. Applying Six Sigma methodology to collaborative forecasting;The International Journal of Advanced Manufacturing Technology,2008
2. Manufacturing system design meets big data analytics for continuous improvement;Procedia CIRP,2016
3. Weatherproofing supply chains: enable intelligent preparedness with data analytics;Transportation Journal,2016
4. The future of manufacturing industry: a strategic roadmap toward Industry 4.0;Journal of Manufacturing Technology Management,2018
5. Big Data analytics and IoT in logistics: a case study;The International Journal of Logistics Management,2018
Cited by
13 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献