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Purpose-The aim of this paper is to investigate the impacts of the noise from the diesel engine power generators used for production activities in an urban environment. Design/methodology/approach-This study has used the Enterprise Edition of NoiseMap 2000 Version 2.7.1 to investigate the impacts of the noise from the diesel engines electric power generators used in a factory in Ikorodu, an urban environment in Lagos, Nigeria. Five sections of the factory with diesel engines electric power generators were considered. The immediate and distant environments covering about 10 km of the factory host environment were considered as receptors to the noise for this study. Findings-It was found out that when all the generators operate simultaneously in the factory, the ambient noise was 30.0-152.5 dB(A) with the minimum contribution within the factory being 70.0-84.4 dB(A) and the maximum contribution of 57.2-70.8 dB(A) outside the factory fence line. Though the maximum noise is 152.5 dB(A), the maximum noise of 70.8 dB(A) beyond the fence line shows a compliance with 70 dB(A) industrial and commercial area limit but breaches the 45 dB(A) and 55 dB(A) residential area limit of the World Bank. Research limitations/implications-As much as it would be desirable ambient noise level could not be measured in all the receptors' locations covered by the modeling. However, the capability of the modeling software adopted makes this to have no negative impact on the quality of the findings of this study. Practical implications-The study will assist the public to determine the noise level safe region around diesel engine electric power generators. Originality/value-The paper highlights the challenges in which ambient noise from the use of off-grid generators used for industrial purposes could pose to the neighboring receptor environments.

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Purpose The purpose of this paper is to propose an approach to evaluate product performance of returned products, using four key performance attributes as the basis for improving sustainability through product recovery. Design/methodology/approach A fuzzy logic approach is developed to account a trade-off scenario for a manufactured product with recovery options. This approach is demonstrated using a numerical example and is validated using a case study in the automotive parts and components industry. Findings Product utilisation value (PUV) is found to be a useful index that manufacturers can use to assess product recovery options, as it brings together a number of conflicting parameters into a rationalised value for decision making. In addition, PUV provides a rationalised approach for comparing and selecting the most appropriate recovery configuration option. Research limitations/implications The authors only utilise four key performance measures to derive PUV. Further research is needed to modify and incorporate other measures that are important to decision makers to improve sustainability in manufacturing supply chains. Practical implications The proposed approach may motivate decision makers to consider sustainable recovery options by comparing PUVs of products for primary and secondary markets. The case study demonstrated the conflict and complexity organisations face in a global supply chain of a competitive industry. Originality/value The authors propose an approach to optimise trade-off considerations of selected performance attributes through PUV. This PUV as a benchmark can help improve recovery of the returned products and reduce landfill.

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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.