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Predictive modelling for smart and sustainable logistics

Author

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  • Chuks Medoh
  • Arnesh Telukdarie

Abstract

Logistics systems (LS) are complex networks that are indiscriminately interconnected and comprising facilities, data, raw material, and product. Quantifying the performance of LS requires a comprehensive understanding of the structure and behaviour of these systems. This is subject to technological complexities. This paper aims to develop a predictive modelling suitable to quantify the performance of LS. The components of the SSLM consist of, business process based, influencing factors and associated activities. This paper adopts a mixed-method approach to quantify the relative performance of each factor via simulation and design of experiment (DOE) approach. The approach provides for the modelling of the statistical significance of the interactions and effect of each factor. The results demonstrate the ability to integrate LS in forecasting the SSLM network optimisation. The innovations reinforce the context of smart and sustainable logistics practices. The SSLM facilitates managers' capacities beyond traditional approaches to quantifying the performance of LS.

Suggested Citation

  • Chuks Medoh & Arnesh Telukdarie, 2024. "Predictive modelling for smart and sustainable logistics," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 49(4), pages 462-485.
    • Handle: RePEc:ids:ijlsma:v:49:y:2024:i:4:p:462-485
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