Title: Predictive modelling for smart and sustainable logistics

Authors: Chuks Medoh; Arnesh Telukdarie

Addresses: Faculty of Engineering and the built Environment, The University of Johannesburg, Johannesburg, South Africa ' Faculty of Engineering and the built Environment, The University of Johannesburg, Johannesburg, South Africa

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.

Keywords: logistics; sustainability; simulation; smart and sustainable logistics model; SSLM; logistics systems; LS.

DOI: 10.1504/IJLSM.2024.143386

International Journal of Logistics Systems and Management, 2024 Vol.49 No.4, pp.462 - 485

Received: 27 Sep 2021
Accepted: 23 Apr 2022
Published online: 17 Dec 2024 *

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