The South African Journal of Industrial Engineering

Note From The Technical Editor

2026-05-22T14:59:04+00:00

“The authority of scientific opinion remains essentially mutual; it is established between scientists, not above them.” Michael Polanyi

The quality and timeliness of peer review depend on finding the right reviewers for each manuscript. As industrial engineering continues to cover a wide range of topics, accurate reviewer information remains important to a strong review process.

This edition of the South African Journal of Industrial Engineering contains 13 articles across areas such as digital transformation, manufacturing, optimisation, reliability, sustainability, waste management, engineering education, and business model innovation. This breadth reflects the range of expertise needed to support the journal’s review process.

SAJIE is strengthening its reviewer database to support effective reviewer matching and to maintain constructive, timely, and knowledgeable feedback to authors.

Existing reviewers are encouraged to update their profiles with current institutional details, areas of expertise, and reviewing interests. Members of the industrial engineering community who would like to contribute as reviewers are also invited to register on the SAJIE platform and indicate their reviewing interests.

Eldon Burger
Technical Editor

Industry 4.0 Readiness and Maturity in Indonesia's Manufacturing Industries: An Empirical Study of Key Success Factors

2026-02-02T09:58:12+00:00

This study assesses the readiness and maturity of Industry 4.0 in Indonesia’s manufacturing industries by identifying and empirically validating key success factors. A measurement model was developed through a systematic literature review and expert interviews, and validated using survey data from manufacturing practitioners. Confirmatory factor analysis (CFA) was used to evaluate construct validity and model fit. The findings establish a concise framework comprising organisational and managerial, people, process, product and service, technological, and external factors influencing Industry 4.0 readiness and maturity. The model should support industrial engineering decision-making for digital transformation in manufacturing systems in developing economies.

Smart Warehouse Management Using Digital Twins and Machine Learning

2026-01-16T08:22:14+00:00

In warehouse operations, digital twin technology integrates physical systems with software components, thereby improving overall productivity. In this study, we developed a digital twin system using the R programming language to analyse product demand patterns and support the slotting storage configuration of outbound processes in a warehouse. By integrating radio-frequency identification (RFID) tag data with Flexsim simulations, the system enhances productivity tracking by 42% – measured by the number of pallets processed per hour – and reduces the time required for dispatching products in the distribution centre by 41%.

Navigating VUCA Challenges in Manufacturing Environment: An Integrated ISM and Fuzzy MICMAC Approach

2025-12-02T14:21:34+00:00

This study explores the challenges of managing volatility, uncertainty, complexity, and ambiguity (VUCA) in manufacturing. Using interpretive structural modelling (ISM) and fuzzy MICMAC, it identifies and prioritises key enablers of decision-making. ISM structures the relationships between enablers, while fuzzy MICMAC categorises them, based on influence and dependence. The results highlight agile leadership and adaptive organisational structure as the most influential enablers, while customer retention strategies and crisis preparedness are highly dependent. This study provides a structured framework to enhance organisational resilience and adaptability in complex manufacturing environments.

Enhancing Industrial Fan Performance: A Data-driven Framework for Energy Savings and Cost Reduction

2025-12-02T14:26:10+00:00

South Africa faces the challenge of balancing energy demand with sustainable development goals. Heating, ventilation, and air conditioning (HVAC) systems consume about 40.6 billion kWh of energy annually, which accounts for 20% of the country’s overall energy consumption, according to the Eskom Energy Management Information Pack from July 2015. Many fans used in these systems operate inefficiently, leading to increased energy and maintenance costs. Often, these fans are selected on the basis of standard sizes rather than being custom designed, resulting in oversizing and higher operational expenses. Optimising the entire system could lead to greater energy savings than simply optimising individual components. To assist in this effort, the US Department of Energy’s Office of Energy Efficiency and Renewable Energy has developed a free online fan system assessment tool (FSAT). This tool helps manufacturers, end-users, and engineers of industrial fan systems to identify and capitalise on energy optimisation opportunities. This paper outlines the basic features and functions of the FSAT, emphasising its role in promoting the sustainable development of facilities and addressing energy-efficiency gaps. The FSAT evaluates the efficiency, potential energy savings, and cost-effectiveness of a fan system by examining the energy consumption framework of a sample facility. It analyses operational parameters and energy usage patterns, providing detailed options for improving energy consumption. According to the findings, using an energy-efficient motor along with an optimised fan could lead to an estimated annual energy savings of 24.84% compared with the selected fans, and savings of 1.49% when compared with the overall facility energy usage.

Enhancing Facility Reliability with Backup Options and Budget Constraint

2025-11-27T08:24:09+00:00

This study considers facility failures in supply chain networks in which facilities must be located at candidate sites to serve customers. To enhance reliability, we incorporate backup options and a limited fortification budget. Customers assigned to fortified facilities do not require backup, while those assigned to unfortified ones do. The problem is formulated as a linear integer model and solved using Lagrangian relaxation. The relaxed model yields a variant of the binary knapsack problem, addressed through a novel solution method. The proposed approach achieves an average optimality gap of 0.1% with a computational time of 20 seconds.

A Functional Systematic Review of Digital Supply Chain Technologies in Municipal Solid Waste Management with a Saudi Benchmark

2026-02-09T09:47:19+00:00

This study conducts a systematic literature review to examine the adoption of digital supply chain technologies in municipal solid waste management, comparing global practices with emerging initiatives in Saudi Arabia. A systematic literature review, supported by content analysis and functional classification, was conducted on 70 peer-reviewed studies to identify the dominant technological domains shaping digital transformation in municipal solid waste management. Global evidence reveals five primary technology clusters: Internet-of-Things-based monitoring, artificial-intelligence-driven optimisation, blockchain-based authentication, big data analytics, and geographic-information-systems-supported planning, which collectively enhance operational efficiency, enable real-time visibility, and support citizen engagement. International best practices further demonstrate how integrated architectures that combine digital twins, robotics, and predictive analytics facilitate circular-economy transitions. In contrast, digital municipal solid waste management initiatives in Saudi Arabia remain largely confined to small-scale pilot projects, conceptual studies, and simulation models, with limited system integration. Key constraints include weak platform interoperability, the absence of end-to-end lifecycle tracking, and limited adoption of automated operational technologies. The findings indicate that advancing circular-economy performance in Saudi municipalities would require a transition from fragmented experimentation towards coordinated, large-scale digital implementation supported by regulatory alignment, strategic investment, and robust national data-governance frameworks. Strengthening digital infrastructure, expanding advanced analytics capabilities, and institutionalising mechanisms for citizen engagement would be essential to achieving Vision 2030 circularity objectives and aligning Saudi municipal solid waste management practices with global standards.

Barriers to Digitalising Circular Economy Practices in Municipal Waste Management: A Comparative Study of Saudi Arabia and Global Trends

2025-12-15T07:37:32+00:00

The circular economy (CE) enables revolutionary waste management of municipal solid waste (MSW) through various technologies, including IoT, AI, and blockchain. The technologies allow real-time monitoring, predictive analysis, data-driven decision-making, and enhanced traceability to implement effective and sustainable CE practices in urban waste systems. Implementing digital supply chain technologies in CE practices encounters complex barriers that prevent adoption. This study systematically examines the barriers preventing the adoption of digital supply chain technology for CE strategies in municipal solid waste management (MSWM) globally, with a focus on Saudi Arabia. The analysis of 58 peer-reviewed studies through qualitative content analysis reveals seven main categories of barriers: technological, legal, financial, data-related, regulatory, institutional, and behavioural.

The analysis shows that Saudi municipalities face unique difficulties because of their fragmented infrastructure, limited public engagement, underdeveloped regulatory frameworks, and insufficient legal protections. An analysis of the local literature demonstrates that insufficient attention to digital skills development and data governance problems creates additional obstacles to implementation. The research shows that successful implementation requires regulatory bodies to work with digital infrastructure developers to engage the public and to establish sustained support for smart technology systems. The study uses its findings to develop policy recommendations to help Saudi municipalities to implement digital CE practices. The recommendations aim to support Vision 2030’s national objectives while participating in worldwide discussions about sustainable digital waste management systems.

Information and Communication Technology for E-waste Awareness and Management in Rural Communities of South Africa

2025-07-28T09:04:12+00:00

This paper provides insight into the importance of the proper disposal of electronic waste, also known as e-waste, which contains hazardous substances such as lead, mercury, and cadmium that can harm both the environment and human health. This is a serious problem for people living in rural communities in South Africa, as they grow food in the soil and drink water from river flows and dams, which may be contaminated. This study, based on a qualitative approach and on semi-structured interviews, revealed, through the use of the social capital theory, a lack of awareness and poor management of e-waste in these communities, with practices such as burning and landfill disposal being common. The research suggests using information and communication technology tools, such as an e-waste registration and collection app, to improve awareness and management. It recommends launching educational programmes to inform rural communities about the dangers of e-waste and to promote safe disposal practices.

Integrating Interpretive Structural Modelling and Decision-Making Trial and Evaluation Laboratory for the Implementation Strategy of Hydrological Information System and Water Quality, Directorate of Water Resources Engineering, Ministry of Public Works and Public Housing, Indonesia

2025-12-04T16:42:29+00:00

This research aimed to design an implementation strategy for hydrological information system and water quality (SIHKA), a portal managing hydrology and water quality data. SIHKA has been integrated with 37 river basin organisations across Indonesia. Despite being operational, SIHKA’s performance was hindered by data complexity and stakeholder dynamics. Using the interpretive structural modelling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) multi-criteria decision-making models, this research identified stakeholders as critical factors, with objectives and constraints serving as causal factors. The integrated approach produced a prioritised, cause-and-effect-based strategy, enabling centralised real-time data management with stakeholder support.

A Framework for Advancing Industrial Engineering Through Engineering Education Using Virtual Reality

2025-08-26T08:32:08+00:00

This study investigates the incorporation of virtual reality (VR) into industrial engineering (IE) education to close the gap between theoretical knowledge and real-world application. A VR-based framework was created by conducting a systematic literature review and applying the design science research methodology (DSRM) to improve hands-on learning, to model intricate industrial systems, and to increase student engagement. The framework aims to create engaging, hands-on experiences to improve understanding and to ensure preparedness for industry. Although suggestions for improvement were provided, evaluations from experts and students using a questionnaire demonstrated the framework’s potential to enhance learning outcomes. This study emphasises how VR could modernise IE education.

Exploring Set Partitioning in Combinatorial Optimisation: Revisiting a TSP Example

2026-03-10T13:13:58+00:00

Large-scale combinatorial problems are often computationally intractable, and alternative solution approaches are frequently used to find effective approximate solutions for these problems. This paper introduces a six-step methodology, adapted from an existing framework in the literature, and based on a set partitioning scheme, aimed at generating near-optimal solutions for combinatorial problems. The proposed approach specifically considers the classic travelling salesman problem, and the methodology’s performance is demonstrated on a real-world 2508-node instance. The results confirm that the proposed set partitioning approach yields satisfactory solution quality and offers practical implementation advantages.

An Investigation Into the Quality of Life Accrued by Households Through 5S Implementation

2026-03-08T18:30:17+00:00

This research focuses on the impact of 5S adoption on households. Much research has been done on the outcome of quality concepts in many institutions and industries; but only a very few, or no, studies have been done on households. Thus, the objective of this research is to find out the outcome of 5S adoption with a special focus on South Indian households. A structured pilot questionnaire was circulated to 50 employees and their spouses in a leading company in Coimbatore. The respondents were well versed in 5S, Lean, Kaizen, and safety. The analysis indicates that 5S adoption in homes could lead to continuous improvement, reduced waste, and improved safety, all of which would be beneficial to families.

Evaluating Business Model Innovation Approaches: A Criteria-based Analysis and Foundation for Future Research

2025-12-15T09:38:55+00:00

Business model innovation (BMI) approaches are critical for maintaining a competitive advantage, yet assessment methods are limited. This foundational study identifies eight criteria to assess BMI approaches. The research uses a hybrid comparative analysis, assessing four prominent approaches using a multi-criteria decision analysis framework, anchored in quantitative content analysis, as an input to the analytical hierarchy process, combined with qualitative validation. The findings indicate that, while current approaches achieve theoretical saturation in value creation, they exhibit critical deficits in financial feasibility and temporal efficiency, revealing a significant gap between design capability and execution readiness. The analysis provides a pathway for academics and industry practitioners seeking to optimise business models.