The South African Journal of Industrial Engineering

Note From The Technical Editor

Eldon Burger — Mon, 31 Aug 2026 00:00:00 +0000

“I feel that the greatest reward for doing is the opportunity to do more.” Jonas Salk

The 2026 SAIIE Awards & Gala Dinner was held on 13 August 2026 at the Wallenberg Conference Centre at STIAS in Stellenbosch. The annual event recognises outstanding contributions to industrial engineering across academia, industry, education, and the wider profession.

Among these awards, the Best Industrial Engineering Paper Published in SAJIE recognises an outstanding paper published in the journal. The 2026 award was presented for the paper “Developing an Enterprise Risk Management Roadmap for the Passenger Rail Agency of South Africa”, authored by Thina-Grace Mangcu, Ferdinand Hassan, and Schalk Grobbelaar from the University of Pretoria.

SAJIE also congratulates Dr Teresa Hattingh, who received the 2026 SAJIE Editor’s Award in recognition of her contribution to the journal. Dr Hattingh was also named a SAIIE Honorary Fellow.

Each year, SAIIE recognises an outstanding paper published in SAJIE through the Best Paper Award. Readers are encouraged to keep an eye out for the annual call for nominations and to nominate papers that they believe deserve recognition.

This edition of the South African Journal of Industrial Engineering contains 14 articles across areas such as digital transformation and technology adoption, sustainability and renewable energy, infrastructure and operations, predictive maintenance, healthcare technologies, engineering education, construction, and project management. Seven of these articles include authors with international affiliations, reflecting the continued international participation in the journal.

Eldon Burger
Technical Editor

Fundamental Principles and Characteristics of Technology Roadmapping for Multiple Emerging Technologies in the South African Mining Industry

K. Diole, T.P. Letaba — Mon, 31 Aug 2026 00:00:00 +0000

This study uses a three-round Delphi method to establish a consensus framework for technology roadmapping in the South African mining industry. With 22 experts, it validates five core principles – strategic alignment, needs-driven processes, collaboration, strategic questioning, and risk management – and essential characteristics, including defined milestones and local context tailoring. The identified priority technologies are automation, artificial intelligence, and sustainable energy. The framework integrates Environmental, Social and Governance factors and dual maturity assessments. This research provides a validated, context-specific tool for mining companies to de-risk technology investment and enhance sustainable competitiveness.

A Strategic Guideline for Dredging Operations for Port Sustainability: A Case Study of a River Port in South Africa

K. Veerasamy, J.L. Jooste — Mon, 31 Aug 2026 00:00:00 +0000

This research addresses the difficulties that port authorities experience in managing maintenance dredging activities. A strategic guideline for optimising dredging practices is developed to address these challenges. The guideline addresses dredging budgets, port sustainability, and navigational safety. Through a mixed-method approach, dredging data of a South African river port is analysed and insights collected from port authority practitioners. The results identify gaps between planned and actual dredging volumes, financial inefficiencies, equipment limitations, and the impact of adverse weather conditions on productivity. Based on the results, guidelines are proposed to bridge these gaps and to contribute to our knowledge for similar cases.

A Conceptual Framework for Analysing the Adoption of Cloud Computing in the Banking Sector

N.M.S.O. Ramos, J.O. Mugabe — Mon, 31 Aug 2026 00:00:00 +0000

Cloud computing has emerged as a critical enabler of innovation, efficiency, and scalability in banking operations. It is providing scalable, efficient, and cost-effective solutions in the banking sector. The adoption of cloud computing in banking is the subject of numerous studies, but they are mainly case-based or context-specific, and lack generalisable frameworks for comparative analysis between different banking contexts. There are no coherent conceptual frameworks for structural analysis of cloud computing adoption in the banking industry. However, an integrated and coherent conceptual framework is necessary to allow for structured analysis, to enable identification of interrelationships among variables, and to guide empirical research. This study develops a conceptual framework for cloud computing adoption in the banking industry, identifying key factors in three dimensions: technological (perceived usefulness, relative advantage, cost saving, perceived risk, system quality, trust, security, perceived ease of use, complexity, technology readiness, privacy, information quality, and data security); organisational (top management support, innovativeness, performance expectancy, training and education, attitude, organisational competency, service quality, and image); and environmental (competitive pressure, regulatory support, trading partner support, regulatory readiness, and user satisfaction).

Prioritisation of Barriers to Bioenergy Industry Development: A Weight-Based Assessment to Inform Policy Design

F. Yuliani, A. Hidayatno, A.D. Setiawan — Mon, 31 Aug 2026 00:00:00 +0000

Bioenergy advances Indonesia’s net zero emission targets by using biomass from agricultural, forestry, and livestock residues; however, progress is limited by political, economic, social, technological, environmental, and legal barriers. This study combines the analytical hierarchy process, stakeholder clustering, and the ‘5 Whys’ root-cause analysis to prioritise barriers, to compare the perceptions of governments, financial institutions, and private companies, and to identify structural drivers. The most significant investment barriers are high capital costs, complex permitting, inconsistent policies, feedstock price fluctuations, and limited financing. Through developing a policy-to-barriers influence matrix, policy packages recommend long-term agreements with transparent tariffs, the designation of national strategic projects, and the reform of fossil-fuel subsidies.

UTAUT and Technology Adoption in Construction: An Examination of Existing Applications

F.S. Hassan, S. Grobbelaar — Mon, 31 Aug 2026 00:00:00 +0000

This study investigates the application of the Unified Theory of Acceptance and Use of Technology (UTAUT) within construction industry research. The synthesis reveals that many studies employ extended UTAUT models to examine the adoption of information systems technologies in stable organisational settings, for which UTAUT was originally developed. The study confirms UTAUT’s strong explanatory power: behavioural intention consistently predicts use behaviour, and performance expectancy remains the dominant determinant of intention. The study recommends prioritising further testing of the baseline model, investigation of the adoption of construction materials and systems, and explicitly accounting for construction’s project-based context.

A Random-Forest-Based Framework for Predicting Schedule Achievement in Cloud Enterprise Resource Planning Projects Using the Critical Path Method and the Programme Evaluation and Review Technique

Mon, 31 Aug 2026 00:00:00 +0000

This paper introduces a robust framework for improving project timeline predictions in cloud-based enterprise resource planning (ERP) implementations by combining traditional project management techniques (the critical path method [CPM] and the programme evaluation and review technique [PERT]) with advanced machine learning methods. The primary problem is that traditional project management techniques such as CPM fail to handle uncertainty in project timelines effectively. The features for the dataset were derived from the CPM and PERT framework and were matched with 4M (man, method, machine, material) to identify which kinds of uncertainties happened in the project. Hypothetically, the random forest algorithm was used as the primary predictive tool, but the research also used a neural network model to do the benchmark in metrics such as accuracy, precision, recall, and F1-score. In the results, random forest outperformed as predicted. By integrating CPM and PERT-derived features, the model identified critical factors, including “method” and “machine”, that strongly influence project efficiency. The framework achieved a near-perfect discrimination (AUC: 0.997), demonstrating its effectiveness in navigating uncertainties and enabling data-driven decision-making for enhanced project outcomes.

Remote-Controlled Assistive Robotic Arm for Implementation in Highly Infectious Wards: A Case Study on Tuberculosis

T. Zinzombe, G. Tigere, S. Grobbelaar — Mon, 31 Aug 2026 00:00:00 +0000

Tuberculosis (TB) wards pose significant risks to healthcare workers (HCWs) owing to airborne Mycobacterium tuberculosis transmission, exacerbated by inadequate ventilation and prolonged exposure. These conditions increase latent TB infection rates and psychological stress, compromising HCW well-being and patient care quality. This paper presents the work completed to develop a five-degrees-of-freedom remote-controlled robotic arm to mitigate these risks in TB wards. Equipped with a Bluetooth interface and real-time camera, the arm enables precise, remote task execution, such as sample collection and equipment handling, minimising direct contact and airborne exposure. Its sterilisable design ensures compliance with infection control protocols. The system was designed to enhance HCW safety, reduce psychological strain, and improve operational efficiency by automating high-risk tasks. This innovation could inform robotic solutions for other infectious disease settings, addressing critical healthcare worker protection and patient care gaps.

Simulating a Self-Bag-Drop System at an International Airport

S. Jacobs, W. Bean — Mon, 31 Aug 2026 00:00:00 +0000

A low-cost airline is considering implementing a self-bag-drop system, because the current check-in system results in excessive passenger waiting times. The research presented in this paper uses discrete-event simulation to investigate the feasibility of the proposed system. Results from multiple simulation experiments demonstrate that the implementation of self-bag-drop counters could significantly reduce passenger waiting times when appropriately aligned with user adoption levels. This study contributes to the field of airport operations management by illustrating the benefits of self-bag-drop systems in a South African case study and by helping to understand the potential impacts and risks of a self-bag-drop system in a developing country.

From Data Void to Circularity: A Multi-Level Analysis of Digital Transformation Barriers in Saudi Municipal Solid Waste Management

B. Aljabri, M. Alkhatani, W.A.M. Al-Garadi — Mon, 31 Aug 2026 00:00:00 +0000

Digital transformation is often framed as the key to advancing circular-economy practices in municipal solid waste management. However, most digital models assume the existence of reliable and traceable waste data. This study challenges that assumption by examining the barriers to digital adoption in Saudi Arabia’s municipal solid waste management sector, where waste flows remain fragmented and partially informal. Using a triangulated mixed-methods design – municipal focus groups (Makkah, Taif, Jeddah), staff (n=53) and citizen (n=408) surveys, and regulatory input from the National Centre for Waste Management – the study identifies the constraints at household, organisational, and governance levels. Its findings reveal that the core barrier is not poor data quality but the absence of source-level material traceability, creating a structural “data void”. Informal recovery, manual sorting, weak infrastructure, and legacy contracts limit digital feasibility. The study reframes digital failure as a governance and traceability problem, and proposes an integrated reform pathway that is aligned with circular-economy objectives.

The Development of an Adapted Agile-Stage Model for Application in Timber Building Design: A South African Study

C. Singh, S. Grobbelaar — Mon, 31 Aug 2026 00:00:00 +0000

The timber construction industry lacks innovation, favouring traditional materials such as concrete. The global drive towards sustainability is increasing. Timber is an alternative sustainable building material, requiring diffusion into the South African industry. This study presents a conceptual Agile Stage-Gate model (ASGM) and the possible benefits and limitations of the model’s application. Qualitative interviews and quantitative surveys with South African timber construction industry experts were used. By exploiting the diffusion of innovation theory in the form of an adapted ASGM, this research could positively shift the perception of timber as a construction material, potentially increasing its acceptance in South African society.

Systematic Review of Micro-Credentials in Engineering Education

M.G. Kanakana-Katumba, K.A. Bello, T.Y. Chuengwa, A. Adeodu, T.K. Ramasu — Mon, 31 Aug 2026 00:00:00 +0000

The rapid evolution of the job market demands upskilling and reskilling, challenging traditional higher education systems. Micro-credentials, offering verifiable skills that are often delivered online, present a promising solution. This study conducted a systematic literature review of 5,940 publications, narrowing them down to 75 for detailed analysis. The findings show that integrating micro-credentials into higher engineering education is gaining awareness as a way to meet the shifting needs of learners and industry. Institutions are increasingly required to provide relevant, adaptable courses that are aligned with engineering career paths. This study offers valuable insights for stakeholders who aim to enhance the relevance of engineering education in a digital era.

Vulnerability Analysis of Sustainable Software Systems Incorporating Environmental Factor-Based Uncertainty

Divya, J.N.P. Singh, A. Anand, V.S.S. Yadavalli — Mon, 31 Aug 2026 00:00:00 +0000

Maintaining software’s integrity and reliability throughout its lifecycle depends on effective vulnerability detection and patching, especially during the operational phase when systems face dynamic and often unpredictable environments. The complexity of real-world software patching is studied to estimate metrics such as residual vulnerabilities, detection rates, and overall system dependability. However, environmental factors, including system heterogeneity, third-party software dependencies, and variations in deployment settings, can affect the success of remedial efforts. This study presents a modelling framework that considers the influence of environmental factors on overall system performance and patching rates. The proposed approach provides a more comprehensive method for evaluating software vulnerabilities during the operational period by integrating these components. The model’s descriptive and predictive capabilities are demonstrated using real-world patching data, providing valuable insights into metrics such as patching failure rates, residual vulnerabilities, and the impact of environmental uncertainty on security outcomes.

A Digital Twin and IoT-Enabled Predictive Maintenance and Energy Optimisation Framework for Hospital Wastewater Treatment Systems

S. Embarki, Y. El Kihel, B. El Kihel — Mon, 31 Aug 2026 00:00:00 +0000

This study develops an integrated digital twin framework for electrical optimisation and predictive maintenance in hospital wastewater treatment systems. From an industrial engineering perspective, the framework links real-time monitoring, load scheduling, power-quality control, condition assessment, and maintenance planning in a single decision-support architecture. Physical measurements from IoT sensors are synchronised with virtual electrical and process models so that facility managers can evaluate operating states, identify energy inefficiencies, and prioritise interventions. Optimisation combines power factor correction, harmonic mitigation, peak demand reduction, and variable-frequency-drive control, supported by machine learning. Predictive maintenance combines electrical, thermal, and vibration indicators to assess equipment condition. Validation on a laboratory-scale pilot system showed sustained power-quality improvements, a 21.7% reduction in energy consumption, and a 22% reduction in peak demand. These gains corresponded to annual savings of 22,107 MAD and a simple payback period of 3.8 years. The results demonstrate the practical value of the proposed framework for energy-intensive systems, while further full-scale validation is required before generalisation to hospital facilities with different layouts and duty cycles.

Exploring Renewable Energy Trading Based on Blockchain Technology and Decentralised Finance: Systematic Literature Review

E. Fernando, M. Tampubolon, Meyliana, R. Wiputra — Mon, 31 Aug 2026 00:00:00 +0000

Renewable energy trading is evolving in an effort to create a fairer, more efficient, and more sustainable energy system by involving the public as prosumers. However, its implementation faces problems such as lack of trust, data insecurity, and limited digital infrastructure. Blockchain offers a solution to ensure the transparency and immutability of transactions, while decentralised finance strengthens the ecosystem through participatory financing, incentives, and energy tokenisation. This study uses the Kitchenham systematic literature review method on 33 Scopus articles to identify trends, problems, and development directions. The results demonstrate the potential of smart contracts and decentralised finance, although regulatory barriers and asset volatility remain significant.