This study developed sensor monitoring equipment for palm oil extraction processes to track digester and press operations and their impact on fibre oil loss and nuts cracking in the press cake. Using a human monitoring and control system revealed delays, inaccuracies, and possible losses. Analysis of scenarios A-D, considering running capacity, fibre oil loss, and nuts cracking, indicated monthly losses ranging from US$32,058 to US$44,853. The sensor monitoring and human control system provided accurate feedback, reducing losses. The reduction scenarios of 10% and 20% suggested potential monthly reductions of US$3,206 to US$8,971

Retail distributors routinely encounter a variety of supplier constraints, including those related to the size and timing of inventory replenishment orders, such as minimum order quantities and minimum reorder intervals. In addition, constraints on the transportation of replenishment stock, such as minimum order values and container filling requirements, necessitate the joint replenishment of commodities in the case of multi-commodity inventories. These supplier constraints typically lead to larger order quantities and excess inventory, giving rise to the need for a joint replenishment model that is capable of mitigating the impact of increased inventory levels. In this paper, we propose a novel simulation-optimisation modelling approach in support of the complicated joint replenishment decisions faced by inventory managers in the presence of the aforementioned constraints. The modelling approach is capable of consolidating multi-product inventory replenishment orders, guided by minimum reorder intervals and minimum order quantities, subject to container filling and/or minimum order value constraints. It aims to optimise safety stock levels heuristically with a view to reducing the increased inventory costs associated with the particular supplier constraints imposed. We show in a real-world case study that the modelling approach is capable of leading to substantial cost savings during joint inventory replenishment decisions.

The rapid advancement of technology in the air traffic management (ATM) industry demands effective methods to identify, prioritise, and plan for emerging technologies. This research used qualitative analysis through expert interviews to investigate these methods, highlighting the importance of structured frameworks such as technology intelligence, technology foresight, and technology roadmaps, along with industry collaboration. A framework of preferred methods was developed to guide ATM organisations in navigating technological uncertainties. The study emphasises proactive planning and early identification to enhance competitiveness and operational efficiency, thus contributing to the broader understanding of emerging technologies in the ATM industry.

Rotating machinery, particularly shafts, is prone to failure owing to cyclic loading, bending stresses, and vibrational oscillations. To enhance their longevity and minimise failures, a predictive maintenance strategy is proposed that integrates Hotelling’s T-squared clustering. Clustering identifies key operational profiles, while embedded sensors gather vibration, temperature, and current data for feature extraction via principal component analysis. The results show that predictive monitoring identifies the remaining useful life of shafts by leveraging data-driven insights, emphasising material-specific characteristics for precise prediction of failure and improved reliability.

This study investigates the impact of rail passenger information systems (PISs) on service delivery in South Africa. Poor service delivery causes rail transport and profitability to decline, as national statistical reports show. PISs in the rail sector have been largely regarded as operational support systems. An interpretive explorative case study’s findings suggest that PISs have characteristics that qualify them as strategic information systems (SISs) to extend a business strategy. A framework is proposed to guide rail organisations on how PISs could be used as SISs to improve service delivery and profitability in this sector.

The paper explores the feasibility of using clustering for the predictive maintenance of nutrunners in a modern high-volume manufacturing environment. During the period of the study, the failure rate of one nutrunner was significantly higher than that of the others. The clustering algorithms that were evaluated were agglomerative hierarchical clustering (AHC), density-based spatial clustering of applications with noise (DBSCAN), and a self-organising feature map (SOFM). The performance metrics used to compare and evaluate the clusters were the silhouette coefficient score (SC) and the variation rate criterion (VRC). It was found that it is feasible to use clustering to improve the maintenance strategy of nutrunners in the automotive industry.

Firms must constantly adjust to changing market conditions. Understanding the firm and its strategies is an important component of strategic management. The study used a survey approach to gather the views of individuals working in South African sawmills on strategy and competitive advantages. The results highlighted that the participants saw the availability of wood resources as essential for the sawmill industry to function competitively. The participants also indicated that they used differentiation as a strategy in their sawmills. Future research should focus on globally competitive sawmills to assess how their competitive advantage and strategy differ from those of sawmills in South Africa.

Small and medium enterprises (SMEs) play a crucial role in the economic development of developing countries. To remain competitive with larger organisations, SMEs must produce high-quality products at competitive prices. To improve product quality, overall equipment effectiveness (OEE), and profitability, SMEs need to optimise the use of their resources. However, the current production strategies used by this sector are insufficient to meet these objectives. In this context, developing and implementing a new strategy to improve quality, productivity, and OEE in SMEs is essential. This paper presents an innovative model, called ‘interpretive maintenance quality function deployment’ (IMQFD), that is aimed at enhancing these key parameters and supporting the growth of the SME sector in the midst of the ongoing industrial revolution.

Poor inventory management negatively affects a company’s profits. Too little stock limits potential sales and customer satisfaction, while too much stock increases storage costs and potential damage. This study combined supervised and deep reinforcement learning (DRL) for optimal decision-making that would maximise profits in the supply chain of Company X. The performance was compared with a benchmark heuristic that stocks inventory based on a seven-day forecast. The DRL models achieved a marginally lower net profit, but satisfied significantly lower customer demand compared with the benchmark heuristic, thus showing its potential to help to optimise the supply chain structure, operation, and parameters.

This study investigates the impact of optimised processes on individual customer experience management (CXM) scores, and explores key metrics such as net promoter score (NPS), customer satisfaction score (CSAT), and customer effort score (CES). Employing a mixed-method approach, the research integrates quantitative surveys and qualitative insights to understand how process optimisation influences customer experience. Findings reveal that optimised processes positively affect NPS and CES ratings, enhancing customer loyalty and reducing effort. Further studies are suggested to explore potential impacts on CSAT scores. These insights contribute to customer experience and business process management (BPM), emphasising the importance of continuous improvement and customer-centric strategies for achieving higher CXM scores and driving business success.