This paper investigates the multifaceted dimensions and systemic interdependencies of the 5D energy transition framework (Decarbonization, Decentralization, Digitalization, Democratization, and Diversification) within the context of South Africa’s energy sector. Amidst increasing environmental regulations, climate imperatives, and the pursuit of Sustainable Development Goal 7, South Africa faces persistent challenges related to infrastructure limitations, economic constraints, and energy poverty. Through a systematic literature review and the application of Causal Loop Diagrams (CLDs), this study identifies critical knowledge gaps in theoretical integration, practical implementation, contextual adaptation, and policy frameworks. The analysis highlights both reinforcing and balancing feedback loops that shape the energy transition, emphasizing the necessity for integrated strategies that address operational risks, financial constraints, workforce development delays, and regulatory complexities. The findings inform the development of a contextually grounded, integrated 5D transition framework tailored to the realities of South African power utilities. The research concludes by outlining key objectives and research questions, providing a foundation for future empirical work and policy development to advance sustainable, inclusive, and resilient energy systems in South Africa.

This paper presents the collaborative development of a soft robotic gripper, advancing from academic research to a validated industrial solution through a structured Technology Analysis and Patent to Business (P2B) approach. The proposed gripper is based on a closed-structure architecture that enables adaptive and uniform grasping, making it suitable for handling fragile objects with varying geometries. Following patent protection and early-stage prototyping, the project evolved through close interaction between academia, industry, and end users, allowing the identification of a critical customer need in glass bottle handling processes. The work demonstrates how multidisciplinary collaboration can bridge the gap between research and real-world application. The resulting gripper presents a simplified and robust design with reduced maintenance requirements, lower energy consumption, and the ability to operate across multiple bottle types. Additionally, the adoption of biodegradable materials contributes to environmentally sustainable manufacturing practices. Experimental validation in a production environment highlights the feasibility and performance of the proposed solution, positioning it as a competitive alternative to conventional industrial grippers in high-demand manufacturing systems.

The South African automotive aftersales service industry is facing increased pressure to improve operational efficiency amidst rising customer expectations, intensifying competition, and rapid technological change. This study investigates workflow improvement and resource utilisation in an automotive aftersales service facility using the DMADV methodology in conjunction with discrete event simulation modelling. Empirical data on technician productivity, equipment utilisation, service throughput, and travel distances were collected through on-site observations, staff interviews, and operational records. Discrete-event simulation models were developed using FlexSim software. Results indicate that a valet-managed vehicle movement scenario yields the most significant performance improvements, including substantial reductions in technician travel distance, increased throughput, and improved equipment utilisation. Model credibility entailed a structured walkthrough with the service manager, ensuring alignment between simulated outcomes and operational realities. The findings demonstrate the effectiveness of combining DMADV with simulation modelling to support data-driven decision-making in service operations. Furthermore, the study provides practical insights for automotive aftersales facilities seeking to enhance workflow efficiency without major infrastructure changes.

This paper presents a simulation–reinforcement learning (RL) framework for evaluating inventory replenishment policies in Electric Vehicle (EV) charger supply chains under uncertain operating conditions. The study is motivated by the need to support charger distribution planning in settings where demand growth, procurement delay, and warehouse operations interact in ways that are difficult to capture through static inventory rules alone. The proposed workflow begins by parameterizing the supply chain through demand forecasting, supplier selection, facility location analysis, and inventory policy inputs. These elements are then embedded within a hybrid simulation environment developed in AnyLogic to represent warehouse, transportation, and replenishment dynamics over time. On top of this environment, an RL layer is introduced to learn adaptive replenishment decisions through repeated interaction with the simulated system. The policy is defined over periodic review intervals and is evaluated through operational indicators including service level, fulfilled and unfulfilled orders, stockouts, backlog, inventory level, holding cost, and total cost. Preliminary findings establish the feasibility of treating the EV charger supply chain model as a policy learning environment and show that the simulation responds meaningfully to variations in demand intensity, lead time, handling resources, and replenishment settings. The study therefore provides a simulation integrated policy evaluation framework for EV charger logistics and establishes the foundation for subsequent benchmarking of adaptive replenishment policies against conventional fixed reorder rules.