Organizations face increasing challenges in maintaining their competitiveness in highly dynamic and uncertain environments. This paper presents an initial System Dynamics model for strategy definition and strategy implementation under deep uncertainty. The proposed model combines System Dynamics with an iterative decision-making approach and considers the dynamic relationships between strategic decisions, implementation activities, competitiveness and financial capabilities. The simulation results show how the speed of strategy implementation influences competitiveness and financial capabilities. They also show the importance of continuously adapting strategic actions during the implementation process. From a management perspective, the model provides a structured approach to better understand the relationships between strategic decisions, implementation activities and their impact over time. It supports managers in identifying actions with high strategic potential, continuously monitoring their implementation and adjusting priorities when assumptions or environmental conditions change. Furthermore, the results indicate that delays in strategy implementation can reduce competitiveness. Such delays can also tie up financial resources that could otherwise be available for future strategic actions. The model therefore provides an initial basis for supporting strategy definition and implementation in highly complex and dynamic environments. Further research will include empirical calibration and validation of the proposed model using qualitative and quantitative data from the German automotive industry.

The economic growth as well as sustainable development of industries and entire country are determined to a great extent by the transport infrastructure development. The organisations that manufacture and develop critical infrastructure such as transport and/or energy must be efficiently managed, as they are key role players in the economic development of a country. To improve economic growth, sustainable efforts to innovate and develop engineering infrastructure can be made through various planning processes that consider the state of social well-being. There has been a widespread of system pressing issues, which resulted from the rolling power blackouts to congested transport systems in South Africa. The negative impact has resulted in economic decline. This paper aims to explore the planning concept of national well-being and determine technical factors that will improve the energy engineering management system with a strong focus on maximizing the interdependency of well-being strategies to improve freight rail business success.

Business Intelligence & Analytics (BI&A) tools have come to the fore in enabling data-driven decision-making for organisations operating in the Industry 4.0 era. However, various factors significantly impede the adoption of the technology, such as legacy systems, data management process and capability, and management support. This paper, thus, aims to investigate and provide insight into the key antecedents that influence organisational readiness to adopt BI&A in the context of South African production and manufacturing industries operating in a developing economy. This is achieved through a systematic literature review. In the same regard, this paper proposes a conceptual model emerging from the reviewed literature that draws on the technology acceptance model and technology-organisation-environment frameworks in order to eventually understand the degree of influence of each antecedent. This paper aims to serve as a resource for scholars and practitioners interested in the adoption of BI&A in developing countries.

This study investigates the role of cognitive biases in decision-making within the Circular Economy and explores how these biases can be validated and addressed through empirical survey data. By developing a Conceptual Causal Loop Diagram and merging it with a foundational CLD, a comprehensive model was created to simulate the dynamics of CE systems. Key variables such as media influence, response to information, and behaviour change were incorporated into the Stock and Flow Diagram, enhancing the model's ability to represent realworld sustainability challenges. The principal results show that variations in biases and other factors can significantly impact CE outcomes, including individual actions, business priorities, and environmental responsibilities. The study highlights key variables, including media influence, response to information, behaviour change, and their roles in shaping CE outcomes. These components contextualize the study within the broader challenge of understanding and mitigating biases to foster sustainability, framing the research within the CE's ongoing efforts to achieve sustainable economic and environmental outcomes. Noteworthy conclusions include the identification of leverage points for promoting sustainability and the importance of understanding biases to improve decision-making processes in CE. The findings reveal biases saturate within approximately 100 months, while the perceived urgency for sustainability declines sharply within 50 months unless actively reinforced. This research contributes to the broader understanding of human factors influencing the transition to a sustainable economy.

The surge in data volume and the pressing need for practical decision-making tools have made Business Intelligence (BI) a pivotal tool. This paper presents the development of an integrated framework to facilitate the implementation of BI systems across diverse sectors. Conducting a thorough systematic literature review focused on BI frameworks, tools, and implementation success factors; the study examines existing literature findings. The BI success factors are presented in four thematic groups: technology, organisation, social and environment. The analysis identified data sourcing, movement and storage, processing and analysis, and visualisation and reporting as the critical components of the BI framework. This research contributes to ongoing BI studies by offering an integrated BI framework for successful implementation across sectors to enhance organisational decision-making processes and overall performance.

As linear production results in significant waste generation, adopting a circular economy (CE) approach helps reduce resource inputs, energy consumption, emissions, and material leakage by slowing, closing, and minimising material and energy loops. Additive manufacturing (AM), commonly called 3D printing, is one of the digital technologies capable of advancing a sustainable CE. This research explores the potential of 3D printing to advance CE practices, focusing on its benefits across CE activities and stages and identifying industries that can leverage this technology. A systematic literature review was conducted by searching the titles, abstracts, and author keywords in the Web of Science database and Scopus databases using search terms “3D printing”, “circular economy”, and “sustainability”. The review highlights that raw material input and recycling are critical CE activities where 3D printing has a considerable impact. Among the various CE stages, “reduce” and “recycle” experience the most significant benefits from adopting 3D printing. Additionally, the research identifies the polymer industry as a key sector that can benefit from this technology. These findings enhance the understanding of $3 D$ printing's role in supporting CE principles and provide valuable insights for industries aiming to adopt sustainable manufacturing practices.

In this article the aerodynamics of a rooftop greenhouse is evaluated using Computational Fluid Dynamics (CFD). This entails using a scale model and wind tunnel tests, as well as understanding the effects of design changes of the rooftop greenhouse on the aerodynamics. A wind tunnel test was conducted using a reduced scale model based on the full-scale rooftop greenhouse. Using these experimental results, a numerical model was developed in Star-CCM+ and the flow around the rooftop greenhouse was simulated. Once the numerical model results were found to be in good comparison to the experimental results, the greenhouse was redesigned to improve the aerodynamics. A numerical model was developed for the redesigned greenhouse and simulated again using CFD. The results indicated a reduction in the high-pressure points that were seen in the original model and less circulating patterns occurred which reduces the damage potential on the greenhouse due to wind flow. This article therefore provides insight into the design of a greenhouse to withstand high pressure from the wind on top of the roof of a building thus being more aerodynamically efficient.

Research commercialisation of innovative, new scientific and technology knowledge originating from universities and public research organisations depends on the transfer of the entrepreneurial intention of research scientists into actual entrepreneurial behaviour. The scientific research output generated from government funded research and development institutions is not always commercialised through the creation of technology-based firms. Hence, there remains a gap in understanding the factors that influences the relationship between entrepreneurial intention and entrepreneurial behaviour of research scientist. This study is based on a literature review to develop the conceptual framework and identify factors that influence the implementation of entrepreneurial behaviour of research scientists to participate in research commercialisation through the creation of technology-based firms. This study contributes to the field of technology-based entrepreneurship by identifying critical factors required for closing the literature gap on the relationship between entrepreneurial intention and entrepreneurial behaviour of research scientists.

This study presents a dynamic causal loop diagram (CLD) as the expected outcome, representing a dynamic hypothesis, to explore the relationships within sustainable Technology Management initiatives, Circular Economy model adoption, and the integration of Artificial Intelligence (AI). Rooted in Systems Engineering and Technology Management, our investigation employs holistic systems thinking to reveal feedback loops and causal connections, supporting strategic planning and decision-making in technology-centric ecosystems. The developed CLD illustrates that adopting Circular Economy models influences AI integration within sustainable Technology Management initiatives, enabling advanced data analytics and predictive modeling to strengthen agile and sustainable practices. This research addresses crucial aspects of incorporating Circular Economy practices and seamlessly integrating AI. The significance lies in the increasing need for sustainable practices, emphasizing the recognition of biases, societal perceptions, and collaborative dynamics among stakeholders. By providing actionable intelligence, this paper serves as a bridge from theory to practice, offering a guide for sustainably managing technology. The CLD is a roadmap for making informed decisions in our ever-changing world.

The desire to increase reliability and productivity in petrochemical industries has driven these organisations to adopt newer technologies that promise predictable operations. Information technology has for years been identified as an enabler of higher competitiveness. The perceived opportunity has driven this rapid technology usage by petrochemical industries, including associated workflows and processes. This paper aims to explore the successful acceptance and adoption of these technologies with the usage of the Technology Adoption Model (TAM). The research method used in this paper to validate the maturity of adoption of these newer technologies is an exploratory literature review. The outcome of this research paper will be utilized to consider an early model that can be utilized to develop a sustainable technology adoption approach that will consider both internal and external aspects affecting technology adoption, especially in petrochemical maintenance management.

This research study assesses the maintenance practices on mining machinery as a case study in a surface platinum mine. The current literature commonly tends to highlight the significance of maintenance in respect to mining, yet it rarely provides specific analysis of various maintenance strategies and their effect on equipment performance in some specialized settings such as surface platinum mines. This research utilizes a robust, data-driven approach and reliability analysis to evaluate impact of maintenance strategies on some key performance indicators for maximizing platinum production. The research investigates historical maintenance records and key equipment performance metrics to identify connections between monitored maintenance practices such as planned and unplanned maintenance activities and important equipment performance indicators such as availability, mean time between failure and mean time to repair. The research outlined in this paper aims at providing a sound and evidence-based solution to evaluate maintenance regimes that are created for a surface platinum mine. By determining the most suitable maintenance strategy among the ones that are revealed through data analysis, the study aims at providing the decision-makers the recommendations on the equipment performance and effectiveness of maintenance practices.

Operational efficiency in the rail engineering company is impacted by the performance of supply chain management. The procurement processes at state-owned entities within the realm of supply chain management are characterized by extended durations, mostly attributable to the legislative framework governing their procedures. The purpose of this study is to evaluate the existing supply chain management strategy TO maximise operational efficiency. The study will explore demand planning, supply chain efficiency, effectiveness, and inventory management solutions. The study focused on the rail Engineering case study company in the Republic of South Africa. The primary data involved semi-structured interviews with experienced participants who are managers and specialists in operation management and supply chain management in rolling stock maintenance in rail Engineering company and engaging in various techniques to improve the efficiency of operations. This study provides insight into the supply chain management techniques used in the rail industry to ensure operational efficiencies. Additional analysis may be required to develop cost-effective solutions for implementing a comprehensive demand planning and management department, including the automation of the entire process to seamlessly integrate suppliers. The rail operations are minimally researched, especially regarding their operational efficiency and practices in supply chain management. The results of this study appear to have applicability to other state-owned enterprises that also have strict requirements.

The Fourth Industrial Revolution has ushered in unprecedented technological advancement, demanding that companies seize the opportunities it offers or risk obsolescence in a rapidly evolving market landscape. Maintenance 4.0 (M4.0), as a subset of Industry 4.0, is a paradigm shift in maintenance driven by transformative technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics (BDA). Implementing Maintenance 4.0 presents a complex challenge, exacerbated by lacking a comprehensive readiness assessment framework and tailored implementation strategies. The research addresses this gap in the literature to develop a Maintenance 4.0 readiness matrix and implementation strategies and evaluate readiness application within a food manufacturing company. The critical readiness factors were categorized from literature studies into technological, organizational, and external environmental factors. The study employed a cross-sectional survey design with a population of management and engineering practitioners. Data collection used survey questionnaires and statistical techniques to analyze gathered data. The key findings were that the food manufacturing company was M4.0 ready in culture, structure, customers, service providers, regulations, and standards. However, the company was unprepared for technology infrastructure, financial resources, business cases, 4IR technology, awareness, skilled resources, change, leadership, and strategy. Findings provide valuable insights into the organization's preparedness and assist management in developing comprehensive strategies when adopting Maintenance 4.0.

This paper investigates the suitability of linear programming techniques to optimize profitability and productivity in the fast-moving consumer goods (FMCG) industry. The impact of six-sigma implementation on the dual objective function problem is also explored. A mixed integer linear programming (MILP) model suitable for the FMCG industry is selected with reference to the classification of lot sizing models found in literature, the classification is based on the type of problem that the model intends to solve. Two literature derived case studies that are representative of the FMCG industry are used to answer the research question in this work through a simulation research approach in the General Algebraic Modelling System (GAMS) software. The study found that linear programming can be used as an optimization technique in the FMCG industry with the lot-sizing concept. The implementation of six-sigma has a big impact on profitability and productivity as it reduces number of defects and failures, thus improving quality of products and maximizing the output with minimal re-work. This research in engineering management can be used to build a business case for resources to be directed at implementing six-sigma as it provides an alternative way to show the benefits of six sigma implementation.

Mining remains the primary driver of human and economic development in South Africa. The effectiveness of the Manganese mining in the Northern Cape depends on the efficiencies in using technology inputs such as power and logistics. The paper reviews the impact of underinvestment in economic infrastructure on the ability of the Manganese Mining Value Chain to unlock development potential in the metalliferous mineral’s rich Northern Cape. The paper argues that the dynamic equation based on the development of rail and the expansion of electricity generation capacity”, their use in the Manganese beneficiation value chain has direct causality towards growth.Power and logistics variables are endogenous to the dynamic structure of the Manganese value chain, and its ability to thrive depends on its internal structure. We take the system dynamics view that only elements within the boundary of a system, such as the Manganese value chain, determine its dynamic behavior. The evaluation of the impact of power and rail logistics on the development in the Northern Cape over ten years, a multi-variate simulation, is based on a system dynamics environment, acknowledging that the impact is more likely to be because of several variables acting against each other.