Theses and Dissertations (Industrial and Systems Engineering)

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Integration of Multi-State Systems in a series of EPQ models for deteriorating products
(University of Pretoria, 2024-04-11) Adetunji, Olufemi; Yadavalli, Venkata S. Sarma; u19300477@tuks.co.za/kapyatshinagi@gmail.com; Kapya, Tshinangi Fabrice
This thesis contains a collection of problems dealing with the modelling and optimisation of multi-state production systems, hence addressing challenges within the broader category of flexible manufacturing. These systems are often subjected to random degradations, failures, age of machines, human errors, power supply disruptions, or changes in demand. In literature, many inventory models have been developed under the assumption that the lifetime of systems is infinite, meaning the performance of a system or equipment remains unchanged and is fully usable for satisfying future demand. Some other models have extended this assumption by considering the functioning of systems (or equipment) under binary modelling conditions in which two states are considered: operational state and failure state. However, a growing body of literature is beginning to take into consideration the numerous scenarios that may occur during the lifetime of an equipment. These situations contribute to the multiplicity of the possible states of systems. Such systems are called multi-state systems (MSS). MSS are generally subject to several failure modes, in particular degradation and age of the systems, with various effects on their performance. The operational characteristics of MSS allow them to continue to function; however, they have a reduced level of performance, demonstrating the adaptability and scalability of the equipment. In the literature, techniques to increase the performance of binary systems are often based on strategies including redundancy or preventive maintenance. In the case of multi-state systems (MSS), continuity of service is ensured by reconfiguration. The objective of this research is to develop models for managing inventory models for deteriorating items in a multi-state manufacturing environment. In many research based on the binary modelling conditions, ensuring the continuity of the production is an important issue. These models assume complete shutdowns of production systems upon failure of manufacturing resources, which can be extremely costly and lead to substantial manufacturing losses. By addressing these limitations that are present in many of the current literature, the models proposed in this thesis are more practical and thus beneficial for operations management practitioners when making decisions involving multi-state systems in manufacturing processes. For such systems, the breakdown or failure of any component only minimally or at least partially disrupts their performance. In this way, the system can continue to provide service with an acceptable level of degradation. The contribution of this thesis is the development of three mathematical models to optimise a series of Economic Production Quantity (EPQ) systems for deteriorating products. The first model deals with A lot-sizing model for a deteriorating product with shifting production rates, freshness-, price-, and stock-dependent demand with price discounting. The system consists of one machine producing a single type of product. When the component of the machine breaks down, the system is minimally or at least partially disrupted. Thus, it may continue to operate at a rate lower than the initial rate until a specific inventory level is reached. Initially, demand is influenced by its selling price and the level of stock displayed. As freshness declines, demand then depends on the product's freshness condition. As production continues, there is also a shift in production rate over time. To account for declining freshness affecting consumer interest and purchasing behaviour, discounts are applied after a certain period. The optimisation problem was solved using numerical methods and supported by sensitivity analysis to demonstrate its practical implications. However, at this stage, the model does not explore how raw materials with imperfect quality could impact this system. The second scenario presents a two-echelon supply chain inventory model for perishable products, incorporating a shifting production rate, stock-dependent demand rate, and imperfect quality raw material. This novel model extends the classic EPQ as well as the first novel developed in this thesis to account for the use of raw materials with imperfect quality in the production process. Two scenarios are formulated within this framework: one involves selling imperfect raw materials at a discounted price after a screening period, while the other entails keeping imperfect items in stock until they are returned to the supplier at the end of an inventory cycle. Both scenarios consider product deterioration as well as shifts in production rate. Numerical solutions were derived for these scenarios. The findings indicate that maximising profit may involve selling the proportion of imperfect raw material rather than retaining it until a new lot arrives from the supplier. This approach is particularly crucial in manufacturing systems where imperfect products appear in both the raw materials and finished goods. The results were validated through a sensitivity analysis. The third model expands previous novels by considering the scenario of a production system that continually declines, leading to an increasing rate of defects over time. It takes into consideration various elements including deterioration of finished products, stock levels, product quality, and the influence of corporate social responsibility (CSR). CSR plays a critical role in enhancing the reputation of the company, building customer loyalty, and increasing sales by demonstrating a commitment to ethical practices and societal well-being. The objective of the model presented in this scenario is to identify the optimal inventory level and cycle time that minimise the total cost per cycle. To illustrate the effectiveness of this model, numerical examples are provided along with sensitivity analysis. The findings show that the profit generated can increase by as much as 14 % if manufacturers integrate a setup cost policy and selling price decisions. Extending product shelf life by 60 % can increase the net profit by as much as 7 %. In another model involving a two-echelon supply chain system, the profit can be increased by as much as 360 % and 386 %, respectively, if the selling price and the demand enhancement parameter for inventory level increase by 20 %. Furthermore, the unit selling price can decrease the total cost by as much as 34 %. Operations managers can use all these mechanisms to increase profits in their production systems. Under reasonable conditions, other industrial fields like automotive, mineral processing plants, assembly lines, as well as the production of mechanical components, may also also benefit from the results obtained.
Heuristic solutions to minimise makespan in a hybrid flow shop scheduling environment with energy consumption constraints in steel making
(University of Pretoria, 2024-02-14) Adetunji, Olufemi; u14202469@tuks.co.za; Baloi, Brighton Miyelani
Due to its advantage over other alternative energy efficient methods in cost savings, there has been an increasing interest in applying energy efficient scheduling in energy intensive industries faced with a conundrum of optimising production while consum ing less energy. In this dissertation, the energy-efficient hybrid flow shop scheduling problem is addressed to minimise the makespan without violating the total energy consumption threshold and where the threshold is not violated. None of the authors that apply the speed scaling mechanism under uniform parallel machines in the EHFSP domain consider each machine’s rate of response to a change in processing speed, the attack time. Therefore, the main contribution of this dissertation lies in addressing this gap,the consideration of attack time during speed scaling. The proposed algorithms seek to find the best makespan that incurs the minimum energy consumption where such alternative may exist. Energy is set as a constraint in re sponse to the dilemma faced by energy-intensive industries to continue fostering and sustaining competitive production by using less energy under an energy constraint. To solve the problem, two algorithms were proposed, each of which is an integration of some other scheduling heuristics, meta- and hyper-heuristics. The first is called the Improved Hyper heuristic NEH (IHNEH) algorithm, while the second is called the Improved Hyper heuristic GA (IHGA) algorithm. Each of the two algorithms operate in three stages, and share the first step, which is where the hyper heuristic is used to select a low-level heuristic for implementation in both solutions. The second step is what distinguishes the two solution. For the IHNEH algorithm, the NEH algorithm is used as the job sequencing procedure, while the IHGA makes use of the GA procedure. It was found that even though both algorithms were improved using the same improvement method, the IHNEH still displayed a superior performance over the IHGA especially for medium to large size problems in terms of the makespan and the energy consumption. The poor performance of the IHGA might be due to the random generation of the initial job sequence instead of using a constructive heuristic. The goodness of heuristic was used to measure the effectiveness of the two methods, and the computational results indicate that the methods were able to produce makespan values that deviate from the actual makespan by at most 2% for small size jobs. In terms of energy consumption, the IHNEH was able to produce energy consumption values that deviate from the actual energy consumption by 0% for small size jobs. For medium and large size jobs, the IHNEH had a deviation from the best makespan and energy consumption of 0, outperforming both the IHGA and the Branch and Bound under the time bound imposed. This implies that the IHNEH is a good technique for this problem given the energy threshold applied. Future studies can change that and study their influence on the Cmax and the energy consumption. Future work can also focus on extending the energy threshold further and study how many jobs can be processed without violating the threshold. The attack time values of machines were not based on actual industry data, however, future work could focus on obtaining these values to obtain more practical results. Also, speed scaling was not applied using actual machine speeds, but rather using speed factors, therefore future work could consider using actual speeds of machines. There are other alternative meta-heuristics that were not considered such as the PSO, and SA which are also capable of producing good results, and therefore they can also be used. Another suggestion for future research is initialising the GA with non random chromosomes to improve its performance. The performance of the GA is not only dependent on the quality of the initial solution and the termination criteria whose sensitivity analysis was presented, there are other factors that influence the GA such as the size of the population, the mutation and the crossover rate, therefore, it would be interesting to perform a sensitivity analysis of these parameters for future work.
Development of a lean six sigma framework for identification and minimisation of inefficiencies in construction projects.
(University of Pretoria, 2024-02-15) Michael, Ayomoh; u16005563@tuks.co.za; Mophethe, Modiehi Mathabo Mirriam
Introduction: This dissertation has focused on the systematic identification, analysis, and reduction of inefficiencies within the construction project domain through the utilisation of Lean Six Sigma. With the construction sector facing challenges such as budget overruns, delays, and resource misallocation, the need for effective strategies to enhance project efficiency becomes imperative. The Lean Six Sigma methodology, known for its success in various industries, offers a structured and data-driven approach to continuous improvement. This research adopts a comprehensive approach by incorporating literature reviews, case studies, and empirical data collection to explore the integration of Lean Six Sigma principles within the context of construction project management. The study begins by establishing the theoretical foundation, elucidating the fundamental concepts of Lean Six Sigma and its historical effectiveness in streamlining operational processes. Subsequently, it delves into a thorough evaluation of the unique constraints and complexities associated with construction projects, emphasising the importance of a tailored approach to enhance efficiency. Purpose: The objective of the dissertation is to carry out an extensive examination of the elements that lead to delays in projects and assess their subsequent effects. By comprehending the consequences and origins of these inefficiencies, adjustments were made to the lean six sigma tool to enhance the processes involved in construction projects and strive to reduce these inefficiencies to the greatest extent possible. As a result, both time and cost overruns were minimised, leading to savings in operational expenses. Consequently, the research endeavours to uncover the underlying causes of process inefficiencies and implement lean six sigma tools as effective solutions to address these inefficiencies. Approach: The practical aspect of the dissertation involves the utilisation of Lean Six-Sigma tools and methodologies in a real-life construction project, focusing on the identification of bottlenecks, waste, and unpredictability. To identify inefficiencies, a value stream map was constructed, while control charts were employed to measure variation. To gain a deeper understanding of the factors contributing to process inefficiencies, a fishbone diagram and factor analysis were utilised. Additionally, an EOQ model was employed to predict material lead time, which aids in effective planning. Furthermore, a scheduling and project monitoring tool, namely the CiteOps software, was developed, along with a visual dashboard created using PowerBi, enabling remote tracking and monitoring of project efficiency. The successful implementation of Lean Six Sigma in construction was illustrated through practical examples from previous studies, highlighting the adaptability and effectiveness of this framework. Findings: During the research, it was discovered that project delays and inefficiencies were largely influenced by lead time, delayed order placement, and task corrections. The correlation coefficient of lead time and placement was found to be 0.66, indicating a strong relationship between these factors and their significant impact on the efficiency of the project process. To mitigate long lead times, an EOQ model was implemented to forecast lead time and plan accordingly. The utilisation of project management software, such as CiteOps software, enhanced accountability among team members and facilitated better planning, enabling the timely identification and resolution of delays. By addressing these issues early on, their impact on the project was minimised. It is recommended to continue utilising the software and EOQ model to minimise the influence of factors that contribute to process inefficiency. Research limitations: The data used for the time study is only for the period when the project delays were at their climax and not from when they first occurred. This means the data used in this research may not be an accurate representation of the issue. The data sampled for this project may be insufficient due to limited availability. Originality: In this dissertation, the DMAIC approach was customised by combining the Six Sigma techniques, statistics, and differential equations to better quantify and understand the impact that delays have on the effective time spent on project completion. Keywords: Six Sigma, Lean, Lean Six Sigma, Inefficiencies, Inefficiency Minimisation, DMAIC
Dynamic system evaluation of fluctuating processor raw material on value chain financial sustainability
(University of Pretoria, 2023) Bean, Wilna; u16056036@tuks.co.za; Ottermann, Helga
As the global population grows, food self-sufficiency in developing countries becomes increasingly important and difficult (FAO, 2023). Due to variables like weather, agricultural production remains volatile, providing processors with an inconsistent supply of raw materials, making it difficult to operate at a reliable and sustainable utilisation and supply food consistently and competitively in the global market. This project aimed to evaluate the impact of a change in available raw material volume at the processing node on the total value chain’s financial sustainability. The project’s goal included illustrating and measuring the effect of seed availability, as well as determining the ideal amount of seed to be processed for maximum value chain financial sustainability. This was done with a system dynamic model that represented the Tanzanian sunflower value chain, including producers, traders and processors and measured financial sustainability with the net income indicator. Steps to develop the system dynamic model included the problem and background articulation, dynamic hypothesis with the causal loop diagram development, the mathematical formulation in AnyLogic, model verification and validation, and scenario analysis. The developed model represented the Tanzanian sunflower value chain accurately and assisted in gaining insight into managing different raw material availability disruptions (increasing and decreasing seed availability by 10% to 50%), which quantified the impact on each node’s financial sustainability with the net income indicator. This illustrated the efficiency gains due to economies of scale and supply and demand price trade-offs. Furthermore, a significant scientific contribution was to optimise the entire value chain to maximise the total value chain net income by determining the ideal amount of seed to be processed. The results illustrated how the model could contribute to quantifying a variety of different scenarios to analyse the impact of different interventions on the entire value chain system. The model quantified the financial sustainability of the value chain, however, as with most research, the model can be further improved to refine the results and incorporate different performance indicators (like environmental and governance) which may widen the reach of the results.
A sustainable operations management model in a non-profit organisation
(University of Pretoria, 2023) Bean, Wilna L.; u20818778@tuks.co.za; Harmse, Martha Fredricka Petronella
The sustainability of a non-profit organisation (NPO) in the South African education and research sector must be improved. Previously they had significant impact, but became under severe stress especially during COVID-19. This is an instance of NPOs in general whose sustainability is at risk. Although NPOs can improve their sustainability through operations management, the implementation of sustainable operations management requires further investigation. They can apply various models to improve the implementation of sustainable operations management, but a gap remains to develop such models. The purpose of this study is to develop an appropriate sustainable operations management model (SOMM) in the specific NPO. By following an action design research approach, the purpose of the study simultaneously is to develop a theory of how appropriate SOMMs can be developed in other NPOs. Most applications of action design research however involve information systems and technology. This study applies a less technologically orientated approach based on design research in education. Starting from a problem formulation phase, the actual problem is identified, conceptualised, and formulated as a case study that represents a class of research problems. Concepts are analysed through a literature review, and long-term commitment is obtained from the NPO. A building, intervention and evaluation phase starts with the contextualisation of a SOMM in the participating NPO, a research procedure is developed to address the actual problem, an interpretive framework and design ecology are developed to address the class of research problems, and effectiveness criteria are established. A SOMM is then iteratively developed through building, intervention and evaluation cycles until it is sufficiently refined. A reflection phase is executed in parallel with the previous two phases to capture the learning that occurs. Lastly, a formalisation phase addresses the reflexivity of the researcher and a design theory is formulated of how appropriate SOMMs can be developed in other NPOs. A practical contribution is made towards a SOMM in the NPO based on a definition of a model as a meta-theoretical framework to develop understanding, facilitate communication, propose improvements and to surface underlying assumptions. Sustainable operations management is defined as the management of human, natural, physical, financial and social capital and processes involved to satisfy self-defined needs and build resilience over the long term. The design starts by evaluating the current sustainability of the NPO, applies an integrated organisational perspective of a SOMM, regards sustainable operations management as an organised complex problem, and implements discordant pluralism. This entails organisational models for sustainability and systems thinking approaches namely a biomatrix entity systems perspective, viable system modelling, system dynamics, soft systems methodology, the Cynefin framework, and dynamic equilibrium modelling. The NPO confirms that the SOMM is effective in providing guidance to address their self-defined needs. These needs evolve through the development of the SOMM due to mutual influences between the model and the NPO. This ill-defined problem is addressed by changing the perceptions of the NPO to satisfy their needs, identify other needs, and to build resilience over the long term. The SOMM fosters and reinforces commitment to multiple, competing strategies by addressing paradox so that the NPO becomes more fluid, enhances their reflexive self-regulation through supportive capabilities, and becomes more sustainable. Design principles for the class of SOMMs in NPOs are based on the strategic selection of the case study, the interpretive framework, and the design ecology. A theoretical contribution is made towards sustainable operations management in NPOs in terms of key focus areas identified through content analysis of literature, and towards sustainable operations management in general with reference to the increasing number of hybrid organisations. The study also contributes to the theory of operations management modelling through the development of a research procedure to develop such a model. Furthermore, a contribution is made to a transformative research agenda of sustainability science in a design research mode. The study emphasises that enhanced sustainability does not imply predictability or a homeostatic balance to be achieved and maintained, but continuous tensions that must be creatively addressed. A less technologically orientated approach to action design research is proposed, and future research opportunities are identified.
Optimum predictive modelling, holistic integration and analysis of energy sources mix for power generation and sustainability in developing economies : a case of the Nigerian power system
(University of Pretoria, 2023) Ayomoh, Michael; hanif.ibrahim@tuks.co.za; Ibrahim, Hanif Auwal
Nigeria being the most populous black nation on earth, with a high birth rate and growing industrial, commercial, transportation, and agricultural activities has been caught up with the dilemma of insufficient power supply which has left the nation lagging in terms of socio-economic development among sister nations. With an aggressive transition to renewables all over the world to meet energy obligations and mitigate greenhouse gas (GHG) emissions, Nigeria is left with no choice but to join the transition in a bid to uphold the Sustainable Development Goals 7 & 13 (clean and affordable energy & climate action). The power generation mix of Nigeria is largely dependent on natural gas hence, largely in conflict with the mentioned SDGs. Despite these sources of electricity being far fetched from meeting the growing demand for power usage, the non-renewable energy source are noted for creating a significant level of environmental pollution, global warming, and health-related risks. As the need to bring down the rising annual global temperatures to 1.5 degrees in various Conference of Parties (COP) grow in awareness, it’s obvious that Nigeria has a significant role to play towards the actualization of this mission. The ever-increasing demand for electricity, as well as its impact on the environment, necessitates expanding the generation mix by utilizing indigenous sustainable energy sources. Power generation planning that is sustainable and efficient must meet various objectives, many of which conflict with one another in which multi-objective optimization is one of the techniques used for such optimization problems. Using multi-objective optimization, a model for Nigeria’s power supply architecture was developed to integrate indigenous energy sources for a sustainable power generation mix. The model has three competing objectives i.e reducing power generating costs, reducing CO2 emissions and increasing jobs. To solve the multi-objective optimization problem, the Hybrid Structural Interaction Matrix (HSIM) technique was utilized to compute the weights of the three objectives: minimization of costs, minimization of CO2 emissions, and maximization of jobs creation. The General Algebraic Modeling System (GAMS) was used to solve the multi-objective optimization problem. According to the simulations, Nigeria could address its power supply shortage and generate up to 2,100 TWh of power by 2050. Over the projected period, large hydropower plants and solar PV will be the leading option for Nigeria's power generation mix. Furthermore, power generation from solar thermal, incinerator, nuclear, gas plants, combined plants, and diesel engine will all be part of the power supply mix by 2050. In terms of jobs expected to be created, about 2.05 million jobs will be added by 2050 from the construction and operation of power generation plants with CO2 emissions attaining 266 MtCO2 by 2050. The cost of power generation is expected to decline from a maximum of 36 billion US$ in 2030 to 27.1 billion US$ in 2050. Findings in this research concludes that Nigeria can meet its power supply obligations by harnessing indigenous energy sources into an optimal power supply mix. Furthermore, to establish the basis for the power generation mix projection, system drivers responsible for the rising demand of electricity and reduce pace of transition to renewable energy sources were identified from a systems thinking point of view after which they were prioritized using the HSIM concept. Also, the impact of renewable energy on power accessibility, affordability and environmental sustainability was investigated using the system dynamics approach. It was obtained that factors including urbanization, industrialization, agricultural/commercial services growth rates, and pollution are the primary reasons for the rising demand for electricity. The slow transition to renewables in Nigeria is directly linked to the absence of subsidies and government grants, non-existing or few renewable energy financing institutions, scarcity of experienced professionals, barriers to public awareness and information, and ineffective government policies. The outcome from the system dynamics approach on accessibility, affordability, and environmental sustainability of the electricity supply are thought to be enhanced if indeed the country's plan of using 36% renewables in the mix of power sources is to be met.
Development of an integrated system of solution for decision support of crop health diagnosis : case of a machine learning enabled unmanned aerial vehicle
(University of Pretoria, 2023) Ayomoh, Michael; elizabetholivier19@gmail.com; Olivier, Rachel Elizabeth
The agricultural sector developed a need to utilise technology to make informed decisions about crops. Remote sensing technologies, which typically utilises satellite, airborne, or ground-based sensors, has been increasingly used in precision agriculture lately. However, Unmanned Aerial Vehicles (UAVs) or drones have become a more cost-effective and versatile solution, providing higher resolution imagery and greater flexibility in flight time, frequency, and crop visibility. The project opportunity stems from the growing usage of UAVs in agriculture. The problem statement addresses the need for a comprehensive framework for selecting, designing, and implementing a crop monitoring UAV system, which has not yet been identified. This project developed an integrated system of solution for a machine learning enabled drone that combines different attributes into a unique solution. The literature review highlighted several aspects to consider for a drone remote sensing system and illustrated how such a system fits into precision agriculture applications. Required equipment and technologies identified for a system include a machine learning enabled UAV, control systems, sensors, and data processing tools. A case study research approach is deemed appropriate as it allows for the review of literature and available solution options before designing a solution. Attributes were identified and modelled to create a unique decision support framework for a crop monitoring solution system following their relevance and combinatorial characteristics. The integrated system is divided into three solution paths, each with critical user decisions and recommended selection processes. Possible solutions are categorised by farm and aircraft specifications to facilitate simpler selection. The research objectives were addressed through the identification of these attributes and through designing the main decision systems along with the categorisation of potential solution options. A case study research approach is deployed throughout the project to allow for the integration of literature and available solution options to the holistic system and each smaller decision sub-system. The methodology was iterated within each main decision path to define and analyse a unique case for each decision system and create a solution based on the information available for the specific decision system. Despite this research being skewed towards qualitative investigations, some quantifications from the research findings include that from the 31 UAV models considered for analysis, they can be categorised into six categories relating to UAVs characteristics and two categories related to the farm characteristics. The categories are designed to group together those aircrafts with similar characteristics or specifications, to allow for an easy reference and selection by the user. The presented solution addresses the complexity of the system and identified literature gaps through an encompassing and integrated system of solution. Future work includes creating a comprehensive database that includes all possible solution options and developing a functioning decision support system based on the developed solution system.
Assessment of the lean frameworks and barriers in implementing lean manufacturing in South African manufacturing industries
(University of Pretoria, 2022) Adetunji, Olufemi; Maware, Catherine; u12026710@tuks.co.za; Mbewe, Josephine Koketso
Global competition and high customer expectations have forced manufacturing organizations to always consider ways in which they can be competitive, adaptive, and resilient in the face of change. Lean manufacturing is one of the most common improvement initiatives that businesses explore to improve their operations and reach targeted business goals such as financial savings, reduced inventory, reduced turn-around time, and manufacturing flexibility, to mention but a few. Literature is replete with studies on the drivers of Lean success, Lean barriers and enablers, and Lean frameworks. However, a gap was identified from reviewing previous Lean research, where it was observed that while there has been diverse studies on Lean implementation barriers and enablers and their impacts on operational performance, on the development of various implementation frameworks for Lean, and on the review and classification of these framework based on different criteria such as practicality, amongst others, there has not been research that considers the interdependence of these concepts, specifically the relationship between the Lean enablers and the design of Lean frameworks. Consequently, a model was developed in the current study, using Partial Least Squares Structural Equation Modelling (PLS-SEM), to hypothesize the relationships between the three variables: Lean framework design effectiveness, Lean implementation enablement, and business operational success. Data was collected using a survey that was distributed across different manufacturing fields. The research questions were developed with justification from literature and SmartPL4 software was used for the analysis of the data. The main findings are that there exists a relationship of considerable strength between Lean enablers and the design of Lean framework. Furthermore, Lean implementation enablement has a positive influence on business operational success. These findings are important because they highlight the importance of organizations implementing Lean to be Lean-ready by considering Lean enablers. This applies for both managers and practitioners of Lean. The relationship between Lean enablers and Lean framework design shows the importance for designers of frameworks to organically design framework and consider the enablers of Lean, as opposed to simply modifying existing frameworks by trying to improve their shortfalls, without reflecting on what positions their organizations are for the success of their Lean implementation.
Identifying the key decisional aspects for multifocal financial service providers in South Africa
(University of Pretoria, 2022) De Vries, Marne; u15134068@tuks.co.za; Schnetler, Henry Ignatius
In South Africa during the last decade, the topic of Financial Service Providers (FSPs) has received significant attention from practitioners and associations, but limited academic literature exists on the topic. In 2012 an industry survey was done that highlighted the key barriers to the growth of FSPs. A similar research study was conducted in 2016 that focused on risks for FSPs that confirmed the findings of the 2012 survey. Since then, no other academic studies that focussed on multifocal FSPs within South Africa were found. A literature review based on the structure of a systematic literature review was conducted in 2020, followed by several informal interviews providing the necessary context to phrase the main research question of this study: What are the aspects that need to be considered for the strategic future of multifocal FSPs in South Africa? Answering the main research question, a mixed methods research methodology was used, applying convenience sampling to conduct two cycles of data-gathering. Using the initial decision aspects identified during a literature review, the first data-gathering cycle used four diverse FSPs via expert interviews, to extract decision aspects, producing extended decision aspects. The second cycle of data-gathering used a survey of nine independent financial advisors to validate whether the extended decision aspects identified from the interviews, as well as business and academic literature, are relevant to industry, producing refined decision aspects. As part of the second data-gathering cycle, the 2012 survey questions were replicated to provide context for the survey participants and their financial services and operating context. Demonstrating the practical use of the decision aspects, the study extracts relevant decision aspects to indicate how the aspects could be used to guide decisionmaking for the four FSPs interviewed during the first data-gathering cycle. The main contribution of this dissertation is to present a comprehensive list of decision aspects that will be useful to multifocal FSPs in guiding their enterprise re-design initiatives. A secondary contribution is to demonstrate how the decision aspects can be used to guide the four FSPs, during future strategic decision-making. The study concludes with future research opportunities identified from the findings.
The impact of the Lean-Green philosophy on Zimbabwean manufacturing industry
(University of Pretoria, 2023) Adetunji, Olufemi; Maware, Catherine; tmctino@gmail.com; Machingura, Tinotenda
Due to the pressure of global competitiveness and its local ramifications on manufacturing businesses in the global south, many organisations are struggling to be both profitable and environmentally compliant as local regulatory institutions begin to demand environmental friendliness from the manufacturers. Over the past few years, organisations have adopted different methodologies to improve their performance. These methodologies include Lean Manufacturing (LM) and Green Manufacturing (GM). This research was carried out in four parts, using two methods, one qualitative and the other quantitative. The first part was based on a qualitative method where a Systematic Literature Review (SLR) was conducted using the Population, Intervention, Comparison, Outcome (PICO) format, and ATLAS.ti. This part seeks to understand the impact of the joint implementation of Lean and Green techniques on the performance of organisations from a literature perspective and propose ways to improve the synergies while limiting the mutually detrimental effects. It is apparent from the literature that implementing Green methodologies is not always complementary to Lean, but the nature of this relationship and the extent of their interaction have not been fully studied. Buoyed by the increasing demand for improved productivity and environmentally conscious manufacturing, research in the area of Lean-Green Manufacturing has experienced significant growth over the last decade, while there has not been a review of the work done since then. This first section, therefore, seeks to review Lean-Green articles published post-2013 and compare the findings to that of Dües et al. (2013) to understand the current state of the research. A systematic literature search was done to identify the Lean-Green articles from Scopus, Web of Science, and Google Scholar databases that were published post-2013. The PICO strategy was used to develop and answer research questions. ATLAS.ti version 22 was used to analyse the 141 papers and develop research themes. The results indicated that LM and GM have strong synergies, and when integrated, they tend to deliver superior organisational performance than individually. These findings agree with the pre-2013 results but with some additions, such as synergies in sustainable performance and value addition. Therefore, it helps to align LM and GM so that the full benefits of the complementary relationship are realised, and where dichotomy exists, it guides its amelioration. The other parts were carried out using the quantitative method by collecting data through a survey and analysing the data using Structural Equation Modelling (SEM). The second part investigates the complementary nature of LM and GM on how they impact operational and environmental performance. It examines whether a combined Lean-Green implementation leads to better organisational performance than when LM and GM are implemented individually. It also explores whether being environmentally compliant leads to improved organisational performance. A survey was conducted on the Zimbabwean manufacturing industry. Out of the 782 questionnaires distributed, 302 valid responses were obtained and analysed using SEM in SMART-PLS. The results indicated that both LM and GM impact environmental and operational performance. However, GM indirectly affects operational performance through environmental performance. In addition, when LM and GM were combined, the impact was greater than when they were used separately. Therefore, the companies that have successfully implemented LM can implement GM more easily because of their complementary nature. Integrating LM and GM reduces most forms of waste, causing improved environmental performance, community relations and customer satisfaction. The third part evaluates whether the integration of LM and GM impacts sustainable performance more than when they are implemented separately. Also, it investigates whether being environmentally compliant has an impact on social and economic performances of organisations. It investigated whether an improvement in environmental performance can make organisations improve their economic and social performance. Thus, demonstrating that environmental compliance should not only be viewed as a requirement for compliance but as a way of improving social and economic performances. The results of the SEM showed that integrating GM and LM has a greater impact on economic, social, and environmental performance than when implemented separately. Moreover, an improvement in environmental performance led to improvement in both social and economic performances. Thus, Lean-Green positively impacts social performance by improving workers' health and safety, labour and community relations. The last part assessed the impact of internal and external Lean-Green barriers on sustainable performance. More recently, organisations have been integrating LM and GM to harness their combined benefits, and some have successfully integrated the two methods. However, even after successful implementation, other organisations fail to achieve their goal of improving their sustainable performance due to extant Lean-Green barriers. Thus, organisations need to know and understand these barriers, because without such understanding, performance improvements may be jeopardised. Thus, this research aims to investigate the impact of internal and external barriers faced by organisations post implementation and how they affect their intended goal of improving sustainable performance. The results showed that internal and external barriers impede organisations from achieving their goals, and hence, they deserve attention.
Effective prepositioning of relief inventory for humanitarian operations in the Central African Region
(University of Pretoria, 2023) Bean, Wilna; justusngunjiri4@gmail.com; Ngunjiri, Justus
Inventory management is a crucial aspect of humanitarian operations. Various inventory models and policies have been developed over the years to improve the efficiency of humanitarian inventory management. These models consider various elements, including sourcing, storage, prepositioning, distribution, and transportation. While the existence of literature and models supplied guidance and breakthroughs towards more informed decision-making, the complex setting of disasters has continued to preclude their application. Over-simplification, impracticality, and particularity of decision variables pose a challenge in using specific models in exceptionally distinct disasters owing to their complexity and ever-changing nature. This implies that the ability to manage inventory efficiently and its distribution depends on the preparedness and prevailing conditions in the post disaster period. This study focused on approaching these shortcomings by adopting an integrated approach which starts with the characterisation of inventory management challenges unique to disaster settings. Gaps within developed models are identified, and an inventory prepositioning and aid distribution model is developed and applied to bridge some gaps. Therefore, this study presents two models (deterministic and stochastic programming with recourse) for prepositioning modelling. The models are implemented as multi-objective mixed-integer linear programming relief inventory prepositioning models for the Democratic Republic of Congo (DRC) and Central African Republic (CAR). The models minimise shortages and enhance equitability while minimising the total response time in areas with poor road network in a cross-border distribution setting. The model is solved using a pre-emptive optimisation approach, and a sensitivity analysis is conducted to evaluate the influence of the budget, priority items proportion, and capacity variation in the model input. Results indicate that the models are sensitive to changing parameters. Of the two models, the stochastic model was determined to have higher reliability but required a higher budget to match the performance of the deterministic model. Results analyses confirm that the models can add value to humanitarian organisations when planning facility locations, inventory prepositioning, and conflict area-distribution centre assignments in the DRC and CAR. This study, therefore, contributes to the body of knowledge and humanitarian organisations in Africa.
An economic order quantity model for imperfect and deteriorating items with freshness and inventory level-dependent demand
(University of Pretoria, 2023) Adetunji, Olufemi; Sebatjane, Makoena; u14200482@tuks.co.za; Coghlan, Deidre Ann
Consumer purchasing behaviour is influenced by many factors. Depending on the circumstances, these factors may become relevant drivers of important supply chain decisions. Expiration dates have an influence on the purchasing decision of consumers for perishable goods. Another behavioural influence that stimulates demand is the volume of goods that are available on display as part of the purchase transaction. Furthermore, the fact that certain goods deteriorate over time must also be evaluated within the context of the study of perishable goods. The market is increasingly seeking goods that have no inherent defects or imperfections. This investigation seeks to determine the impact of imperfect quality, deterioration, freshness and inventory level and also, how those issues can be improved upon in workable situations. This paper proposes an inventory model that stipulates the demand as a function of freshness and the inventory level. In addition, the inventory depletes through both deterioration and demand, and the product quality is not always perfect. The objective of the inventory model is to maximise the system’s profit, hence the study has developed a theoretical mathematical model for imperfect and deteriorating items with freshness and inventory level-dependent demand. A numerical example was used to illustrate the practical application of the model in a real life environment. Sensitivity studies were conducted to determine the impact of changes or variations to the inputs that are used in the model. The findings were that the date of expiry, the elasticity of demand and the selling price of the perfect products are the main constituents that affect the profitability.
Integrated Modelling of Functional Capabilities and Reliability Analysis of Outdoor Autonomous Vehicle Intelligence
(University of Pretoria, 2023) Ayomoh, Michael; brianndlovu36@gmail.com; Ndlovu, Brain Ndumiso
The autonomous vehicles concept and development were founded in the 1980s, but they became more famous and advanced more than a decade ago. Autonomous vehicles were created due to the advancement of different technologies, and it was believed to portray the progress of the 21st century. This idea led people to think these autonomous vehicles might help reduce or mitigate road accidents. However, firstly, according to the National Law Review, early accidents were recorded, and some were deadly. Secondly, the African continent has been left behind concerning technological advancement; hence, it is currently not ready for so-called smart cities. Therefore, the problem this dissertation looked into is that there is an issue of complexity associated with autonomous vehicles (with independent levels 4 and 5). The study aimed to objectively understudy the reliability of the intelligent autonomous vehicle amidst inter- and intra-complexities associated with autonomous ground vehicle navigation requirements. Therefore, an appropriate methodology had to be selected to fulfill the aim. Thus, two research methodologies were considered for this dissertation, which are (1) design science research and (2) systems thinking methodologies. Additionally, a unification of these two methods was established, and a framework was designed. An optimal physical structure was developed using the established framework and analysing autonomous vehicles’ sensor fusions. Furthermore, the reliability analysis model was formulated. The use of systems and reliability engineering theories and applications were adopted to develop and model the optimal structure and reliability model. Finally, the reliability of the autonomous vehicles with respect to traffic rules was calculated. It was found that there is a 99.94% chance that the autonomous vehicle will fail at least one of the traffic rules in 20 minutes.
Towards adapting nexus for practical use to elucidate inter-team dependencies : a large-scale agile case study
(University of Pretoria, 2022) De Vries, Marne; laurenalisachristopher@gmail.com; Christopher, Lauren Alisa
Agile methodologies were originally created for small, collated teams; but large enterprises saw and wanted the benefits of agile for their projects that consisted of multiple developers, who may even be globally distributed. This raised the need for scaled agile and the issue of how to implement a small-scale solution on a much larger scale. Bringing together the empirical data found, along with experiences and information from practitioners who interact with these frameworks daily, an evaluation was done as to what solution would the most ideal for a Fintech company, comparing SAFe, LeSS, DAD, and Nexus as possible options. Through case study research, interviews and a survey, the issues of inter-team communication, collaboration, and dependencies were raised within the enterprise’s banking build. Nexus was selected as the best large-scale agile framework for the enterprise. This case study uses a Fintech company, building a digital banking platform, and in need of a large-scale agile solution, as they had globally distributed development teams working on the same project. Inspecting the banking build just after it’s MVP (minimum viable product) launch, the learnings of Nexus, it’s implementation and running within the enterprise, were used to understand how Nexus increased collaboration by daily communication which resolved dependencies between teams quicker by highlighting and focussing on inter-team dependencies in the Nexus Daily Scrum using a Kanban board which contained only these dependencies. Using these learning and comparing them to the theoretical version of Nexus, the study presented conclusions towards the adaptation of Nexus in a practical environment in order to better resolve inter-team dependencies. The case study provides knowledge and learnings surrounding Nexus and for those looking to implement a scaled agile solution. The enterprise, due to various factors, implemented only some of the Nexus components which leads to discussions on applications for future studies, as well as non-Nexus specific learnings that can be used as possible research topics.
A participative modelling tool supporting the story card method
(University of Pretoria, 2022) De Vries, Marne; u16020155@tuks.co.za; Opperman, Petra
When business-oriented software needs to be developed within a scaled context, the story card method (SCM) assists in structuring emerging software requirements within a taxonomy that represents enterprise operation. However, agile team members first need to develop a common understanding about enterprise operation when they construct the enterprise operation taxonomy. The COVID-19 pandemic emphasised the need to use digital participative design practices when in-person face-to-face participation is not possible, especially when team members are geographically dispersed. A key concern was identified during a previous design iteration of the SCM and confirms that the current software modelling tool that is being used, in combination with the SCM, does not encourage active participative modelling (PM), due to the latency of the tool. This study aims to investigate whether a new PM modelling tool is useful to post-graduate participants within a tertiary educational context, when they apply digital PM within the context of their own enterprise using the SCM. The study starts with a literature review, indicating that problems related to PM also exist within a broader context than this study. A design science research (DSR) approach was followed in this study to evolve the existing SCM artefact and address the concern related to the previous software modelling tool. As multiple PM tools are available, a list of minimum requirements was used to short-list two tools. A comparative analysis of the two tools is provided, motivating the selection of a single tool that was used to support the SCM. In applying the SCM, 36 participants were involved. Of the 36 participants, 25 completed a survey to evaluate the usefulness of the tool and whether the tooling encouraged participative design. Using a demonstration case to illustrate the notion of participative design to the post-graduate participants, using the selected tool in combination with the SCM, feedback was obtained about the participative modelling tool that was used by post-graduate participants. Finally, a conclusion is provided on the usefulness of the PM tool and whether the findings could be generalised beyond the combined use with the SCM.
A lot-sizing model for a multi-state system with deteriorating items, variable production rate and imperfect quality
(University of Pretoria, 2022-02-14) Adetunji, Olufemi; Yadavalli, Venkata S. Sarma; u19300477@tuks.co.za; Kapya, Tshinangi Fabrice
The management and control of inventory has become a core part of management, which plays a significant role through achieving efficient and profitable operations of a business organization. Hence, considerable efforts have been made to develop models that can be implemented to optimize inventory systems without compromising customer needs. The classic Economic Production Quantity (EPQ) model is the most widely used of these models; however, this model presents certain limitations, leading researchers to extend some of the assumptions to increase its applicability to present-day organizations. In manufacturing, studies of the functional state of equipment have, for a long time, been based on binary modelling conditions where two states were considered: the operational state and the complete failure state. However, a growing literature takes into consideration the numerous scenarios that may occur during the lifetime of some equipment. Such systems are called Multi-State Systems (MSS). Thus, in this dissertation, a perishable replenishment policy is developed based on the MSS concept to optimize an EPQ model that operates in a degraded state, producing both perfect and imperfect products, under constant demand and backlog dependent-demand. The cycle was assumed to start with a particular production rate until a point when the inventory reached a certain level, and after which the failure mode was activated due to the deterioration of certain components, and the production rate was reduced to a lower rate to ensure the continuity of supply until the maximum inventory level was reached. Production then stopped to restore the machine and the cycle started again. The model assumed that inventory was subject to deterioration, the demand rate was constant, and partial backlogging was allowed. The work done included an exploration of the modelling methods, analysis and evaluation of the performance of the multi-state system in which the level of service relies on the state of the equipment during the production cycle. An evaluation and optimization of the system’ performance indicators such as inventory levels, backorder level, cycle time and the total cost function were carried out. Due to model complexity, the Newton-Raphson approach was used to solve the model and numerical examples are provided to illustrate the solution procedure. Based on the results, the presence of imperfect quality outputs forced the system to produce more items to meet the needs for perfect quality items. As the proportion of imperfect quality items produced increased, the proportional increase in cost seems to have grown more quickly. As the production rate in the first production-consumption cycle increased, the total cost function increased; this was mainly due to higher production cost, holding and disposal costs incurred. However, as the inventory holding cost rate increased, the optimal inventory levels decreased, the cycle time decreased, but the shortage and the total cost increased. The decrease in production rate during the second production-consumption cycle was shown to have increased the cycle time and the inventory level in the first cycle, but decreased the inventory level in the second cycle and the total cost. Sensitivity analysis showed that working with low values of cost parameters provided better results in terms of optimizing the total cost. The EPQ model presented in this research can be used by production managers, working in industries such as assembly lines, steel factories, hydrometallurgical plants under different operational scenarios, as a guideline when making production decisions
Mobile robot optimum trajectory Development using a hybrid reactive navigation model
(University of Pretoria, 2021) Ayomoh, Michael; thabang.ngwenya@up.ac.za; Ngwenya, Thabang
Path planning for mobile robot navigation in workspaces with varying obstacles complexity levels was addressed in this research. The domain problem is that for a specific class of obstacles referred to as the concave shaped and lengthy obstacles, the likelihood of local minima trap occurring is often significantly high. For instance, a labyrinth premised on concave shaped obstacles often misleads a navigating robot into the concave hollow region in a bid for the robot to reach its desired target point. Apart from the use of reactive algorithms, for an autonomous navigation process which is often premised on continuous path trajectory development, the literature clearly alleges that most non-reactive algorithms get trapped in the concave hollow and along the edges of lengthy obstacles. The purpose of this research is to adapt a reactive mobile robot (MR) navigation algorithm premised on the Hybrid Virtual Force Field (HVFF) concept for the exploration of robot navigation in both developed and literature based obstacle constrained workspaces. The obstacles considered in this research work are mostly premised on concave shaped and lengthy obstacles cul-de-sac. The HVFF approach evolved from the Virtual Force Field (VFF) approach which is premised on the Potential Field Method (PFM). This method of path planning operates by utilizing the resultant of forces emanating from the combination of repulsive and attractive forces acting on a navigating robot. The algorithmic validation was carried out via the conduct of simulation trials using the Python software. The simulations conducted span across newly developed workspaces and literature based workspaces for a comparative study. Furthermore, the behaviour of the robot navigation with and without the HVFF algorithm per workspace was presented. Of a particular interest was the navigation time with and without the HVFF algorithm per workspace. The results obtained in all the simulations showed a much efficient navigation completion time with the use of the HVFF algorithm. Efficiency in arriving at the target point implies that the robot was able to come out of the local minima trap each time it entered the hollow region of a concave shaped obstacle or around the edges of a lengthy stretched out obstacle. The time difference recorded between deploying the HVFF approach and not deploying the HVFF algorithm across the different simulations conducted spanned between 14.27 to 287.44 seconds which corresponds to a percentage gain time of 31.87% and 89.70% including a simulation with an unending target point (TP) arrival time for the without HVFF algorithm. As the concave trap increased in its depth, the tendency of the robot to escape from the trap becomes much more difficult. The outputs of this research justify the effectiveness and efficiency of the HVFF algorithm.
Strategic location modelling for reaction vehicles of the private security industry in South Africa
(University of Pretoria, 2014-08) Yadavalli, Venkata S. Sarma; kellerman@fourie.co.za; Kellerman, Rikus
Since the early 1960s location problems have been used throughout various industries and in various countries. During recent years the field of location problems has become increasingly popular due to the fact that it is applicable in real life situations – especially in emergency services such as hospital, police station and ambulance locations to name a few. Despite the fact that location problems are so widely used with great success, it is still not being used to full potential in industries where it can have a major impact. One of these industries is the private security industry in South Africa. This dissertation addresses various mathematical models that can assist the management of privately owned security companies to determine strategic locations for their reaction vehicles, these locations will increase both resource utilization and improve the level of service they provide to customers. These models are used in different scenarios to see how the models adapt to input changes.
System dynamics modelling for sensitivity analysis evaluation of driving factors on decoupled aquaponic systems in South Africa
(University of Pretoria, 2022) Ayomoh, Michael K.; u13012470@tuks.co.za; Roux, Adriaan J. G.
Despite being water and energy proficient, the practice of aquaponics has remained underdeveloped and underutilized in a water scarce society like the Republic of South Africa. As the population of humans on the globe continues to grow geometrically with climate change also being aided, more proficient and safe means of food security premised on water and energy efficiency is becoming the prerogative of governments across different nations. This research has presented a system dynamics model of a decoupled aquaponics system to investigate the sensitivity of parameters in the design of aquaponics systems. Two major driving variables considered in this research include energy and water utilisation for efficient design. A couple of ventilation flow, heating and energy based models were built into the system dynamics model for the conduct of simulation. The results revealed that the top performing countries in respect of energy and water efficiency include locations with hot humid climates such as Brazil, Nigeria and Malaysia. In South Africa, Durban was the best performing city with a peak energy demand of 18.4 MW and a total yearly energy usage of 4550 MW. Durban had a 7.3% higher cumulative energy compared to Brazil. Durban had a net water return of 124.8 ×10^3 m^3. Given the humid and hot climate in the city of Durban, it is considered to be competitively suitable for aquaponics operations. Other regions in South Africa could still be suitable to operate aquaponics systems however, this might be less energy and water efficient. The outcome of this research can be utilized by local governing authorities to ensure sustainable policy design and implementation.
Extracting input data for residential waste collection capacitated arc routing problems
(University of Pretoria, 2021) Bean, Wilna; llewellyn.steyn@gmail.com; Steyn, Llewellyn James
Residential waste collection is an essential but expensive public service provided by governments throughout the world. A key contributor to the cost of waste management is collection cost, making the potential for cost savings on waste collection an area of focus. One way to reduce collection cost is through the use of vehicle routing to improve collection routes. While various vehicle routing problem definitions exist for waste vehicle routing, the most compelling is the Mixed Capacitated Arc Routing Problem with Time Restrictions and Intermediate Facilities (MCARPTIF). A challenge facing the MCARPTIF however is that the input parameters necessary to solve real world instances of the problem are difficult to estimate. These include the time taken to drop off waste, the collection and traversal time per street segment and the waste generation rate per street segment. Global Positioning System (GPS) devices and publicly available data sets offer an opportunity to provide insight into some of these parameters and to develop more realistic MCARPTIF instances and subsequently collection routes. This dissertation aims to demonstrate how these parameters can be efficiently estimated. Using GPS data and known landfill locations, landfill visit durations are estimated at a landfill in a metropolitan area. Landfill visit durations are estimated to average 16 minutes. In addition, landfill durations are shown to increase with congestion within the facility. Using GPS data and publicly available street network data from the same metropolitan area, the average vehicle velocity when collecting waste over seven case study areas was found to be 3.857 km/h. The vehicle velocity when traversing street segments within the case study areas was found to average 6.843 km/h. A synthetic population based on census data and per capita waste generation estimates was used to estimate waste generation rates per street segment for a number of case study areas. All of the above mentioned variables were compared to known parameter assumptions used in literature and differ considerably. Lastly the parameter estimates were used to solve a number of real world instances of the MCARPTIF and were compared to instances using parameters from literature. Differences between instances solved using parameters estimated in this dissertation and those based on assumptions from literature illustrate the importance of using accurate input data for waste collection routing applications.

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