Zugl.: Erlangen, Nürnberg, Universiẗat, Diss., 2009.

This is the first research to empirically study the congruity in supply chain operations strategies. The tests show that the congruity in supply chain operations strategies are related to performance and also moderate the association between performance and the types of supply chain relationships. 308 manufacturing firms were studied for empirical tests. Various methods including multiple regressions, cluster analyses, and logistic regressions are applied in this study. Based on the tests, five hypotheses are supported and only one hypothesis is inconclusive. This dissertation contributes in different thresholds. First, by studying operations strategies through a different angle (the supply chain instead of a firm) this study reconciles results in the relationships between performance and operations strategies and between performance and supply chain relationships. Therefore, this study contributes to reconciling two long-term puzzles in both operations strategies and supply chain management literatures. Second, this research draws on three different disciplines, operations strategies, supply chain management and organization theory (resource dependence theory). This interdisciplinary approach provides a broader perspective in operations management research. Third, through this research, guidelines are provided for not only powerful firms but also the weak ones on how to improve their performance through developing different types of supply chain relationships and integrating their operations strategies with their supply chain members.

Supply Chain Integration (SCI) has been found by previous research to be correlated withincreased business performance. However, the rate of implementation of the concept isdependent on the industry, implying that this is determined by industry-specific factors. Thepurpose of this study was therefore to investigate how the Swedish wooden house industryapproaches SCI, and thereby identify industry-specific factors that influence integration. Toaccomplish this, an exploratory multiple case study was conducted, in which Swedish woodenhouse manufacturers and associated actors were interviewed. The study confirmed that thewooden house industry is affected by factors limiting SCI implementation, and that companiesin the industry are not integrated to any significant degree. Further, two industry-specificfactors were discovered to be inhibiting integration. First, the degree of product customizationgranted to customers by house manufacturers determines how much control over procurementdecisions is retained, and thereby potential for integration with suppliers. Customer orientationwas found to inhibit SCI in the wooden house industry due to its effect on productcustomization, in spite of previous literature classifying it as a driver. Secondly, thefragmentation of the contractor market was found to force house manufacturers to deal with amultitude of small actors to perform the on-site construction function, limiting investments inrelationships for any one given actor. The study also found that levels of internal integrationwas generally not at a level at which the benefits of SCI can be fully realized.

Thesis (MPAM.) -- University of Limpopo, 2021 / The South African government introduced the Supply Chain Management Framework in 2003, with a vision of creating a seamless system intended to play a critical role in service delivery to communities while achieving the objectives of cost-effectiveness, fairness, equity, transparency and ethics. This study focused on the impact of Supply Chain Management Policy Implementation in the Limpopo Office of the Premier. Qualitative data were collected, primarily in the form of semi-structured interviews using an interview schedule consisting of both closed and open-ended questions with the Chief Financial Officer, Director (SCM), Deputy Directors (Demand, Acquisition, Logistics and Inventory, Asset and Transport Management) and SCM nineteen SCM practitioners. These officials provided sufficient information concerning the effect of Supply Chain Management policy implementation in the Limpopo Office of the Premier. The study established that failure to implement SCM policy hurts service delivery. The effective implementation results in improved service delivery, whereas poor implementation results in the poor quality of service to the public, fraud and corruption, irregular and fruitless expenditures as well as negative departmental image.

Modern supply chains are often characterized by carefully planned and interconnected processes that seamlessly integrate with each other. Supply chains are often efficientduring a stable business environment but are highly vulnerable to risks and disruptions.When any link in the supply chain is negatively impacted, challenges can arise for all the companies in the supply chain. This has expanded the goal of supply chain management tocreate resilient supply chains that incorporate event readiness, provide efficient and effective responses, and can return to their original state after a disruption. During Covid19 businesses all over the world were affected and the pandemic led to disruptions in supply chains. Small and medium-sized enterprises (SME) were especially affected because of lack in financial resources, technical capabilities, and low resilience. SMEs play a crucial role in the global economy but there is still a limited amount of research onhow SME can achieve resilience in their supply chains. Furthermore, there is a lack ofresearch comparing changes in supply chain resilience before and after Covid-19. To address this research gap, the purpose of the study is to explore how Covid-19 affected the supply chain resilience in Swedish SMEs. To understand the effects of Covid-19 we will examine supply chain resilience in SMEs before, during and after the pandemic. To address our purpose, we have conducted a qualitative study using an inductive approach and exploratory research design. We collected primary data from semi-structuredinterviews with nine managers from manufacturing Swedish SMEs. The data gathered through the interviews was analysed by using a thematic analysis that led us to our findings. Our findings revealed that prior to the pandemic, risk awareness was not a top priority for the respondent companies. However, Covid-19 exposed vulnerabilities in their supply chains highlighting the importance of both proactive and reactive strategies. Swedish SMEs experienced various challenges including transportation problems, component shortages and fluctuations in customer demand. Tomeet these challenges, the companies showed flexibility by changes in product assemblingand increased inventory levels during Covid-19. Furthermore, strengthened communication with suppliers and customers played a significant role in increasing supply chain visibility and collaboration. As a result of the Covid-19 disruption, our study show that the responding companies have become more aware of risks and incorporated practices into in their operations as adoption of alternative suppliers, optimization of safety stocks and improved communication. Overall, the findings suggested that the pandemic prompted the respondent SMEs to strengthen their supply chain resilience. By learning from the impact of the crisis, these companies have become more resilient and created supply chains that can better withstand future disruptions. This study contributes to understanding how supply chain resilience in Swedish SMEs was affected by Covid-19 and how SMEs can enhance their supply chain resilience

Background: COVID-19 pandemic had a vast and significant impact on today’s societies, in terms of economic, societal, and even environmental aspects. Global supply chains had to adapt to this changing landscape rapidly. Both supply and demand witnessed repercussions. The effects of COVID-19 on supply chains have been evident, with lockdowns and inconsistent supply and demand causing issues for international trade. Indeed, many companies were constrained to stop supply and production and could not deliver their goods.   Purpose: This study aims to investigate the impact of the COVID-19 pandemic on transportation operations in logistics companies, more precisely by using the case of DHL to emphasize our study. The purpose is to analyze the impacts that the crisis had on Logistics operations, by focusing on DHL and the way the company was impacted, understood and overcame the disruption. This study will take into consideration economic, social and environmental aspects.   Method: The research will be based on a case study and empirical evidence to provide a comprehensive understanding of the impact of the COVID-19 pandemic on DHL’s supply chain operations and more precisely on logistics and transport. We used the Triple Bottom Line approach to measure the economic, social, and environmental performances of DHL. Further, the analysis used in this paper is an inductive thematic analysis, to emphasize the findings and provide a better accuracy to our results.   Results: The study aims to identify the effects that COVID-19 had on logistics operations, and it more particularly seeks to highlight the strategies used by logistics companies that could be useful for other companies that need to build more resilient and flexible supply chains in the face of future disruptions. The findings of this thesis will be valuable to businesses, supply chain managers, and policymakers seeking to understand the role of improving their supply chains. Thanks to the interviews that were conducted, the findings highlighted several strategies that have been implemented by companies, such as the acceleration in automation, efforts put towards employee conditions, and restructuring companies.

M. Tech. (Department of Logistics Management, Faculty of Management Sciences), Vaal University of Technology. / The successful implementation of supply chain strategy has received significant attention among literature and practitioners, due to its importance in contributing to company success. The success of the FMCG industry in South Africa has also been attributed to the effectiveness of its supply chain strategies. However, the industry faces numerous challenges such as miscellaneous risks that include the potential loss of key suppliers, innovation, and risk, planning and forecasting, power outages in manufacturing plants and the fact that only a limited amount of safety stock can be held to offset uncertainty. It also faces difficulties in improving supply and demand, which further extends to transforming supplier relationships into effective collaboration. Thus, this study applies supply chain strategy to the FMCG industry in South Africa, and seeks to establish the connection between supply chain strategies, flexibility (SCF) and performance (SCP) in the sector. The methodological procedure in this study was guided by the positivist paradigm which is embedded in quantitative and causal designs. A sample of 228 respondents purposively drawn from the FMCG industry in Gauteng province participated in the survey. The collected data were tested using descriptive and inferential statistics. The specific statistical techniques applied included the analysis of frequency distributions, percentages, mean scores, exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and hypotheses tests using partial least squares-based structural equation modelling. The empirical results showed that two supply chain strategies, namely, organisational learning and supplier integration contribute significantly to SCF. In turn, SCF contributes positively to financial and non-financial performance. Supply chain strategies such as innovation, risk management and customer orientation were statistically insignificant in predicting SCP. The study is significant because it identifies that supply chain strategies are important in developing flexible FMCG supply chains in South Africa. It also confirms the importance of SCF in improving the performance of FMCG supply chains. The study may therefore be used as a reference source for information on the improvement of SCP in FMCG supply chains.

In the era of Industry 4.0, conventional supply chains are undergoing a transformation into digital supply chains with the wide application of digital technologies such as big data, cloud computing, and Internet of Things. A digital supply chain is an intelligent and value-driven process that has superior features such as speed, flexibility, transparency, and real-time inventory monitoring and management. This concept is further included in the framework of Supply Chain 4.0, which emphasizes the connection between supply chain and Industry 4.0. In this context, data analytics for supply chain management presents a promising research opportunity. This thesis aims to investigate the use of data analytics in supply chain decision-making, including modelling, monitoring, and optimization. First, this thesis investigates supply chain monitoring (SCMo) using data analytics. The goal of SCMo is to raise an alarm when abnormal supply chain events occur and identify the potential reason. We propose a framework of SCMo based on a data-driven method, principal component analysis (PCA). Within this framework, supply chain data such as inventory levels and customer demand are collected, and the normal operating conditions of a supply chain are characterized using PCA. Fault detection and diagnosis are implemented by examining the monitoring statistics and variable contributions. A supply chain simulation model is developed to carry out the case studies. The results show that dynamic PCA (DPCA) successfully detected abnormal behaviour of the supply chain, such as transportation delay, low production rate, and supply shortage. Moreover, the contribution plot is shown to be effective in interpreting the abnormality and identify the fault-related variables. The method of using data-driven methods for SCMo is named data-driven SCMo in this work. Then, a further investigation of data-driven SCMo based on another statistical process monitoring method, canonical variate analysis (CVA), is conducted. CVA utilizes the state-space model of a system and determines the canonical states by maximizing the correlation between the combination of past system outputs and inputs and the combination of future outputs. A state-space model of supply chain is developed, which forms the basis of applying CVA to detect supply chain faults. The performance of CVA and PCA are assessed and compared in terms of dimensionality reduction, false alarm rate, missed detection rate, and detection delay. Case studies show that CVA identifies a smaller system order than PCA and achieves comparable performance to PCA in a lower-dimensional latent space. Next, we investigate data-driven supply chain control under uncertainty with risk taken into account. The method under investigation is reinforcement learning (RL). Within the RL framework, an agent learns an optimal policy that maps the state to action during the process of interacting with the non-deterministic environment, such that a numerical reward is maximized. The current literature regarding supply chain control focuses on conventional RL that maximizes the expected return. However, this may be not the best option for risk-averse decision makers. In this work, we explore the use of safe RL, which takes into account the concept of risk in the learning process. Two safe RL algorithms, Q-hat-learning and Beta-pessimistic Q-learning, are investigated. Case studies are carried out based on the supply chain simulator developed using agent-based modelling. Results show that Q-learning has the best performance under normal scenarios, while safe RL algorithms perform better under abnormal scenarios and are more robust to changes in the environment. Moreover, we find that the benefits of safe RL are more pronounced in a closed-loop supply chain. Finally, we investigate real-time supply chain optimization. The operational optimization problems for supply chains of realistic size are often large and complex, and solving them in real time can be challenging. This work aims to address the problem by using a deep learning-based model predictive control (MPC) technique. The MPC problem for supply chain operation is formulated based on the state space model of a supply chain, and the optimal state-input pairs are precomputed in the offline phase. Then, a deep neural network is built to map the state to input, which is then used in the online phase to reduce solution time. We propose an approach to implement the deep learning-based MPC method when there are delayed terms in the system, and a heuristic approach to feasibility recovery for mixed-integer MPC, with binary decision variables taken into account. Case studies show that compared with solving the nominal MPC problem online, deep learning-based MPC can provide near-optimal solution at a lower computational cost. / Thesis / Doctor of Philosophy (PhD)

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