Browsing by Subject "Supply Chain Management"

Now showing 1 - 5 of 5
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    Essays on the Role of Network Structure in Operational Performance
    (2019-06) Chen, Kedong
    Research on supply chain networks is an important emerging field. A network perspective is essential because a supply chain is more of a network of organizations involved in various stages of manufacturing and product distribution, than independent firms or simple linear chains. In today's volatile world of interdependence and connectivity among firms and facilities, supply chain management must go beyond single organizations and embrace a holistic view of entire networks. Managers who fail to take into account firms' or facilities' relationships with respect to the rest of the network may produce biased performance evaluations and ineffective improvement strategies. In my dissertation, I investigate the effect of network structure on firms' operational performance. The dissertation consists of three inter-related essays. The first essay explores how a warehouse's inventory efficiency is affected by its structural position in the network. The second essay prescribes optimal strategies to invest resilience resources in the supply chain network against supply shocks. The third essay clarifies the learning behavior of a supply network that improves resilience through its suppliers' disruptions. The dissertation takes a multi-method approach by utilizing data analytics, stochastic optimization, agent-based simulation, multi-level analysis, etc. The dissertation is motivated by and grounded in real supply chains. The network data and the operational context are related to world-renowned manufacturing and/or logistics companies. This dissertation is informed by business practice and difficulties. Its prescriptions and implications will, in turn, inform organizations.
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    Health care supply chain design for emerging economies.
    (2010-08) Kohnke, Emily Jane
    This dissertation research is motivated by the global mismatch in the supply and demand of quality health care for underserved communities. To begin addressing the identified need for additional health care services in many communities, this dissertation unfolds a design for the health care supply chain. This design is based on the coordination constructs of access, awareness and affordability and will advance our understanding of how to increase the quality and volume of care in underserved communities by connecting the development of care to the delivery of care. The dissertation is comprised of three studies that are designed to: (i) uncover the nature, measurement and relationships between the three mechanisms (affordability, awareness and access) and propose an integrative framework to inform supply chain design for delivering quality health care to underserved communities; (ii) empirically analyze the relationships in the proposed framework and, (iii) extend the framework by examining inter-organizational relationships and roles between partners in the health care supply chain and how they influence the delivery of care. This research was conducted in collaboration with Children's HeartLink, a medical non-profit organization which partners with health care organizations in developing countries around the globe to provide health care services for individuals suffering from congenital heart conditions. The research setting for this study was the First Hospital of Lanzhou University, located in the Gansu province of China.
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    Impact of External Mechanisms on Energy Efficiency Investments at Small-and-medium-sized Suppliers
    (2018-02) Nguyen, Jason Quang
    The manufacturing sector is the world’s largest energy consumer, responsible for more than one third of the total final energy consumption of the global economy, and is com- prised of many small and medium-sized manufacturers (SMM). Governments, large cor- porations and third-party agencies have dedicated tremendous resources to regulatory policies and incentive mechanisms to encourage SMMs to invest in Energy Efficiency (EE) improvements. However, significant EE improvement opportunities at SMMs re- main unimplemented. In order to fully capitalize these potentials, it is thus essential to develop a better understanding of the investment decisions faced by SMMs. Previ- ous research has mainly considered EE investment decisions by an isolated single firm. However, for SMMs, it is also important to account for the potential influence that sup- ply chain interactions with large, powerful buyers and competing suppliers may have on such investment decisions. In the three studies of this research, we examine the impact of mechanisms governed by regulators, buyers and third-party agencies on the EE investment decisions of SMMs, taking into consideration supply chain interactions with both their buyers and competing suppliers. In the first study, we investigate when it is beneficial for a buyer to offer EE instru- ments, including assessment assistance and procurement commitment, and how these interact with third-party assessment assistance to impact the supplier’s EE investment level. We find that assessment assistance helps reduce the EE gap but procurement commitment is required to eliminate it. However, the buyer offers procurement com- mitment only when the alternate supplier is sufficiently expensive. Not surprisingly, third-party assessment assistance is important for unlocking EE improvement when the assessment cost is high. Nevertheless, when the costs of both the assessment and the al- ternate supplier are moderate, the addition of third-party assistance can actually harm EE investment by deterring the buyer from offering her own instruments. Energy mar- ket characteristics influence these outcomes in several ways. We find that an increase in the volatility or cross period correlation of energy prices reduces the buyer’s incentive to offer both assessment assistance and procurement commitment, leading to lower EE investment. However, an increase in the expected energy price generally increases the buyer’s incentive to offer both instruments, expanding the regions where the EE gap is reduced or even eliminated. In the second study, we examine the impact of carbon pricing on social welfare, taking into consideration the negative externality of energy and the positive externality generated by domestic manufacturing. We show that in the absence of external juris- diction competition, the first-best social welfare is achieved by setting the carbon price at the negative externality of energy. These results continue to hold in the presence of external jurisdiction competition, but only when the external costs of energy are suffi- ciently low. Setting a carbon price at the negative externality of energy when it is high is no longer optimal and the first-best outcome social welfare is not achieved. When so- cial welfare losses happen, neither of the two common remedies, carbon price relief and EE investment subsidy, can singlehandedly restore the losses. We show that a balanced combination of both remedies is required to achieve the first-best social welfare level. In the third study, we shift the focus to settings where a buyer is not aware of the supplier’s EE improvement opportunities. For example, western buyers like Wal-mart or Target are typically not well informed about the EE improvement potentials of their Chinese suppliers and need engagement from third-party agencies to learn the informa- tion. We analyze the conditions under which a third-party agency should work only with a supplier or also engage the buyer to achieve the higher EE investment from the supplier, as well as the impact of the third-party agency’s tactic on the supplier’s prof- itability. We find that buyer engagement can either help or hinder the supplier’s EE investment level, depending on the cost of the buyer’s alternative supply source. We also find that the potential benefit of engaging the buyer on the supplier’s EE invest- ment is reduced as the alternate supply source becomes more expensive. Regarding the impact on the supplier’s profitability, we show that buyer engagement always reduces the supplier’s profit as the engaged buyer squeezes all the associated EE cost savings. We further analyze the impact of energy market uncertainty on these directional re- sults. In particular, an increase in the volatility or cross period correlation of energy prices reduces the chance that third-party agency’s engagement with the buyer posi- tively influences the supplier’s EE investment level. However, when an improvement in EE investment does occur, its magnitude is larger for higher volatility or cross period correlation of energy prices. A higher volatility or cross period correlation of energy prices also reduces the detrimental impact of buyer engagement on the supplier’s profit. Our findings provide insights for policy makers interested in increasing EE investment and reducing the energy efficiency gap that plagues many supply chains.
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    Managing Human Capital in Supply Chains: Perspectives on Technological Advancement and Social Responsibility
    (2016-07) Liu, Xiaojin
    Managing human capital and technology is critical for sustainable and responsible operations in global supply chains. Facing new technologies and the necessity for the appropriate accompanying skills in global supply chains, organizations must manage well both the technology and the human capital, and must do so both internally and externally. This dissertation focuses on integrating technology and human capital in supply chains, with special emphasis on improving long-term operational sustainability and social responsibility. The investigation unfolds in four arenas: 1) technology development; 2) human capital; 3) work design; and 4) working conditions. Following this conceptual structure, I employ statistics, econometrics, and data analytics to analyze complex data sets from the health care and garment manufacturing industries, providing empirical evidence relating to the four areas and identifying current challenges for maintaining sustainable development in the supply chains of these industries. This dissertation is organized into three essays. Focusing on the health care industry, Essay I investigates how workforce capabilities shape the implementation effectiveness of Clinical Decision Support (CDS) systems, one critical component of an Electronic Health Record (EHR) system. Under the knowledge management framework of technology, the dissertation develops a model on the integration between explicit knowledge embedded in the technology system and tacit knowledge from workforce capabilities, and their impact on care delivery effectiveness in clinical organizations. The results show that more extensive CDS system implementation can enhance care delivery effectiveness, while low levels of related workforce capabilities have a significantly negative impact. The findings on the interaction relationships between the two types of knowledge vary across the types of workforce capabilities. Specifically, trainer needs (i.e., low workforce capability in training on information technology use) negatively moderate the relationship between CDS and care delivery effectiveness, suggesting that this type of workforce capability can strengthen the effectiveness of CDS. However, both informatics needs (i.e., low workforce capability in health informatics skills), and EHR/IT staff needs (i.e., low workforce capability in preparing and maintaining EHR/IT systems) have positive moderating effects. Counter-intuitively, we find that these two types of workforce capability, in fact, dampen the effectiveness of CDS. These findings indicate that a complex relationship exists for the integration of explicit and tacit knowledge related to technology implementation. Essay II investigates how geographical, socioeconomic, organizational, and technological contexts affect telemedicine use and its effectiveness in the health care industry. The dissertation employs the technology-organization-environment (TOE) framework as the theoretical underpinning to examine antecedents and consequences of telemedicine adoption in clinics. Combining data from multiple sources relevant to clinical organizations, our empirical analysis indicates that differences in geographic location characteristics and organizational barriers have significant impact on telemedicine adoption. Specifically, rural and low poverty regions are positively associated with telemedicine adoption, while cost and low local demand are barriers. We further examine the implication of telemedicine adoption on organizational outcomes. The results suggest that telemedicine adoption is related to the effectiveness of care delivery in clinics. More extensive use of telemedicine is associated with greater care delivery effectiveness. However, examining the interaction among technologies, we find that telemedicine reduces the effectiveness of CDS systems – i.e., the benefit of telemedicine is greater in clinics with a lower level of CDS adoption. Essay III uses the Bangladesh ready-made garment (RMG) industry to investigate how buyers in the global garment industry coordinate and collaborate to improve working conditions in supplier factories in Bangladesh. In line with the literature on supply chain trust and risk management, the dissertation explores the implications of three types of working condition risks on buyer sourcing strategy. We characterize these risks as structural risk, fire risk, and electrical risk. We collect data from two large consortiums: North American retailers comprise the Alliance for Bangladesh Worker Safety (Alliance), and European retailers comprise the Accord on Fire and Building Safety in Bangladesh (Accord). We examine the implications of each type of risk for buyer trust and buyer sourcing strategy. The empirical results support the contention that buyers are sensitive to working condition risks in a supplier factory. When working condition risks in a supplier factory increase, buyer trust in the factory decreases. Our analysis, however, shows that this relationship varies with the type of the risk. Specifically, among the three types of studied risks, fire and electrical risks are associated with decreased buyer trust, while structural risk has a marginal negative effect. Further, the negative relationship between working condition risks and buyer trust is contingent on the size of the supplier factory. The results indicate that for a given level of risk, buyers have greater trust in larger factories compared to smaller factories. This may imply that buyers expect large factories to share the responsibility and take corrective actions toward improving working conditions. In conclusion, the dissertation provides new, theoretically grounded empirical insights into managing human capital and technology for sustainable and responsible operations in global supply chains. It echoes the call from the extant literature that organizations are expected to have important and integral social, psychological, and ecological responsibilities. My research contributes to the development of supply chain management theory and applies these theories to real world industrial practice. The dissertation concludes with a discussion of the key findings from each of the three essays. Limitations and directions for future research are also identified.
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    Supply Chain Pricing, Risk-Return Analysis, and Online Resource Allocation
    (2018-07) Chen, Xiao
    This dissertation studies a few models in two categories of operations management. The first part of the dissertation focuses on supply chain management related topics. We consider a supply chain model with one supplier and one retailer who acts as a newsvendor. The first model in this dissertation focuses on the supplier and the retailer's optimal policies in a multi-period newsvendor model. We derive the optimal pricing and ordering policies for demand with Increasing Generalized Failure Rate (IGFR) property and obtain comparative statics for the optimal prices. We discover that under certain conditions of the demand distribution, the supplier's optimal prices are increasing in time. Moreover, the price increments are increasing in the backorder cost and the optimal prices are increasing in the backorder cost as well. We also perform a distribution-free analysis of the multi-period newsvendor model and provide the structure of the worst-case distribution. In addition to the pricing and ordering decisions, we also analyze the risk-return trade-off in single-period newsvendor models using the mean-variance approach. We discuss the classic newsvendor model which uses the wholesale-price contract and two variations of the model, a spot market model and a revenue-sharing contract model. We derive the risk-return curve for the retailer and the corresponding distribution in closed-form for a two-point distribution and a three-point distribution in the classic model. When the demand follows a multi-point distribution or a continuous distribution, we provide a linear program to compute the risk-return curves and show the curves' upper bounds. An approximation algorithm is introduced to efficiently calculate the risk-return curve in the continuous distribution models. Introducing some variation to the basic model, we consider a supply chain setting with a spot market where unsatisfied demand can purchase from the supplier at the market price. The supplier's decisions are the wholesale price and the buffer inventory for the spot market. We derive the supplier's optimal decisions and study the supplier's risk-return trade-off under uniform and exponential distributions. Another problem that we consider is the risk-return analysis under a revenue-sharing model. We derive the supplier's optimal pricing policy and characterize the effect of φ on both the supplier and the retailer's decisions and risks. Numerical experiments are conducted to demonstrate the results. The second part of this thesis concerns resource allocation in an online setting, specifically, the online matching problems. Online matching problems are used as the backstage algorithm by search engines to match advertisements with each search. We focus on the online matching problem with concave return functions and a random permutation model. In this dissertation, we introduce two online learning algorithms to solve the associated matching problem. The main idea is to utilize the observed data in the allocation process and project it into the future. We begin with the one-time learning algorithm that only uses the data to compute an allocation rule once. This algorithm achieves near-optimal performance when input data satisfy certain conditions. To further improve the performance, we introduce a dynamic learning algorithm which updates the allocation rule at a geometric pace, at time εn, 2εn, 4εn and so on. This algorithm achieves near-optimal performance with fewer restrictions on the input data conditions. We compare the performance of the one-time learning algorithm, the dynamic learning algorithm, and the greedy algorithm in numerical experiments.