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<p>While the energy field has been primarily dominated by fossil fuels such as coal and oil, there is evidence that renewable energy sources are starting to gain a stronger foothold in the energy market to accommodate growth (Debbage, 2008; Intelligent, 2008; Sterzinger, 2006). This has been the result of greater social concern, as well as tax and other government incentives (Intelligent, 2008; Debbage, 2008). Due to these trends, a growing market opportunity exists for cities and states to increase their renewable energy component production (Intelligent, 2008; Regional, 2013; Debbage, 2008; IPCC, 2014). The primary purpose of this study was to survey existing manufacturers in the Bluegrass Region of Kentucky to obtain information and identify manufacturers who: were currently in the renewable energy market, interested in entering the renewable energy market, or have no interest in entering the renewable energy market. Respondents also addressed potential barriers to the growth of the renewable energy field including workforce development, government policy, and investment capital. A total of 25 companies responded to the survey. Correlation analysis was used and determined that no significant correlation existed between surveyed companies who identified themselves as suppliers of renewable energy components and those companies who were identified as possible suppliers of renewable energy components within the REPP (Renewable Energy Policy Project) standards. This study builds on previous methodology used by Debbage (2008) for North Carolina. </p>

This thesis deals with the problems of design and operation of process supply chains. Process supply chains face many challenges due to volatile market conditions, production and transportation delays, and stiff market competition, which ultimately affect their profitability. Supply chain management (SCM) is the process of managing the flow of materials and information within supply chain to optimize the SC performance. SCM is carried out using a hierarchical decision-making framework, where the top most layer looks at network design and the bottom-most layer deals with scheduling day-to-day activities. In this research, the systems engineering principles are applied to devise an improved methodology for supply chain optimization (SCO). First, we consider the design of supply chain in the presence of demand uncertainty. The representation of network topology plays an important role in deriving the optimal network design. In real practice, the shipping cost for transferring goods from one location to another is determined based on service time and quantity. More importantly, the cost associated with establishing a transportation linkage is relatively small for existing transportation infrastructure and can be changed if beneficial. The flexibility of changing the transportation routes is included in the network topology representation by the explicit inclusion of time limited transportation contract agreements. Further, the customer demand is volatile, and it is very difficult to predict accurately. To handle the demand uncertainty, a two-stage stochastic programming formulation is applied in the SC design approach. Next, we consider the problem of handling uncertainty in SC planning by applying a system engineering control principle, robust model predictive control (MPC). The uncertainty in model parameters (yield) and demand are captured by stochastic programming. In this approach, the planning activities are represented by a hybrid model with decisions governed by logical conditions/rulesets. An MPC based rolling horizon control framework is used to schedule the planning activities, where the SC performance is expressed using a multi-criterion objective comprising customer service and economics. The uncertainty in demand and yield are propagated by two mechanisms - an open-loop approach, and an approximate closed-loop strategy. Finally, we consider the problem of integration of SC planning and scheduling. Due to the use of different time scale models for planning and scheduling, the decision derived from the planning layer may result in infeasibility when those targets are implemented at the scheduling level, which ultimately affects the supply chain efficiency. To address this issue, we model tactical and operational planning activities using an integrated hybrid time modeling approach in which the first few planning periods are formulated using an operational planning model and the remaining time periods are modeled with a tactical planning model. The main rationale for formulating an integrated model is that customer demand forecast becomes less accurate for a future time, therefore making a detailed planning model unnecessary. A key benefit of using a hybrid modeling approach is that it avoids the problem of infeasibility encountered in the hierarchical decision framework, as well as the computational burden associated with the use of a detailed planning model over a long time horizon. We employ an MPC based rolling horizon framework as a tactical decision policy where the integrated model is used to predict the system behavior. / Thesis / Doctor of Philosophy (PhD)

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科技的採用問題，一直是資訊管理領域一個重要的議題。而以往研究，為了解為何科技會無法被採用，在研究上會分析科技導入至採用初期所面臨到的障礙與挑戰，以協助組織排除科技採用的困難。然而，對於科技的採用而言，採用困難的發生，並不只局限於科技的「前採用階段」而已，在科技被採用以後，同樣也會面臨到科技因為不適用所帶來的採用問題。為此，本論文之研究目的，即在於理解科技在「後採用階段」，發生科技採用困難的本質與原因。 在本論文中，作者主要從實務契合度的觀點，來理解供應鏈系統的採用困難。實務契合度的觀點認為，科技具有內嵌「實務」的特性，每一種科技，根據它的目的，都會被植入一些實務，來協助採用者進行組織活動。因此，所謂的科技採用就是將科技所內嵌的實務，轉移到採用者身上。然而，由於採用者處於他們所屬社會/組織系統中，可能也有他們自己一套的實務活動方式，所以科技內嵌實務與採用者實務之間的不契合，就會發生在科技採用的過程中。在本研究中，根據這樣的看法，作者以詮釋性質化研究的方式，分析了一家筆記型電腦公司的供應鏈系統採用問題。從中發現，根據組織結構、產品材料技術與產業網路的條件原因所影響，供應商會和該公司形成出不同類型的採購實務，而供應鏈系統所提供出的「實務」，因為和這些供應商與該公司的採購實務之間，有著全然不同的特性與假設，故導致了系統採用上的困難。本論文相信，這樣的研究成果，將會加深對科技採用問題的理解，並對供應鏈系統的管理，有實務的貢獻。 / Technology adoption has always been a central research issue in the information systems discipline. Despite years of investigative effort, most of past studies primarily focus on the pre-adoption stage, but very few studies examine post-adoption behaviors, post IT application, and consequences of adoption. In this dissertation, unlike the typical studies to analyze adoption difficulties during pre-adoption stage, I focus on the post technology adoption stage and aim to understand why technology adoption may become failures in organizations after effective deployment for a period of time. In this dissertation, I consider an adoption of supply chain system from the practices fit perspective. The practices fit lens argues that organizational practices (such as procurement practices) are embedded in the technology (such as supply chain systems), and the technology adoption could be as a transfer of the practices to the recipient’s adopters. However, since adopters lie in their specific social/organizational context over time, they have gradually developed a variety of practices to deal with organizational matters. In this circumstance, after the technology is adopted, the difficulties of adoption may occur because of the incompatibility between adopters’ practices and technology practices. From this perspective, I apply an interpretive stance in the study to report a case of supply chain system, and to analyze how and why practices misfit may induce the problems of the adoption. The case study reveals that there are different types of procurement practices for an organization to conduct procurement activities with their suppliers. Each type of procurement practices is affected by the organizational structures, product technologies, and industrial network. However, the supply chain system is not to take account of these conditions into the system principles. As a result, the system is considered inapplicable for supporting the procurement activities by adopters. Important implications are provided to enhance the theoretical development of technology adoption. Practical insights are discussed with regard to supply chain management.