Browsing by Subject "Supply Chain Management"
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Item An Effective Implementation of Operational Inventory Management(2010-01-16) Sellamuthu, SivakumarThis Record of Study describes the Doctor of Engineering (DE) internship experience at the Supply Chain Systems Laboratory (SCSL) at Texas A&M University. The objective of the internship was to design and develop automation tools to streamline lab operations related to inventory management projects and during that process adapt and/or extend theoretical inventory models according to real-world business complexity and data integrity problems. A holistic approach to automation was taken to satisfy both short-term and long-term needs subject to organizational constraints. A comprehensive software productivity tool was designed and developed that considerably reduced time and effort spent on non-value adding activities. This resulted in standardizing and streamlining data analysis related activities. Real-world factors that significantly influence the data analysis process were identified and incorporated into model specifications. This helped develop an operational inventory management model that accounted for business complexity and data integrity issues commonly encountered during implementation. Many organizational issues including new business strategies, human resources, administration, and project management were also addressed during the course of the internship.Item Border Crossing Modeling and Analysis: A Non-Stationary Dynamic Reallocation Methodology For Terminating Queueing Systems(2012-10-19) Moya, HiramThe United States international land boundary is a volatile, security intense area. In 2010, the combined trade was $918 billion within North American nations, with 80% transported by commercial trucks. Over 50 million commercial vehicles cross the Texas/Mexico border every year, not including private vehicles and pedestrian traffic, between Brownsville and El Paso, Texas, through one of over 25 major border crossings called "ports of entry" (POE). Recently, securing our southwest border from terrorist interventions, undocumented immigrants, and the illegal flow of drugs and guns has dominated the need to efficiently and effectively process people, goods and traffic. Increasing security and inspection requirements are seriously affecting transit times. Each POE is configured as a multi-commodity, prioritized queueing network which rarely, if ever, operates in steady-state. Therefore, the problem is about finding a balance between a reduction of wait time and its variance, POE operation costs, and the sustainment of a security level. The contribution of the dissertation is three-fold. The first uses queueing theory on the border crossing process to develop a methodology that decreases border wait times without increasing costs or affecting security procedures. The outcome is the development of the Dynamic Reallocation Methodology (DRM). Currently at the POE, inspection stations are fixed and can only inspect one truck type, FAST or Non-FAST program participant. The methodology proposes moveable servers that once a threshold is met, can be switched to service the other type of truck. Particular emphasis is given to inspection (service) times under time-varying arrivals (demands). The second contribution is an analytical model of the POE, to analyze the effects of the DRM. First assuming a Markovian service time, DRM benefits are evaluated. However, field data and other research suggest a general distribution for service time. Therefore, a Coxian k-phased approximation is implemented. The DRM is analyzed under this new baseline using expected number in the system, and cycle times. A variance reduction procedure is also proposed and evaluated under DRM. Results show that queue length and wait time is reduced 10 to 33% depending on load, while increasing FAST wait time by less than three minutes.Item Modified (Q, r) Inventory Control Policy for an Assemble-to-Order Environment(2010-10-12) Seijo, Roberto L.The traditional (Q,r) inventory control model assumes that the date at which the order is entered is the same as the date at which it is requested or expected to be delivered. Hence, the penalty cost is incurred when the customer places the order if inventory is unavailable. This is a reasonable assumption for retail systems and most distribution centers (DC), but not for an assemble-to-order (ATO) environment. In this scenario, there is a delivery time which is usually pre-negotiated and in addition to considering the manufacturing process time and in some cases the outbound transportation time, it also has some safety time built-in. This safety time is defined by the manufacturer and represents information related to when the penalty is incurred. The main objective of this research is to develop a modified (Q,r) policy that incorporates the safety time, and to evaluate this policy in terms of expected inventory cost and expected penalty cost / late orders. The problem is addressed following the heuristic approach discussed by Hadley and Whitin (1963). Two main models are developed based on the following assumptions: 1) early shipments are allowed by the customer, and 2) no early shipments are allowed. The behavior of both models is analyzed mathematically and by means of numerical examples. It is shown that from a manufacturer perspective, the first model is preferred over the traditional (Q,r) model. However, it poses a threat for the long term business relationship with the customer because the service level deteriorates, and for the implications that early shipments have on the customer inventory. The behavior of the second model is strictly related to the problem being addressed. Its merits with respect to the traditional and the "early shipment" model are discussed. This discussion is centered on the coefficient of variation of the lead-time demand, the ratio (IC/pi), and the location of the supplier. A final model which is a hybrid of the previous two shipping policies is developed. The models developed in the course of this research are generalizations of the traditional (Q,r) model.Item Strategies for Competitive Advantage and Supply Chain Management: Synergy Opportunities(2010-10-12) Abdulla, Saeed A.Integrating research from the strategic management and the supply chain management (SCM) literatures promises a fertile area of research that can enrich both areas. In this work, an attempt was made to answer the recent calls for incorporating perspectives from each field into the other. These calls were further encouraged by the new competitive landscape characterized by hypercompetition and network versus network competition. Thus, the field of Strategy, with its emphasis on gaining and sustaining competitive advantage, and SCM, with its emphasis on managing processes spanning organizational boundaries, stand to benefit greatly by this integration. The introduction chapter briefly describes what this research tried to achieve. In the supply chain management literature review chapter, the importance of managing supply chains in this era of network versus network competition is shown and the strategic demand network management (SDNM) concept is presented as an evolution of supply chain management and as a more suitable name reflecting the processes involved. In the third chapter, a selected list of supply chain management practices is presented and explained. The fourth, fifth and sixth chapters will endeavor to carry on three developments. These developments seek to integrate strategy and SCM research in three ways. In the first development, the dynamic capability perspective from the strategy field and the SDNM capability are integrated in order to suggest how demand network management enables dynamic capabilities. On the other hand, dynamic capabilities perspective were used to guide the SDNM practices. In the second development, alliance management capability from the strategy field was integrated with SDNM capability and SDNM practices to show how concepts from both areas can enrich the other. And finally the third development builds on the first two developments to explore how SDNM capability can facilitate strategic entrepreneurship (SE) and SE based boundary decisions.