by Tang Man-kwong, Pau Wai-man. / Bibliograph: leaves 119-120 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1983

退貨産品在國民經濟中發揮重要作用，近些年來在工業界和學術界引起廣泛關注。由于受到退貨産品的巨大經濟潛能驅動以及迫于相關法律的強大壓力，企業開始積極整合現有供應鏈流程和産品退貨流程，但是在這個過程中生産計劃和庫存管理運營遇到了前所未有的挑戰。本論文分析了可修護單壹産品的多周期庫存系統管理問題，其中該庫存系統包含隨機的新産品需求和産品保修需求。所探討的系統還包含了以下特征： i. 産品供應源的完美替換。新訂購産品和維修後的退貨産品並無二異。ii. 需求優先級。在單個周期內，新産品需求優先級高于保修需求。iii. 新産品訂購和退貨産品維修特性。兩項流程交付時間都假設爲零。在每個周期中，維修退貨産品過程涉及且考慮維修損失。我們探尋到了所考察系統的最優庫存補貨和處理控制策略。 / Product returns play an important role in the economy and have attracted more and more attention both from industry and academia in the past few years. Motivated by enormous economic potential of and dramatic legislation pressure on product returns, companies today actively integrate product-return processes with their existing forward supply-chain processes, but must confront the considerable challenge of production planning and inventory control problems compared to traditional problems without returns and repairs. / This thesis analyzes a multi-period single-item repairable inventory system with stochastic new and warranty demands. The system investigated has the following features: i. perfect substitution of supply sources. Newly-procured items are indistinguishable from items repaired from repairable warranty returns. ii. demand priority. New demand has higher priority than warranty demand within a period and thus needs to be satisfied first. iii. properties on the processes of purchasing and repairing. We assume zero lead-time for both processes. Further we capture the "repair loss" in repairing returns in each period. We identify the optimal inventory replenishment and disposal policies for the investigated system. / Detailed summary in vernacular field only. / Lin, Yizhong. / Thesis (Ph.D.) Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 80-87). / Abstracts also in Chinese.

M.Com. (Informatics) / This study gives background information on technology like Artificial Intelligence (AI) and Expert Systems (ES). The application realm of this technology is the manufacturing environment. The strategies that are addressed are, Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM), Manufacturing Resource Planning (MRP II) and Computer integrated Manufacturing (CIM). We proposel further process or step namely: Inter Organlzational Information Sharing (1015). CIM integrates all internal information sources. lOIS creates standard and secure interfaces so that the organization and other organizations especially suppliers, manufacturers and distributors can share predefined information. The sharing of this information can help make forecasting more accurate and help the organization espond faster to the consumer's ever changing needs. Due to the fact that inventory plays such an important role in the manufacturing process, a software program was developed to aid the managers in small to medium sized organizations to reduce inventory.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Title also in Chinese. / Summary in Chinese. / Thesis (M.B.A.)--Chinese University of Hong Kong. / Bibliography: leaves 94-95.

Thesis (MTech (Business Information Systems))--Cape Peninsula University of Technology, 2018. / Enterprise resource planning (ERP) systems have a good reputation for automation and strength in the integration of business processes (BPs), therefore more and more organisations are adopting ERP systems. The City of Cape Town (CCT), a metropolitan municipality, is one of the organisations that have adopted an ERP system. CCT provides a wide range of services to residents and citizens in the Western Cape Province of South Africa. The ERP system at CCT manages the Supply Chain Management (SCM) processes, among others, to ensure good service delivery. ERP systems are able to provide statistical reports on stock-outs; however, the information provided is insufficient to link a specific stock-out to its causes within the SCM process at CCT. This makes it difficult to select the most appropriate solution to minimise the causes and number of stock-outs. The study aims to explore the possible reduction of the causes of stock-outs at CCT stores, therefore the study has adopted a qualitative research methodology with a case study research strategy. It uses subjectivism to understand the truth, with an inductive approach applied to five different interviewee groups, namely the ERP Support Department consultants, the Procurement Department, the Inventory and Stores Management Department, the Master Data Maintenance Department, and the reservation creators. A purposive sampling method is used because the study targets knowledge and experience on the subject from the interviewees in the investigation of the problem. The case is the SCM division at CCT, the unit of analysis is the CCT stores, and the unit of observation is the employees who work for the SCM division of CCT. All ethical procedures and policies of the Cape Peninsula University of Technology (CPUT) are adhered to. For the data collection, in depth semi-structured questions have been developed, and the data were analysed using thematic analysis. The leading causes of stock-outs were discovered by analysing the themes. The results revealed the themes, in descending order according to the causes of stock-outs, as follows: procedures, service delivery, suppliers, stock-outs, human resources, and systems. All the objectives were achieved, thereby answering the research questions.

The objective of this research project was to identify the limitations associated with schedule-based Material Requirements Planning (SBMRP) and to present a knowledge-based expert system (KBES) approach to solve these problems. In SBMRP, the basic strategy is to use backward or forward scheduling based on an arbitrary dispatching rule, such as First-In First-Out. One of the SBMRP weak points is that it does not use such job information as slack times, due dates, and processing times, information which otherwise is important to good scheduling decisions. In addition, the backward scheduling method produces a better schedule than the forward scheduling method in terms of holding and late costs. Dependent upon job characteristics, this may or may not be true and should be tested. This study focused on the means to overcome these two weak points by the use of a KBES. Heuristic rules were developed through an experiment-based knowledge acquisition process to generate better schedules, rather than relying solely upon forward or backward scheduling. Scheduling performance was measured, based on the minimization of the sums of holding and late costs. Due to complexities of the problem, heuristic methods were used rather than analytic methods. In particular, five loading rules were selected, based upon their close relationship to selected job characteristics, including processing times and due dates. Combined loading methods (CLMs) were developed to obtain better performance, derived by combining the two existing SBMRP scheduling strategies with five loading heuristic rules. This resulted in the generation of 10 CLMs for further evaluation. Since this study proposed a new approach, an expert human scheduler was not available. To overcome this problem, knowledge acqusition through computer experimentation (KACE) was utilized, based upon an architecture of five components: job generator, scheduler, evaluator, rule generator (an extended version of ID3), and the KBES. The first three components were used to generate a large number of examples required by the rule generator to derive knowledge. This derived knowledge was incorporated into the KBES. Experimental results indicated that the KBES outperformed the two existing SBMRP methods. Based on sensitivity analysis, the KBES exhibited robust performance with regard to every job parameter except number of parts. As the number of parts was increased, KBES performance was subject to degradation since the possibility of interactions or conflicts between parts tended to increase, resulting in shifting the threshold ratio of total available time to total processing time. Thus, it is strongly recommended that a new KBES capable of accommodating 30 parts or more should be developed using the KACE method. / Graduation date: 1991

The demand from modern day customers for quality products, supplied in any quantity and within a short lead-time, forces organisations to stock the correct amount of inventory in the correct locations in its supply chain. Establishing the correct inventory levels within an organisation’s supply chain is complicated by the various stochastic processes occurring in a supply chain. The thesis is aimed at the development of a generic Just-In-Time (JIT) supply chain optimisation software tool, whereby the correct inventory levels for an organisation can be determined. These inventory levels will ensure that the organisation will achieve a predefined customer service level at the minimum cost to the company. The tool was developed and satisfactory results were obtained using the Harmony Search Algorithm (HSA) for optimising the inventory levels.

Thesis. 1979. Ph.D.--Massachusetts Institute of Technology. Alfred P. Sloan School of Management. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY / Bibliography: p. 336-338. / Ph.D.

Supply chain management is one of the fundamental topics in the field of operations research, and a vast literature exists on the subject. Many recent developments in the field are rapidly narrowing the gap between the systems handled in the literature and the real-life problems companies need to solve on a day-to-day basis. However, there are certain features often observed in real-world systems that elude even these most recent developments. In this thesis, we consider a number of these features, and propose some new heuristics together with methodologies to evaluate their performance. In Chapter 2, we consider a general two-echelon distribution system consisting of a depot and multiple sales outlets which face random demands for a given item. The replenishment process consists of two stages: the depot procures the item from an outside supplier, while the retailers' inventories are replenished by shipments from the depot. Both of the replenishment stages are associated with a given facility-specific leadtime. The depot as well as the retailers face a limited inventory capacity. We propose a heuristic for this class of dynamic programming models to obtain an upper bound on optimal costs, together with a new approach to generate lower bounds based on Lagrangian relaxation. We report on an extensive numerical study with close to 14,000 instances which evaluates the accuracy of the lower bound and the optimality gap of the various heuristic policies. Our study reveals that our policy performs exceedingly well almost across the entire parameter spectrum. In Chapter 3, we extend the model above to deal with distribution systems involving several items. In this setting, two interdependencies can arise between items that considerably complicate the problem. First, shared storage capacity at each of the retail outlets results in a trade-off between items; ordering more of one item means less space is available for another. Second, economies of scope can occur in the order costs if several items can be ordered from a single supplier, incurring only one fixed cost. To our knowledge, our approach is the first that has been proposed to handle such complex, multi-echelon, multi-item systems. We propose a heuristic for this class of dynamic programming models, to obtain an upper bound on optimal costs, together with an approach to generate lower bounds. We report on an extensive numerical study with close to 1,200 instances that reveals our heuristic performs excellently across the entire parameter spectrum. In Chapter 4, we consider a periodic-review stochastic inventory control system consisting of a single retailer which faces random demands for a given item, and in which demand forecasts are dynamically updated (for example, new information observed in one period may affect our beliefs about demand distributions in future periods). Replenishment orders are subject to fixed and variable costs. A number of heuristics exist to deal with such systems, but to our knowledge, no general approach exists to find lower bounds on optimal costs therein. We develop a general approach for finding lower bounds on the cost of such systems using an information relaxation. We test our approach in a model with advance demand information, and obtain good lower bounds over a range of problem parameters. Finally, in Appendix A, we begin to tackle the problem of using these methods in real supply chain systems. We were able to obtain data from a luxury goods manufacturer to inspire our study. Unfortunately, the methods we developed in earlier chapters were not directly applicable to these data. Instead, we developed some alternate heuristic methods, and we considered statistical techniques that might be used to obtain the parameters required for these heuristics from the data available.