Two approaches were used to evaluate multiple-use inventories. The first approach was to analyze inventory data currently being collected. Assuming that all variables being inventoried are necessary in making decisions for multiple-use management, simple correlation, multiple linear regression, and factor analysis techniques were employed. Data from the Jefferson National Forest in Virginia were analyzed but no strong association between variables currently being observed was detected. The second approach was to define the decisions that are to be made and the variables necessary to make these decisions. Once the variables have been defined, sampling intensity must be determined. The decision-maker was not required to define the sampling intensity based on experience or a rule-of thumb, but rather a cost-loss function for multiple inventories and multiple decisions was minimized. The cost of collecting data will increase as sample size increases, while the expected monetary loss that will occur to the decision-maker from making decisions with inventory data will decrease. Minimizing the cost-loss function determines the sampling intensity that will provide the lowest total (cost plus loss) monetary cost to the decision-maker. Methods were developed to determine necessary information for the cost-loss function, and several methods of minimization were evaluated. / Doctor of Philosophy

M. Tech. Business Information Systems / Small and Medium Enterprises (SMEs) are playing a pivotal role in shaping an international economic landscape through job creation and acting as a catalyst in encouraging indigenous-traditional business activities, particularly in rural areas. However, SMEs operate their business with a variety of challenges. For example, the inventory control system is operated manually which results in intensive human errors. The argument driving the study was that a wireless web-based inventory control system could lead to sustenance and competitiveness of rural SMEs. The objective of this study was to; analyse how rural SMEs conduct business and to analyse how manual inventory control system are used, explore issues and challenges experienced which may be affecting the process of conducting business, and lastly to determine how wireless inventory control system could be used appropriately to improve rural SMEs.

This thesis examines the spare parts mission scaling problem within the land environment of the Canadian Forces. A revision was done to the recently proposed Land Automated Scaling System, thus providing a readily implementable version of this model. This revised model determines the kit of spare parts for a first or second line unit to carry in order to maximize the operational availability of the deployed weapons systems, subject to a volume constraint. Bayesian methods and actual part demand data are used to revise the demand distribution to more accurately reflect the distribution of the number of parts required during a mission, taking into account the environmental conditions and usage mode of the equipment. The model is easy to use, requiring readily available and easily accessible input data. In addition, it can be operated on a Base minicomputer, thus allowing it to be used by the ordinance engineering and supply officers at the unit/formation level to produce and revise their parts scales as their situation requires.

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

The thesis discusses the development of Multi-Warehouse Inventory Control System (MWICS), a uniquely designed web application that targets membership based food wholesalers. The main goal of MWICS is to provide a real-time inventory control ability to all warehouses and present them as if it were single warehouse. The program consists of three main components: user account management sub-system, product and purchase management sub-system, and a warehouse inventory management sub-system. User interfaces are constructed primarily in HTML, PHP, and Javascript. MySQL is used to add, access, and process data.

Thesis--Universität St. Gallen, 2005. / Description based on print version record. Includes bibliographical references (p. 192-208).

Thesis (MCIS - Computer and Information Sciences)--AUT University, 2007. / Includes bibliographical references. Also held in print (vii, 94 leaves ; 30 cm.) in the Archive at the City Campus (T 658.514 ZHA).

Dissertation--Universitat St. Gallen, 2005. / Includes bibliographical references (p. 192-208). Also available in print.

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.

Yau Man-Kuen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 222-234). / Abstracts in English and Chinese. / Chapter Chapter 0 --- Introduction --- p.1 / Chapter PART A: --- Logistics in Hong Kong 一 Overview and Prospects / Chapter A.1 --- Study Objectives --- p.3 / Chapter A.2 --- Methodology --- p.4 / Chapter A.3 --- What is Logistics? --- p.4 / Chapter A.3.1 --- Major Trends --- p.6 / Chapter A.4 --- Key Features of the Logistics in Hong Kong & China --- p.8 / Chapter A.4.1 --- China Industry --- p.8 / Chapter A.4.2 --- National Developments in China --- p.13 / Chapter A.4.3 --- Hong Kong Industry --- p.16 / Chapter A.5 --- Growth Trends & Statistics for Hong Kong --- p.25 / Chapter A.6 --- Competitive Analysis for Hong Kong as a Logistics Hub --- p.45 / Chapter A.6.1 --- Current Industry Strengths --- p.45 / Chapter A.6.2 --- Current Industry Weaknesses --- p.46 / Chapter A.6.3 --- Competitiveness Challenges --- p.47 / Chapter A.6.4 --- Future Opportunities --- p.51 / Chapter A.7 --- Changing Conditions and Infrastructure Needs --- p.54 / Chapter A.7.1 --- Trade --- p.54 / Chapter A.7.2 --- Technology --- p.56 / Chapter A.7.3 --- Investment --- p.56 / Chapter A.7.4 --- Human Resources --- p.57 / Chapter A.7.5 --- Government and Regulation --- p.58 / Chapter A.8 --- Recommendations --- p.61 / Chapter A.9 --- Conclusions --- p.64 / Chapter A.10 --- Future Work --- p.65 / Chapter PART B: --- Inventory Management with Advance Ordering / Chapter Chapter B.1 --- Introduction --- p.66 / Chapter B.1.1 --- Overview --- p.66 / Chapter B.1.2 --- Literature Review --- p.69 / Chapter Chapter B.2 --- Model Formulation --- p.72 / Chapter B.2.1 --- Introduction --- p.72 / Chapter B.2.2 --- Mathematical Model --- p.74 / Chapter B.2.3 --- Preliminaries --- p.76 / Chapter B.2.4 --- Table of variables --- p.77 / Chapter Chapter B.3 --- Study of Window Size0 --- p.79 / Chapter B.3.1 --- Introduction --- p.79 / Chapter B.3.2 --- Mathematical Model --- p.79 / Chapter B.3.3 --- Proof of Window Size0 --- p.81 / Chapter Chapter B.4 --- Study of Window Size1 --- p.94 / Chapter B.4.1 --- Introduction --- p.94 / Chapter B.4.2 --- Mathematical Model --- p.95 / Chapter B.4.3 --- Optimal Ordering Policy for Window Size1 --- p.95 / Chapter B.4.4 --- Special Case of Uniformly Distributed Demand --- p.109 / Chapter B.4.5 --- Discussion of Fukuda's Paper --- p.114 / Chapter Chapter B.5 --- Simulation Study of Window Size1 --- p.120 / Chapter B.5.1 --- Simulation Models --- p.120 / Chapter B.5.2 --- Simulation Program Structure --- p.126 / Chapter B.5.3 --- Simulation Numerical Analysis --- p.131 / Chapter Chapter B.6 --- Simulation Study of Window Size K --- p.172 / Chapter B.6.1 --- Simulation Models --- p.172 / Chapter B.6.2 --- Simulation Program Structure --- p.179 / Chapter B.6.3 --- Simulation Numerical Analysis --- p.181 / Chapter Chapter B.7 --- Conclusion and Further Studies --- p.201 / Appendix (PART A) --- p.204 / Appendix (PART B) --- p.208 / Bibliography (PART A) --- p.222 / Bibliography (PART B) --- p.229