Inventory management using data mining : forecasting in retail trade

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dc.contributor.advisor Lingras, Pawan
dc.creator Zhang, Peng, M.Sc.
dc.date.accessioned 2011-10-21T14:22:39Z
dc.date.available 2011-10-21T14:22:39Z
dc.date.issued 2010
dc.identifier.other HF5429.15 Z435 2010
dc.identifier.uri http://library2.smu.ca/xmlui/handle/01/23736
dc.description xiii, 181 leaves : ill. ; 29 cm. en_CA
dc.description Includes abstract.
dc.description Includes bibliographical references (leaves 176-181).
dc.description.abstract Inventory management, as an important business issue, plays a significant role in promoting business development. This study aims to apply data mining techniques, such as time series clustering and time series prediction techniques, in inventory management. Based on historical business data sets, time series clustering techniques, such as K-Means and Expectation Maximization are used to categorize inventories into reasonable groups. This study then identifies the most effective prediction technique to accurately predict inventory demands for each group. The traditional statistical evaluation metrics, such as Mean Absolute Percentage Error may not always be good indicators in an inventory management system, where the goal is to have as little inventory as possible without ever running out. The thesis proposes a more appropriated evaluation metric based on cost/benefit analysis of inventory forecasts. Results from a simulation program based on the proposed cost/benefit analysis are compared with statistical metrics. en_CA
dc.language.iso en en_CA
dc.publisher Halifax, N.S. : Saint Mary's University en_CA
dc.subject.lcc HF5429.15
dc.subject.lcsh Data mining
dc.subject.lcsh Retail trade -- Management -- Data processing
dc.subject.lcsh Inventory control -- Data processing
dc.title Inventory management using data mining : forecasting in retail trade en_CA
dc.type Text en_CA
thesis.degree.name Master of Science in Applied Science
thesis.degree.level Masters
thesis.degree.discipline Mathematics and Computing Science
thesis.degree.grantor Saint Mary's University (Halifax, N.S.)


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