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    An investigation of forecasting methods for a purchasing decision support system. A real-world case study of modelling, forecasting and decision support for purchasing decisions in the rental industry.

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    MPhil_Thesis_RYang_2012.pdf (1.333Mb)
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    Publication date
    2013-11-15
    Author
    Yang, Ruohui
    Supervisor
    Dahal, Keshav P.
    Cowling, Peter I.
    Keyword
    Forecasting
    Decision support system
    Rental industry
    Rights
    Creative Commons License
    The University of Bradford theses are licenced under a Creative Commons Licence.
    Institution
    University of Bradford
    Department
    Department of Computing
    Awarded
    2012
    
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    Abstract
    This research designs a purchasing decision support system (PDSS) to assist real-world decision makings on whether to purchase or to sub-hire for equipment shortfalls problem, and to avoid shortage loss for rental business. Research methodology includes an extensive literature review on decision support systems, rental industry, and forecasting methods. A case study was conducted in a rental company to learn the real world problem and to develop the research topics. A data converter is developed to recover the missing data and transform data sets to the accumulative usage data for the forecasting model. Simulations on a number of forecasting methods was carried out to select the best method for the research data based on the lowest forecasting errors. A hybrid forecasting approach is proposed by adding company revenue data as a parameter, in addition to the selected regression model to further reduce the forecasting error. Using the forecasted equipment usage, a two stage PDSS model was constructed and integrated to the forecasting model and data converter. This research fills the gap between decision support system and rental industry. The PDSS now assists the rental company on equipments buy or hire decisions. A hybrid forecasting method has been introduced to improve the forecasting accuracy significantly. A dada converter is designed to efficiently resolve data missing and data format problems, which is very common in real world.
    URI
    http://hdl.handle.net/10454/5664
    Type
    Thesis
    Qualification name
    MPhil
    Collections
    Theses

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