Show simple item record

FieldValueLanguage
dc.contributor.authorArvan, Meysam
dc.date.accessioned2019-07-12
dc.date.available2019-07-12
dc.date.issued2019-07-12
dc.identifier.urihttp://hdl.handle.net/2123/20712
dc.description.abstractProduct forecasts are critical input into procurement, inventory, marketing decisions etc. The use of human judgement is common in the real-world forecasting practice. Human intervention occurs mainly to incorporate contextual information. The literature suggests that a forecasting support system (FSS) that systematically guides the forecaster in applying judgement can improve forecast accuracy. Guidance is the core component of such an FSS. A behaviourally-informed FSS (BIFSS), as defined in this thesis, is an FSS that aims to provide systematic guidance to inform the judgement of a forecaster. This thesis firstly investigates the impact of promotions on product sales and judgemental forecasts using industry data. Then, a novel conceptual framework for developing a BIFSS is presented based on the literature of decision support systems (DSS) and judgemental forecasting literature. Decisional guidance as a crucial element in this framework is selected for further investigation. A lab experiment is employed to examine the effectiveness of two types of guidance, interval guidance and adaptive guidance. The moderating impact of promotions as a major contributor to the complexity of forecasting (shown by using the industry observations) is also considered in the experiment design. Task complexity also varies by changing the noise level in the time series. The results confirm the effectiveness of guidance types in improving forecast accuracy. However, there was not a significant difference between the guidance types. It was also found that providing multiple guidance types does not necessarily result in more accurate forecasts. My analyses provide evidence that guidance is particularly effective under the most complex task setting. The positive effect of guidance under less complex settings is not significant. Providing multiple guidance types can better help forecasters overcome a higher complexity than only providing one guidance type.en_AU
dc.publisherUniversity of Sydneyen_AU
dc.publisherSydney Business Schoolen_AU
dc.publisherInstitute of Transport and Logistics Studiesen_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectForecasten_AU
dc.subjectproduct demanden_AU
dc.subjectSupport systemsen_AU
dc.titleGUIDED JUDGEMENT FOR DEMAND FORECASTING IN THE PRESENCE OF SALES PROMOTIONSen_AU
dc.typePhD Doctorateen_AU
dc.type.pubtypeDoctor of Philosophy Ph.D.en_AU
dc.description.disclaimerAccess is restricted to staff and students of the University of Sydney . UniKey credentials are required. Non university access may be obtained by visiting the University of Sydney Library.en_AU


Show simple item record

Associated file/s

Associated collections

Show simple item record

There are no previous versions of the item available.