Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting
Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the g...
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sg-smu-ink.sis_research-24362018-07-13T02:53:53Z Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting CHOY, Murphy CHEONG, Michelle Lee Fong Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand forecasting techniques may fail given the unusual characteristics of the function. Proper identification of the underlying demand function and using the most appropriate forecasting technique becomes critical. In this paper, we will attempt to explore the key characteristics of the different types of demand function and relate them to known statistical distributions. By fitting statistical distributions to actual past demand data, we are then able to identify the correct demand functions, so that the most appropriate forecasting technique can be applied to obtain improved forecasting results. We applied the methodology to a real case study to show the reduction in forecasting errors obtained. 2012-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1437 http://www.saycocorporativo.com/saycoUK/BIJ/journal/Vol5No2/Article_7.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Forecasting Lumpy Distribution Time Series Computer Sciences Management Information Systems Operations Research, Systems Engineering and Industrial Engineering |
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Forecasting Lumpy Distribution Time Series Computer Sciences Management Information Systems Operations Research, Systems Engineering and Industrial Engineering CHOY, Murphy CHEONG, Michelle Lee Fong Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting |
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Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand forecasting techniques may fail given the unusual characteristics of the function. Proper identification of the underlying demand function and using the most appropriate forecasting technique becomes critical. In this paper, we will attempt to explore the key characteristics of the different types of demand function and relate them to known statistical distributions. By fitting statistical distributions to actual past demand data, we are then able to identify the correct demand functions, so that the most appropriate forecasting technique can be applied to obtain improved forecasting results. We applied the methodology to a real case study to show the reduction in forecasting errors obtained. |
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CHOY, Murphy CHEONG, Michelle Lee Fong |
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CHOY, Murphy CHEONG, Michelle Lee Fong |
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CHOY, Murphy |
title |
Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting |
title_short |
Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting |
title_full |
Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting |
title_fullStr |
Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting |
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Identification of Demand through Statistical Distribution Modeling for Improved Demand Forecasting |
title_sort |
identification of demand through statistical distribution modeling for improved demand forecasting |
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Institutional Knowledge at Singapore Management University |
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2012 |
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https://ink.library.smu.edu.sg/sis_research/1437 http://www.saycocorporativo.com/saycoUK/BIJ/journal/Vol5No2/Article_7.pdf |
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