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Shardul Phadnis. Rashad, Ahmed (Ahmed Fathy Mustafa Rashad Abdelaal) Spraggon, Santiago Massachusetts Institute of Technology. Engineering Systems Division. Massachusetts Institute of Technology. Engineering Systems Division 2013-09-24T19:43:06Z 2013-09-24T19:43:06Z 2013 2013 http://hdl.handle.net/1721.1/81104 858278757 Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (p. 60). Improving forecast accuracy has positive effects on supply chain performance. Forecast accuracy can reduce inventory levels, increase customer service levels and responsiveness, or a combination of the two. However, the further upstream in the supply chain, the more difficult it becomes to forecast accurately. Demand for consumer products might be subject to factors that are hard to identify and quantify. One way to overcome this is to observe external factors or predictors that might help explain demand. The purpose of this thesis is to explore the factors that potentially influence the demand of a fast-moving consumer product (bottled water), and build a demand signal repository for these factors to help the manufacturer generate more accurate forecasts. We identified more than 30 such factors that might affect demand, using interviews and industry research. We tested more than 200 causal models of the relationship between observed demand and the predicting factors. The resulting model explained almost 60% of demand for two out of three customers using daily buckets and over 85% using weekly buckets compared to less than 50% using time-series techniques. Using the results of this extensive analysis, we propose a new forecasting model. We also identified additional factors that could not be included this analysis due to the lack of data; adding these to the model may further improve the forecast accuracy. by Ahmed Rashad and Santiago Spraggon. M.Eng. in Logistics 60 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Engineering Systems Division. Assembling the crystal ball : using demand signal repository to forecast demand Using demand signal repository to forecast demand Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e11c13ac-a495-4399-b290-67c3d6298ea4"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Assembling the crystal ball : using demand signal repository to forecast demand</Title> <Subtitle>Using demand signal repository to forecast demand</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2013</PublicationDate> <Authors> <Author> <DisplayName>Rashad, Ahmed (Ahmed Fathy Mustafa Rashad Abdelaal)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Spraggon, Santiago</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Engineering Systems Division.</Keyword> <Abstract>Improving forecast accuracy has positive effects on supply chain performance. Forecast accuracy can reduce inventory levels, increase customer service levels and responsiveness, or a combination of the two. However, the further upstream in the supply chain, the more difficult it becomes to forecast accurately. Demand for consumer products might be subject to factors that are hard to identify and quantify. One way to overcome this is to observe external factors or predictors that might help explain demand. The purpose of this thesis is to explore the factors that potentially influence the demand of a fast-moving consumer product (bottled water), and build a demand signal repository for these factors to help the manufacturer generate more accurate forecasts. We identified more than 30 such factors that might affect demand, using interviews and industry research. We tested more than 200 causal models of the relationship between observed demand and the predicting factors. The resulting model explained almost 60% of demand for two out of three customers using daily buckets and over 85% using weekly buckets compared to less than 50% using time-series techniques. Using the results of this extensive analysis, we propose a new forecasting model. We also identified additional factors that could not be included this analysis due to the lack of data; adding these to the model may further improve the forecast accuracy.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>