Explaining beer demand: A residual modeling regression approach using statistical process control |
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| Institution: | 1. Industrial Engineering Department, Middle East Technical University, 06531 Ankara, Turkey;2. Krannert School of Management, Purdue University, West Lafayette, IN 47907, USA;1. Institute of Agro-products Processing Science and Technology, Chinese Academy of Agricultural Sciences/Key Laboratory of Agro-Products Processing/Laboratory of Agro-products Quality Safety Risk Assessment, Ministry of Agriculture, Beijing 100193, PR China;2. Functional and Evolutionary Entomology, Gembloux Agro-Bio-Tech, University of Liége, Passage des Déportés 2, 5030 Gembloux, Belgium;1. Department of Ecology, Faculty of Sciences, University of Málaga, 29071, Málaga, Spain;2. Tvärminne Zoological Station, University of Helsinki, J.A. Palménin tie 260, 10900 Hanko, Finland;1. Department of International Development Engineering, Graduate School of Engineering, Tokyo Institute of Technology, 2-12-1 I4-2 Ookayama, Meguro-ku, Tokyo 152-8550, Japan;2. Department of Environmental Sciences, School of Food, Agricultural and Environmental Sciences, Miyagi University, 2-2-1 Hatadate, Taihaku-ku, Sendai City, Miyagi 982-0215, Japan;3. School of Environmental Science and Engineering, Kochi University of Technology, 185 Miyanokuchi, Kami City, Kochi 782-8502, Japan;1. School of Civil Engineering, The University of Sydney, Sydney, NSW, Australia;2. Institute for Risk and Reliability, Leibniz Universität Hannover, Hannover, Germany;3. Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK;4. International Joint Research Center for Engineering Reliability and Stochastic Mechanics (ERSM), Tongji University, Shanghai, China |
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| Abstract: | We develop a medium-term model as well as a short-term model for understanding the factors affecting beer demand and for forecasting beer demand in Turkey. As part of this specific model development (as well as regression modeling in general) we propose a procedure based on statistical process control principles (SPC) and techniques to (1) detect nonrandom data points, (2) identify common missing, lurking variables that explain these anomalies, and (3) using indicator variables, integrate these lurking variables into the model. We validate our proposed procedure on several test examples as well as on the medium-term beer demand model. Both the medium and short-term models yield very satisfactory results and are currently being used by the company for which the study was conducted. In addition to the residual modeling regression approach developed using SPC, a major contribution to the success of the project (and the modeling in general) is the mutual collaboration between analyst and client in the modeling process. |
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