Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Explaining beer demand: A residual modeling regression approach using statistical process control
Date
1999-01-25
Author
Köksalan, Mustafa Murat
Erkip, N
Moskowitz, H
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
181
views
0
downloads
Cite This
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.
Subject Keywords
Management Science and Operations Research
,
Economics and Econometrics
,
Industrial and Manufacturing Engineering
,
General Business, Management and Accounting
URI
https://hdl.handle.net/11511/51813
Journal
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
DOI
https://doi.org/10.1016/s0925-5273(98)00207-2
Collections
Department of Industrial Engineering, Article
Suggestions
OpenMETU
Core
Analysis of an inventory system under supply uncertainty
Gullu, R; Onol, E; Erkip, N (Elsevier BV, 1999-03-20)
In this paper, we analyze a periodic review, single-item inventory model under supply uncertainty. The objective is to minimize expected holding and backorder costs over a finite planning horizon under the supply constraints. The uncertainty in supply is modeled using a three-point probability mass function. The supply is either completely available, partially available, or the supply is unavailable. Machine breakdowns, shortages in the capacity of the supplier, strikes, etc., are possible causes of uncerta...
The dynamics of firms in a micro-to-macro model: The role of training, learning and innovation
Ballot, G; Taymaz, Erol (Springer Science and Business Media LLC, 1997-12-01)
We analyze the co-evolution of the performances of firms and of the economy in an evolutionary micro-to-macro model of the Swedish economy. The model emphasizes the interactions between human capital (or competences) and technological change at the firm level and their effects;on aggregate growth, taking into account the micro-macro feedbacks. The model features learning-by-doing, incremental and radical innovations, user-producer learning at the firm level, and a change in the techno-economic paradigm. We ...
Analysis of demand and pricing policies in Turkey beer market
Özgüven, Cemhan; Güven, Yusuf Çağlar; Department of Industrial Engineering (2004)
The purpose of this work is to study the beer market in Turkey in respect of demand analysis and with a view to assess whether the marketing and in particular pricing policies adopted by industry players in the period 1997-2002 have been efficient. Of specific interest is the near duopolistic structure of the market and the question whether pricing policies followed during the period 1997-2002 have been determinant in the observed sales volumes. The investigation focuses first on the analysis of the determi...
Selecting quality improvement projects and product mix together in manufacturing: an improvement of a theory of constraints-based approach by incorporating quality loss
Köksal, Gülser (Informa UK Limited, 2004-12-01)
The quality of products and processes needs to be improved continuously in today's competitive environments. Unless these improvement efforts are focused properly, companies might not achieve desirable results in terms of sales, quality and productivity. Many quality improvement (QI) approaches have a limited evaluation of the factors involved in the selection of QI projects. Theory of constraints (TOC) has been proposed by some researchers as a remedy for the better selection of QI projects. However, these...
Demand estimation, relevant market definition and identification of market power in Turkish Beverage Industry
Kalkan, Ekrem; Taymaz, Erol; Department of Economics (2010)
This dissertation aims to contribute to the field of economics of competition policy by analyzing the demand structure and the market power in the Turkish beverage industry and in the cola market in particular. First, a demand system for the beverage products has been estimated by using a multi-stage linearized Almost Ideal Demand System (AIDS). Using the own-price elasticity of cola in a SSNIP test (Small but Significant Non-Transitory Increase in Price), it is shown that cola market consists of a distinct...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. M. Köksalan, N. Erkip, and H. Moskowitz, “Explaining beer demand: A residual modeling regression approach using statistical process control,”
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
, pp. 265–276, 1999, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/51813.