Data Mining in Franchise Organizations

2008 ◽  
pp. 2722-2733 ◽  
Author(s):  
Ye-Sho Chen ◽  
Robert Justis ◽  
P. Pete Chong

Franchising has been used by businesses as a growth strategy. Based on the authors’ cumulative research and experience in the industry, this paper describes a comprehensive framework that describes both the franchise environment — from customer services to internal operations — and the pertinent data items in the system. The authors identify the most important aspects of a franchising business, the role of online analytical processing (OLAP) and data mining play and the data items that data mining should focus on to ensure its success.

2011 ◽  
pp. 217-229 ◽  
Author(s):  
Ye-Sho Chen ◽  
Robert Justis ◽  
P. Pete Chong

Franchising has been used by businesses as a growth strategy. Based on the authors’ cumulative research and experience in the industry, this paper describes a comprehensive framework that describes both the franchise environment — from customer services to internal operations — and the pertinent data items in the system. The authors identify the most important aspects of a franchising business, the role of online analytical processing (OLAP) and data mining play and the data items that data mining should focus on to ensure its success.


Author(s):  
Ye-Sho Chen ◽  
Grace Hua ◽  
Bob Justis

Franchising has been a popular approach given the high rate of business failures (Justis & Judd, 2002; Thomas & Seid, 2000). Its popularity continues to increase, as we witness an emergence of a new business model, Netchising, which is the combination power of the Internet for global demand-andsupply processes and the international franchising arrangement for local responsiveness (Chen, Justis, & Yang, 2004). For example, Entrepreneur magazine—well known for its Franchise 500 listing—in 2001 included Tech Businesses into its Franchise Zone that contains Internet Businesses, Tech Training, and Miscellaneous Tech Businesses. At the time of this writing, 40 companies are on its list. Netchising is an effective global e-business growth strategy (Chen, Chen, & Wu, 2006), since it can “offer potentially huge benefits over traditional exporting or foreign direct investment approaches to globalization” and is “a powerful concept with potentially broad applications” (Davenport, 2000, p. 52). In his best seller, Business @ the Speed of Thought, Bill Gates (1999) wrote, “Information technology and business are becoming inextricably interwoven. I don’t think anybody can talk meaningfully about one without talking about the other” (p. 6). Gates’ point is quite true when one talks about data mining in franchise organizations. Despite its popularity as a global e-business growth strategy, there is no guarantee that the franchising business model will render continuous success in the hypercompetitive environment. This can be evidenced from the constant up-and-down ranking of the Franchise 500. Thus, to see how data mining can be “meaningfully” used in franchise organizations, one needs to know how franchising really works. In the next section, we show that (1) building up a good “family” relationship between the franchisor and the franchisee is the real essence of franchising, and (2) proven working knowledge is the foundation of the “family” relationship. We then discuss in the following three sections the process of how to make data mining “meaningful” in franchising. Finally, future trends of data mining in Netchising are briefly described.


2008 ◽  
pp. 75-83
Author(s):  
He´ctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).


Author(s):  
Héctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).


2013 ◽  
Vol 846-847 ◽  
pp. 1141-1144
Author(s):  
Dan Dan Chen ◽  
Zhi Gang Yao

A comprehensive analysis on a large amount of ship equipment consumption data accumulated over the years is achieved through the establishment of data warehouse, online analytical processing, regression analysis, cluster analysis, etc. by means of data mining. The analysis results present important references for equipment guarantee department in terms of equipment preparation and carrying, etc. and provide the comprehensive analysis and utilization on massive ship maintenance support data with technical means.


2011 ◽  
pp. 809-819
Author(s):  
Ye-Sho Chen ◽  
Grace Hua ◽  
Bob Justis

Franchising has been a popular approach given the high rate of business failures (Justis & Judd, 2002; Thomas & Seid, 2000). Its popularity continues to increase, as we witness an emergence of a new business model, Netchising, which is the combination power of the Internet for global demand-andsupply processes and the international franchising arrangement for local responsiveness (Chen, Justis, & Yang, 2004). For example, Entrepreneur magazine—well known for its Franchise 500 listing—in 2001 included Tech Businesses into its Franchise Zone that contains Internet Businesses, Tech Training, and Miscellaneous Tech Businesses. At the time of this writing, 40 companies are on its list. Netchising is an effective global e-business growth strategy (Chen, Chen, & Wu, 2006), since it can “offer potentially huge benefits over traditional exporting or foreign direct investment approaches to globalization” and is “a powerful concept with potentially broad applications” (Davenport, 2000, p. 52). In his best seller, Business @ the Speed of Thought, Bill Gates (1999) wrote, “Information technology and business are becoming inextricably interwoven. I don’t think anybody can talk meaningfully about one without talking about the other” (p. 6). Gates’ point is quite true when one talks about data mining in franchise organizations. Despite its popularity as a global e-business growth strategy, there is no guarantee that the franchising business model will render continuous success in the hypercompetitive environment. This can be evidenced from the constant up-and-down ranking of the Franchise 500. Thus, to see how data mining can be “meaningfully” used in franchise organizations, one needs to know how franchising really works. In the next section, we show that (1) building up a good “family” relationship between the franchisor and the franchisee is the real essence of franchising, and (2) proven working knowledge is the foundation of the “family” relationship. We then discuss in the following three sections the process of how to make data mining “meaningful” in franchising. Finally, future trends of data mining in Netchising are briefly described.


2011 ◽  
pp. 141-156
Author(s):  
Rahul Singh ◽  
Richard T. Redmond ◽  
Victoria Yoon

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.


Author(s):  
Chandra S. Amaravadi

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).


Author(s):  
Nicolás Marín ◽  
Carlos Molina ◽  
Daniel Sánchez ◽  
M. Amparo Vila

The use of online analytical processing (OLAP) systems as data sources for data mining techniques has been widely studied and has resulted in what is known as online analytical mining (OLAM). As a result of both the use of OLAP technology in new fields of knowledge and the merging of data from different sources, it has become necessary for models to support imprecision. We, therefore, need OLAM methods which are able to deal with this imprecision. Association rules are one of the most used data mining techniques. There are several proposals that enable the extraction of association rules on DataCubes but few of these deal with imprecision in the process and give as result complex rule sets. In this chapter the authors will present a method that manages the imprecision and reduces the complexity. They will study the influence of the use of fuzzy logic using different size problems and comparing the results with a crisp approach.


2008 ◽  
pp. 2964-2977
Author(s):  
Rahul Singh ◽  
Richard T. Redmond ◽  
Victoria Yoon

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.


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