Facility Location Decision for Global Entrepreneurial Small-to-Medium Enterprises Using Similarity Coefficient-based Clustering Algorithms

Decisions on location selection are critical for the survival of small-to-medium entrepreneurial organizations from the time they are established until later stages of operation and expansion. The selection of location for small and medium entrepreneurial businesses requires a selection strategy that incorporates relevant factors, quantifies these factors and develops a methodology that analyzes data for better decision-making. In the era of globalization where borders have become easier to transcend, many small ventures tend to choose more attractive international markets as a potential location for their operations where they can obtain higher returns on their investment. Thus, significant changes in the location decision process of the small and medium entrepreneurial companies have received great attention in the literature about small firms with global orientation as a response to the international entrepreneurship phenomenon. Therefore, consideration should be given to factors and attributes that reinforce the appeal of the international market to new businesses. These factors and attributes will provide the decision maker with an effective methodology for data analysis that will provide a framework for decision-making in the selection of locations for the entrepreneurial organization. In this research, the most frequent and critical attributes to select the best location for the entrepreneurial firms (globally) are extracted from relevant literature. Then, a similarity-based cluster analysis approach is introduced to quantify these attributes based on the existing data of economic metrics, such as technological advancement, expenditures on education, expenditures on research and development, the quality of the labor force, unemployment rates, domestic competitiveness, etc. Subsequently, the resulting outcomes are used to identify groups of prospective sites that fit the needs of the entrepreneurial firm. Last, the validity of the adopted methodology will be tested via numerical examples

Manufacturing Site Selection in the Global Context

The decision making regarding global site selection has been always a challenging and strategic problem. Recently, due to the globalization of the problem many new factors such as political, social, regulatory, government, environmental consideration, etc. gained importance in the decision making process. One of the goals in this thesis is to identify the relevant factors in manufacturing site selection and incorporate them into the data analysis. The collection of a wide range of factors that impact the manufacturing site selection problem at a country level, the quantification of these factors, and incorporation of them into the decision making process needs a quantitative, comprehensive, and flexible approach. In this research hundred countries has been considered for factor analysis and classification. To cluster these countries according to their manufacturing site selection attributes, thirty-four frequently cited attributes are chosen. These factors, also, can be quantified with major economic, business, social, political, and environmental metrics. Factor analysis techniques have used to investigate interrelationships between selected attributes. Our analysis showed that some of these factors can be dropped from our data set. Finally, two types of clustering algorithms, Agglomerative Hierarchical and K-means, are employed to classify countries according to their similarity regrading quantified attributes. We have shown that this approach provides a framework to help the decision making regarding manufacturing facility location selection

Internet-based solutions to support distributed manufacturing

With the globalisation and constant changes in the marketplace, enterprises are adapting themselves to face new challenges. Therefore, strategic corporate alliances to share knowledge, expertise and resources represent an advantage in an increasing competitive world. This has led the integration of companies, customers, suppliers and partners using networked environments. This thesis presents three novel solutions in the tooling area, developed for Seco tools Ltd, UK. These approaches implement a proposed distributed computing architecture using Internet technologies to assist geographically dispersed tooling engineers in process planning tasks. The systems are summarised as follows. TTS is a Web-based system to support engineers and technical staff in the task of providing technical advice to clients. Seco sales engineers access the system from remote machining sites and submit/retrieve/update the required tooling data located in databases at the company headquarters. The communication platform used for this system provides an effective mechanism to share information nationwide. This system implements efficient methods, such as data relaxation techniques, confidence score and importance levels of attributes, to help the user in finding the closest solutions when specific requirements are not fully matched In the database. Cluster-F has been developed to assist engineers and clients in the assessment of cutting parameters for the tooling process. In this approach the Internet acts as a vehicle to transport the data between users and the database. Cluster-F is a KD approach that makes use of clustering and fuzzy set techniques. The novel proposal In this system is the implementation of fuzzy set concepts to obtain the proximity matrix that will lead the classification of the data. Then hierarchical clustering methods are applied on these data to link the closest objects. A general KD methodology applying rough set concepts Is proposed In this research. This covers aspects of data redundancy, Identification of relevant attributes, detection of data inconsistency, and generation of knowledge rules. R-sets, the third proposed solution, has been developed using this KD methodology. This system evaluates the variables of the tooling database to analyse known and unknown relationships in the data generated after the execution of technical trials. The aim is to discover cause-effect patterns from selected attributes contained In the database. A fourth system was also developed. It is called DBManager and was conceived to administrate the systems users accounts, sales engineers’ accounts and tool trial monitoring process of the data. This supports the implementation of the proposed distributed architecture and the maintenance of the users' accounts for the access restrictions to the system running under this architecture

DATA MINING: A SEGMENTATION ANALYSIS OF U.S. GROCERY SHOPPERS

Consumers make choices about where to shop based on their preferences for a shopping environment and experience as well as the selection of products at a particular store. This study illustrates how retail firms and marketing analysts can utilize data mining techniques to better understand customer profiles and behavior. Among the key areas where data mining can produce new knowledge is the segmentation of customer data bases according to demographics, buying patterns, geographics, attitudes, and other variables. This paper builds profiles of grocery shoppers based on their preferences for 33 retail grocery store characteristics. The data are from a representative, nationwide sample of 900 supermarket shoppers collected in 1999. Six customer profiles are found to exist, including (1) "Time Pressed Meat Eaters", (2) "Back to Nature Shoppers", (3) "Discriminating Leisure Shoppers", (4) "No Nonsense Shoppers", (5) "The One Stop Socialites", and (6) "Middle of the Road Shoppers". Each of the customer profiles is described with respect to the underlying demographics and income. Consumer shopping segments cut across most demographic groups but are somewhat correlated with income. Hierarchical lists of preferences reveal that low price is not among the top five most important store characteristics. Experience and preferences for internet shopping shows that of the 44% who have access to the internet, only 3% had used it to order food.Consumer/Household Economics, Food Consumption/Nutrition/Food Safety,

A geographic knowledge discovery approach to property valuation

This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely data driven and does not require previous knowledge of the area applied. To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm. For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the investigation of relationships between spatial entities beyond first degree connectivity. A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality