A Neuro-Fuzzy Partner Selection System for Business Social Networks

A Neuro-Fuzzy Partner Selection System for Business Social Networks

T. T. Wong (The Hong Kong Polytechnic University, Hong Kong) and Loretta K.W. Sze (The Hong Kong Polytechnic University, Hong Kong)
DOI: 10.4018/978-1-61350-168-9.ch006


Enterprises are now facing growing global competition and the continual success in the marketplace depends very much on how efficient and effective companies are able to respond to customer demands. Business social network sites (BSNS) have provided a powerful tool to link up manufacturers, suppliers, distributors, and customers. Among the emerging business social networks, decision support functionality addressing the issue of selecting business partners is an important domain to be studied, and it is the objective of this chapter to propose a practical partner selection decision support system. Essentially, a neural-network data mining system is used to generate information for subsequent fuzzy multi-objective analysis. It demonstrates the benefits of integrating information technology, artificial intelligence, and multi-objective decision making to form a practical aid that capitalizes on the merits of BSNS. A special feature is that the trust among companies can be incorporated as an evaluation criterion.
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Globalization and information technology have become key factors in gaining competitiveness for businesses in recent years (Lee & Lai, 2007) and the continual success in the marketplace depends very much on how efficient and effective companies are able to respond to customer demands. Business social network sites (BSNS) have provided a powerful tool to link up manufacturers, suppliers, distributors and customers to facilitate the rapid exchange of useful information. The formation of smart organizations is gathering momentum to meet this challenge. The main aim of business social networking is meant to establish dynamic organizations by the synergetic combination of dissimilar companies with different core competencies, thereby forming a consortium to perform a business activity to achieve maximum customer satisfaction. Among the emerging business social networks, the decision support functionality, which addresses issues such as selection of business partners, coordination in the distribution of manufacturing processes and the solution of production problems, is an important domain to be studied and it is the objective of this chapter to propose a viable partner selection decision support system.

Nowadays, many enterprises manufacture and distribute their products or services globally, and quite a number of smart organizations are formed through BSNS and are expected to evolve to a strategically important e-business model. Although business social networks play an important role in linking the core and partner companies, it remains subservient to the human that form the smart organizations. A review of the literature indicates that neither researchers nor practitioners agree on a single model of inter-firm trust that applies to all partner evaluation contexts.

As more and more BSNS are becoming ubiquitous, organizations preparing to make use of such sites need to recognize the implications this transition will have on their business processes and organizations as a whole. Organizations need to: (i) encourage staff to enter into business network collaborations, (ii) manage transition and adapt quickly, and (iii) leverage human resources to optimize performance. In other words, organizations need to get “smart” and one would find without difficulty that most SMEs fall into this category. SMEs comprise over 98% of business establishments in Hong Kong and employ about 60% of the working population in the private sector. The Government of the Hong Kong Special Administrative Region (HKSAR) attaches great importance to supporting Hong Kong's SMEs. The reasons that small and medium enterprises (SME) are becoming so prevalent in Hong Kong nowadays include low overhead, flexibility, minimum investment, and high productivity. By owning few resources and focusing on the organization’s expertise, the company can maintain high levels of productivity while allowing her partners to do the same. Both partners in a smart organization and the individuals who work for the partners are allotted greater flexibility. The partners can focus on core competencies, while individual workers may have the ability to telecommute from their homes. In a smart organization, companies are linked by the free flow of information. There is no hierarchy, no central office, and no vertical integration: just the skills and resources needed to complete the project at hand. Each participating company contributes what it is best at. It can be seen that since no single company will have all the skills necessary to compete in the global market, these arrangements will become the norm. One of the keys to the success of the smart organization is the use of business social network sites to facilitate these alliances.

Key Terms in this Chapter

Fuzzy Logic: Fuzzy logic is a form of many-valued logic derived from fuzzy set theory to deal with uncertainty in subjective belief. In contrast with “crisp logic”, where binary sets have two-valued logic, fuzzy logic variables can have a value that ranges between 0 and 1. Furthermore, when linguistic variables are used, these unit-interval numerical values may be described by specific functions.

Small and Medium Enterprises (SMEs): The Government of Hong Kong specifies that manufacturing enterprises with fewer than 100 employees and non-manufacturing enterprises with fewer than 50 employees in Hong Kong come under the category of SMEs.

OLAP(Online Analytical Processing): OLAP is a data structure that allows quick analysis of data. It can also be defined as the capability of manipulating and analyzing data from multiple perspectives. The arrangement of data into cubes overcomes some limitations of relational databases. OLAP cubes can be thought of as extensions to the two-dimensional array of a spreadsheet. For example a company might wish to analyze some manufacturing data by product, by time-period, by city, by type of revenue and cost, and by comparing actual data with a budget.

Neural Network (NN): Also known as Artificial Neural Network (ANN), is a non-linear statistical data modeling tool. It processes information using a connectionist approach to computation. In most cases a NN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. Modern neural networks are usually used to model complex relationships between inputs and outputs or to find patterns in data.

Multi-Objective Decision Making: Multi-objective decision making (MODM), also known as multi-criteria decision making, is a systematic procedure targeting at supporting decision makers faced with conflicting evaluations. The procedure aims at highlighting these conflicts and deriving a way to come to a compromise in a more transparent manner. Evaluation criteria in MODM are derived or interpreted subjectively as indicators of the strength of various preferences.

Data Mining: Data mining, a branch of computer science, is the process of extracting patterns from large data sets by combining statistical analysis and artificial intelligence with database management. Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and business partner selection.

Neuro-Fuzzy System (NFS): Also known as Fuzzy Neural Network (FNN) refers to combinations of artificial neural networks and fuzzy logic that synergize these two techniques by combining the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. Neuro-fuzzy system incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. The main strength of neuro-fuzzy systems is that they are universal approximators with the ability to solicit interpretable IF-THEN rules.

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