A Fuzzy-Based Decision Support Tool for Appraisement of Supplier's Quality Assurance Practices

Recently competition in the global marketplace has stimulated immense attention being paid by the enterprises towards securing highest quality, cost effective components and materials, consistently delivered on time. This objective can only be achieved by establishing long term, close working relationships with suppliers, who adopt a proper quality philosophy. Supplier Quality Assurance is the confidence in a supplier's ability to deliver a commodity or service towards satisfying customer's needs. Supplier Quality Assurance can be achieved through interactive relationship between the customer and the supplier; it aims at ensuring the product's ‘suitably fit' to the customer's requirements with little or no adjustment or inspection. In the present context, the study develops a decision-making framework to assure as well as to assess suppliers' existing quality philosophy, current policy and related practices. An Interval-Valued Fuzzy Set (IVFS) theory has been adopted to develop such an evaluation model.

Fuzzy Nominal Classification using Bipolar Analysis

The process of assigning objects (candidates, projects, decisions, options, etc.) characterized by multiple attributes or criteria to predefined classes characterized by entrance conditions or constraints constitutes a subclass of multi-criteria decision making problems known as nominal or non-ordered classification problems as opposed to ordinal classification. In practice, class entrance conditions are not perfectly defined; they are rather fuzzily defined so that classification procedures must be design up to some uncertainty degree (doubt, indecision, imprecision, etc.). The purpose of this chapter is to expose recent advances related to this issue with particular highlights on bipolar analysis that consists in considering for a couple of object and class, two measures: classifiability measure that measures to what extent the former object can be considered for inclusion in the later class and rejectability measure, a degree that measures the extent to which one should avoid including this object into that class rendering final choice flexible and robust as many classes may be qualified for inclusion of an object. This apparent theoretical subject finds applications in almost any socio-economic domain and particularly in digital marketing. An application to supply chain management, where a certain number of potential suppliers of a company are to be classified in a number of classes in order to apply the appropriate strategic treatment to them, will be considered for illustration purpose.

Multi-Criteria Fuzzy Analysis of Competitiveness

The article presents the possibility of using multi-criteria fuzzy analysis for assessing the regional competitiveness. This estimation can be used for place marketing strategy development and based on results of socio-economic development. The proposed approach is characterized by comparative estimation, when the level of development of one region is determined by the development of other areas. The final evaluation is the level of the cluster which the object being analyzed belongs. This allows ignoring minor fluctuations in total indexes. The results of robust and fuzzy groups of regions are analyzed. This grouping is characterized by similar levels of development and helps to define the directions of further development of the regions.

Multi-Criteria Decision Making Models for Sustainable and Green Supply Chain Management Based on Fuzzy Approach

Understanding different aspects of sustainability, supply chain management (SCM), and decision making policies and relating them to performance measurement have been increasingly investigated in the last decade. In contrast to traditional SCM, which typically focuses on economic and financial business performance, sustainable SCM (SSCM) is characterized by explicit integration of environmental or social objectives which extend the economic dimension. For evaluating the sustainability of SCM as well as its greenness, we have to consider many and different index and criteria. One of the best tools for assessing the SSCM and GSCM is multicriteria decision making (MCDM) techniques. Many studies have been conducted in this area. Moreover, there are many uncertainty factors which may reduce the accuracy of MCDM result. Actually, Uncertainty is always a worsening factor in any decision support models, and dilutes the planned objectives of such models. For decreasing this uncertainty, fuzzy logic has been combined with MCDM approach. In fact, the main purpose of this chapter is considering the recent studies in area of SSCM and GSCM regarding to applications of fuzzy MCDM techniques. At the end of this chapter, based on out investigations in applications of fuzzy MCDM in SSCM and GSCM and regarding to research gaps, some suggestions for future studies have been proposed.

Selection of Digital Marketing Tools Using Fuzzy AHP-Fuzzy TOPSIS

Technological developments resulted in creative, faster, target-oriented and knowledge-based marketing strategies more prone to interactions. Digital marketing is an example of marketing types arisen by technological developments. Digital marketing tools are rapidly growing since they can be performed through internet; they are faster and thus have an ascending trend. The amount of investment to be made to each digital marketing tool has become a significant decision making problems for the businesses. In this chapter, Fuzzy AHP and Fuzzy TOPSIS techniques were employed for the selection of digital marketing tools. Multi-criteria analysis revealed the Remarketing advertising as the most suitable digital marketing tool. Additionally, the social media advertising, advertising-per-impression, advertising-per-click, affiliate advertising, banner marketing and email-advertising were respectively found to be as the other significant marketing tools.

A Literature Survey on the Usage of Fuzzy MCDM Methods for Digital Marketing

Digital marketing is the integrated processes of building and maintaining customer relationships using online channels to establish the exchange and flow of products, and services in the market. It is the marketing that bridges electronic technology with psychology in the marketplace. Fuzzy sets can handle the uncertainty and human-manner linguistic evaluations in digital marketing. The objective of this chapter is to summarize and classify the literature on digital marketing, which uses the fuzzy multicriteria decision making methods (MCDM), and predict the future directions for digital marketing. Our chapter will include the main definitions and principles of digital marketing with its challenges. We also give a brief definition of the fuzzy MCDM used methods in the literature. We use graphical illustrations techniques for summarizing the survey results.

Applying the Fuzzy Analytical Network Process in Digital Marketing

The fuzzy analytical network process (FANP) is introduced as a potential multi-criteria-decision-making (MCDM) method to improve digital marketing management endeavors. Today's information overload makes digital marketing optimization, which is needed to continuously improve one's business, increasingly difficult. The proposed FANP framework is a method for enhancing the interaction between customers and marketers (i.e., involved stakeholders) and thus for reducing the challenges of big data. The presented implementation takes realities' fuzziness into account to manage the constant interaction and continuous development of communication between marketers and customers on the Web. Using this FANP framework, the marketers are able to increasingly meet the varying requirements of their customers. To improve the understanding of the implementation, advanced visualization methods (e.g., wireframes) are used.

Fuzzy Structural Models and Based Applications in Digital Marketplace

One of the well-known topics of decision making is Multi-Criteria Decision Making (MCDM). Fuzzy set theory helps to provide a useful way to address a MCDM problem. Without models, MCDM methods cannot be practiced effectively, therefore, it is interesting to clarify the structure among criteria. But the shortcoming of MCDM is unable to capture imprecision or vagueness inherent in the information. Fuzzy set theory has great potential to handle such situations and fuzzy structural models have been developed. In this chapter widely used structural models i.e. Interpretive Structural Modeling (ISM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Cognition Maps (CMs) are first summarized briefly along with their mathematical formulation and then diffusion these models into fuzzy set theory is explained along with a literature review of the based applications of these models in the digital marketplace.

Fuzzy and Soft Poly-Optimization in the Digital Environment

TThe standard optimality problem is when the triad is given: <x, F(x), O(x)>, where x is the decision variables vector, F(x) is an overall optimality criterion and O(x) is a set of constraints, defined in a fully mathematical form. But in the real practice in business, logistics, medicine, and even in engineering – where a decision–maker has for his/her disposal comparatively good unique mathematical models – it is very rarely. Another trouble may be, the computations are time consuming and/or algorithms do not converged satisfactory. In this chapter examples of two non-standard techniques of poly-optimization (Multi Attribute Decision Making, Multi Criteria Decision Making) are presented. The first is a dialog procedure of finding satisfactory solution on hierarchical play of constraints, with extensive computer simulations, what is called here as a soft optimization. The other is a standard poly-optimization on fuzzy mathematical definition of criteria and of a model. A few examples are given.

Some Generalized Intuitionistic Fuzzy Geometric Aggregation Operators with Applications in Multi-Criteria Decision Making Process

The aggregation operators play an important role in the fusion of multidimensional information in decision making process. In this study, a series of generalized aggregation operators such as: the generalized intuitionistic fuzzy weighted geometric (GIFWG) operator, the generalized intuitionistic fuzzy ordered weighted geometric (GIFOWG) operator and the generalized intuitionistic fuzzy hybrid geometric (GIHG) operator are proposed under intuitionistic fuzzy environment by controlling the power of the argument values with an additional parameter p. Some of the important properties and some special cases of these operators are also included in this study. Further, the developed approach is utilized to deal with multi-criteria decision making (MCDM) problems. Numerical examples are constructed to illustrate the developed approach effectively.