Integration of Fuzzy Logic Techniques into DSS for Profitability Quantification in a Manufacturing Environment

Author(s):  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Timothy Ganesan

Production control, planning, and scheduling are forms of decision making, which play a crucial role in manufacturing industries. In the current competitive environment, effective decision-making has become a necessity for survival in the marketplace. This chapter provides insight into the issues relating to integration of fuzzy logic techniques into decision support systems for profitability quantification in a manufacturing environment. The chapter is divided into five sections with a general introduction of the topic, followed by a thorough literature review on the existing techniques. Thereafter, fuzzy logic algorithms using logistic membership functions and resource variables for decision making aiming at quality improvement are discussed. A case study involving a textile firm is then described with the computational results and findings, and finally, future research directions are presented.

2012 ◽  
pp. 242-261 ◽  
Author(s):  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Timothy Ganesan

Production control, planning, and scheduling are forms of decision making, which play a crucial role in manufacturing industries. In the current competitive environment, effective decision-making has become a necessity for survival in the marketplace. This chapter provides insight into the issues relating to integration of fuzzy logic techniques into decision support systems for profitability quantification in a manufacturing environment. The chapter is divided into five sections with a general introduction of the topic, followed by a thorough literature review on the existing techniques. Thereafter, fuzzy logic algorithms using logistic membership functions and resource variables for decision making aiming at quality improvement are discussed. A case study involving a textile firm is then described with the computational results and findings, and finally, future research directions are presented.


Author(s):  
Tanushri Banerjee ◽  
Arindam Banerjee

There are several challenges faced by decision makers while deploying Business Analytics in their organization. There may not be one resolution approach that is suitable for creating a Business Analytics culture in all organizations. However, it is easy to perceive that most India-based organizations may have similar issues of data organization that may be impeding their progression in the field of Analytics. Based on their research, the authors have proposed a framework for adoption of Analytics in Indian firms in their book “Weaving Analytics for Effective Decision Making” by SAGE. They propose to use that model for explaining certain domain specific adoption of Business Analytics in organizations in India. They have used a case study of a Global Bank which is in the process of establishing its consumer lending USA operations, an offshore captive operation, in India to describe the process of building an Analytics team in an organization in India. Data processed using R has been added as screenshots for supporting the findings.


2020 ◽  
Vol 16 (3) ◽  
pp. 279-297
Author(s):  
Jennifer Capler

PurposeThis article details a qualitative descriptive case study of affective factors of effective decision-making of one local government organization in the United States of America. The specific problem was that many elected American local government representatives lack effective decision-making strategies. This research focus indicated a lack of qualitative research on the real-world experience of factors that were taken into consideration during decision-making within American local government organizations.Design/methodology/approachUsing a local government organization in southwest Illinois, elected representatives were interviewed and observed. The interviews and observations surfaced how the representatives made decisions. Data were analyzed using manual coding and theming to determine themes and patterns.FindingsThe results produced six themes about factors, including emotional intelligence, which impacted decision-making. They are: (1) remembering the past, (2) communication and respect, (3) spurring economic growth and development, (4) fairness, (5) recognizing and removing emotions and bias and (6) accountability.Research limitations/implicationsBeing a single case study, this research is limited in generalization. The research was limited to the identification of current, real-world experience of elected local government representatives.Practical implicationsThe findings of this research can be used to create more effective decision-making practices for local government organizations of similar size.Originality/valueThis is the first study to review, in-depth, the decision-making and emotional intelligence factors of local government organizations in the United States of America. The conceptual background, discussion, implications to local government organizations, limitations and recommendations for future studies are discussed.


2019 ◽  
Vol 9 (4) ◽  
pp. 293-302
Author(s):  
Oded Koren ◽  
Carina Antonia Hallin ◽  
Nir Perel ◽  
Dror Bendet

Abstract Big data research has become an important discipline in information systems research. However, the flood of data being generated on the Internet is increasingly unstructured and non-numeric in the form of images and texts. Thus, research indicates that there is an increasing need to develop more efficient algorithms for treating mixed data in big data for effective decision making. In this paper, we apply the classical K-means algorithm to both numeric and categorical attributes in big data platforms. We first present an algorithm that handles the problem of mixed data. We then use big data platforms to implement the algorithm, demonstrating its functionalities by applying the algorithm in a detailed case study. This provides us with a solid basis for performing more targeted profiling for decision making and research using big data. Consequently, the decision makers will be able to treat mixed data, numerical and categorical data, to explain and predict phenomena in the big data ecosystem. Our research includes a detailed end-to-end case study that presents an implementation of the suggested procedure. This demonstrates its capabilities and the advantages that allow it to improve the decision-making process by targeting organizations’ business requirements to a specific cluster[s]/profiles[s] based on the enhancement outcomes.


2014 ◽  
Vol 989-994 ◽  
pp. 1704-1711
Author(s):  
Ding Hua Zhang ◽  
Liang Cheng ◽  
Ren Wei Wu ◽  
Jing Wang ◽  
Jing Yi Liu

This paper presents the findings of a study on decision making models for the migrant’s worker group incident emergency management based on Analytic Hierarchic Process (AHP). Analytic Hierarchic Process (AHP) helps to quantitate and layer the complex migrant’s worker incident emergency management problem, the aim of the work is to improve the support for analysis and decision through the importance comparing of various factors associated layer by layer.A case study conduct in Wukan village Shantou city of Guangdong province revealed that AHP model can do effective decision making


Author(s):  
Edward Shinnick ◽  
Geraldine Ryan

The advent of the World Wide Web and other communication technologies has significantly changed how we access information, the amount of information available to us, and the cost of collecting that information. Individuals and businesses alike collect and interpret information in their decision-making activities and use this information for personal or economic gain. Underlying this description is the assumption that the information we need exists, is freely available, and easy to interpret. Yet in many instances this may not be the case at all. In some situations, information may be hidden, costly to assimilate, or difficult to interpret to ones own circumstances. In addition, two individuals who look at the same information can reach different conclusions as to its value. One person may see it as just a collection of numbers, another sees a market opportunity. In the latter case, information is used in an entrepreneurial way to create a business opportunity. Advances in technology have created opportunities to do this by creating information systems that can support business decision-making activities. Such decision support systems are playing an increasingly important role in determining not only the efficiency of businesses but also as business opportunities themselves through the design and implementation of such systems for other markets and businesses. However all is not easy as it may first seem. Quality decision making and effective decision support systems require high quality information. The implicit assumption in talking about decision support systems is that the required information is always available. It is somewhere “out there” and must just be collated to make use of it. However, very often this is not the case. Information that is scarce or inaccessible is often more valuable and can be the very reason for many firm’s existence. The importance for firms to process information to do with its business environment on issues such as, market trends, events, competitors, and technological innovations relevant to their success is prevalent in the management and IS literature.1 The theme of this article is to analyse the role information plays in managerial decision making at individual, group, and firm level from an economics perspective. We argue that access to information is essential for effective decision making and look at problems associated with insufficient information; the effects that such information deficits have in shaping and designing markets are then explored. We start by exploring the nature of information and the issue of asymmetric information. We examine the different solutions put forward to address information deficits, such as advertising, licensing, and regulation. Finally we conclude by outlining likely future research in markets with information deficits.


2021 ◽  
pp. 307-316
Author(s):  
Daniela Borissova ◽  
Zornitsa Dimitrova

The management of business information processes needs effective decision-making models. That means to involve different methods, techniques, and principles to improve competitiveness and to achieve the planned business results. In this context, the article deals with the problem of group decision-making under uncertain conditions. To cope with such problems some well-known optimization strategies of Wald, Laplace, Hurwitz, and Savage are modified to take into account the experts’ opinions with different importance when forming the final group decision. Numerical testing is based on a case study for CRM software selection. The results are discussed based on the proposed models under two different cases derived from the case study. The conducted numerical testing of the proposed models demonstrates their applicability to cope simultaneously with multiple experts’ evaluations and uncertainty conditions.


2020 ◽  
Vol 29 (2) ◽  
pp. 98-101
Author(s):  
Samantha Watkins

Many theories have been proposed for the decision-making conducted by nurses across all practices and disciplines. These theories are fundamental to consider when reflecting on our decision-making processes to inform future practice. In this article three of these theories are juxtaposed with a case study of a patient presenting with an ST-segment elevation myocardial infarction (STEMI). These theories are descriptive, normative and prescriptive, and will be used to analyse and interpret the process of decision-making within the context of patient assessment.


2020 ◽  
Vol 38 (6) ◽  
pp. 660-672 ◽  
Author(s):  
Selman Karagoz ◽  
Muhammet Deveci ◽  
Vladimir Simic ◽  
Nezir Aydin ◽  
Ufuk Bolukbas

As the number of end-of-life vehicles (ELVs) increases rapidly, their management has become one of the most important environmental topics worldwide. This study is conducted to evaluate various alternatives for location selection of an authorized dismantling center (ADC) for ELVs using a multi-criteria decision-making (MCDM) approach. An intuitionistic fuzzy MCDM-based combinative distance-based assessment (CODAS) approach is proposed to aid waste managers and solve their problem. The intuitionistic fuzzy weighted averaging operator is utilized to aggregate individual opinions of decision-makers into a group opinion. The intuitionistic fuzzy Euclidean and Hamming distances are used to calculate the assessment score of alternatives. A real-life case study of Istanbul is provided to illustrate how this novel intuitionistic fuzzy MCDM-based CODAS approach can be used for alternative selection in real-world applications. The comparison with the available state-of-the-art intuitionistic fuzzy set-based MCDM approaches approves the validity and consistency of the proposed intuitionistic fuzzy CODAS approach. The intuitionistic fuzzy CODAS, WASPAS, and TOPSIS approaches generate exactly the same ordering of alternatives for the new ADC in Istanbul. The results show that the intuitionistic fuzzy CODAS approach indicates valid results and is an effective decision-making technique for vagueness and uncertainty nature of linguistic assessments.


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