Understanding customer requirements through quantitative analysis of an improved fuzzy Kano’s model

2017 ◽  
Vol 231 (4) ◽  
pp. 699-712 ◽  
Author(s):  
Lina He ◽  
Xinguo Ming ◽  
Miao Li ◽  
Maokuan Zheng ◽  
Zhitao Xu
Keyword(s):  
Quantitative Analysis ◽  
Global Market ◽  
Requirement Analysis ◽  
Primary Concern ◽  
Customer Requirements ◽  
Customer Requirement ◽  
Proposed Model ◽  
Kano’S Model ◽  
Maximizing Deviation Method ◽  
Importance Assessment

Customer requirement analysis has become a primary concern for companies who compete in the global market. Kano’s model, as a customer-driven tool, has been widely used for customer requirement analysis in product improvement. Although a number of authors have improved the traditional Kano’s model, there has been a limitation of dealing with the fuzzy and uncertainty of human thought under multi-granularity linguistic environment. Furthermore, the traditional Kano’s model faces problems regarding quantitative data computation and customer requirements importance assessment. In this article, an improved fuzzy Kano’s model is proposed to analyze customer requirements under uncertain environment. A 2-tuple linguistic fuzzy Kano’s questionnaire is developed to model the uncertainty and diversity of customers’ assessments using 2-tuple linguistic variables under multi-granularity linguistic environment. Then, a comprehensive and systematic methodology is presented to prioritize customer requirements through quantitative analysis of improved fuzzy Kano’s model. This method integrates subjective judgments assigned by decision maker, objective weights based on maximizing deviation method and customer satisfaction contribution to determine the priority ratings of customer requirements. A case study of combine harvester development is presented to evaluate the proposed model.

Integrated Quantitative Analysis of Customer Satisfaction Based on Kano's Model

2012 ◽  
Vol 224 ◽  
pp. 358-361
Author(s):  
Zhi Jun Fan ◽  
Zhao Liang Jiang
Keyword(s):  
Quantitative Analysis ◽  
Customer Satisfaction ◽  
Product Configuration ◽  
Customer Requirements ◽  
Kano’S Model ◽  
Group Satisfaction

The satisfaction of customer requirements (CRs) is the objective of product configuration. A methodology Based on the Kano's model was proposed to explore customers' stated needs and unstated desires and to resolve them into different categories which have different impacts on customer satisfactions (CSs). The customer satisfactions are classified into group satisfaction and individual satisfaction, and each of them has three types with Kano theory. Group requirements items were selected frequently by the same kind of customers. Individual requirements were specified by the customer himself. Based on a combination of group satisfactions and individual satisfactions, the integrated satisfaction was determined. A case study is provided to illustrate the effectiveness of the presented method.

Physical programming and conjoint analysis-based redundancy allocation in multistate systems: A Taguchi embedded algorithm selection and control (TAS&C) approach

2009 ◽  
Vol 223 (3) ◽  
pp. 215-232 ◽  
Author(s):  
V V Kumar ◽  
M Tripathi ◽  
M K Pandey ◽  
M K Tiwari
Keyword(s):  
Conjoint Analysis ◽  
Optimal Allocation ◽  
Programming Approach ◽  
Primary Concern ◽  
Algorithm Selection ◽  
Allocation Model ◽  
Redundancy Allocation ◽  
Physical Programming ◽  
Proposed Model ◽  
And Control

Amidst increasing system complexity and technological advancements, the manufacturer aims to win the consumer's trust to maintain his or her permanent goodwill. This expectation directs the manufacturer to address the problem of attaining desired quality and reliability standards; hence, the measure of performance of a system in terms of reliability and utility optimization poses an issue of primary concern. In order to meet the requirement of a reliable and trouble-free product, optimal allocation of all conflicting parameters is essential during the design phase of a system. With this in mind, this paper presents a physical programming and conjoint analysis-based redundancy allocation model (PPCA-RAM) for a multistate series—parallel system. Use of physical programming approach is the key feature of the proposed algorithm to eliminate the need for multi-objective optimization. Physical programming methodology provides an adequate balance among various associated performance measures and thus provides an efficient tool for formulating the objective function of a practical redundancy allocation problem. The proposed model has been addressed by a novel methodology called Taguchi embedded algorithm selection and control (TAS&C). An illustrative example has been presented to authenticate the efficiency of the proposed model and algorithm. The results obtained are compared with the genetic algorithm (GA), artificial immune system (AIS), and particle swarm optimization (PSO), where TAS&C was seen to significantly outperform the rest.

Research on Customer Requirements Expansion Based on TRIZ and Grey Theory

2010 ◽  
Vol 29-32 ◽  
pp. 1235-1240 ◽  
Author(s):  
Jiang Hua Ge ◽  
Jian Yuan Xu ◽  
Ya Ping Wang ◽  
Fen Wei
Keyword(s):  
Product Family ◽  
Expansion Method ◽  
Complete Information ◽  
Grey Theory ◽  
Customer Requirements ◽  
Customer Requirement ◽  
Ontology Model ◽  
Potential Market ◽  
Horizontal Expansion ◽  
Market Requirement

To accurately understand the current customer requirements, mine and predict those of potential market, requirement expansion method based on TRIZ was proposed to realize horizontal expansion of customer requirements and grey theory was applied to vertical expansion to maximize the diversity of customer requirement. Meanwhile, ontology model of customer requirements was built to represent in detail complete information of customer requirement, it provided the basis for determining product family reasonably and comprehensively.

Ontologies to Support Customer Requirement Formulation in Aerospace Design

2017 ◽  
Author(s):  
Mark T. Lemke ◽  
Robert B. Stone ◽  
Ryan A. Arlitt
Keyword(s):  
Open Source ◽  
Research Field ◽  
Research Center ◽  
Major Contributor ◽  
Customer Requirements ◽  
Customer Requirement ◽  
Major Goal ◽  
Engineering Discipline ◽  
Nasa Ames Research ◽  
Common Understanding

The major goal of customer requirement formulation is to achieve a common understanding between the project stakeholders and the engineering requirements. Many times, this process can be ambiguous, incomplete, and time consuming especially when more than one engineering discipline is involved. Therefore, adequate requirement formulation tools can be a major contributor to solving these challenges. The use of ontologies provides a standardized way of describing concepts in a domain of interest and the relationships between these concepts to better understand the domain as a whole. This paper describes the methodology used to create an ontology derived from twenty customer requirements of a mid-size, twin-engine, commercial transport-class aircraft provided by NASA Ames Research Center. One key stipulation that NASA had was that this ontology effectively captures the relationships that exist between the hardware and software level of each customer requirement. The final ontology was created using Protégé OWL, an open source ontology editor, which will be used by NASA in order to improve the customer requirement creation phase of future NASA products. The ontology and requirements were further generalized into a set of common patterns for describing requirements in this domain. These pattern templates provide a tool to ensure that common styles of requirements have been considered, and that these common styles are uniform. This research paper fills a gap in the customer requirement research field by introducing the use of ontologies and common patterns to reduce ambiguity and repetition.

scholarly journals ModCOVNN: a convolutional neural network approach in COVID-19 prognosis

2021 ◽  
Vol 7 (2) ◽  
pp. 125
Author(s):  
Ahmed Wasif Reza ◽  
Jannatul Ferdous Sorna ◽  
Md. Momtaz Uddin Rashel ◽  
Mir Moynuddin Ahmed Shibly
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Primary Concern ◽  
Neural Network Approach ◽  
Good Efficiency ◽  
X Ray ◽  
Proposed Model ◽  
Driven System ◽  
History Of ◽  
Pcr Test

COVID-19 is a devastating pandemic in the history of humankind. It is a highly contagious flu that can spread from human to human. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. However, identifying COVID-19 patients with a Polymerase chain reaction (PCR) test can sometimes be problematic and time-consuming. Therefore, detecting patients with this virus from X-ray chest images can be a perfect alternative to the de-facto standard PCR test. This article aims at providing such a decision support system that can detect COVID-19 patients with the help of X-ray images. To do that, a novel convolutional neural network (CNN) based architecture, namely ModCOVNN, has been introduced. To determine whether the proposed model works with good efficiency, two CNN-based architectures – VGG16 and VGG19 have been developed for the detection task. The experimental results of this study have proved that the proposed architecture has outperformed the other two models with 98.08% accuracy, 98.14% precision, and 98.4% recall. This result indicates that proper detection of COVID-19 patients with the help of X-ray images of the chest is possible using machine learning methods with high accuracy. This type of data-driven system can help us to overcome the current appalling situation throughout the world.

Knowledge Absorptive Capacity for Technological Innovation Capabilities

2014 ◽  
pp. 418-443
Author(s):  
Murad Ali ◽  
Kichan Park
Keyword(s):  
Absorptive Capacity ◽  
Developing Economies ◽  
Qualitative Content Analysis ◽  
Case Study Research ◽  
Global Market ◽  
Development Stages ◽  
External Knowledge ◽  
Foreign Companies ◽  
Proposed Model ◽  
Past Experiences

This chapter presents the development stages of a theoretical model of Knowledge Absorptive Capacity (KAC) that shows how most, if not all, firms in developing countries initiate, implement, assimilate, improve, and develop external knowledge. The chapter reviews the literature, models, and frameworks related to knowledge absorptive capacity. The chapter utilizes a qualitative content analysis as an explanation method in case study research to validate the proposed model. The chapter then analyzes Korean firms as a case in point to illustrate how Korean firms have built their knowledge absorptive capacity. The model consists of four stages: 1) knowledge initiation, 2) knowledge imitation, 3) knowledge improvement, and finally, 4) knowledge innovation or 4KI. The framework shows four development stages at Korean firms as: 1) entrance of foreign companies into the Korean market and their reluctance to transfer their knowledge and information sharing to Korean firms, initiating its knowledge absorptive capacity, 2) Korean firms started knowledge absorptive capacity by means of imitating knowledge from external (especially foreign firms), 3) it then developed knowledge absorptive capacity by means of improving external knowledge, and finally, 4) capability to create their own knowledge and becoming one of the leading economy in the world which challenges firms from advanced countries in the global market. The chapter also highlights the developmental changes in the electronics industry of Korea. Keeping past experiences in consideration, the authors conclude that this model provides useful implications for developing economies, known as latecomers following the same pattern of KAC.

Sign in / Sign up

Export Citation Format

Share Document