Verifying Dynamic Kano’s Model to Support New Product/Service Development

Purpose: Although firms try to shorten time-to-market, the duration of product development projects might anyway jeopardize the assumptions made at the beginning of the design process. This includes the definition of product attributes for ensuring customer satisfaction, thus forecasting techniques could be worthwhile. Within Kano’s method, trajectories of quality attributes have been identified and they can be potentially useful to the scope, but they have not been carefully verified. Design/methodology/approach: The paper takes on the above verification challenge by exploring studies of customer satisfaction conducted by means of Kano’s model regarding manifold industrial fields. The paper focuses on changes in the relevance of customer requirements reported in different contributions and analyses data statistically. Findings: The dynamic trajectories outlined in Kano’s model are partially confirmed and they are valuable in the mid-term to predict changes in customer preferences. The use of quantitative indicators portraying the extent of customer satisfaction and dissatisfaction leads to more reliable predictions. Research limitations/implications: In order to use as many data as possible, information has been gathered from different industrial fields, which can exhibit different paces in changes of customer preferences. Practical implications: The results benefit firms willing to have a clearer picture of customer main drivers for customer satisfaction at the time of market launch, although customer surveys are conducted at the beginning of product development projects. Originality/value: The paper puts into question previous assumptions about modifications of customer preferences, which, however are just empirically supported and assesses how these can be exploited in a reliable way.Peer Reviewe

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

Key Performance Indicators for Sustainable Campus Assessment: A Case of Andalas University

Sustainable campus has became an important issue amongst universities around the world. Universities can generate a significant impacts to environment due to the high usage of energy, extensive transportation, massive waste, high consumption of materials, and extensive development of buildings and facilities. Thus, there is a need to assess the sustainable campus performance. This paper proposes a set of key performance indicators (KPIs) for sustainable campus assessment consisting of six categories divided into a total of 35 indicators. Analytical Hierarchy Process (AHP) method is applied to determine the importance weight of the KPIs. The results indicated the most important category for the sustainable campus assessment is education with an importance weight of 0.2665, while energy and climate change is regarded as the least important category. It is hoped the proposed KPIs can assist the universities to achieve the higher performance in sustainable campus

Evaluating the Effectiveness of Design for the Environment Tools to Help Meet Sustainability and Design Goals

Environmental impacts of electronics are a growing concern because the amount and type of materials used in production of the devices, the impacts to the environment from discarded electronics and the early retirement of products due to rapidly evolving devices, changing design trends, and perceived technological obsolescence. Design for the Environment is a sustainability strategy that aims to reduce the environmental impacts through techniques that enable sustainability solutions during the design decision-making process. In order to suit the diverse needs of sustainable design practitioners, there has been a large number of tools for Design for the Environment (DfE) developed, confusing product designers and engineers about which tool to choose to meet sustainability and design goals. Therefore, there is a need for methods that help designers choose DfE tools that are reliable, objective, effective, and easy to integrate in the regular product design and development activities. This thesis project develops a methodology to help designers screen, test and validate the results of applying DfE tools recommendations, when searching for the most effective techniques. First, the project proposes a method to classify tools under common DfE categories of tools, screen the tools, and identify potential techniques. Next, the author of this thesis, who is the designer on this document, designs an electronics device, under regular design parameters, for testing a set of potential DfE techniques. Prior to testing DfE tools, the author develops a set of sustainability metrics to measure the impacts of the electronic device and the reductions in environmental impacts obtained from the application of each DfE tool recommendations. After assessing the impacts of the device using the metrics, there were three DfE tools tested, Autodesk Eco Materials Adviser (EMA), DfE Matrix, and Electronic Product Assessment Tool (EPEAT) to determine product environmental burdens, propose solutions, and make design recommendations that improve the product environmental profile. Each tool identified materials, life cycle stages, and components that cause the product environmental burdens; these findings were targets for redesign. Addressing the tools findings resulted in three redesigns of the electronic device re-assessed with the sustainability metrics to measure the reductions of the environmental impacts. The metrics were useful to validate the results of applying the tools and help the product designer and sustainability practitioner developing this thesis to identify the most effective tools, the benefits, weaknesses, and strengths of using diverse tools

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse