Purpose
– Total quality management (TQM) is a process and philosophy to achieve customer satisfaction in long term by improving the products, processes and services effectively and efficiently. TQM implementation is turning into a complex practice due to the increasing number of effective factors and key elements labelled as critical success factors (CSFs). The purpose of this paper is to analyse the relations between CSFs of TQM and to provide decision makers has a clear picture of relations by determining the most affecting – both the number of CSFs which this factor affects and the its effect degree on relevant CSFs are higher comparing to other factors – of this factors affected factors – both the number of CSFs and their effect degree on these factors are higher – that influences a successful TQM implementation.
Design/methodology/approach
– The paper refers to fuzzy cognitive maps (FCMs) that allow dynamic modelling of a system in consideration of a complex network structure and the effects of factors to each other. The method demonstrates causal representations between CSFs under uncertainty to represent the relations and interaction between them and performs qualitative simulations to analyse the factors that have the highest impact on continuous improvement of quality management process. The evaluations are performed by five academicians whose professions are on both the areas of TQM and FCM.
Findings
– FCM analysis shows how the most affecting and affected factors influence the other CSF in order to manage a successful TQM implementation.
Originality/value
– The critical factors of TQM implementation are in the focus of most of the empirical studies in the literature. However, none of them considers the dynamic interactions between the factors. This study employs FCM to explore the CSFs that influence the TQM implementation process considering the relations among them to observe the most affecting and affected factors based on the changes of determined CSFs.
Job evaluation is the process of systematically determining a relative internal value of a job in an organization. The most widespread method applied in job evaluation process is the point-factor method. In this method, for determining the worth of a job, a set of compensable factors are identified. In this study, a fuzzy multi-criteria approach is developed for job evaluation. In the first stage, factors are weighted by using a Fuzzy Analytic Hierarchy Process (F-AHP) method. Afterwards, the obtained weights are used in the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) for scoring jobs. At last these scores are used to support for an ideal compensation system. An illustrative case study demonstrating the applicability of the model is given. Finally a sensitivity analysis for the deviations in the criteria weights is also made.
SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis is a commonly used and an important technique for analyzing internal and external environments in order to provide a systematic approach and support for a decision making. SWOT is criticized mostly for considering only qualitative examination of environmental factors, no priority for various factors and strategies, and no vagueness of the factors under fuzziness. In this paper, fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) integrated with fuzzy AHP (Analytical Hierarchy Process) is used to develop fuzzy multi-criteria SWOT analysis in order to overcome these shortcomings. Nuclear power plant site selection, which is a strategic and important issue for Turkey's energy policy making, is considered as an application case study that demonstrated the applicability of the developed fuzzy SWOT model.
Process Failure Modes and Effects Analysis (PFMEA) concept, has been developed based on the success of Failure Modes and Effects Analysis (FMEA) to include a broader analysis team for the realization of a comprehensive analysis in a short time. The most common use of the PFMEA involves manufacturing processes as they are required to be closely examined against any unnatural deviation in the state of the process for producing products with consistent quality. In a typical FMEA, for each failure modes, three risk factors; severity (S), occurrence (O), and detectability (D) are evaluated and their multiplication derives the risk priority number (RPN). However there are many shortcomings of this classical crisp RPN calculation. This study introduces a fuzzy hybrid approach that allows experts to use linguistic variables for determining S, O, and D for PFMEA by applying fuzzy 'technique for order preference by similarity to ideal solution' (TOPSIS) and fuzzy 'analytical hierarchy process' (AHP). An application to a spindle manufacturing process expresses the relevance of the fuzzy hybrid model in PFMEA.
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