A conceptual model for the implementation of lean product development

Companies are faced with the need to address their product development challenges innovatively in order to stay competitive in today's market. One way of doing that is the integration of lean thinking in their product development process. However, due to the lack of clear understanding of the lean thinking performance measurements, the near absent of a holistic and unifying measuring method and the near or non-existence of an evaluating conceptual model to allow for the evaluation of the performance of the lean product development processes, many companies are unable to fully implement the lean thinking principle in their Product development process. In dealing with these issues, this article has therefore proposed a conceptual model which is based on some core critical success factors for the examination of lean performance in the product development process

An intuitionistic fuzzy multi-criteria decision-making method based on an exponential-related function

Intuitionistic fuzzy multiple criteria decision making (MCDM) method which is based on an exponential-related function, adopted in the Technique for order preference by similarity to ideal solution (TOPSIS) has been proposed in this study. The exponential-related function which is used for comparing intuitionistic-fuzzy-sets (IFS), and as a replacement for the traditional exponential score function which is only effective for determining priority weights that involve pairwise-comparison, has been applied, for computing the separation measure from the fuzzy positive and negative ideal solution to determine the relative closeness-coefficients of alternatives. The main advantage of this method includes (1) its ability to account for Decision-makers (DMs) attitudinal-character in the decision-making process as-well-as to represent the aggregated effect of the positive/negative evaluations in the performance ratings of the alternatives based on the IFS-data and (2) The simplicity of the method both in its concept and computational procedures. To demonstrate the feasibility of the method, it has been applied for the evaluation of some hypothetical design-related problems and for a real-life case study

Development of a TQM-based Framework for Product Infant Failure Assessment

Product infant failures have been traced to the product development and production stage. Researchers and practitioners have opined that total quality management (TQM) can be used to properly managed these failures. While their suggestions have helped in this regard, there is limited information on how to scientifically aggregate criteria that can be used to specifically identify the most suitable TQM technique for product infant failure improvement, especially at the development stage. Hence, this study proposes a fuzzy-based multi-criteria framework for this problem. The framework uses intuitionistic fuzzy set to handle vague and imprecise judgment and Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) to rank selected TQM techniques.  Real-world data sets were used to evaluate the framework performance, while TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) was used to validate the framework performance. Based on the results obtained, the IFWG-VIKOR and TOPSIS methods rank the most and least suitable TQM practices as TM4 and TM11, respectively. The framework can be used to provides insights into the management of techniques that can address infant product failure issues at the early stage of product development.

Cross-Infection and Risk Prevention in Production Environment: A Multi-criteria Decision-making Approach

Manufacturing relies heavily on the physical presence of human resources, and disease outbreaks that force separation, such as: COVID-19, harm output. As a solution to the problem, this paper proposes a new Hybrid Multi-criteria Decision-making (H-MCDM) model based on the Intuitionistic Fuzzy Weighted Geometric (IFWG) operator, the Intuitionistic Fuzzy Hamacher Interactive Weighting Averaging (IFHIWA) operator, and the Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IF-TOPSIS) method for the evaluation, management, and prevention planning. In addition to the role that modeling played in solving the cross-infection problem, the results obtained from putting the model through its implementation show that the use of protective materials during manufacturing operations is the most suitable among the finite alternatives considered, when the safety of the workforce/workplace, cost of implementing risk-mitigating actions, virus transmission level, and ease of implementation are all taken into account. Thus, the proposed model has been validated through a feasibility test, so contributes to maintaining production and reducing cross-infections in workplaces while an epidemic is ongoing

A Proactive Decision-Making Model for Evaluating the Reliability of Infrastructure Assets of a Railway System

Railway infrastructure is generally classified as either fixed or movable infrastructure assets. Failure in any of the assets could lead to the complete shutdown and disruption of the entire system, economic loss, inconvenience to passengers and the train operating company(s), and can sometimes result in death or injury in the event of the derailment of the rolling stock. Considering the importance of the railway infrastructure assets, it is only necessary to continuously explore their behavior, reliability, and safety. In this paper, a proactive multi-criteria decision-making model that is based on an interval-valued intuitionistic fuzzy set and some reliability quantitative parameters has been proposed for the evaluation of the reliability of the infrastructure assets. Results from the evaluation show that the failure mode ‘Broken and defective rails’ has the most risk and reliability concerns. Hence, priority should be given to the failure mode to avoid a total system collapse

A Generalized Triangular Intuitionistic Fuzzy Geometric Averaging Operator for Decision-Making in Engineering and Management

Triangular intuitionistic fuzzy number (TIFN) is a more generalized platform for expressing imprecise, incomplete, and inconsistent information when solving multi-criteria decision-making problems, as well as for expressing and reflecting the evaluation information in several dimensions. In this paper, the TIFN has been applied for solving multi-criteria decision-making (MCDM) problems, first, by defining some existing triangular intuitionistic fuzzy geometric aggregation operators, and then developing a new triangular intuitionistic fuzzy geometric aggregation operator, which is the generalized triangular intuitionistic fuzzy ordered weighted geometric averaging (GTIFOWGA) operator. Based on these operators, a new approach for solving multicriteria decision-making problems when the weight information is fixed is proposed. Finally, a numerical example is provided to show the applicability and rationality of the presented method, followed by a comparative analysis using similar existing computational approaches

Extended TOPSIS model for solving multi-attribute decision making problems in engineering

In this paper, we propose an exponential-related function (ER) and develop an intuitionistic fuzzy TOPSIS model based on the function (IF-TOPSISEF) to solve multi-attribute decision making (MADM) problems in which the performance ratings are expressed in intuitionistic fuzzy sets (IFSs). The main advantage of this new approach is that the exponential-related function is able to represent the aggregated effect of the positive and negative evaluations in the performance ratings of alternatives based on the intuitionistic fuzzy set (IFS) data. It also serves as a mean for the computations of the separation measures of each alternative from the intuitionistic fuzzy positive and negative ideal solutions to determine the relative closeness coefficients. To demonstrate the effectiveness of the proposed method, the proposed IF-TOPSISEF is applied for the evaluation of the concept designs of a part in an HDD machine (The drill pipe slider), and for some hypothetical examples

Extended TOPSIS model for solving multi-attribute decision making problems in engineering

In this paper, we propose an exponential-related function (ER) and develop an intuitionistic fuzzy TOPSIS model based on the function (IF-TOPSISEF) to solve multi-attribute decision making (MADM) problems in which the performance ratings are expressed in intuitionistic fuzzy sets (IFSs). The main advantage of this new approach is that the exponential-related function is able to represent the aggregated effect of the positive and negative evaluations in the performance ratings of alternatives based on the intuitionistic fuzzy set (IFS) data. It also serves as a mean for the computations of the separation measures of each alternative from the intuitionistic fuzzy positive and negative ideal solutions to determine the relative closeness coefficients. To demonstrate the effectiveness of the proposed method, the proposed IF-TOPSISEF is applied for the evaluation of the concept designs of a part in an HDD machine (The drill pipe slider), and for some hypothetical examples. Growing Science Ltd. All rights reserved.

Development of a Fault Detection and Localization Model for a Water Distribution Network

Water distribution networks are complex systems that aid in the delivery of water to residential and non-residential areas. However, the networks can be affected by different types of faults, which could lead to the wastage of treated water. As such, there is a need to develop a reliable leakage detection and localization system that can detect leak occurrences in the network. This study, using a simulated dataset from EPANET, presents the application of supervised machine learning classifiers for leak detection and localization in the water distribution network of the University of Port Harcourt Choba campus. The study compared three machine learning classification tools that are used in pattern recognition analysis: the support vector machine, k-nearest neighbor, and artificial neural network. The robustness and effectiveness of the proposed approach are compared with those of the performance of the classifiers for leakage detection in the network of the case study. The results show that the support vector machine performs the best, with 79% accuracy, while the respective accuracies for the remaining classifiers are 70% for the k-nearest neighbor and 61% for the artificial neural networks. The high accuracy demonstrated by the models shows that they are able to detect and address issues relating to fault detection in a water distribution network. This model could provide a leakage detection system to be applied to buildings for the efficient management of water in their networks

A Hybrid Fuzzy Model for the performance evaluation of Biomethane gas as a renewable energy source

Biomethane gas which has been applied as a renewable energy source in some of the industries, like automotive, food manufacturing, aerospace, and maritime, is regarded as the engine block of the economic advancement of most of the developed and developing countries. Hence, there is need to continuously explore the behavior of the energy source and to assess its performance when applied in some key engines and systems used in the different industries. In this paper, however, an integrated multicriteria-based model termed “triangular intuitionistic fuzzy hamming distance (TIFHD)” has been proposed for the performance evaluation of the biomethane gas as an energy source used for a specific system and engines with respect to the following criteria, environmental factor (CE), economic (CEc), social (CS), energy balance (CEb), and energy sustainability (CEs). The performance ranking results from the evaluation shows the following ranking order EN2 > EN1> EN3> EN4,with Stirling engine (EN4) and diesel-cycle engine (EN2) as the system and engines with the best and least biomethane gas performance respectively. EN1 and EN3 are industrial oven and the Otto cycle engine (gas motors) that is normally used in the food manufacturing and automotive industries, respectivel