Benchmarking fuzzy logic and ANFIS approaches for leanness evaluation in an Indian SME

2017 ◽  
Vol 24 (4) ◽  
pp. 973-993 ◽  
Author(s):  
Rohit Agrawal ◽  
P. Asokan ◽  
S. Vinodh
Keyword(s):  
Fuzzy Logic ◽  
Lean Manufacturing ◽  
Fuzzy Inference ◽  
Evaluation Model ◽  
Medium Size ◽  
Inference System ◽  
Content Type ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Leanness Evaluation

Purpose The purpose of this paper is to present a study that is focused on application of fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) approaches for leanness evaluation in an Indian small- and medium-size enterprise (SME). Design/methodology/approach Lean manufacturing concepts are being adopted by SMEs to sustain in the competitive manufacturing landscape. Performance of lean system needs to be assessed using appropriate methods. A model for measuring lean performance is proposed with five enablers, 30 criteria and 90 attributes. Leanness index is computed using fuzzy logic approach and benchmarked with ANFIS approach. Findings Leanness index computed using fuzzy logic approach is found to be (4.47, 5.97, 7.55) and that of ANFIS approach is found to be 5.84 to facilitate benchmarking of leanness evaluation. After finding weaker areas, certain improvement initiatives are being deployed. Research limitations/implications The developed model for leanness evaluation has been test implemented in an SME. In future, the model could be test implemented in several SMEs. Practical implications A case study conducted in an SME involved in heavy engineering fabrication is presented. Therefore, the inferences derived from the study has practical propensity. Originality/value The development of leanness evaluation model for SMEs and deployment in an industrial scenario are the original contributions of the authors.

scholarly journals Aplikasi Fuzzy Logic dalam Diagnosa Penyakit Diabetes Mellitus pada PUSKESMAS di Jakarta Timur

2016 ◽  
Vol 2 (3) ◽  
pp. 21-30 ◽  
Author(s):  
Zaimatun Niswati ◽  
Aulia Paramita ◽  
Fanisya Alva Mustika
Keyword(s):  
Diabetes Mellitus ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fuzzy Inference ◽  
Treatment Decision ◽  
Inference System ◽  
Family History Of Diabetes ◽  
Patients With Diabetes ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Abstract— Patients with diabetes mellitus increased from year to year. This is due to delays in diagnosis of the disease and also because of unhealthy lifestyles. This study aims to create an application of decision support systems in the field of health, namely the diagnosis of the disease Diabetes Mellitus with Fuzzy Inference System (FIS) Mamdani, so that a layman can perform early diagnosis and immediate treatment. Decision Support System Techniques developed to improve the effectiveness of decision-makers. Samples are six puskesmas in East Jakarta. This application uses five variables as inputs consisting of glucose 2 hours after a meal, Diastolic blood pressure, body mass index, family history of diabetes, total pregnancies and one variable as output. The data obtained be processed using fuzzy logic approach to programming Matlab and made Graphical User Interface (GUI). Result is an expert system for diagnosis of Diabetes Mellitus .The trial results by midwives and nurses puskesmas is 100% of these applications in accordance with the doctor”s diagnosis. It is to help improve the quality of service in the Puskesmas in East Jakarta, thus satisfying the users and puskesmas be able to compete both nationally and internationally. 

scholarly journals Course recommendation system using fuzzy logic approach

2020 ◽  
Vol 17 (1) ◽  
pp. 365
Author(s):  
Mohd Suffian Sulaiman ◽  
Amylia Ahamad Tamizi ◽  
Mohd Razif Shamsudin ◽  
Azri Azmi
Keyword(s):  
Fuzzy Logic ◽  
Recommendation System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Education Environment ◽  
User Design ◽  
Interest Level ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
The Right

Course selection is a key for success in student’s academic path. In today’s education environment, various courses offered by different academic institutions required the students to explore the course outline manually. Most of them are lacking in knowledge, having dilemma and making blind selections to choose the right course. Therefore, it is essential to have a course recommendation to provide guidance to a student to choose the course related with their interest and skill. This paper proposed to develop a course recommendation system using fuzzy logic approach. The development methodology of this system involves several phases include requirements planning, user design and construction for prototyping, testing and cutover. This study used the fuzzy rules technique in order to calculate each associated student’s skill and interest level based on Mamdani fuzzy inference system method. Then, the rules will generate final outcome which recommend the suitable course path and provide the details to a user based on their course test. The result shows the functionality of this system has been achieved and works well. This study is significantly helping the students to choose their course based on the interest and skill.

scholarly journals Fuzzy Logic Approach for Routing in Internet of Things Network

2021 ◽  
Vol 15 (1) ◽  
pp. 18-24
Author(s):  
Reena P. Pingale ◽  
S. N. Shinde
Keyword(s):  
Fuzzy Logic ◽  
Network Lifetime ◽  
Network Performance ◽  
Fuzzy Inference ◽  
Residual Energy ◽  
Link Quality ◽  
Inference System ◽  
Input And Output ◽  
Fuzzy Logic Approach ◽  
Logic Approach

A performance of network is evaluated by considering different parameters. The network lifetime depends on many factors Residual energy, Link lifetime and Delay. The Major Challenge in IoT is to the increased lifetime of low power and lossy network (RPL).The process considering input and output to evaluate Network performance by considering the above factors. The proposed system makes use of FIS (Fuzzy Inference System) for selecting the best path to maximize network lifetime. The outcome obtained by using MATLAB and Network performance is increased. The excellent route is selected if Residual Energy is 194, Link quality is 51.2 and Delay is 1.05 then excellent route quality is 73.4%.

Assessment of readiness level for implementing lean six sigma in healthcare organization using fuzzy logic approach

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
V. Vaishnavi ◽  
M. Suresh
Keyword(s):  
Fuzzy Logic ◽  
Conceptual Model ◽  
Six Sigma ◽  
Healthcare Organization ◽  
Lean Six Sigma ◽  
Medium Size ◽  
Content Type ◽  
Readiness Level ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Purpose Lean Six Sigma (LSS) is a widely accepted business improvement methodology in healthcare, which aims to improve operations and quality and reduce cost, medical errors and waiting time by combing the principles of lean thinking with Six Sigma methodologies. To implement LSS successfully in healthcare organizations it is necessary to know the readiness level before starting the change process. Thus, the purpose of this paper is to assess the readiness level for the implementation of LSS in healthcare using a fuzzy logic approach. Design/methodology/approach The current study uses a fuzzy logic approach to develop an assessment model for readiness to implement LSS. The conceptual model for readiness is developed with 5 enablers, 16 criteria and 48 attributes identified from the literature review. The current study does the study in a medium-size hospital from India. Findings The fuzzy readiness for implementation of LSS index (FRLSSI) and fuzzy performance importance index (FPII) are calculated to identify the readiness level for the implementation of LSS in the case hospital. The FRLSSI is computed as average ready with (3.30, 5.06 and 6.83) and the FPII computed helps to identify 15 weaker attributes from 48 attributes. Research limitations/implications The current study uses only one hospital for study. In the future, the model can be tested in many hospitals. Practical implications The current study would be used by the managers of a healthcare organization to identify the readiness level of their organization to implement LSS. The proposed model is based on the identification of enablers, criteria and attributes to assess the readiness level of a healthcare organization and it helps to improve the readiness level to implement LSS effectively. Originality/value The present study contributes to the knowledge of readiness for the implementation of LSS in a healthcare organization. The conceptual model is developed for assessing the readiness level of a healthcare organization and it helps to improve the readiness level for successful implementation of LSS. Weaker attributes are identified and necessary corrective actions should be taken by the management to improve the readiness. The continuation of the assessment readiness model over a period of time would help to improve the readiness level of healthcare for the implementation of LSS.

scholarly journals Bankruptcy Prediction Model with Risk Factors using Fuzzy Logic Approach

2021 ◽  
Vol 6 (2) ◽  
pp. 102-110
Author(s):  
Teoh Yeong Kin ◽  
Akmal Haziq Ahmad Aizam ◽  
Suzanawati Abu Hasan ◽  
Anas Fathul Ariffin ◽  
Norpah Mahat
Keyword(s):  
Risk Factors ◽  
Fuzzy Logic ◽  
Prediction Model ◽  
Fuzzy Inference ◽  
Bankruptcy Prediction ◽  
Financial Flexibility ◽  
Inference System ◽  
Financial Firms ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Forecasting bankruptcy remains crucial, especially during this pandemic. Managers, financial institutions, and government agencies rely on the information regarding an impending bankruptcy threat to make decisions. This paper developed a straightforward bankruptcy prediction model using the fuzzy logic approach for individuals and companies to evaluate their performance and analyse the tendency of getting bankrupt. A sample of 250 respondents from banks and financial firms were tested using the qualitative risk factors, namely, industrial risk, management risk, financial flexibility, credibility, competitiveness, and operational risk. This study provides a comprehensive analysis using the Fuzzy Inference System (FIS) editor in the MATLAB software, where the model's accuracy is compared to the actual results. The results show an accuracy rate of 99.20%, indicating that this approach can determine the likelihood of bankruptcy. The fuzzy logic approach can improve prediction accuracy while also guiding decision-makers in detecting and preventing possible financial crises in their early phases.

Leanness evaluation in health-care organizations using fuzzy logic approach

2020 ◽  
Vol 28 (6) ◽  
pp. 1201-1225 ◽  
Author(s):  
M. Suresh ◽  
V. Vaishnavi ◽  
Rajesh D. Pai
Keyword(s):  
Health Care ◽  
Fuzzy Logic ◽  
Health Care Organizations ◽  
Health Care Organization ◽  
Point Of View ◽  
Care Organization ◽  
Content Type ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Leanness Evaluation

Purpose Lean practices are one of the fundamental practices adopted by health-care organizations to improve service quality and to reduce cost. In this context, the measurement of leanness in health-care organizations has become imperative. The purpose of this study is to measure the leanness of a hospital using fuzzy logic. Design/methodology/approach The design of the research includes two major steps. First, the identification of enablers, criteria and attributes of leanness constitutes the measures of assessment. Second, the above measures in the case hospital are assessed by using fuzzy logic approach. Findings This study suggests that leanness assessment is essential to identify the current lean capability of a health-care organization. This would help the health-care organizations to improve their lean performance further. The findings of the study suggest that the leanness of the case hospital is “Lean” (fuzzy range: 5.61, 7.24 and 8.91). Practical implications This study brings in three important implications from managerial point of view. First, it helps the management to assess the current level of leanness of the hospital. Second, it identifies the attributes that prevent the organization from being more lean. Third, it provides suggestive measures to address the weaker attributes and enables the enhancement of lean performance further. Originality/value The leanness assessment framework developed in the hospital operations is found to be original, and it adds value to the leanness assessment in health-care operations.

scholarly journals Measuring flood resilience: a fuzzy logic approach

2017 ◽  
Vol 35 (5) ◽  
pp. 470-487 ◽  
Author(s):  
Victor Oluwasina Oladokun ◽  
David G. Proverbs ◽  
Jessica Lamond
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Business Case ◽  
Individual Property ◽  
Inference System ◽  
Content Type ◽  
Wide Range ◽  
Resilience Index ◽  
Fuzzy Logic Approach ◽  
The Individual

Purpose Flood resilience is emerging as a major component of an integrated strategic approach to flood risk management. This approach recognizes that some flooding is inevitable and aligns with the concept of “living with water.” Resilience measurement is a key in making business case for investments in resilient retrofits/adaptations, and could potentially be used to inform the design of new developments in flood prone areas. The literature is, however, sparse on frameworks for measuring flood resilience. The purpose of this paper is to describe the development of a fuzzy logic (FL)-based resilience measuring model, drawing on a synthesis of extant flood resilience and FL literature. Design/methodology/approach An abstraction of the flood resilience system followed by identification and characterization of systems’ variables and parameters were carried out. The resulting model was transformed into a fuzzy inference system (FIS) using three input factors: inherent resilience, supportive facilities (SF) and resident capacity. Findings The resulting FIS generates resilience index for households with a wide range of techno-economic and socio-environmental features. Originality/value It is concluded that the FL-based model provides a veritable tool for the measurement of flood resilience at the level of the individual property, and with the potential to be further developed for larger scale applications, i.e. at the community or regional levels.

scholarly journals Supply chain risk assessment approach for process quality risks

2017 ◽  
Vol 34 (7) ◽  
pp. 940-954 ◽  
Author(s):  
Abhijeet Ghadge ◽  
Xie Fang ◽  
Samir Dani ◽  
Jiju Antony
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Supply Chain ◽  
Failure Modes ◽  
Design Stage ◽  
Security Risks ◽  
Content Type ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Purpose The purpose of this paper is to proactively analyse and mitigate the root causes of the product and security risks. The case study approach examines the effectiveness of the fuzzy logic approach for assessing the product and process-related failure modes within global supply chain context. Design/methodology/approach The case study of a Printed Circuit Board Company in China is used as a platform for conducting the research. Using data triangulation, the data are collected and analyzed through interviews, questionnaires, expert opinions and quantitative modelling for some interesting insights. Findings Fuzzy logic approach for failure mode and effect analysis (FMEA) provides a structured approach for understanding complex behaviour of failure modes and their associated risks for products and processes. Today’s managers should conduct robust risk assessment during the design stage to avoid product safety and security risks such as recalls. Research limitations/implications The research is based on the single case study and multiple cases from different industry sectors may provide some additional insights. Originality/value The study attempts to mitigate the root causes of product and processes using fuzzy approach to FMEA in supply chain network.

Evaluation of agility in supply chains: a case study of an Indian manufacturing organization

2017 ◽  
Vol 28 (2) ◽  
pp. 212-231 ◽  
Author(s):  
Bharat Singh Patel ◽  
Cherian Samuel ◽  
S.K. Sharma
Keyword(s):  
Fuzzy Logic ◽  
Supply Chain ◽  
Human Perception ◽  
North India ◽  
Scoring Method ◽  
Content Type ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Manufacturing Organization

Purpose The purpose of this paper is to report a case study carried out to assess the agility and identify obstacles to agility in a supply chain. A human perception-based framework is used for the calculation of agility. The case study was carried out in a North India-based manufacturing organization. Design/methodology/approach In this study, the concept of a multi-grade fuzzy logic approach is used. Using this concept, the overall agility index has been determined. The fuzzy logic approach has been used to overcome the disadvantages such as impreciseness and vagueness using a scoring method. Findings From the analysis, it is observed that the organization on which the study was performed is “very agile.” After evaluating the agility level, the fuzzy performance importance index is calculated, which helps to identify the barriers of agility in the supply chain. These barriers help decision makers to implement appropriate improvement measures for improving agility level. Overall, 11 barriers were identified in the study. Research limitations/implications Managers of the contemporary manufacturing organization have to measure the agility level of the organization and identify barriers to agility in order to survive in a competitive environment. The obstacles identified in this study are used to improve the performance of the organization. The enterprise should improve on the weak areas in order to achieve the highest agility level. Originality/value The agile supply chain (ASC) enablers proposed by previous researchers are not sufficient for the evaluation of agility of a supply chain. There are a few more ASC enablers such as customer satisfaction, flexibility and adaptability that also play a vital role in making a supply chain agile. Adding these three ASC enablers, a total of seven ASC enablers along with their attributes are being considered for the development of a conceptual model.

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