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Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar.

CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI).

The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers.

The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

This paper proposes a new approach source node exclusion method (SNEM) to evaluate terminal pair reliability of complex communication networks.

The proposed approach breaks a non‐series‐parallel network to obtain its sub‐networks by excluding the source node from rest of the network. The reliabilities of these sub‐networks are thereafter computed by first applying the series‐parallel‐reductions to it and if any sub‐network results into another non‐series‐parallel network then it is solved by the recursive application of SNEM.

The proposed method has been applied on a variety of network and found to be quite simple, robust, and fast for terminal pair reliability evaluation of large and complex networks.

The proposed approach is quite simple in application and applicable to any general networks, i.e. directed and undirected. The method does not require any prior information such as path (or cut) sets of the network and their pre‐processing thereafter or perform complex tests on networks to match a predefined criterion.

The proposed approach provides an easy to develop and easy to use tool to determine terminal pair reliability of a communication network. The approach is particularly useful for communication network designer and analysts.

The purpose of this paper is to ascertain overall system health and maintenance needs with degree of certainty using condition‐monitoring data with hierarchical fuzzy inference system.

In process plants, equipment condition is ascertained using condition‐monitoring data for each condition indicator. For large systems with multiple condition indicators, estimating the overall system health becomes cumbersome. The decision of selecting the equipment for an overhaul is mostly determined by generic guidelines, and seldom backed up by condition‐monitoring data. The proposed approach uses a hierarchical system health assessment using fuzzy inference on condition‐monitoring data collected over a period. Each subsystem health is ascertained with degree of certainty using degree of match operation performed on fuzzy sets of condition‐monitoring data and expert opinion. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge.

The proposed approach has been applied to a large electric motor (> 500kW), which is treated as four subsystems i.e. power transmission system, electromagnetic system, ventilation system and support system. Fuzzy set of condition‐monitoring data of each condition indicator on each subsystem is used to ascertain the degree of match with the expert opinion fuzzy set, thus inferring the need for periodical overhaul. Subjective expert opinion and quantitative condition‐monitoring data have been evaluated using hierarchical fuzzy inference system with a rule base. It is found that the certainty of each subsystem's health is not the same at the end of 600 days of monitoring and can be classified as “very good”, “good”, “marginal” and “sick”. Degree of certainty has helped in taking a managerial decision to avoid “over‐maintenance” and to ensure reliability. Large volumes of condition‐monitoring data not only helped in assessing motor overhaul health, but also guide the maintenance engineer to suitably review maintenance/monitoring strategy on similar systems to achieve desired reliability goals.

Condition‐monitoring data collected for long periods can be utilized to understand the degree of certainty of degradation pattern in the longer time frame with reference to domain knowledge to improve effectiveness of predictive maintenance towards reliability.

The paper gives an opportunity to evaluate quantitative condition‐monitoring data and subjective/qualitative domain expertise using fuzzy sets. The predictive maintenance cycle “Monitor‐analyse‐plan‐repair‐restore‐operate” is scientifically regulated with a degree of certainty. Approach is generic and can be applied to a variety of process equipment to ensure reliability through effective predictive maintenance.

This paper aims to prioritize preventive maintenance actions on process equipment by evaluating the risk associated with failure modes using predictive maintenance data instead of maintenance history alone.

In process plants, maintenance task identification is based on the failure mode and effect analysis (FMEA). To eliminate or mitigate risk caused by failure modes, maintenance tasks need to be prioritized. Risk priority number (RPN) can be used to rank the risk. RPN is estimated invariably using maintenance history. However, maintenance history has deficiencies, like limited data, inconsistency etc. To overcome these deficiencies, the proposed approach uses the predictive maintenance data clubbed with expert domain knowledge. Unlike the traditional single step approach, RPN is estimated in two steps, i.e. Step 1 estimates the “Possibility of failure mode detection” and Step 2 estimates RPN using output of step 1. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge. Fuzzy inference system is developed using MATLAB® 6.5.

The proposed approach is applied to a large gearbox in an integrated steel plant. The gearbox is covered under a predictive maintenance program. RPN for each of the failure modes is estimated with the proposed approach and compared with the maintenance task schedule. The illustrative case study results show that the proposed approach helps in detection of failure modes more scientifically and prevents “Over maintenance” to ensure reliability.

This approach gives an opportunity to integrate the predictive maintenance data and subjective/qualitative domain expertise to evaluate the possibility of failure mode detection (POD) quantitatively, which is otherwise purely estimated using subjective judgments. The approach is generic and can be applied to a variety of process equipment to ensure reliability through prioritized maintenance scheduling.

The purpose of this paper is to identify the inter-partner dynamics-based enablers of joint venture (JV) competitiveness. In addition, this paper models the interactions among identified enablers/factors to project the strength of their relationship with JV competitiveness.

ISM- and total interpretive structural modeling (TISM)-based fuzzy TISM approach has been used to examine the interactions and strength of interactions among identified enablers of JV competitiveness.

The analysis concludes that inter-partner dynamics-based enablers, such as partner fit, power symmetry and trust, have strong driving power and low dependence power and are at the lowest level of hierarchy in fuzzy TISM model. Variables like collaborative communication, organizational learning and absorptive capacity are linkage variables and they have high dependence as well as driving power and they lie in the second level of fuzzy TISM hierarchy. Strategic flexibility is found to have high dependence power and has weak driving power. The outcome variable JV competitiveness found to have zero driving power and highest dependence power.

The findings have implications for practitioners and policy makers. JVs may achieve competitiveness by managing identified enablers (inter-partner dynamics).

Present paper is one among the few efforts that address the issue of JV competitiveness (post-formation of JV). Methodologically also, this study is one among few initial efforts of using modified fuzzy TISM to explore and understand the linkage among enablers and outcome variables. Modified fuzzy TISM process carries out transitivity checks along with the successive pair-wise comparisons and simplifies the fuzzy TISM approach.

The primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.

This research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.

This study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.

This analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.

This study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

The purpose of this study is to assess the effect of chicken breeds on quality characteristics of meat nuggets.

The formulation of meat nuggets prepared from meat of Cobb-400, Vanraja, Aseel and Kadaknath separately consisted of 60 per cent lean meat. The emulsion was prepared by standard method and moulded into nuggets. Cooking was performed under pressure (120°C/15 Psi for 30 min).

Emulsion and cooked nuggets both showed no significant differences in pH values among the breeds. Higher moisture and fat content was observed in emulsion and nuggets prepared from Cobb-400, while respective protein and ash was maximum in Kadaknath and Vanraja meat-based emulsions and nuggets. The per cent emulsion stability (87.04 ± 0.45) and cooking yield (85.24 ± 0.06) was reported highest in Cobb-400, which indicates the better water holding capacity and suitability of Cobb-400 meat for the development of nuggets at six weeks of age. The mean sensory scores for colour and appearance (7.12 ± 0.28), as well as flavour (7.00 ± 0.04), were significantly (p < 0.05) higher in Cobb-400 nuggets and lowest in Kadaknath (6.21 ± 0.03 and 6.65 ± 0.06). However, no significant differences were noticed in other sensory attributes among treatments.

The fatty acid and amino acid profile analysis may be helpful to understand the original nutritional difference in prepared nuggets.

The study will be off immense help in optimum utilization of meat of locally available chicken breeds for breed-specific and cost-effective product formulations.

The products will be acceptable to all commodities because it is made up of chicken meat.

The effect of chicken breeds on meat nuggets is relatively new aspect and essential to establish suitability of meat of locally available chicken breeds for product development.

This study aims to identify and prioritize various growth-accelerating factors in the Indian automotive industry. It further develops a hierarchical model to examine the mutual interactions between the factors, their dependence and their driving power.

This study first identifies the growth-accelerating factors and then uses the modified total interpretive structural modeling (m-TISM) framework, which is an extended version of TISM. It further uses MICMAC analysis to analyze the mutual interrelation between the identified factors.

This study highlights the interrelation amongst the factors using m-TISM model. A hierarchical model shows the level of autonomous, dependence, linkage and independent factors considering the Indian automotive industry. This study also provides the understanding related to the interdependence of growth-accelerating factors.

The government and practitioners could evaluate the growth-accelerating factors which have higher driving power for implementing efficient policies and strategy formulation. By implementing m-TISM model in the Indian automotive industry, auto manufacturers can become more productive and profitable. Future studies could use other methods such as expert opinion to derive the factors, and further model could be verified using structural equation modeling technique.

This study uses a novel m-TISM framework for the analysis of growth-accelerating factors in the context of the Indian automotive industry. It further provides a detailed theoretical and conceptual understanding relating to the philosophy and establishes an interrelation amongst these under-researched growth-accelerating factors.