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The purpose of this paper is to ascertain and understand the relationship dynamics among the quality-enabled factors (QEFs) affecting the initiation stage of production system life cycle (PSLC). This study presents an approach for refining the decision making in the initiation stage of the production system.

In this paper, ten QEFs have been identified for the initiation stage of PSLC. An interpretive structural modelling (ISM) approach has been utilized to cultivate an organizational association among these identified QEFs. The results of ISM approach are used as an input to fuzzy Matriced’ Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis, to identify the driving and dependence power of QEFs.

The key consequences of this paper are to prioritize the strategic QEFs in reducing the risks linked with initiation stage of production system. The integrated model obtained by ISM-fuzzy MICMAC illustrates that there exists two clusters of QEFs, one is having high driving power and low dependency power which requires extreme consideration and of strategic importance (such as honesty and sincerity in collecting and analyzing field data) and other is having high dependence power and low driving power and are resultant effects (such as strategic decision-making ability).

The integrated ISM-fuzzy MICMAC model developed is not statistically corroborated; consequently structural equation modelling (SEM) approach which is also known as linear structural relationship approach could be utilized to examine the validity of developed hypothetical model.

This is first study to identify ten QEFs in initiation stage of production system and further, to deploy integrated ISM-fuzzy MICMAC approach to recognize and categorize the QEFs influencing the initiation stage of production system.

The purpose of this paper is to generate awareness of contributions made by benchmarking toward building performance of Indian service industries in globally market. Ranking of Benchmarking is done on the basis of their application which give confidence for the managers to adopt in their Industries so that they may become best in their field.

Methodology consists of three phase: define, phase include definitions, factors of benchmarking as literature outcomes, questionnaire survey and outcome of survey. In the second phase, analysis of collected data and applications of multi-criteria decision-making approaches [technique for order preference by similarity to ideal solution (TOPSIS) and analytical network process (ANP)] are used. The last phase includes comparison of results which gives validation in similarities of ranking obtained.

The study identifies seven different benchmarking techniques used for service industries. Using TOPSIS and ANP approaches shows similarity that external benchmarking, performance benchmarking and internal benchmarking are the first three ranks that give basis for several critical success factors s, namely, planning, reliability, standardization, time behavior, usability, etc., as part of benchmarking using in service industries.

The limitation is the assumptions made by multi-criteria decision-making approaches which may effect the analysis of the study as these are taken theoretically.

This study is a first attempt to find similarities in both techniques while comparing benchmarking in Indian service industries.

The paper aims to discuss current Technical Education scenarios in India. It proposes modelling the factors affecting quality in a technical institute and then applying a suitable technique for assessment, comparison and ranking.

The paper chose graph theoretic approach for quantification of quality-enabled model. Further, the paper has validated the approach by taking examples of institutions and applying the methodology.

The paper provides a systematic methodology to build a quality model for quantification of various factors in a technical institute. The qualitative effect represented in form of a single numerical index is a novel method for such representations. The illustrated methodology in the paper is equally useful for comparison and ranking of a set of institutes.

Because of the chosen factors in the approach, the methodology may not be equally suitable based on type and size of institute and the geographical location.

The paper includes demonstration on application of methodology for comparing the quality in a quantitative manner. The dynamic model allows changing factors and/or their effects as per requirement.

In future, the methodology can be taken up by government/regulatory bodies and can convey the comparisons of institutions to stakeholders including students and parents.

This paper attempts to develop a novel method for comparing quality that can be used by accreditation bodies.

To represent the effect of ‘human factors in total quality management (TQM) environment’ in terms of a single numerical index by considering their inheritances and interactions.

Various human factors affecting the TQM culture in an organization are identified and discussed for the sub factors affecting them. These factors are interacting with each other and their overall effect helps an organization in attaining TQM enabled needs. The paper attempts to represent the overall effect of human factors quantitatively by developing a mathematical model using graph theoretic approach. In this approach, interaction among identified human factors is represented through digraph, matrix model and a multinomial.

The extent of human aspects present in an organization, conducive to TQM culture is represented in terms of the “human index”. It provides an insight into the human factors at system and subsystem level. The developed procedure may be useful for self‐analysis and comparison among organizations.

Since, human behaviour is difficult to predict, so are the human factors. The paper considers general factors, which may vary depending on type of organization, size of organization and geographical location. There is a scope of research in factor specific organizations.

It provides a useful methodology for organizations to assess human aspects and improve upon therein. Procedure for stepwise application of methodology is given with example that may help an industry to implement it.

The paper attempts to quantify the intangibles through systematic approach and is of value to industries to improve upon their work environment.

Advanced manufacturing system (AMS) offers opportunities for industries to improve their technology, flexibility and profitability through a highly efficient and focused approach to manufacturing effectiveness. Selecting a proper AMS is a complicated task for the managers as it involves large tangible and intangible selection attributes. Failure to take right decision in selecting proper AMS alternative may even lead industry to losses. The purpose of this paper, therefore, is to rank the AMS alternatives by using fuzzy grey relational analysis, which will help managers when choosing an appropriate AMS.

This research proposes a multi‐attribute decision‐making (MADM) method, fuzzy grey relational analysis (FGRA), for AMS selection. The methodology is explained as follows. AMS alternatives and selection attributes will be chosen. The qualitative attributes will be converted into quantitative using fuzzy conversion scale. Then these data will be pre‐processed to normalize every value. This step is done to convert all alternatives into a comparability sequence. According to these sequences a reference sequence (ideal target sequence) is defined. Then, the grey relational coefficient between all comparability sequences and the reference sequence is calculated. Finally, based on these grey relational coefficients, the grey relational grade between the reference sequence and every comparability sequences is calculated. If a comparability sequence translated from an alternative has the highest grey relational grade between the reference sequence and itself, then that alternative will be the best choice. Fuzzy logic is used here to convert linguistic data into crisp score.

The proposed method is validated and compared by taking two examples from literature. The traditional statistical techniques require large data sets for evaluating attributes while grey theory on the contrary solve the multi attribute decision making problems with small data sets. This methodology will significantly increase the efficiency of decision making and overall competitiveness for manufacturing industries. This approach will motivate more and more industries to invest in AMS.

This method will help managers to weigh the AMS alternatives before actually buying them, which will in turn save money and time. This will build confidence of the top management for investing in costly technology such as AMS.

From time to time, various researchers have proposed various techniques to select the AMS. However, a survey on current evaluation methods shows that they are all less objective, lack accurate data processing, involve large calculations because of their complexity. In this paper, the authors attempt to solve the problem of AMS selection with FGRA, which is more logical, axiomatic, generates results in fewer steps with less calculations and is easy to understand. This paper succeeds in getting AMS alternatives' ranking using fuzzy grey relational analysis.

The length of the debate regarding a new business idea and resource allocation between Ritu Jhajharia, Vinay Chaurasia and Sandeep Poonia on an evening in March 2016, was enough to make the piping hot pasta go cold. After much deliberation, The Rolling Circle, a Mumbai-based marketing firm concentrated on the digital marketing business arena was born with plans of potentially viable business strategies, blueprints of employee and other key resources' allocations, strategies for client procurement and vesting of some amount of investment money in initial pitches. Yet, in the last two plus years, the company has procured 40 plus clients and are looking to finish their account books with Rs. 1 crore revenue in their banks by 2018. Despite initial financial crunch, the company continued to get businesses. One day in February 2018, the resignation of co-founder Vinay Chaurasia was a blow to the foundation of the company. While clocking their best business so far and continuously growing since its inception, The Rolling Circle is now faced with one of the biggest dilemmas in the company lifecycle: in their line of business, there is neither a constant flow of clients nor a stability in terms of the employees working in the organisation; the founding team is grappling with the glaring strategic question of whether they should recruit high-profile employees in the organisation first and then continue onboarding premium clients, or they should first sign with premium clients and then look out for high-profile employees to serve those clients? Who will wait: the client or the employees?

This research has been taken up by the human resource center of organization X, for designing a curriculum for a training course named “Basic Computer Application”, by optimizing topics and finalizing teaching strategies, based on needs and expectations collected from past and prospective training participants from different ranks of employees of X. The study aims to discuss this issue.

The research strategy adopted is based on a survey conducted using a specially designed questionnaire. The questionnaire was circulated to 256 personnel selected randomly having computer backgrounds as well as non-computer backgrounds, who had either attended past batches of the same training course or attended different training courses. The data thus collected was processed using VOC Table and the affinity diagram and subsequently fed into the house of quality (HOQ) for obtaining desired output.

The study has identified and optimized topics for the course, based on employee needs/wants, using the two-stage QFD approach. After delivering the course with an optimized curriculum and teaching strategy, it is observed that the overall participant satisfaction performance of the course has increased from 3 to 4.6, which is higher than the goal set at 4. This indicates an overall enhancement in the satisfaction of participants with the delivery of the new improved course curriculum.

As indicated by the survey findings, the competence level of personnel with computer backgrounds is higher than that of personnel without computer backgrounds, which would result in different design considerations while constructing QFDs for these two entirely different groups of participants. This study has been limited to the course curriculum design for personnel with non-computer backgrounds only. The curriculum design for personnel with computer backgrounds will be addressed separately, in a future study.

This is an original study conducted on randomly selected employees across the country from among personnel working for different establishments of organization X. The approach taken in this study for optimizing a course curriculum is unique in the way that it uses a two-stage QFD, which is traditionally used for the design of a product, its components, and the subsequent manufacturing process. This study has employed a planning matrix that incorporated participant-given ratings for each individual need and also participant-decided benchmarking ratings of other similar courses. Estimation of final target has been done by factoring in participants' competence ratings for each individual topic, for a more truthful representation of participant opinion on the final output. This study has also adopted a unique approach of considering only top 80% (in terms of frequency of occurrence) of total responses while calculating weighted averages, instead of calculating the mean value of all data points. In this way, only the significant contributors have been considered while analyzing data.

The research paper presents analysis and prioritization of barriers influencing the improvement in the effectiveness of manufacturing system. The purpose of this paper is to develop an integrated fuzzy-based multi-criteria decision-making (F-MCDM) framework to assist management of the case company in the selection of most effective manufacturing system. The framework helps in prioritizing the manufacturing systems on the basis of their effectiveness affected by the barriers.

In this paper, on the basis of experts’ opinion, five barriers have been identified in a brain-storming session. The problem of prioritization of manufacturing system is a multi-criteria decision-making (MCDM) problem and hence is solved by using the F-MCDM approach using dominance matrix.

Manufacturing systems’ effectiveness for Indian industries is influenced by barriers. The prioritization of manufacturing systems depends on qualitative factor decision-making criteria. Among the manufacturing systems, leagile manufacturing system is given the highest priority followed by lean manufacturing system, agile manufacturing system, flexible manufacturing system and cellular manufacturing system.

The selection of an appropriate manufacturing system plays a vital role for sustainable growth of the manufacturing company. In the present work, barriers which influence the effectiveness of manufacturing system have been identified. On the basis of degree of influence of barriers on the effectiveness of the manufacturing system, five alternative manufacturing systems are prioritized. The framework will help the management of the case company to take reasonable decision for the adoption of the appropriate manufacturing system.

The results of the research work are very useful for the manufacturing companies interested in analyzing the alternative manufacturing systems on the basis of their effectiveness and their sensitivity toward various barriers. The management of Indian manufacturing company will take decision to adopt a manufacturing system whose effectiveness is least sensitive toward barriers. Effectiveness of such manufacturing system will improve with time without having retardation due to barriers. With improved effectiveness of the manufacturing system, the manufacturing company would be able to survive with global competition. The result of the present work is based on the inputs from the case company and may vary for the other manufacturing company. In the present work, only five alternative manufacturing systems and five barriers have been considered. To obtain the better result, MCDM approach with more number of alternative manufacturing systems and barriers might be considered.

The research work is based on the fuzzy analytic hierarchy process framework and on the case study conducted by the authors. The work carried out is original in nature and based on the real-life case study.

This paper aims to examine the role of technology-enabled leadership (TEL) in achieving performance-enhancement outcomes. This empirical investigation is from a dynamic capabilities perspective.

A conceptual framework presents a general model with an overarching dynamic capabilities theory. The research model defines key variables – macroeconomic turbulence (MET), TEL, analytics-based responsiveness (ABR), knowledge-driven innovation (KDI) and performance enhancement outcomes (PEOs). Empirical tests of eight hypotheses are conducted using an original survey instrument based on the respondents (n = 203).

In response to MET, TEL is crucial in implementing ABR in strategic planning aspects and KDI in operational dimensions. In turn, ABR and KDI are key mediating variables that achieve a desirable level of PEOs.

Despite the limitations associated with survey-based research, the findings suggest robust analytical results. For example, the alternative model suggests that MET negatively moderates the positive impact of TEL on ABR, while KDI positively moderates the positive impact of ABR on PEOs.

Outstanding firms demonstrate both TEL and data-savvy decision-making processes. Knowledge-intensive innovation allows firms to achieve multiple performance outcomes that help firms survive and thrive in challenging market environments.

There has been a growing concern about how firms use customers’ data in choosing their business practices. Customers are concerned about privacy and data security issues if firms misuse the data while pursuing profit-based goals. However, this empirical investigation confirms that business analytics improve firm performance (e.g. firm productivity enhancements), ultimately benefiting the customers. Providing relevant data to firms has potentially positively enhanced customer services and thus benefits societal well-being.

Using an original survey instrument, this research empirically tests a research model that defines the complex paths between TEL and competitive performance outcomes.

Having complexity theory as the overarching conceptual rationale, this paper presents a research model that defines external drivers, strategic and internal business practices and performance outcomes in service firm contexts.

Using an original survey instrument, the authors empirically examine business process management (1) adopting the mediating roles of shared goal practices and technology imperative practices (2) involving moderating roles of competitive market environments and joint implementation practices.

In response to dynamic market complexity, firms implement business process management through shared goal practices and technology imperative practices for achieving customer service outcomes. The findings also suggest the moderating roles of competitive pressure and joint implementation practices.

Generalizations here are limited to service firms. Increasing customer service expectations (e.g. simplicity, convenience, visual images and rapid responses) require firms to involve cross-functional work throughout their organizational processes.

In digital environments, business process management requires socio-technological synergy through shared goal practices and technology imperative practices.

Building on a theory-driven research model, a survey instrument provides tools to examine business process management of service firms that sense dynamic market complexity challenges and translate them to achieve desirable customer service outcomes.