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The purpose of this paper is to propose the use of graph theoretic structural modeling for assessing the possible reduction in complexity of the work flow procedures in an organization due to lean initiatives. A tool to assess the impact of lean initiative on complexity of the system at an early stage of decision making is proposed.

First, the permanent function-based graph theoretic structural model has been applied to understand the complex structure of a manufacturing system under consideration. The model helps by systematically breaking it into different sub-graphs that identify all the cycles of interactions among the subsystems in the organization in a systematic manner. The physical interpretation of the existing quantitative methods linked to graph theoretic methodology, namely two types of coefficients of dissimilarity, has been used to evolve the new measures of organizational complexity. The new methods have been deployed for studying the impact of different lean initiatives on complexity reduction in a case industrial organization.

The usefulness and the application of new proposed measures of complexity have been demonstrated with the help of three cases of lean initiatives in an industrial organization. The new measures of complexity have been proposed as a credible tool for studying the lean initiatives and their implications.

The paper may lead many researchers to use the proposed tool to model different cases of lean manufacturing and pave a new direction for future research in lean manufacturing.

The paper demonstrates the application of new tools through cases and the tool may be used by practitioners of lean philosophy or total quality management to model and investigate their decisions.

The proposed measures of complexity are absolutely new addition to the tool box of graph theoretic structural modeling and have a potential to be adopted by practical decision makers to steer their organizations though such decisions before the costly interruptions in manufacturing systems are tried on ground.

The purpose of this paper is to identify the key enabler for total productive maintenance (TPM) implementation in Indian small and medium enterprises (SMEs) by using graph theoretic approach (GTA). There are certain enablers for TPM implementation which helps the organization to implement it successfully. It is very essential to identify the nature and impact of these key enablers.

A large number of the enablers (27) have identified for TPM implementation in Indian SMEs from the available literature, questionnaire survey and expert opinion. These TPM enablers have categorized into six major categories.

In this research work, the intensity of identifying enablers has been calculated to show their impact or influence in TPM implementation. The value of intensity of TPM enablers shows the role or impact of individual enabler in the implementation of TPM in Indian SMEs.

This study provides an easy-to-use methodology for the practical decision makers in the manufacturing industry to improve their performance by implementing TPM in Indian SMEs. A detailed methodology has prepared to identify the enablers for TPM implementation in Indian SMEs by using GTA. This study also explains that how to check the feasibility of an organization to implement TPM in Indian SMEs successfully.

TPM is an improvement concept which holds the potential to improve manufacturing organizations, but its implementation is not easy in Indian SMEs. The reason behind the unsuccessful implementation of TPM in most of the organizations is the ignorance of impact of innumerable enablers and barriers.

The purpose of this paper is to study the impact of restructuring in the manufacturing system at the conceptual stage using graph theoretic model.

Some restructuring decisions are conceptualized which reflect the aim of the organization to gradually evolve the manufacturing system towards a leaner structure. This is achieved by way of defining simplified procedures so that lesser hindrance in terms of cycles of interactions is encountered. The restructuring decisions are represented by five restructured configurations of the manufacturing system, through gradual removal of appropriate interaction links. The graph theoretic models are developed for original configuration and each of the new restructured configurations and the resulting structural characterization information is used to compare the structure of restructured configurations with the original configuration. The value of the coefficient of dissimilarity of each of the new configurations with respect to the original configuration is obtained to have a quantitative estimate of the simplification that may be achieved by different contemplated restructuring decisions.

The present work shows that the restructuring decisions can be represented by different configurations in the form of schematic diagrams involving minor changes in the interaction structure among subsystems of the manufacturing system. The quantitative analysis using coefficient of dissimilarity for restructuring decisions indicated that there are varying levels of impact created by five comparable restructuring decisions considered in the study. The findings have a potential to guide the restructuring efforts by identifying a focus area that can produce greater impact of restructuring.

The findings are valid for a particular case manufacturing organization which does not involve itself in extensive design activity. The study is based on the assumption that the schematic diagram and graph theoretic model captured all the relevant influencing factors of the manufacturing system.

The study provides an easy to use methodology for the practical decision makers in manufacturing industry striving to improve the performance of their organization. It can provide the analysis of restructuring decisions at the conceptual stage itself without the necessity of disturbing the normal functioning of the organization. There is a scope for identifying focus areas where the restructuring may yield comparatively greater dividends.

The study of restructuring by representing it in the form of changes in interactions among subsystems of a manufacturing system and investigation of the impact of such restructuring efforts at the conceptual stage using graph theoretic model has been carried out for the first time.

Reliability assessment does require an effective structural modelling approach for systems, in general and manufacturing systems are no exception. This paper aims to develop it for large manufacturing systems using graph models, a systems approach.

Structural graph models for reliability at various hierarchical levels are developed by considering a CNC cam shaft grinding machine. The system reliability expression is obtained by converting the reliability graphs into equivalent matrices, which helps to evaluate and analyse system.

Using the obtained reliability expressions at various hierarchical levels of the system, it is possible not only to evaluate its reliability from structure point of view but also to identify weak structural elements from reliability point of view.

The approach can be extended to include the influence of other parameters, such as human, component and environment, etc., on the system reliability.

The approach helps to design and develop manufacturing systems from reliability consideration by assessing their possible alternatives among these.

The suggested methodology is useful for reliability evaluation of large and complex manufacturing systems.

The purpose of this paper is to identify potential constraints and determine the constraint structure in a steel manufacturing plant. “Potential constraint” is defined as a factor that is either a constraint at present or can become one in the future and “constraint structure” is used to denote the network of influences between the potential constraints in an organization.

A three-step methodology was followed. First, potential constraints in a steel manufacturing plant were identified with a literature review and expert inputs. Then, the fuzzy decision-making trial and evaluation laboratory (fuzzy DEMATEL) technique was applied to uncover the structure and finally, an ex-post validation and refinement of the results was done with help from other experts.

A total of 10 key potential constraints to steel manufacturing were identified. The two outputs of fuzzy DEMATEL – the influence scatter plot (ISP) and the influence network diagram (IND) – together reveal the constraint structure. The 10 potential constraints could be classified into three types – influencers, mediators and influenced – respectively. Of these “Top management commitment (TMC)” and “Clear vision and long-term planning (CLP)” influence other factors the most, and are themselves influenced the least; while “Customer Relationship Management (CRM)” is most influenced by other factors, while influencing other factors the least.

Potential constraints and the constraint structure can help decision makers in a steel manufacturing plant to identify which organizational factors to address and achieving the plant's goals.

This is the first study that analyzed organizational level constraints in a steel manufacturing context.

One of the major challenges for developing countries is the lack of mechanisms for the evaluation of critical success factors (CSFs) of quality initiatives, which hampers the journey toward sustainability. Lean Six Sigma (LSS) has been one of the most widely used initiatives supporting quality improvement with wastes reduction and facilitating sustainability. To expedite LSS and its spread, it is important to evaluate key CSFs. Accordingly, the purpose of this paper is to provide an approach for the evaluation of LSS-CSFs for Indian small and medium enterprises (SMEs).

The paper uses a graph theoretic approach and demonstrates the evaluation of LSS-CSFs by proposing an index. The development of index is illustrated using a set of seven prioritized CSFs based on the literature review paper (Lande et al., 2016).

This study guides about the translation of CSFs in the form of an index (number) and will benefit both researchers and practitioners, who wish to study the role of key CSFs for implementation and audit requirements for sustainability.

Authors remain confined only to Indian SMEs.

LSS possesses the potential to enhance the performance of manufacturing SMEs, but its evaluation is not easy. This attempt for offering a useful evaluation scheme involving CSFs, in the areas of LSS in developing country contexts, is the first. The approach also facilitates both quality audits and benchmarking between different sets of CSFs. The approach is generalizable and can be extended in other areas.

Easily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers.

In total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC.

A scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors.

The authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.

A rich agenda for future research in the field of Lean Manufacturing (LM) is available in the academic literature. The purpose of this paper is to determine the LM future research methodologies suggested in the literature and to classify them into themes. Classifying these themes into broad categories is also an aim of the present study.

For the purpose of the present study, a systematic literature review (SLR) of peer reviewed journal articles in LM was conducted. A total of 214 articles published in 46 journals during 2010–2020 were collected from four major management science publishers, namely, Emerald Online, Elsevier/Science Direct, Springer Link and Taylor and Francis. To organize the qualitative data into meaningful themes and these themes into broad categories, the quality tool “affinity diagram” was applied.

The review of LM articles that are increasing over time reveals the “vital few” academic journals, which have published most of the sample articles. The plethora of the suggested future research methodologies are analytically presented and classified into meaningful themes, namely, the size of the research sample and its composition, several types of study (other than surveys), longitudinal studies, applying advanced statistical analysis and (mathematical) modeling techniques, objective, real and quantitative data, surveys, mixed/multiple research studies, reliability and validity analysis, using computer-aided technology for data collection and processing and research collaborations. These themes in turn are classified into broad categories, namely, study, data and statistical analysis and modeling.

This SLR is not comprehensive because the number of the databases searched is restricted to four. Moreover, the literature review is limited to peer reviewed journal articles regarding Lean only in the manufacturing sector, while the subject reviewed is limited to the future research methodologies. The subjectivity of classifying the large number of the future research methodologies into themes and these themes into broad categories is also a limitation of the present SLR. Based on these limitations, future literature review studies can be carried out.

Researchers can be analytically informed about the future research methodologies suggested in the literature and their respective key themes and broad categories, to design original research studies of high academic and practical value.

This study goes beyond previous SLRs on LM by presenting analytically the plethora of the future research methodologies suggested in the literature as well as by identifying natural patterns or groupings of these methodologies.

Generalization ability of genetic programming (GP) models relies highly on the choice of parameter settings chosen and the fitness function used. The purpose of this paper is to conduct critical survey followed by quantitative analysis to determine the appropriate parameter settings and fitness function responsible for evolving the GP models with higher generalization ability.

For having a better understanding about the parameter settings, the present work examines the notion, applications, abilities and the issues of GP in the modelling of machining processes. A gamut of model selection criteria have been used in fitness functions of GP, but, the choice of an appropriate one is unclear. In this work, GP is applied to model the turning process to study the effect of fitness functions on its performance.

The results show that the fitness function, structural risk minimization (SRM) gives better generalization ability of the models than those of other fitness functions.

This study is of its first kind where two main contributions are listed addressing the need of evolving GP models with higher generalization ability. First is the survey study conducted to determine the parameter settings and second, the quantitative analysis for unearthing the best fitness function.

The purpose of this study is to measure the effects of density, moisture, fiber content on unconfined compressive strength (UCS) of soil by formulating the models based on evolutionary approach and artificial neural networks (ANN).

The present work proposes evolutionary approach of multi-gene genetic programming (MGGP) to formulate the functional relationships between UCS of reinforced soil and four inputs (soil moisture, soil density, fiber content and unreinforced soil strength) of the silty sand. The hidden non-linear relationships between UCS of reinforced soil and the four inputs are determined by sensitivity and parametric analysis of the MGGP model.

The performance of MGGP is compared to those of ANN and the statistical analysis indicates that the MGGP model is the best and is able to generalize the UCS of reinforced soil satisfactorily beyond the given input range.

The explicit MGGP model will be useful to provide optimum input values for design and analysis of various geotechnical infrastructures. In addition, utilization of Water hyacinth reinforced fiber reinforced soil will minimize negative impact of this species on environment and may generate rural employment.

This work is first of its kind in application and development of explicit holistic model for evaluating the compressive strength of heterogeneous soil blinded with fiber content. This includes the experimental and cross-validation for testing robustness of the model.