Lean-green performance management in Indian SMEs: a novel perspective using the best-worst method approach

Investigating the barriers of blockchain technology integrated food supply chain: a BWM approach

Purpose

Blockchain is an evolving technology that has an impact on a variety of industries and related operations including food supply chain. There are several challenges associated in the implementation of blockchain in the food supply chain. As a result, the goal of this research is to identify and analyse the barriers associated with the implementation of blockchain in food supply chains.

Design/methodology/approach

A comprehensive literature review is conducted to identify 16 primary barriers associated with the implementation of blockchain technology in the food supply chain and experts finalise these identified barriers and further categorise them into four groups. Furthermore, the best worst method is used to prioritise the finalised barriers.

Findings

The findings suggest that “technological barriers” and “organisational barriers” are primary barriers among the identified barriers for the implementation of blockchain. These barriers could be mitigated through supply chain collaboration, efficient blockchain technology development through research and development, and increasing technical competence.

Research limitations/implications

In terms of limitation, there is a possibility that some barriers were overlooked in the literature review process, and expert judgement might be prejudiced. This paper examines the blockchain implementation in the food supply chain, to assist policymakers in overcoming these barriers and ensuring effective adoption.

Originality/value

This study focuses on the effective implementation of blockchain technology in the food supply chain in the context of emerging economies.

Prioritization and ranking of lean practices: a case study

Purpose

The paper aims to identify, prioritize and rank lean practices in the context of an Indian automotive component manufacturing organization using interpretive ranking process (IRP) and interpretive structural modeling (ISM) approaches.

Design/methodology/approach

Lean practices are identified from the literature. Then, two hierarchical models were are developed using two distinct modeling approaches – ISM and IRP with expert opinions from an Indian automotive component manufacturing organization to analyze the contextual relationships among the various lean practices and to prioritize and rank them with respect to performance dimensions.

Findings

In the study, the hierarchical structural models are developed using ISM and IRP approaches for an Indian automotive component manufacturing organization. In ISM-based modeling, lean practices can be categorized into five levels. Top priority should be given to the motivators followed by value chain, system/technology and organization centric practices. IRP model shows the dominance relationship among the various lean practices with respect to performance dimensions.

Practical implications

The models are constructed from the organizational standpoint to evaluate their impact to the implementation of lean manufacturing. The study leverages the organizations to prioritize limited resources as per the hierarchy. Managers get the inter-linkages and ranking of various lean practices, which leads to a better perspective for the effective implementation of lean. The structural models also assist management to assign proper roles to employees/departments for effective lean implementation.

Originality/value

There is hardly any structural model of lean practices in the literature for clustering, prioritizing and ranking of lean practices. The study fills this gap and develops the hierarchical models of lean practices through IRP and ISM approaches for an Indian automotive component manufacturing organization. The results from both approaches are compared for illustrating the benefits of one over the other.

Analysing drivers of efficiency in the leather industry: a two-stage double bootstrap DEA approach

Purpose

The Indian leather industry contributes to economic growth at a significant environmental cost. Due to the rising global demand for sustainable leather products, promoting efficient input utilisation has become vital. This study measures input efficiency and its determinants for leather industry in order for it to improve its future performance.

Design/methodology/approach

In the first stage, bootstrap data envelopment analysis (DEA) approach is used for measuring efficiency and analysing firms' differences based on their geographical location, organisational structures, urban-rural location and sub-industrial groups. A second stage regression examines efficiency determinants using size, age, skill and capital-labour intensity as the explanatory variables.

Findings

Efficiency result shows a significant potential of minimising inputs by 47% provided the firms adopt best practices. West Bengal firms, urban located firms, individual and proprietorship owned firms and leather consumer goods firms are found to be relatively efficient to their counterparts. Size, skilled managerial staff and labour-intensive firms positively affect efficiency.

Practical implications

Construction of well-connected roads for accessing urban retail markets and provision of reliable electricity would improve efficiency of rural firms. Small-scale enterprises have a larger share in Indian leather industry; therefore, policy should focus on enhancing the firms' scale and investing in training facilities to skill employed labour for ensuring optimal use of inputs.

Originality/value

Previous studies on the leather industry have used the conventional DEA efficiency measurement approach. This study uses DEA bootstrapping model for robust efficiency estimates and provides consistent inferences about the determinants.

Identification of specific metrics for sustainable lean manufacturing in the automobile industries

Purpose

This study explains the importance of performance measures and identifies the specific performance measures of sustainable lean manufacturing (SLM) for automobile industries. Awareness towards sustainability and continuous improvement approaches demand monitoring of the sustainable lean impact on organization/industry, and hence, identifying the specific performance metrics is of peak importance.

Design/methodology/approach

In this study, specific metrics for social, economic and environmental performance are identified from a systematic literature review of 82 significantly related journal articles. The importance of the identified metrics is assessed with the help of questionnaire responses from a group of industrial experts.

Findings

Performance indicators are statistically analyzed category wise and assessed. The key metrics are summarized based on the survey data followed by a discussion with industrial experts. From this study, performance measures have been identified and validated through hypothesis testing for Indian automobile industries. Certification of IATF16949 implementation found an important vertical for SLM implementation. In this study, SLM implementation initiatives are discussed, and reward scheme for outstanding performers are identified as important initiatives are followed by small improvement culture.

Practical implications

The proposed discussion of this study is useful for industrialist and researchers, as SLM performance measures are well explained for Indian automobile industries. In this study, future research direction is also explained related to other industries. These summarized performance measures will help to maintain SLM in industries.

Originality/value

This paper presents the original literature review based on the study of SLM, as no extensive study is available where SLM performance measure explained for automobile industries. Key initiatives and vertical of SLM are well explained for Indian automobile industries. This study proposed a complete framework for SLM implementation considering competitive manufacturing targets.

Benchmarking framework for sustainable manufacturing based MCDM techniques

Purpose

The purpose of this paper is to develop a model for sustainable manufacturing by adopting a combined approach using AHP, fuzzy TOPSIS and fuzzy EDAS methods. The proposed model aims to identify and prioritize the sustainable factors and technical requirements that help in improving the sustainability of manufacturing processes.

Design/methodology/approach

The proposed approach integrates both AHP, Fuzzy EDAS and Fuzzy TOPSIS. AHP method is used to generate the weights of the sustainable factors. Fuzzy EDAS and Fuzzy TOPSIS are applied to rank and determine the application priority of a set of improvement approaches. The ranks carried out from each MCDM approach is assessed by computing the spearman's correlation coefficient.

Findings

The results reveal the proposed model is efficient in sustainable factors and the technical requirements prioritizing. In addition, the results carried out from this study indicate the high efficiency of AHP, Fuzzy EDAS and Fuzzy TOPSIS in decision making. Besides, the results indicate that the model provides a useable methodology for managers' staff to select the desirable sustainable factors and technical requirements for sustainable manufacturing.

Research limitations/implications

The main limitation of this paper is that the proposed approach investigates an average number of factors and technical requirements.

Originality/value

This paper investigates an integrated MCDM approach for sustainable factors and technical requirements prioritization. In addition, the presented work pointed out that AHP, Fuzzy EDAS and Fuzzy TOPSIS approach can manipulate several conflict attributes in a sustainable manufacturing context.

Benchmarking smart manufacturing drivers using Grey TOPSIS and COPRAS-G approaches

Purpose

The objective of the study is to identify and analyse drivers of smart manufacturing using integrated grey-based approaches. The analysis facilitates industry practitioners in the identification of preference of drivers through which smart manufacturing can be implemented. These drivers are explored based on existing literature and expert opinion.

Design/methodology/approach

Modern manufacturing firms have been adopting smart manufacturing concepts to sustain in the global competitive landscape. Smart manufacturing incorporates integrated technologies with a flexible workforce to interlink the cyber and physical world. In order to facilitate the effective deployment of smart manufacturing, key drivers need to be analysed. This article presents a study in which 25 drivers of smart manufacturing and 8 criteria are analysed. Integrated grey Technique for Order Preference by Similarity to Ideal Solution (grey TOPSIS) is applied to rank the drivers. The derived ranking is validated using “Complex Proportional Assessment – Grey” (COPRAS-G) approach.

Findings

In total, 25 drivers with 8 criteria are being considered and an integrated grey TOPSIS approach is applied. The ranking order of drivers is obtained and further sensitivity analysis is also done.

Research limitations/implications

In the present study, 25 drivers of smart manufacturing are analysed. In the future, additional drivers could be considered.

Practical implications

The study presented has been done with inputs from industry experts, and hence the inferences have practical relevance. Industry practitioners need to focus on these drivers in order to implement smart manufacturing in industry.

Originality/value

The analysis of drivers of smart manufacturing is the original contribution of the authors.