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The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).

In the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.

The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.

A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.

This study investigates the quantitative aspect of the various strains of operational excellence (OE) and competitive-potential (CP) in the SME sector. It has five steps, i.e., identifying the key performance constructs of OE and their hypothesized relationship pattern from literature, validating these constructs through factor analysis, formalizing their empirical relationships by structural-equation-modeling (SEM), path analysis of performance constructs with the empirical results, and lastly proposing a framework for OE deployment in SMEs.

Data for the deployment scores of operational excellence procedures (OEPs) were collected through a structured questionnaire survey. Nine hundred participants from a stratified random sample were approached for the survey, and 473 responses were received. Sample stratification was based on Gender, Education, Experience, Position, Department and Industry. Respondents had 5–30 years of experience managing manufacturing operations, holding the manager position and above.

The path analysis of the structural model provides unique insights into OE's practical aspects in SMEs (small and medium enterprises). For example, Contractual-conformance and Process-efficiency play pivotal roles as both have a significant positive impact on CP. Supplier efficacy, Consistency and Product-excellence do not improve CP unless mediated by Contractual-conformance or Process-efficiency.

The study provides important implications for academia, policymakers and managers. The study identifies and validates the operational excellence key performance practices and proposes a framework for manufacturing organizations. SME managers can follow the framework to develop effective operational excellence strategies to help them achieve their organizational goals. Additionally, the study emphasizes the need for continuous culture in SMEs, which will help to support operational excellence deployment. Overall, the implications presented in the study will help SMEs to enhance their competitiveness and operational performance.

The study explores the empirical investigation of the operational excellence deployment in SMEs. The study uses a mixed method approach for research design, including qualitative and quantitative approaches, and uses SEM to test the proposed framework. Validation of OE's six key performance constructs and establishing their empirical relation is an attempt to advance the Operations excellence theory. Unlike large enterprises, SMEs demonstrate an incohesive response to the practices pertaining to Supplier efficacy, Consistency and Product-excellence. This unique response pattern requires special treatment, which is incorporated into the proposed framework.