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In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

The purpose of this article is to develop an integrative framework of accelerator design to answer the question of what activities accelerators perform and how they function within a structured framework. Research on the functioning of accelerators as a mechanism for startup engagement produced multiple empirical results. However, the comparability of relevant research is strongly limited, currently hindering theoretical developments. Existing accelerator design models often differ and only partially overlap, which leaves extant literature with a fragmented and discordant conceptual understanding.

Based on a meta-synthesis method using qualitative analysis of 36 accelerator design articles, an integrative framework is developed. After identification of relevant literature, a renowned method for extracting, coding and synthesizing data on individual and cross-study level is applied to identify accelerator design constructs. Eventually, identified accelerator design constructs are integrated into a framework resting on the activity system lens of business model design.

The article reconciles fragmented knowledge on accelerator design and shows how accelerator design can be holistically conceptualized by 32 key activities clustered in eight design dimensions. The framework is complemented by an initial guideline for measurement. The findings further highlight formerly disregarded aspects of governance and community formation from a processual and structural perspective.

This article is the first to present a comprehensive picture of accelerator design integrating multiple empirical findings of prior research into a single coherent framework. This framework offers a shared foundation for future research exploring the delineations, functioning and impact of accelerators. From a practical perspective, the article provides managers of accelerators a guide to design, review and improve programs according to their value creation goals.

The purpose of this paper is to explore the reintegration process of Wounded, Injured or Sick Employees (WISE) of the Dutch Military Armed Forces.

The research method is an exploratory, qualitative case study. A purposive sampling was drawn, including 10 WISE, and 6 reintegration stakeholders. A total of 16 interviews were conducted to explore the individual, organisational and socio-environmental factors that influence reintegration of WISE.

Findings show the importance of involvement and participation of members of the social environment in the reintegration process. Findings show that the complexity of the plethora of WISEs' injuries and disabilities requires a more person-centric reintegration approach with personalized-customized provisions, rather than a policy-driven approach to the reintegration, in order to enhance the reintegration experience and to arrive at beneficial individual and organisational reintegration outcomes.

This cross-sectional study on a limited sample of WISE and reintegration stakeholders does not allow for making inferences about the long-term effects of the reintegration process on reintegration outcomes of the wider population of WISE. Future longitudinal research, encompassing a larger sample, could examine the long-term career, organisational and societal implications of reintegration of WISE within and outside the Military Armed Forces.

This paper presents a “Wounded Warrior Workplace Reintegration Program”, aimed at deriving beneficial outcomes for all stakeholders involved in the reintegration trajectory.

This paper contributes to the literature by presenting a Model of Occupational Reintegration of WISE that considers the factors at an individual, social-environmental, and institutional level as determinants of successful reintegration.