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This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.

A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.

The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.

The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

The purpose of this study is to experimentally analyse the effect of flexible and stiffened membrane wings in the lift generation of flapping micro air vehicle (MAV).

This is analysed by the rectangle wing made up of polyethylene terephthalate sheets of 100 microns. MAV is tested for the free stream velocity of 2 m/s, 4 m/s, 6 m/s and k^* of 0, 0.25, 1, 3, 8. This test is repeated for flapping MAV of the free flapping frequency of 2 Hz, 4 Hz, 6 Hz, 10 Hz and 12 Hz.

This study shows that the membrane wing with proper stiffeners can give better lift generation capacity than a flexible wing.

Only a normal force component is measured, which is perpendicular to the longitudinal axis of the model.

In MAVs, the wing structures are thin and light, so the effect of fluid-structure interactions is important at low Reynold’s numbers. This data are useful for the MAV developments.

The effect of chord-wise flexibility in lift generation is the study of the effect of a flexible wing and rigid wing in MAV. It is analysed by the rectangle wing. The coefficient of normal force at different free stream conditions was analysed.

A literature review revealed that government of various developing economies have put an effort on health-care supply chain through the executing critical factors (CFs) directly. Although they have attained some significant benefits in this tactic, but it was not up to satisfactory level. One of the reasons can be attributed to the fact that government/policy makers are not quantifying the impact of CFs on health-care supply chain. This paper aims to propose a methodology to quantify and estimate the impact of CFs on government-supported health-care supply chain (GHSC).

The Graph Theoretic Approach is proposed for estimating the impact and utility of CFs on an Indian GHSC. This study is also extended to scenario analysis for comparing results with different performance situation.

The results obtained from this study show that performance of Indian GHSC is satisfactory, but performance gaps exist which need to be reduced. In this research work, 12 CFs are identified under two significant categories (SCs), i.e. enablers and barriers and the intensity of enablers and barriers have been calculated to show the impact or influence of CFs on GHSC. The value of intensity shows that the role or impact of enabler category (i.e. performance measurement, employee recognition and reward, technology adoption, training cell, inbuilt analytical tool for IT system) is higher on Indian GHSC in comparison to barriers category to enhance the performance of GHSC.

The obtained numerical results are completely in specific to the Indian perspective only; hence, they cannot be generalized for other countries. Simultaneously, this study is related to government supported health-care system; hence, the selection of expert panel was crucial due to the unavailability of doctors and other stakeholders of government system.

The proposed approach is aimed at providing a procedure for evaluating the impact of CFs on HSC in general and GHSC in specific. This study is an attempt to assist government and top management of GHSC to assess the impact of CFs on GHSC and accordingly define its course of actions.

Although various issues related to the CFs have been broadly identified and analyzed, no dedicated study has been reported in the field for quantification of impacts of CFs. Furthermore, this proposed model has an ability to recognize the specific contribution of each CF and overall contribution.

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 paper is to propose and evaluate the selection of materials for the selective laser sintering (SLS) process, which is used for low-volume production in the engineering (e.g. light weight machines, architectural modelling, high performance application, manufacturing of fuel cell, etc.), medical and many others (e.g. art and hobbies, etc.) with a keen focus on meeting customer requirements.

The work starts with understanding the optimal process parameters, an appropriate consolidation mechanism to control microstructure, and selection of appropriate materials satisfying the property requirement for specific application area that leads to optimization of materials.

Fabricating the parts using optimal process parameters, appropriate consolidation mechanism and selecting the appropriate material considering the property requirement of applications can improve part characteristics, increase acceptability, sustainability, life cycle and reliability of the SLS-fabricated parts.

The newly proposed material selection system based on properties requirement of applications has been proven, especially in cases where non-experts or student need to select SLS process materials according to the property requirement of applications. The selection of materials based on property requirement of application may be used by practitioners from not only the engineering field, medical field and many others like art and hobbies but also academics who wish to select materials of SLS process for different applications.

By integrating organizational support theory (OST) and social cognitive theory, this study investigates types of managers' coaching behavior as experienced by the employees. Furthermore, the study examines whether employees would exhibit greater task and contextual performance when organizational learning is blended with a specific coaching behavior of their manager.

Using primary data from 298 software engineers working in select information technology companies across India, the current study attempts to assess moderating effect of managers' coaching behavior in two relationships, including continuous learning and employees' task performance (CL-TP) and continuous learning and employees' contextual performance (CL-CP).

Result of exploratory factor analysis suggests that managers of select organizations exhibit two major types of coaching behavior: inspiration-based coaching behavior and facilitation-based coaching behavior. On the moderating role of coaching behavior, it is documented that facilitation-based coaching behavior significantly positively moderates both stated (CL-TP and CL-CP) relationships, whereas inspiration-based coaching behavior of supervisors has positive significant effect on CL-TP relationship but negatively moderates the CL-CP relationship.

The extent to which the findings of this study can be generalized is constrained by the limited sample and organizational context.

The most important managerial implication for all learning organizations is that both kinds of coaching behaviors help improving the task performance of the employees, but managers should prefer facilitation-based coaching style in order to generate higher contextual performance of employees.

This study contributes to practitioners and existing literature by explaining how individual performance of employees is affected by the investment made by organizations in facilitating continuous learning.

The purpose of this paper is not only to gauge the extent of technical efficiency in 31 state road transport undertakings (SRTUs) operating in India but also to explore the most influential factors explaining its variations across SRTUs.

Three popular data envelopment analysis (DEA) models, namely CCR, BCC and Andersen and Petersen's super‐efficiency models, have been utilized to compute various efficiency scores for individual SRTUs. A censored Tobit analysis is conducted to see which factors significantly explain the inter‐SRTU variations in efficiency.

The key findings of the DEA analysis are only five SRTUs define the efficient frontier, and the remaining 26 inefficient undertakings have a scope of inputs reduction, albeit by the different magnitude; the extent of average overall technical inefficiency (OTIE) in these SRTUs is to the tune of 22.8 percent, indicating that the sample SRTUs are wasting about one‐fourth of their resources in the production operations; managerial inefficiency (as captured by the pure technical inefficiency) is a relatively more dominant source of OTIE; and operation in the zone of increasing returns‐to‐scale is a common feature for most of the undertakings. The multivariate regression analysis using Tobit analysis highlights that the occupancy ratio is the most significant determinant for all the efficiency measures, and bears a positive relationship with overall technical, pure technical and scale efficiencies. Further, scale efficiency is also impacted positively by the staff productivity.

The results of this paper can be applied from management's perspective. The managers can assess the relative efficiency of their SRTUs in the industry and take corrective measures to improve efficiency by altering input‐output mix.

This paper provides more robust estimates of relative efficiency of the SRTUs and highlights the key determinants of overall technical efficiency.

OGQ was founded by Geet Sethi and Prakash Padukone with the mission to support potential Olympic medal winners, in achieving their dream, with the help of all the stakeholders; and the vision to scout for potential talent and identify their needs. It had eminent personalities from sports who understood the problems with Indian sports and from industry who had a passion for sports and supported it. OGQ supported its athletes for the 2012 London Olympics through voluntary contributions and its athletes won four medals. For the 2016 Olympics, OGQ had a target of eight Olympic medals and was scaling up its support to athletes. Viren Rasquinha, the CEO of OGQ, knew that he had to focus on getting more contributions as he needed more resources to support the athletes. For this OGQ needed to review its communication strategy to the current and potential donors.