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In recent years many developing and developed countries have focused attention on the training of their managers. Based on a questionnaire survey, evaluates for the first time the extent of management training in India and skills emphasis. Also reassesses other dimensions such as training methods, training policy and assessment methods. The findings suggest that the extent of management training in India is low and there are structural differences between organizations that provide training to their managers and those which do not.

Identifies major causes of the lack of managment training in India in order to evolve a viable strategy of management training. Financial constraint emerged as the single most critical factor responsible for the lack of management training in India. Discusses environmental conditions conducive to future training along with non‐trainers′ perceptions of the importance and benefits of skills. Finally, discusses the role Government can play in bringing non‐training organizations into the training fold.

Creating visibility in the supply chain (SC) helps in making it resilient. Integrating the SC with Industry 4.0 key enabling technologies creates visibility and sustainability in SCs. It also fosters intelligent decision-making, thereby making a SC smart. However, how Industry 4.0 technologies affect key performance indicators (KPIs) of a resilient SC and may help achieve sustainability is rarely studied.

Sixteen KPIs were identified from the literature review and analyzed using fuzzy analytic hierarchy process (FAHP) using expert opinions. Further, a sensitivity analysis was conducted for the KPIs by varying the weightage of the criteria. Later, KPIs results were analyzed, and (1) how and which Industry 4.0 technology helps improve the KPI? (2) Resilience relationship with sustainability? were discussed.

The analyses show that the time-oriented (TO) is an essential criterion and organizational (OR) is the less important comparatively. Lead time, time to market and risk assessment frequency are the top KPIs that need a focus. Blockchain, Big Data and Cyber-physical systems enhance KPI's value and, in turn, foster economic, environmental and social sustainability of the SC and help in better decision making in terms of smart contracts, better forecasting and enhanced real-time information sharing.

Identification of the KPIs, the impact of Industry 4.0 technologies and the impact on sustainability; this kind of interplay is rarely evident in the literature. Understanding the findings of this research will help managers develop smart systems that may work intelligently to overcome risks associated and enhance sustainability. Academicians can use the findings and conduct future research that can overcome the limitations of this research.

A nut bolt joint is a primary device that connects mechanical components. The vibrations cause bolted joints to self-loosen. Created by motors and engines, leading to machine failure, and there may be severe safety issues. All the safety issues and self-loosen are directly and indirectly the functions of the accuracy and precision of the fabricated nut and bolt. Recent advancements in three-dimensional (3D) printing technologies now allow for the production of intricate components. These may be used technologies such as 3D printed bolts to create fasteners. This paper aims to investigate dimensional precision, surface properties, mechanical properties and scanning electron microscope (SEM) of the component fabricated using a multi-jet 3D printer.

Multi-jet-based 3D printed nut-bolt is evaluated in this paper. More specifically, liquid polymer-based nut-bolt is fabricated in sections 1, 2 and 3 of the base plate. Five nuts and bolts are fabricated in these three sections.

Dimensional inquiry (bolt dimension, general dimensions’ density and surface roughness) and mechanical testing (shear strength of nut and bolt) were carried out throughout the study. According to the ISO 2768 requirements for the General Tolerances Grade, the nut and bolt’s dimensional examination (variation in bolt dimension, general dimensions) is within the tolerance grades. As a result, the multi-jet 3D printing (MJP)-based 3D printer described above may be used for commercial production. In terms of mechanical qualities, when the component placement moves from Sections 1 to 3, the density of the manufactured part decreases by 0.292% (percent) and the shear strength of the nut and bolt decreases by 30%. According to the SEM examination, the density of the River markings, sharp edges, holes and sharp edges increased from Sections 1 to 3, which supports the findings mentioned above.

Hence, this work enlightens the aspects causing time lag during the 3D printing in MJP. It causes variation in the dimensional deviation, surface properties and mechanical properties of the fabricated part, which needs to be explored.

The purpose of this paper is to empirically study the impact of product patent regime on the productivity of different categories such as ownership, R&D, size and product-wise of Indian pharmaceutical firms using non-parametric data envelopment analysis.

The present study has applied Ray and Desli’s Malmquist productivity index and its decomposition to measure total factor productivity (TFP) change, pure technical efficiency change, scale efficiency change and technical change under variable returns to scale (VRS) technology assumption for 141 Indian pharmaceutical firms during 2000-2001 to 2014-2015.

The study found the negligible impact of product patent regime on productivity. The technological change has played a positive role in the growth of productivity, whereas technical efficiency change depicts the judicious utilization of resources for improving performance. From the results, it is found that R&D intensive firms depict better stability in the TFP than the non-R&D firms. However, Granger causality between R&D and productivity found no relationship. Productivity is more directly affected by investment in fixed assets rather than in R&D, which focusses on incremental value additions in a largely branded/plain generic product market. In case of ownership, private foreign firms found to have registered progress in TFP while others have recorded marginal regress, which probably could be attributed to the superior marketing and management skills of the foreign firms, besides possessing proprietary technology. Both small and large firms have shown positive growth in the new regime as compared to the pre-patent regime. These small firms are able to compete with large firms because of their up-gradation of the technological base by improving access to better foreign technology. TFP growth for all the firms can be attributed to improvement in technology, and innovation in terms of high capital-output ratio. Further, the paper tried to identify the determinants of productivity from panel random effect regression, and it is found that export intensity, age and the new patent regime have negative and significant relationship with productivity, whereas other variables such as R&D, ownership, size and capital imports are insignificant. In the end, the results of sensitivity analysis have confirmed the validity of the selected variables.

The results suggest that Indian pharmaceutical firms need substantive improvement in TFP by improving managerial and scale efficiency. Indian pharmaceutical industry (IPI) needs to improve productivity across the network and drive cost excellence initiatives across the spend base through operational excellence and digital initiatives. The results of this paper can be applied in framing policies for future growth and improvement in the productivity of IPI.

The paper aims to make several new contributions to the existing literature. Most of the research papers only analysed TFP of the industry as a whole and detailed firm-wise analysis is needed to capture the true impact at a unit level. This study has analysed the impact of different categories such as ownership, R&D, size and product-wise, and determinants of productivity. The study has used a broader time period and larger panel data to predict the better picture.

The purpose of this study is to find the best geometries among the cylindrical, enamel and honeycomb geometries based upon the mechanical properties (tensile test, compression test and shear test). Further this obtained geometry could be used to fabricate products like exoskeleton and its supporting members.

The present research focuses on the mechanical testing of cylindrical, enamel and honeycomb-shaped parts fabricated through multi-jet printing (MJP) process with a wall thickness of 0.26, 0.33, 0.4 and 0.66 mm. The polymer specimens (for tensile, compression and shear tests) were fabricated using a multi-jet fusion process. The experimental results were compared with the numerical modelling. Finally, the optimal geometry was obtained, and the influence of wall thicknesses on various mechanical properties (tensile, compression and shear) was studied.

In comparison to cylindrical, enamel structures the honeycomb structures required less time to fabricate and had lower tensile, compressive and shear strengths. The most efficient geometry for fully functional parts where tensile, compressive and shear forces are present during application – cylindrical geometry is preferred followed by enamel, and then honeycomb. It was found that as the wall thickness of various geometries was increased, their ability to withstand tensile, compressive and shear loads also enhanced. The enamel shape structure exhibits greater strain energy storage capacity than other shape structures for compressive loads, and the strength to resist the compressive load will be lower. In the case of cylindrical geometries for tensile loading, the resisting area toward the loading will be higher in comparison to honeycomb- and enamel-based structures. At the same time, the ability to store the stain energy is less. The results of the tensile, compression and shear load finite element analysis using ANSYS are in agreement with those of the experiments.

From the insight of literature review, it is found that a wide range of work is done on fused deposition modeling (FDM) process. But in comparison to FDM, the MJP provide the better dimensional accuracy and surface properties (Lee et al., 2020). Therefore, it is observed that past research works not incorporated the effect of wall thickness of the embedded geometries on mechanical properties of the part fabricated on MJP (Gibson, n.d.). Hence, in this work, effect of wall thickness on tensile, compression and shear strength is considered as the main factor for the honeycomb, enamel and cylindrical geometries.

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.

The purpose of this paper was to investigate the prevalence rates of stress, depression and anxiety and their sociodemographic factors linked with the Indian population following the second round of COVID-19 in India.

A cross-sectional study was carried out using an online questionnaire. In total, 505 individuals participated through convenience sampling. To measure anxiety, depression and stress, the Depression Anxiety Stress Scale (DASS-21), a 21-statement self-reported questionnaire, was used.

Multiple regression analyses were performed to evaluate the sociodemographic characteristics associated with depression, stress and anxiety. Results indicated salary/allowances reduction and alcohol consumption were associated with depression. Multiple regression also indicated that salary/allowances reduction, smoking status and alcohol consumption were associated with stress. In addition, this research also showed that chronic disease, salary/allowances reduction, smoking status and alcohol consumption were associated with anxiety.

During the second COVID-19 wave in India, various individuals were affected. Anxiety, depression and stress were common among Indians after the second wave of COVID-19. Along with other actions to restrict the development of COVID-19, the Indian Government and mental health specialists must pay close attention to the inhabitants' mental health. More large-scale studies on various occupations should be conducted, and new mental health factors should be included.

This study provides empirical insights related the sociodemographic factors and stress, anxiety and depression.

Neglected tropical diseases (NTDs) are a set of infectious diseases that primarily affect low-income countries situated near the equator. Effective diagnostic tools hold the key to stemming the spread of these infectious diseases. However, specificity is a major concern associated with current diagnostic protocols. In this regard, electrochemical deoxyribonucleic acid (DNA) biosensors could play a crucial role, as highlighted by renewed interest in their research. The purpose of this study was to highlight the current scenario for the design and development of biosensors for the detection of NTDs related pathogens. This review highlights the different types of factors involved and the modifications used to enhance sensor properties.

The authors discuss the potential of electrochemical DNA biosensors as efficient, affordable diagnostic tools for the detection of pathogens associated with NTDs by reviewing available literature. This study discusses the biosensor components, mainly the probe selection and type of electrodes used, and their potential to improve the overall design of the biosensor. Further, this study analyses the different nanomaterials used in NTD-based electrochemical DNA biosensors and discusses how their incorporation could improve the overall sensitivity and specificity of the biosensor design. Finally, this study examines the impact such techniques could have in the future on mass screening of NTDs.

The findings provide an in-depth analysis of electrochemical DNA biosensors for the detection of pathogens associated with NTDs.

This review provides an update on the different types and modifications of DNA biosensors that have been designed for the diagnosis of NTD-related pathogens.

The learning outcome is that it will help to sustain your startups in the ever-changing business environment especially in the context of emerging markets.

The present case is about Dilkhush Kumar from village Bangaon, India who developed a cab booking platform “AryaGo”, an innovative platform completely dedicated for rural road transport connectivity. AryaGo is the service line of Aryan Cabs and Rural Trans-solution Pvt. Ltd., a Startup founded in the year 2016 under Startup Bihar, a seed capital support scheme of State Government. The idea was to provide comfort, convenience, safety and affordability to all the commuters travelling from or within far-off villages. The biggest challenge during implementation in villages includes availability of updated geo-mapped images for app development and vehicle tracking. Company was foremost in providing the kiosk-based booking facility for its customers. It took the decision based on the profiling of customers and their preferred booking methods. When a customer booked a cab using IVR, they did not had access to app-based customer panel and hence they could not avail services such as, location tracking, SoS, real time tracking of vehicle. However, heavy invest in IT has put the company in financial stress and Kumar is wondering whether to expand fast or penetrate in the given target market. Should he re-design his business model so as it can really make a difference in terms of service delivery?

Post Graduate/ MBA.

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CSS 8: Marketing