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A spurt in the usage of additive manufacturing (AM) is observed in industrial applications to produce final parts along with rapid prototyping and rapid tooling. Despite the potential benefits of on-demand and on-location production of customised or complex shape parts, widespread implementation of this disruptive production technology is not yet visible. The purpose of this paper is to examine the various barriers to implement AM in the Indian automotive sector and analyse interrelations among them.

Based on the extant literature and discussions with industry experts, ten major barriers are identified. The authors use a modified Fuzzy interpretive structural modelling (Fuzzy-ISM) method to derive strengths of relationships among these barriers, develop hierarchical levels, and thereafter group and rank these barriers.

ISM diagraph is developed to demonstrate how the barriers drive one another. Production technology capabilities and government support emerge as the most critical factors, with high driving power and medium dependence.

While identified barriers may be similar across the automotive industry, generalisation of results for interrelationships and ranks in other industries may be limited.

The findings may be useful to managers to develop suitable mitigation strategies, and take more informed decisions, with individual focus, level focus or cluster focus.

Findings clearly establish that the role of management and government is crucial in mitigating workers’ resistance to AM implementation.

This paper contributes to AM literature by the structured presentation of the barriers to implement AM in the Indian automotive sector. It also extends the Fuzzy-ISM method by presenting calculation of indirect relations using the appropriate max-product composition and in ranking the barriers.

The purpose of this paper is to identify various risk drivers which affect a food processing supply chain and to create a map of how those risk drivers propagate risks through the supply chain and impact important performance measures.

This study involves experts from food processing companies to elucidate the contextual relationships among the risk drivers and between risk drivers and performance measures. This is used to quantify the relationships and to determine the indirect and overall relationships applying Fuzzy Interpretive Structural Modeling.

Three categories of risk drivers which Indian food processing companies need to pay maximum attention to minimize risks are identified. These are supplier dependency and contracting, supplier variability, visibility and traceability and manufacturing disruptions. Analysis shows that collaborating with suppliers and logistics service providers, developing mutually beneficial contracts with them while ensuring that adequate technology investments are made can significantly mitigate risks and consequently improve margins and lead to revenue growth.

This study has been carried out with experts from large food processing companies in India, and hence, the results cannot be generalized across other types of food processing companies.

The proposed methodology can help understand the interrelationships between supply chain risks and between those risks and performance measures. Thus, it can help a food processing company to create business cases for specific supply chain risk mitigation projects.

This study is one of the earliest to create a comprehensive risk propagation map for food processing companies which helps in quantifying the impact the risk drivers have on each other and on performance measures.

The purpose of this paper is to present a framework to manage product returns for reverse logistics by focusing on estimation of returns for select categories of products in the Indian context.

The paper develops a conceptual model and thereafter an integrated modeling framework borrowing from existing literature and industry practices. It utilizes product ownership data, average life cycle of products, past sales, forecasted demand and likely impact of environmental policy measures for estimating return flows. Informal interviews with 84 stakeholders are carried out to estimate significant parameters. Software packages, decomposition methods and heuristics are utilized for solution.

The integrated framework helps in estimating returns for select categories of products and thereafter taking simultaneous decisions on their disposition, location and capacity of facilities and flows of returned products for a given time horizon under various strategic, operational and customer service‐related constraints.

A “push” system where the volumes of returns drive the decisions. Estimations and optimization have been carried out for select product categories and not brands or original equipment manufacturers (OEMs). No free choice of facility locations.

The insights and learning under different scenarios may be utilized as inputs for decision making by various stakeholders such as OEMs and their consortia, local remanufacturers and third party service providers.

At methodological level, our framework combines descriptive modeling with optimization technique, while at topological level; it provides detailed solutions for network configuration and design.

The purpose of this paper is to carry out structural analysis of potential supply chain risks and performance measures in fresh food retail by applying interpretive structural modeling (ISM).

Inputs were taken from industry experts in identifying and understanding interdependencies among food retail supply chain risks on different levels (sourcing and logistics outside the retail stores; storage and customer interface at the stores). Interdependencies among risks and their impact on performance measures are structured into a hierarchy in order to derive subsystems of interdependent elements to derive useful insights for theory and practice.

Using the ISM approach the risks and performance measures were clustered according to their driving power and dependence power. Change in/inadequate government regulations’ are at the bottom level of the hierarchy implying highest driving power and require higher attention and focussed mitigation strategies. Risks like lack of traceability, transport delays/breakdowns and temperature abuse, cross-contamination in transport and storage have medium driver and dependence powers.

The approach is focussed on food retail supply chains in the Indian context and thereby limits the ability to generalize the findings. The academics and experts were selected on convenience and availability.

It gives managers a better understanding of the risks and performance measures that have most influence on others (driving performance measures) and those measures which are most influenced by others (dependent performance measures) in fresh food retail and also a tool to prioritize them. This kind of information is strategic for managers who can use it to identify which performance measures they should concentrate on managing the trade-offs between measures. The findings and the applicability for practical use have been validated by both experts and practicing managers in food retail supply chains.

The work is perhaps the first to link supply chain risks with performance and explains the propagation of risks in food retail supply chains. It contributes to theory by addressing a few research gaps and provides relevant managerial insights for practitioners.

The ocean transportation of automobiles is carried out by specialized Roll‐on/Roll‐off ships, which are designed to carry a large number of automobiles at a time. Many of these shipping companies have vertically integrated or collaborated with other logistics services providers to offer integrated maritime logistics solution to car manufacturers. The purpose of this study is to develop an optimization model to address the tactical level maritime logistics planning for such a company.

The problem is formulated as a mixed integer linear program and we propose an iterative combined Ant colony and linear programming‐based solution technique for the same.

This paper can integrate the maritime transportation planning of internally managed cargoes with the inventory management at the loading and discharging ports to minimize supply‐chain cost and also maximize additional revenue through optional cargoes using same fleet of ships.

The mathematical model does not consider the variability in production and consumption of products across various locations, travel times between different nodes, etc.

The suggested mathematical model to the supply‐chain planning problem and solution technique can be considered in the development of decision support system for operations planning.

This paper extends the maritime inventory routing model by considering simultaneous planning of optional cargoes with internally managed cargoes.

This paper aims to present a systematic approach in the literature survey related to metal additive manufacturing (AM) processes and its multi-physics continuum modelling approach for its better understanding.

A systematic review of the literature available in the area of continuum modelling practices adopted for the powder bed fusion (PBF) AM processes for the deposition of powder layer over the substrate along with quantification of residual stress and distortion. Discrete element method (DEM) and finite element method (FEM) approaches have been reviewed for the deposition of powder layer and thermo-mechanical modelling, respectively. Further, thermo-mechanical modelling adopted for the PBF AM process have been discussed in detail with its constituents. Finally, on the basis of prediction through thermo-mechanical models and experimental validation, distortion mitigation/minimisation techniques applied in PBF AM processes have been reviewed to provide a future direction in the field.

The findings of this paper are the future directions for the implementation and modification of the continuum modelling approaches applied to PBF AM processes. On the basis of the extensive review in the domain, gaps are recommended for future work for the betterment of modelling approach.

This paper is limited to review only the modelling approach adopted by the PBF AM processes, i.e. modelling techniques (DEM approach) used for the deposition of powder layer and macro-models at process scale for the prediction of residual stress and distortion in the component. Modelling of microstructure and grain growth has not been included in this paper.

This paper presents an extensive review of the FEM approach adopted for the prediction of residual stress and distortion in the PBF AM processes which sets the platform for the development of distortion mitigation techniques. An extensive review of distortion mitigation techniques has been presented in the last section of the paper, which has not been reviewed yet.

Gas metal arc-based directed energy deposition (GMA-DED) process experiences residual stress (RS) developed due to heat accumulation during successive layer deposition as a significant challenge. To address that, monitoring of transient temperature distribution concerning time is a critical input. Finite element analysis (FEA) is considered a decisive engineering tool in quantifying temperature and RS in all manufacturing processes. However, computational time and prediction accuracy has always been a matter of concern for FEA-based prediction of responses in the GMA-DED process. Therefore, this study aims to investigate the effect of finite element mesh variations on the developed RS in the GMA-DED process.

The variation in the element shape functions, i.e. linear- and quadratic-interpolation elements, has been used to model a single-track 10-layered thin-walled component in Ansys parametric design language. Two cases have been proposed in this study: Case 1 has been meshed with the linear-interpolation elements and Case 2 has been meshed with the combination of linear- and quadratic-interpolation elements. Furthermore, the modelled responses are authenticated with the experimental results measured through the data acquisition system for temperature and RS.

A good agreement of temperature and RS profile has been observed between predicted and experimental values. Considering similar parameters, Case 1 produced an average error of 4.13%, whereas Case 2 produced an average error of 23.45% in temperature prediction. Besides, comparing the longitudinal stress in the transverse direction for Cases 1 and 2 produced an error of 8.282% and 12.796%, respectively.

To avoid the costly and time-taking experimental approach, the experts have suggested the utilization of numerical methods in the design optimization of engineering problems. The FEA approach, however, is a subtle tool, still, it faces high computational cost and low accuracy based on the choice of selected element technology. This research can serve as a basis for the choice of element technology which can predict better responses in the thermo-mechanical modelling of the GMA-DED process.

To create and sustain a resource-based competitive advantage, managers acquire and develop specialized resources as they grow their firms. The authors argue that an important part of committing to a resource-based strategy is a willingness to keep spending on specialized resources during periods when sales and profits are down. The authors seek to validate this conjecture by examining whether such resource-based commitment to a customer-centered strategy results in improved customer satisfaction. The authors use the stickiness of selling, general, and administrative (SG&A) expenses to capture this commitment empirically. The authors first document that future customer satisfaction is positively associated with SG&A cost stickiness, consistent with the premise that the retention of specialized SG&A resources during low demand periods helps firms to build and maintain relationships with customers over time. Next, the authors test whether expected future benefits of customer satisfaction are enhanced when SG&A cost stickiness is higher. The authors find that the positive relation between Tobin’s Q and customer satisfaction is positively moderated by SG&A cost stickiness. Finally, the authors test whether earnings persistence, a quality of earnings associated with sustained performance over time, is positively associated with the interaction between customer satisfaction and SG&A cost stickiness. The authors find that it is. Their evidence supporting these predictions is consistent with the conjecture that resource-based commitment reflected in cost stickiness is an important dimension of creating and sustaining a resource-based competitive advantage.

As older adults are increasingly active in virtual communities (VCs), these platforms for knowledge exchange present opportunities for companies to use elder human capital. The purpose of this study is to understand the antecedent factors that motivate older adults’ knowledge contribution and knowledge seeking (KS) behaviors in VCs.

Rooted in socio-emotional selectivity and social cognitive theories, this study included five key variables and developed models for older adults’ knowledge contributing (KC)/KS behaviors. This paper tested the hypotheses using data from a sample of 204 older adults in 3 VCs in China.

The results provide support for most of the hypotheses and show that while other members’ participation (MP) acts as a substitute for meaning in life and attitude toward aging, it acts as a complement for outcome expectations (OE) focused on others and OE focused on oneself in their impacts on KC/KS activities.

The study provides practical insights for developing elder human resources via VCs to avoid knowledge loss.

This study described older adults’ unique characteristics when modeling their information and communication technologies-related behaviors and built two models to explain their KC/KS behaviors. It confirmed that the same factor has different levels of impact on older adults’ KC/KS behaviors in VCs. In addition, it confirmed and reinforced the complementary and substitutive effects of other MP as an environmental factor on these behaviors.

Sustainable human resource management (SuHRM), which aims to achieve positive environmental, social and economic outcomes at the same time, has gained prominence across industries. However, the challenges of implementing SuHRM across industries are largely under-studied. The purpose of this study is to identify the grey areas in the field of SuHRM by using an unsupervised learning algorithm on the abstracts of 607 papers published in prominent journals from 1995 to 2023. Most of the articles have been published post-2018.

The analysis of the data (abstracts of the selected articles) has been done using topic modelling via latent Dirichlet algorithm (LDA).

The output from topic modelling-LDA reveals nine primary focus areas of SuHRM research – the link between SuHRM and employee well-being; job satisfaction; challenges of implementing SuHRM; exploring new horizons in SuHRM; reaping the benefits of using SuHRM as a strategic tool; green HRM practices; link between SuHRM and organisational performance; link between corporate social responsible and HRM.

The insights gained from this study along with the discussions on each topic will be extremely beneficial for researchers, academicians, journal editors and practitioners to channelise their research focus. No other study has used a smart algorithm to identify the research clusters of SuHRM.

By utilizing topic modeling techniques, the study offers a novel approach to analyzing and understanding trends and patterns in HRM research related to sustainability. The significance of the paper would be in its potential to shed light on emerging areas of interest and provide valuable implications for future research and practice in Sustainable HRM.