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As the world is becoming more volatile and uncertain, organizations face much complexity in their daily operations. Further, there is a much ambiguity in business operations to achieve the effective utilization of resources. The work optimizes a novel constraint programming model approach of the utilization of shuttle services vehicle while considering cost savings, employee wellbeing and other real an Information Technology enabled service (ITES) industry constraints.

The present work considers a novel extension of the vehicle routing problem related to the shuttle service operation in an ITES industry in VUCA context. Additionally, the model considers the women safety aspects, which engages the company to provide a security guard for women employees in the night shift.

Numerical experiments were conducted on real instances data of ITES industrial partner. The results show that the vehicle utilization increased from 75% up to 96% while ensuring in parallel the wellbeing of employees and women safety during the night shift. Finally, the proposed model is converted to a decision support application allowing ITES partner to plan employees shuttle service operations efficiently.

Study has evaluated the shuttle services optimization for ITES industry using data from industrial which makes it a unique contribution to literature in shuttle operations. Further, the study used constraint programming to evaluate the vehicle utilization and security allocation, thereby introducing new parameter on security allocation in open VRP problem.

Despite the fact that hygiene and sanitation are becoming more critical for improving the present situation in developing nations, the factors that affect them are not well covered in the present research. This paper investigates the quality of the hygiene and sanitization factors and identifies the interrelations between the identified factors.

A graph theory-based approach is proposed to assess the factors influencing the practice, and a critical service index (CSI) is used to quantify the same.

Two Indian villages are used to illustrate the implementation of the suggested approach. This represents the validation of the suggested method, as well as assisting in the development of essential suggestions for increasing the quality of hygiene and sanitization in the Indian context. In spite of the increasing importance of hygiene and sanitation for improving the current situation in developing countries, the factors that influence them are not well-researched.

This study contributes in two ways. First, it provides an organized methodology for quantifying hygiene and sanitation factors and a critical service index that incorporates the findings. The suggested approach may also be used to evaluate and classify other sectors. Second, it shows how the methodology was used to create key recommendations for two Indian villages, which may be considered the first effort in India’s hygiene and sanitation initiatives.

This research discussed improvements in sanitation and hygiene habits among Indian households, which have not been achieved as expected under the Swachh Bharat Mission.

This paper aims to investigate the firm performance of micro, small and medium enterprises (MSMEs) by using artificial intelligence-based supply chain resilience strategies. A theoretical framework shows the relationship between artificial intelligence, supply chain resilience strategy and firm performance.

A questionnaire is developed to survey the MSMEs of India. A sample size of 307 is considered for the survey. The employees working in MSMEs are targeted responses. The conceptual model developed is tested empirically.

The study found that eight hypotheses were accepted and two were rejected. There are five mediating variables in the current study. Artificial intelligence, the independent variable, positively affects all five mediators. Then, according to the survey and analysis of the final 307 responses from MSMEs, the mediating variables significantly impact the dependent variable, firm performance.

This study is limited to emerging markets only. Also this study used only cross sectional data collection methods.

This study is essential for supply chain managers and top management willing to adopt the latest technology in their organisation or firmfor a better efficient supply chain process.

This study investigated artificial intelligence-based supply chain resilience for improving firm performance in emerging countries like India. This study tried to fill the research gap in artificial intelligence and supply chain resilience.

The waste-to-energy (WtE) project plays a significant role in the sustainable development of urban environments. However, the inherent “Not in my backyard” (NIMBY) effect presents substantial challenges to site selection decisions. While effective public participation is recognized as a potential solution, research on incorporating it into site selection decision-making frameworks remains limited. This paper aims to establish a multi-attribute group decision-making framework for WtE project site selection that considers public participation to enhance public satisfaction and ensure project success.

Firstly, based on consideration of public demand, a WtE project site selection decision indicator system was constructed from five dimensions: natural, economic, social, environmental and other supporting conditions. Next, the Combination Ordered Weighted Averaging (C-OWA) operator and game theory were applied to integrate the indicator weight preferences of experts and the public. Additionally, an interactive, dynamic decision-making mechanism was established to address the heterogeneity among decision-making groups and determine decision-maker weights. Finally, in an intuitive fuzzy environment, an “acronym in Portuguese of interactive and multi-criteria decision-making” (TODIM) method was used to aggregate decision information and evaluate the pros and cons of different options.

This study develops a four-stage multi-attribute group decision-making framework that incorporates public participation and has been successfully applied in a case study. The results demonstrate that the framework effectively handles complex decision-making scenarios involving public participation and ranks potential WtE project sites. It can promote the integration of expert and public decision-making preferences in the site selection of WtE projects to improve the effectiveness of decision-making. In addition, sensitivity and comparative analyses confirm the framework’s feasibility and scientificity.

This paper provides a new research perspective for the WtE project site selection decision-making, which is beneficial for public participation to play a positive role in decision-making. It also offers a valuable reference for managers seeking to effectively implement public participation mechanisms.

The purpose of this study is to delve into the intricate terrain of assimilating sustainability practices into digital accounting and finance, centring on the transformative dynamics introduced by artificial intelligence (AI)-enabled FinTech. The primary objective is to scrutinize critical lacunae in existing literature, exploring how organizations can meticulously construct comprehensive sustainability frameworks. Simultaneously, the study investigates the protracted repercussions of AI-enabled FinTech on the enduring sustainability paradigms.

Executing a systematic literature review, the research engaged in the meticulous identification and assessment of a voluminous pool of 1,158 articles. Using a judicious two-phase strategy, the scrutiny distilled a mere 64 pertinent articles, subjecting them to rigorous evaluation encompassing methodologies, contributions and overall quality. The Fuzzy Delphi method was used to elicit expert opinions and facilitate consensus-building, leveraging fuzzy logic to accommodate uncertainties in the data.

The review navigates the convoluted impact of AI across diverse sectors, accentuating its transformative imprint on realms such as health care, finance and transportation. Specifically, in the financial domain, the discerning eye of AI-enabled FinTech optimizes investment portfolios, augments risk assessment, propels financial inclusion and streamlines the intricate landscape of sustainability reporting. The study meticulously pinpoints research gaps encompassing investment optimization, risk management, financial inclusion, sustainability reporting and ethical considerations within the intricate milieu of AI-enabled FinTech. This research contributes to the existing body of knowledge by synthesizing intricate thematic strands, discerning overarching trends and spotlighting critical voids in the synthesis of sustainability practices and AI-enabled FinTech. The findings resonate with far-reaching implications, emphasizing the exigency of comprehensive investigations into the longitudinal sustainability ramifications instigated by AI-enabled FinTech.

The study underscores the imperative of crafting robust ethical frameworks for the equitable and transparent deployment of AI solutions within the intricate landscape of FinTech. Moreover, this research stands poised to shape organizational strategies, inform regulatory frameworks and guide investment decisions, thereby catalyzing the cultivation of conscientious and sustainable financial practices.

This paper aims to provide empirical evidence on adopting artificial intelligence (AI), including generative AI, in knowledge management (KM) processes and its impact on organisational decision-making. Specifically, the study addresses three key research questions: RQ1: How is (generative) AI adopted within KM processes in organisations? RQ2: What factors influence the adoption of AI in these processes, either facilitating or inhibiting it? RQ3: How does AI adoption in KM processes affect organisational decision-making?

An explorative investigation has been conducted through semi-structured interviews with KM and AI experts from a worldwide sample of 52 mostly private, large and for-profit organisations. Interviews have been analysed through a mixed thematic analysis.

The study provides an original framework in which the three investigated concepts are interconnected according to a dual relationship: linear and retroactive and 20 factors affecting AI adoption within KM processes.

The provided model guides managers in improving their organisational decision-making through AI adoption in KM processes. Moreover, according to the rational decision-making model, the authors propose a six-step systematic procedure for managers.

To the best of the authors’ knowledge, this is the first study that simultaneously addresses AI, KM and decision-making and provides an integrated framework showing the relationships between them, allowing organisations to better and practically understand how to ameliorate their decision-making through AI adoption in KM processes.

Vehicle allocation problems (VAPs), which are frequently confronted in many transportation activities, primarily including but not limited to full truckload freight transportation operations, induce a significant economic impact. Despite the increasing academic attention to the field, literature still fails to match the needs of and opportunities in the growing industrial practices. In particular, the literature can grow upon the ideas on sustainability, Industry 4.0 and collaboration, which shape future practices not only in logistics but also in many other industries. This review has the potential to enhance and accelerate the development of relevant literature that matches the challenges confronted in industrial problems. Furthermore, this review can help to explore the existing methods, algorithms and techniques employed to address this problem, reveal directions and generate inspiration for potential improvements.

This study provides a literature review on VAPs, focusing on quantitative models that incorporate any of the following emerging logistics trends: sustainability, Industry 4.0 and logistics collaboration.

In the literature, sustainability interactions have been limited to environmental externalities (mostly reducing operational-level emissions) and economic considerations; however, emissions generated throughout the supply chain, other environmental externalities such as waste and product deterioration, or the level of stakeholder engagement, etc., are to be monitored in order to achieve overall climate-neutral services to the society. Moreover, even though there are many types of collaboration (such as co-opetition and vertical collaboration) and Industry 4.0 opportunities (such as sharing information and comanaging distribution operations) that could improve vehicle allocation operations, these topics have not yet received sufficient attention from researchers.

The scientific contribution of this study is twofold: (1) This study analyses decision models of each reviewed article in terms of decision variable, constraint and assumption sets, objectives, modeling and solving approaches, the contribution of the article and the way that any of sustainability, Industry 4.0 and collaboration aspects are incorporated into the model. (2) The authors provide a discussion on the gaps in the related literature, particularly focusing on practical opportunities and serving climate-neutrality targets, carried out under four main streams: logistics collaboration possibilities, supply chain risks, smart solutions and various other potential practices. As a result, the review provides several gaps in the literature and/or potential research ideas that can improve the literature and may provide positive industrial impacts, particularly on how logistics collaboration may be further engaged, which supply chain risks are to be incorporated into decision models, and how smart solutions can be employed to cope with uncertainty and improve the effectiveness and efficiency of operations.

This study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach.

The methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses.

The study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters.

This study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables.

The study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.

Digital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain.

The study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study.

Blockchain technology offers numerous venues for supply chain applications and research. However, the connections between specific blockchain features and future applications have been unclear to date in its evolution. The purpose of this study is to fill this void.

The authors advance the understanding of blockchain in supply chain management by providing a new research framework built on unique blockchain features as applied across core supply chain functions.

This study’s framework is a feature-function matrix that integrates four overarching supply chain functions (i.e. supplier management, logistics, production processes and customer management) with nine blockchain features (i.e. traceability/provenance, accessibility, visibility, immutability, distributed/shared ledger, validity, peer-to-peer transacting, pseudonymity and programmability). This study’s feature-function framework is supported by a structured, systematic review of reviews using PRISMA methods. The authors use the framework to present a future blockchain research agenda in supply chain management.

The authors provide a new blockchain feature/supply chain function framework and provide a structured path for future research.

This study aims to investigate the evolution of terminology in supply chain management (SCM) and its implications for the field’s strategic orientation. It also aims to understand how SCM terms adapt to interdisciplinary contexts, reflecting shifts in theoretical and practical approaches within the discipline.

This study uses a systematic literature review and analyzes over 3,500 unique SCM-related terms extracted from approximately 33,000 abstracts. By using Descending Hierarchical Classification and factor analysis, the research methodologically identifies key shifts in terminology and discerns underlying patterns.

This study categorizes terminological variations in SCM into three main clusters: product–agent, performance objective orientation and structure. These variations signal not only linguistic changes but also strategic shifts in SCM understanding and practice. Notably, terms such as “green,” “sustainable” and “circular” supply chains have emerged in response to evolving internal and external pressures and trends. In addition, this paper offers a nuanced understanding of these terminological adaptations, proposing a reference framework for navigating SCM’s evolving lexicon and highlighting global usage and geographical and cultural nuances in SCM discourse.

This paper presents a reference framework that complements existing SCM definitions, fostering a shared understanding of SCM variations on a global scale. This framework enhances cultural sensitivity within the field and underscores SCM’s adaptability and flexibility. These insights offer a nuanced view of SCM dynamics, benefiting researchers and practitioners alike. Beyond terminology, this study sheds light on the interplay between language and SCM strategy, providing a valuable perspective for navigating the evolving SCM landscape. The study’s scope is constrained by the analyzed abstracts. Future research could broaden this analysis to encompass more SCM literature or delve deeper into the implications of terminological changes.

This study offers practitioners a reference framework for navigating the evolving lexicon of SCM. This framework aids in understanding the strategic implications of terminological changes, enhancing clarity and context in both academic and practical applications.

By acknowledging global usage and variations, the research underscores the impact of geographical and cultural nuances on SCM discourse. This global perspective enriches the understanding of SCM as a dynamic and culturally sensitive field.

This research is novel in its extensive and systematic exploration of SCM terminology. This study offers a comprehensive analysis of how language evolves in tandem with strategic shifts in the field, providing a unique perspective on the interplay between terminology and strategy in SCM.