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Strategic planning is an organization’s process of describing its strategy, or direction, and making decisions on allocating its resources to track this strategy. SWOT analysis is one of the most commonly used techniques for strategic planning. SWOT examines the strengths (S) and weaknesses (W) agents of the community together with opportunities (O) and threats (T), for selecting and implementing the best strategy which helps in achieving its goals. The purpose of this paper is to enhance the performance of SWOT analysis regarding the quantitative side of strategies, select the best strategy from different strategies and deal effectively with vague and incompatible information, which occurs usually in actual life.

This study used the neutrosophic analytic hierarchy process (AHP) incorporated with SWOT analysis.

By adding the neutrosophic AHP to SWOT analysis, the performance of SWOT analysis is enhanced through determining the quantitative values and dealing with vague and inconsistent information effectively leading to improved decisions.

The developed integrated methodology is validated in a real-life case of Starbucks company. For the case study of Starbucks company, the proposed model helps in determining different strategic plans and, further, ranking these plans effectively, which will help the company to compete with its competitors and develop itself by obtaining a competitive advantage over its competitors in an uncertain business environment.

In the case study of Starbucks company, the proposed model helps to determine the different strategic plans, rank these plans which help the company compete with its competitors, develop itself and grow.

This research is the first to address SWOT analysis with neutrosophic AHP.

Industry 4.0 has received significant attention in today's competitive business market, necessitating a restructuring of functional domains in nearly every manufacturing organization. A comprehensive strategy to improve performance in preparation for Industry 4.0 implementation necessitates several steps, one of which is the establishment of performance outcomes (POs). The aim of this paper is to identify and rank the POs realized due to the adoption of Industry 4.0 enablers.

Based on an extensive literature review and inputs received from experts, a comprehensive list of enablers and the POs was prepared and finalized. This paper proposes a framework based on hybrid solution methodology, namely Neutrosophic Analytical Hierarchy Process (N-AHP) and Neutrosophic Combined Compromise Solution (N-CoCoSo), to rank the POs realized due to the adoption of Industry 4.0 enablers. The N-AHP methodology has been adopted to calculate the relative weights of the Industry 4.0 enablers. In comparison, the N-CoCoSo method has been adopted to rank the POs of Industry 4.0.

The proposed framework is applied to an Indian manufacturing organization to test the organization's practical applicability. Additionally, sensitivity analysis is also carried out to check the steadiness of the proposed framework. The findings of this study revealed that “Improved responsiveness to market conditions in today's competitive business environment” is the top-ranked PO of Industry 4.0, followed by “Enhanced competitiveness and better market share”, “Better product quality, through smart management of production process” and “Reduction in manufacturing waste and environmental sustainability” which could be realized due to adoption of its enablers.

This research would aid practitioners by enhancing the practitioners' capacity to understand and prioritize the various POs resulting from implementing Industry 4.0 enablers. Embracing a clear strategic plan will further assist practitioners in improving the efficiency of Industry 4.0 implementation.

Previous literature has only addressed the relationship between Industry 4.0 enablers and POs in a limited way. This paper attempts to compile a comprehensive list of Industry 4.0 enablers relevant to manufacturing organizations in order to fill this knowledge and research gap.

The purpose of this article is to develop a new model called strategy segmentation methodology (SSM) by combining the Kraljic portfolio matrix (KPM) and the supplier relationship model (SRM) so that the buyer company can effectively conduct its relations with its suppliers.

The importance weights of the criteria defining the dimensions of each model are calculated with the single-valued neutrosophic analytical hierarchy process (SVN-AHP) method. Subsequently, the derived importance weights are employed in the single-valued neutrosophic technique for order preference by similarity to ideal solution (SVN-TOPSIS) method to obtain the scores of the suppliers and their supplied items. In order to illustrate the feasibility of the proposed methodology, a case study in the machinery industry is performed with the related comparative analysis.

The implementation of SSM enables to formulate various strategies to manage suppliers taking into account the items they procure, their capabilities and performance and the supplier–buyer relationship strength. Based on the proposed strategies, it is concluded that the firm in the case study should terminate its relationship with six of its suppliers.

Although KPM has become the basis of purchasing strategies for various businesses, it neglects the characteristics of suppliers and the buyer–supplier relationship. In this study, KPM is integrated with the SRM approach presented by Olsen and Ellram (1997) to overcome these disadvantages of KPM. The novel integration of the two approaches enables the realization of a robust and reliable supplier classification model.

Increasing complexity in construction projects evokes interest in application of innovative digital technologies in construction. Digital twins (DT), which bring these innovative technologies together, have strong interactions with lean construction (LC). To highlight the collaborative nature of DT and LC, the paper explores the interactions between LC and DT and assesses benefits, costs, opportunities and risks (BOCR) of DT in LC to analyze significant obstacles and enablers in DT adoption in LC.

BOCR approach comprehensively considers both the positive and the negative attributes of a problem. At the first step, BOCR criteria for DT are identified through literature review and expert opinions, at the second step dependencies among BOCR criteria for DT in LC are determined by neutrosophic analytic hierarchy process (AHP), through a questionnaire survey. Integrating BOCR into neutrosophic AHP enables achieving more meaningful preference scores.

Cost of skilled workforce is the most important factor and opportunity to reduce waste is the second most important factor in adoption of DT in LC. The results were analyzed to rank the BOCR of adoption of DT in LC.

This study, in a novel way, performs BOCR analysis through neutrosophic AHP to reflect experts' judgments more effectively by neutrosophic AHP's better handling of vagueness and uncertainty. The paper provides a model to better understand the significant factors that influence adoption of DT in LC.

The purpose of this study is to develop a decision-support framework that can be used by decision-makers to suspend public infrastructure projects. Additionally, the study also investigates how to select the most convenient infrastructure project for suspension.

The proposed framework includes an extensive set of factors and a novel comparison mechanism that can reveal the most convenient infrastructure project to be suspended. A comprehensible literature review and focus group discussion (FGD) sessions were conducted to identify factors that should be considered for suspension. Then, the neutrosophic analytic hierarchy process (N-AHP) method was used to determine the relative importance of the factors. Finally, the proposed comparison mechanism was demonstrated through a hypothetical case study and Technique for order of preference by similarity to ideal solution (TOPSIS) analysis.

Results showed that suspension decisions cannot be made merely based on “financial” factors. Instead, the other aspects, namely “Technical and managerial” and “Social and Environmental”, should also be taken into consideration. Second, factors related to the initial investment, cost of refinancing, cash flow, permits and approvals, insufficiency of bidders, degradation of the components, reputation, impact on stakeholders and criticality of the infrastructure were particularly elaborated as the most significant, needing the utmost attention of the decision-makers. Lastly, the results demonstrated that the proposed comparison mechanism has considerable potential to identify the most convenient infrastructure project for suspension.

Public infrastructure projects are often under pressure due to the inflationary state and economic stagnation of countries after major crises. The suspension decision for infrastructure projects necessitates comprehensible assessments to consider all consequences. Studies have widely investigated the contractual and legal aspects of project suspension in light of existing literature. However, little effort has been devoted to identifying the factors that decision-makers should consider before suspending a particular infrastructure project. Furthermore, existing literature does not investigate how to select the most convenient infrastructure project for suspension either. Thus, by developing a specific suspension framework for infrastructure projects by considering various factors, this study is the earliest attempt to examine the contract suspension mechanism of public infrastructure projects. In this respect, the study significantly contributes to the theory of contract management domain and has important managerial implications.

In past years, the global supply chain has witnessed devastating effects of coronavirus (COVID-19) disease. However, the COVID-19 pandemic has renewed the interest of the Sustainable Supply Chain (SSC) stakeholders on sustainability. The stakeholders are now rethinking their business processes and strategy to make them sustainable. In this context, the relevant literature is required to support emerging markets to formulate sustainability-focussed strategies. The purpose of this study is to provide a comprehensive analysis of potential antecedents that leads towards sustainable development of freight transportation in emerging markets.

Initially, the antecedents of the Sustainable Freight Transport (SFT) system are derived from the literature survey followed by verification from the experts. Then, the potential antecedents are categorized under four (social, organizational, operational and environmental) broad categories. Afterwards, a Neutrosophic Analytic Network Process (N-ANP) method is employed to obtain the priority weights of the identified potential antecedents.

The paper identified and ranked 17 antecedents of the SFT system. According to the study’s findings, the top three antecedents of SFT are “the presence of a multimodal transportation system,” “circularity in SFT” and “traffic congestion management”. The results from the study advocate the promotion of existing multi-modal transport facilities which is promising to achieve sustainability. The results suggested the adoption of the digital twin to manage the transport operations.

This study sheds light on how to achieve sustainability in the freight transportation system post-COVID era highlighting the potential antecedents. The study’s findings will assist practitioners in developing SFT strategies in the face of such pandemics in future.

The analytical hierarchical process (AHP)’s main purpose is to assess higher hierarchy levels based on the cooperation of its various levels. It results in a well-designed model-based method in which the weights for the selected attribute are calculated using dimensions, criteria, and indicators. This paper aims to highlight the benefits of blockchain in supply chain management with the help of a literature review along with opinions of experts from various sectors.

With the goal of enhancing the use of blockchain technology in supply chain management, particularly when comparing within the same industry, the AHP methodology has been used. In order to develop the AHP model, a total of eight elements are examined in this study, which are decentralization, resiliency, security, smart contracts, sustainability, traceability, transparency and trust. A calculation of a Desirability Index for conventional supply chain and blockchain-enabled supply chains has been also developed.

Findings where that in a blockchain-enabled supply chain, the global weights of individual benefit variables are considerably larger than in conventional supply chains. When the score of the Desirability Index for conventional supply chain and blockchain-enabled supply chain is compared, the blockchain-enabled supply chain significantly surpasses the conventional supply chain in terms of increasing sustainable development in today’s supply networks.

This study takes into account the AHP methodology applying it on blockchain. This has not been done before in the academic world, at least as far as the authors may be aware of. The originality of combining such process with a recent technology such as blockchain highlights the value of this research.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

Digital technology and SDGs have gained increasing interest from the research community. This chapter aims to explore the field through a holistic review of 188 publications from 2017 to 2022. For the systematic review of 188 articles, a three-step methodology comprising of PRISMA guidelines was performed, bibliometric analysis and text analysis using VOS-Viewer and Sentiment Analysis using RStudio had been undertaken. Bibliographic coupling revealed the following clusters Digital Space (Over all SDG), Localising SDGs, Financial Systems and Growth (SDG 8), Sustainable Supply Chain (SDG 9), Education (SDG 4), Energy Management (SDG 7), Smart Cities (SDG 11 and 13), Gender, Skills, and Responsibility (SDG 5 and 12), Food Management (SDG 1, 2 and 3), Business Innovation (SDG 8 and 9) and ICT (SDG 9). Next, co-occurrence analysis highlighted the following clusters Circular Economy (SDG 8), Higher Education System (SDG 4), Digital health (SDG 3), Industry 4.0 (SDG 9) and Supply Chain Management (SDG 9). Next, text analysis traced the most relevant areas of work within the theme. Finally, sentiment analysis revealed positive sentiments of the field. The research concluded that only a few SDGs had found major focus while the others don't have any solid ground in the literature. This chapter presents a knowledge structure by mapping the most relevant SDGs in the context of digital technology and sets directions for future research.

The purpose of this paper is to present the augmented reality (AR) eyeglass selection problem based on Neutrosophic MULTIMOORA method which is a very new multi-objective method.

The author evaluates five AR goggles according to six different criteria. Criteria have different weights and determined by analytic hierarchy process. The author used neutrosophic MULTIMOORA method in order to evaluate AR eyeglasses.

Five different AR eyeglasses were evaluated and the best one was selected according to six different criteria (benefit and non-benefit). According to Neutrosophic MULTIMOORA method, Sony AR eyeglass is selected as the best one. Neutrosophic MULTIMOORA method uses simple computational equations and it handles multi-objective decision making problems effectively.

Evaluating AR goggles by using the Neutrosophic MULTIMOORA method for the first time is the originality of this paper.