Best-worst Method Research Articles

Dry port location selection for integration with inland waterway transport in developing countries: A case study in Northern Vietnam

Dry port has emerged as a critical element of transport infrastructure, eliciting substantial research and investment for its development. The strategic selection of dry port locations not only enhances the effectiveness of connections between seaports and hinterlands but also supports the sustainable advancement of the logistics industry, given that dry port operations can integrate with more environmentally friendly transportation modes, particularly inland waterway transport. Extensive research has been conducted to identify optimal dry port locations within the framework of inland waterway container terminals. Nevertheless, these studies primarily focus on developed economies, leaving a notable research void in developing countries. This paper aims to propose a methodological framework for selecting the most suitable dry port location, with a particular emphasis on integration with inland waterway transport in developing nations. This study implements a combination of the Best-Worst Method (BWM) and Elimination Et Choix Traduisant la Realité III (ELECTRE III) in this domain. An analytical case study of Northern Vietnam, considering five alternative dry ports, is conducted to demonstrate the efficacy of the proposed framework. Twenty-seven Vietnamese experts, categorized into three groups—policymakers and consultants, dry port investors and operators, and dry port users—participate in the decision-making process, contributing insights to this case study. An aggregated group decision-making approach is employed. Four principal criteria—economic, accessibility, location, and environmental—are utilized to assess and rank the five alternatives. The findings reveal that a reduction in transport cost is the most critical sub-criterion, while environmental considerations and railway accessibility receive the lowest priority.

Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

Identifying and prioritizing the barriers to TQM implementation in food industries using group best-worst method (a real-world case study)

Identifying and prioritizing the barriers to TQM implementation in food industries using group best-worst method (a real-world case study)

Additive manufacturing service bureau selection: A Bayesian network integrated framework

Additive manufacturing service bureaus (AMSBs) are crucial for enabling manufacturing organizations to leverage the benefits of additive manufacturing (AM) technology, such as on-demand manufacturing, production speed, etc., all while eliminating the expense of maintaining inventories. Consequently, many organizations favor AMSBs for expertise, cost efficiency, and access to diverse equipment, materials, and post-processing, reducing the necessity for substantial in-house investments. While researchers have explored evolving business models and the types of AM services offered by AMSBs to some extent, there is a noticeable research gap in selecting the most compatible AMSB for specific customer requirements, which this research would like to address. Initially, this research identifies various types of services offered by AMSBs, classifying them into eight groups: generative, evaluative, explorative, facilitative, constructive, decisive, selective, and assistive. Then, a knowledge-based expert system is introduced to select a suitable type of AM service. Further, 101 AMSB selection criteria are identified and grouped into criteria and sub-criteria by incorporating insights from literature and experts. Then, 26 pertinent criteria were shortlisted through Delphi. Neutrosophic best-worst method is then utilized to quantify criteria weights. Finally, a Bayesian network is used to calculate the selection probability of each AMSB, identifying the AMSB with the highest probability as the most compatible. The robustness of this framework is validated through sensitivity analysis. The practical effectiveness of the framework was demonstrated through a case study involving Ferro Oil-Tech India Private Limited. The analysis of the results provided valuable managerial insights and suggested ways to enhance the business competitiveness of the organization.

Sustainable innovation challenges in wine ecosystem growth: a time-based comparison

Purpose This exploratory study aims to identify and prioritize key sustainable innovation challenges faced by family-owned businesses in the wine ecosystem. It also explores whether the criticality of these challenges differed pre- and post-COVID-19 by comparing the prioritization of these challenges at these two times. Design/methodology/approach Three methodologies were used. First, a systematic literature review identified the key sustainable innovation challenges in the wine ecosystem, which were then summarized into macro-categories (macro-challenges). Second, a panel of experts validated these challenges. Finally, the best-worst method was used using 40 qualitative interviews to prioritize the most critical challenges in two alternative scenarios to explore potential changes in challenge criticality following COVID-19. Findings This study provides evidence that the primary challenge to sustainable innovation (i.e. constraints on businesses because of limited financial resources) was the same pre- and post-COVID-19. In contrast, although the challenge associated with knowledge sharing was identified as a key strategy of wine ecosystem actors, particularly in recent years, it was the least critical factor at both times. Originality/value This exploratory study extends the conceptual boundaries of sustainable innovation by introducing it to the wine ecosystem. It establishes a practical agenda for small and medium-sized enterprises to enable managers who are constrained by limited resources to prioritize challenges. This agenda outlines a path to identifying competitive strategies. This path can also be followed by a range of stakeholders within this ecosystem to achieve resilience and define medium-to-long-term strategies. Furthermore, policymakers and governments can follow this path to formulate more targeted investment management plans and policies, thereby supporting sustainable economic growth in this key sector for many countries.

The Importance of Green Investments in Developed Economies—MCDM Models for Achieving Adequate Green Investments

Green investments help to create less harmful alternatives and adequate funds that contribute to economic growth, sustainable development, and social well-being. The paper aims to evaluate decision making on the choice of green investments based on multi-criteria decision making (MCDM). The applied methods are empirical and analytical based on the study of the literature, multi-criteria modeling, the determination of weights, and the ranking of criteria in deciding the green investment mapping of indicators, and mapping the indicators. The research used groups of indicators that reflect the main characteristics of green growth from the OECD database. The idea is to decide on the best green investment based on green growth criteria, which consist of grouped indicators according to the areas of the green economy rather than according to their values. The results of the Analytical Hierarchy Process (AHP method) showed that half of the investments in the green economy come from public sources (0.51) and the other half are private (0.25) and institutional investors (0.24), while the Best/Worst Method (BWM) revealed that the best criterion for the decision to invest in the green economy is the environmental and resource productivity of the economy, and the worst is the base of natural assets. This paper aims to enable decision-makers to use these results as weights for the overall assessment of green investments in ESG and to simplify the decision-making approach in future analyses.

How Can Risk-Averse and Risk-Taking Approaches be Considered in a Group Multi-Criteria Decision-Making Problem?

We propose an alternative decision-making methodology based on adopting a mixed risk-averse and risk-taking behavior, improving the objectivity of decision-making. We demonstrate the methodology by prioritizing Iranian tourism centers’ activity under pandemic conditions, providing insights to policymakers on those to keep active or reduce the activity of – hence, those worth developing ahead of future disease outbreaks. This research follows a three-step methodology. First, criteria for evaluation are identified and categorized into tourist attractions, infrastructure, and healthcare dimensions. Second, criterion weights are calculated based on expert opinions, collected using a best-worst method-based questionnaire. Third, tourism centers are evaluated by employing risk-averse and risk-taking best-worst methods. We identify popular attractions, general services, and drugstore accessibility as the primary indicators of tourist attractions, infrastructure, and healthcare, respectively. By clustering tourism centers using K-means algorithm, we find that, in order, the cities of Semnan, Kerman and Zahedan are the tourism centers most suited to staying active during disease outbreaks. For multi-criteria decision-making problems that rely on experts’ evaluations, the proposed methodology can improve the reliability of decision-making. The methodology and framework presented can be used to support various types of decision-making, including evaluation, ranking, selection or sorting.

Bayesian network approach for dynamic fault tree with common cause failures and interval uncertainty parameters

Traditional fault tree analysis often assumes that the basic events are independent and the failure parameters are known. Therefore, it is powerless to deal with the correlation among basic events and the uncertainty of failure parameters due to the small failure data. Therefore, a framework based on continuous-time Bayesian network is proposed to evaluate the reliability of fault tree with common cause failures (CCF) and uncertainty parameters. Firstly, the best-worst method (BWM) and hesitant fuzzy set (HFS) are introduced to address the issue of β-factor being influenced by experts’ subjectivity. Then, the interval theory is introduced to deal with the uncertainty parameters. Based on continuous-time Bayesian network, the conditional probability functions of logic gates (i.e. AND gate, OR gate, spare gate, priority AND gate) with CCF are derived, and the upper and lower bounds of failure probability of top event can be solved. Finally, the fault tree of CPU system is given to verify the effectiveness of the proposed framework.

A readiness assessment model for human systems management digitalization in industrial organizations

ABSTRACT Human systems management (HSM) in organizations must invest in digitization to remain competitive. Even though there is no denying digitalization is essential for modern industrial organizations, the success of digitalization projects is rarely contingent on chance. HSM digitalization requires more than just technology. It also requires leadership, people, strategy, innovation, and process readiness. Organizations must select the most suitable digitalization project to initiate the transition and begin by increasing and monitoring readiness for a successful transition. A practical approach to a successful transition begins with increasing and monitoring readiness. The literature suggests a lack of analytical assessment models for HSM digitalization readiness in industrial organizations, especially for small and medium enterprises (SMEs). This research presents a framework with the general best-worst method (GBWM) to examine decision criteria and help organizations, particularly SMEs, choose the most effective digitalization strategy. A case study demonstrates the applicability and efficacy of the proposed method in assessing HSM digitalization readiness in SMEs.

Prioritization of sustainable development plannings for supply chain finance using a novel Triangular neutrosophic-based TOPSIS-CPT-VIKOR model

The development of sustainable supply chain finance (SSCF) promotes the improvement of the long-term production and operational performance of enterprises. It is significant to explore both environmentally friendly and cost-effective solutions, especially in the presence of uncertain information. The triangular neutrosophic number (TrNN) can enable decision-makers (DMs) to express their preferences in high uncertainty. This paper aims to provide a systematic multiple-attribute group decision-making (MAGDM) framework to support DMs in prioritizing sustainable development plannings by combining the best-worst method (BWM), optimization model of maximizing deviation method, and uniformity entropy theory. Moreover, a TrNN-based extended TOPSIS method is developed to measure the differences in DMs’ importance. We incorporate the cumulative prospect theory (CPT) method to account for DMs’ psychological behaviors in risk scenarios. The CPT-VIKOR methodology is employed to rank alternatives, enhancing the precision of decision-making. Finally, the proposed approach is successfully applied to the LNG industry to illustrate its applicability.

Decoding rail derailments: Unraveling the weighted factors influencing safety and sustainability using the best-worst method

The rail network is essential for sustainable transportation, offering various advantages such as reduced greenhouse gas emissions and congestion relief. However, ensuring safety within the rail network is crucial for its long-term viability and public acceptance. Derailment incidents have significant implications for safety, efficiency, and sustainability. This study employs the Best-Worst Method (BWM) to identify and weigh the parameters affecting derailment incidents. The research methodology involved conducting an extensive literature review to extract influential parameters, which were subsequently classified. Additionally, a rigorous data collection process was undertaken to ensure the reliability of the findings. The BWM was then applied, utilizing the expertise of five carefully selected domain experts who met specific selection criteria based on their experience and reputation in the field of railway safety. This expert panel provided valuable insights to determine the relative importance of the identified parameters. The calculated weights revealed the criticality of factors such as fractures in railway lines, illegal rail width, unauthorized locomotive speed, and defects in wagon wheels. Conversely, falling cargo train parts, improper load distribution, and subsidence of the railway line had relatively lesser influence. The results of this study offer valuable information for decision-makers and stakeholders in the rail industry, facilitating resource allocation and the implementation of targeted strategies to enhance rail safety.

A decision-making framework for determinants of an organisation’s readiness for smart warehouse

Demanding, competitive business dynamics drive organisations to continually evaluate and adopt automation in their structural setups, particularly warehouses, for growth and profitability. Industry 4.0 technologies are revolutionising warehouse operations, resulting in cost savings, increased efficiency, and a more environmentally friendly approach for businesses. The readiness of organisations to adopt these technologies is evaluated with the help of hard and soft determinants selected through a theoretical framework of Technology–Organisation-Environment (TOE). In this study, a unique method is used that combines the Modified Delphi method, content validity for scale validation, expert opinion consensus tests for agreement, ranking determinants using the Best Worst Method (BWM), and rank validation through sensitivity analysis for warehousing industry experts and organisations. Warehouse service providers who convert their warehouses into smart modes to serve fast-moving consumer goods companies in Tier-2 cities are chosen for the structural framework. Results present the role of the ‘operational efficiency of technology’, ‘top management’, ‘employee’s education’, and ‘technical background’ as the most effective driving determinants. This research provides valuable information to researchers and managers trying to upgrade their warehouse into a smart warehouse.

Comparison of the Analytic Network Process and the Best–Worst Method in Ranking Urban Resilience and Regeneration Prioritization by Applying Geographic Information Systems

Urbanization without planning causes concerns about biodiversity loss, congestion, housing, and ecosystem sustainability in developing countries. Therefore, resilience and regeneration following urbanization are critical to city planning and sustainable development. Integrating multi-criteria decision-making methods (MCDM) with geographic information systems (GIS) can be a promising method for analyzing city resilience and regeneration. This study aims to use two MCDMs, the Analytic Network Process (ANP) and the Best–Worst Method (BWM), to evaluate the resilience of metropolitan neighborhoods in Tehran. Fourteen criteria were selected to represent the city’s resilience, and the weights of two models were evaluated for their spatial patterns using GIS. The results showed that the building age was the most important criterion in both methods, while the per capita green space was the least important criterion. The weights of the most important criterion, the building age, for the ANP and BWM, were 19.56 and 18.98, respectively, while the weights of the least important criterion, the per capita green space, were 2.197 and 1.655, respectively. Therefore, the MCDM with GIS provides an approach for assessing city resilience and regeneration priority.

Sustainable preventive maintenance of MMC-based concrete building structures in a harsh environment

The construction industry plays a significant role in environmental strain, attributed mainly to its substantial resource consumption, primarily driven by the surge in residential construction. Modern Methods of Construction (MMC) presents an innovative paradigm for designing and constructing infrastructure and buildings more efficiently, using conventional materials with unconventional techniques. The article aims to apply this approach to an MMC-based building structure, minimizing its life cycle impact by optimizing the consumption of building materials, with particular attention to the effects of the maintenance phase from a preventive point of view. This study focuses on assessing the sustainability of reinforced concrete flat slabs employing a hollow structural body system, explicitly emphasizing environmental aggressiveness factors contributing to corrosion, such as carbonation and chlorides. The research explores ten design options for a waterfront public residential building, examining their impact on the economy, the environment, and even society regarding the maintenance cycles required over the structure's lifetime, depending on the preventive strategy employed for each design. In assessing the sustainability of these options, researchers employed a combination of the best-worst method (BWM) and the VIKOR technique, considering nine criteria related to sustainability. The study found that 5 % silica fume concrete is the most cost-effective and environmentally friendly option, with hydrophobic impregnation reducing social impacts. However, compared to one- and two-dimensional evaluations, the study demonstrates the importance of simultaneously considering a design's life cycle's economic, environmental, and social impacts to achieve sustainability in maintenance with a holistic view. This approach led to an 86 % higher sustainability rating for a design using sulforesistant cement in the concrete mix than the baseline.

Analysis of the barriers and possible approaches for adopting Industry 4.0 in the fertilizer sector of Bangladesh

Industry 4.0 is gaining significant attention and recognition in both academic and practical spheres since to its potential to deliver substantial advantages to various industries and enable novel applications. This research aims to investigate the fertilizer production industry, evaluating its current state and identifying the barriers impeding the implementation of Industry 4.0 in this domain. The first investigation on Industry 4.0’s current state in Bangladesh entailed visiting and surveying five significant fertilizer companies. Approximately 50% of the components associated with Industry 4.0 have been successfully integrated into these manufacturing facilities. In order to ascertain the causes behind this negative circumstance, a research study was undertaken to uncover the barriers that are hindering the successful Industry 4.0 implementation. Based on the views expressed by a board of ten experts from industry and university, it has been determined that nine out of the twelve issues initially identified are deemed to be significant hurdles. A widely known decision-making approach based on numerous factors, the Best–Worst Method (BWM) was used to evaluate the ranking of these nine difficulties. Insufficient knowledge about Industry 4.0 was determined to be the most significant barrier, while the possibility of job loss was determined to be the least significant. The ranking was further improved by conducting a sensitivity analysis. Subsequently, many pragmatic strategies for overcoming these obstacles are discussed. This study is expected to have a substantial impact on the fertilizer business in Bangladesh by providing valuable insights to industry owners, policymakers, and stakeholders, therefore facilitating their adoption of the fourth industrial revolution.

Strategies to promote patient-centeredness within the healthcare industry: A grey-based multicriteria decision making methods.

The international policy agenda has recently advocated for the development of patient-centeredness in healthcare service delivery. Consequently, various stakeholders in the healthcare systems have expressed a vital need for identifying strategies and tools that can enhance patient-centeredness. The objective of this paper was to prioritise and benchmark the strategies that can improve patient-centeredness in healthcare service delivery. We employed a multi-stage research scenario that consisted of two phases: a phase including of a scoping review to identify the current strategies to improve patient-centeredness (PC); And, a phase including of a multicriteria best-worst method to assign weights to PC principles, and a questionnaire administered to a sample of experts for benchmarking the strategies derived from the literature using the Grey Multi-Attributive Border Approximation Area Comparison (MABAC-G) method. The most important principle of patient-centeredness was deemed to be access to care, while telehealth tools and Electronic Health Information Systems were respectively suggested as the most efficacious platforms for promoting patient-centeredness. We recommend that administrators and policy makers in the healthcare industry prioritise the implementation and research of strategies such as telehealth tools and electronic health information systems to enhance access and patient-centeredness in the healthcare systems.

Security assessment and diagnosis for industrial water resources using TODIMSort considering Best–Worst Method with double hierarchy hesitant fuzzy linguistic term set

Motivated by the driving force to address global water scarcity, industrial water resources, as the second largest consumption of water resources, its security assessment plays a crucial role in improving the current situation. Hence, this paper proposes a novel methodology to conduct the industrial water resources security (IWRS) assessment. Firstly, a more targeted assessment system based on the framework of the Pressure–State–Response (P–S–R) on IWRS is established. Then, enhanced with a double hierarchy hesitant fuzzy linguistic term set (DHHFLTS), the Best–Worst Method (BWM) now determines subjective weights through DHHFLTS-BWM (DF-BWM). By introducing the Criteria Importance Through Intercriteria Correlation (CRITIC) method, which considers the indicator interactions, objective weights are obtained to modify the subjective weights. Furthermore, the global dominance of all alternatives is calculated by a TODIMSort method and grading them. Moreover, 16 cities in Anhui Province are taken as examples to assess IWRS in the decade from 2011 to 2020. Comparative analysis with original BWM, time series analysis, sensitivity analysis on loss attenuation coefficient, coupling and coordination analysis and obstacle analysis on all indicators are conducted to verify the rationality, effectiveness, and stability of the proposed assessment methodology. Simultaneously, explore the existing issues within IWRS. It can be seen from the results that the performance of Lu’an and Huainan cities is relatively better, while Ma’anshan city shows relatively poorer performance. In addition, per capita water resources and wastewater treatment facilities have a significant impact on the IWRS. Finally, some management suggestions are proposed to enhance the scientific and effective management of industrial water resources and ensure their sustainable utilization.

Reviving the forgotten legacy: Strategies for reviving qanats as sustainable solutions for agricultural water supply in arid and semi-arid regions

In semi-arid and arid regions, qanats are a vital water source for agricultural needs. However, many communities are abandoning these traditional and sustainable water supply systems in favor of modern and more productive methods. Consequently, this research aimed to identify the most effective approaches for rejuvenating qanats in the dry and semi-arid areas of Razavi Khorasan province, Iran. To accomplish this goal, a comprehensive strategy was employed by merging the Strengths, Weaknesses, Opportunities, and Threats (SWOT, Best-Worst Method (BWM), and the Weighted Aggregated Sum Product Assessment (WASPAS).Based on interviews with experts in water resources and SWOT analysis, 27 factors affecting the restoration of qanats were identified and weighted with BWM. The results of BWM-WASPAS highlight the crucial role of a holistic approach in reviving qanats. The study findings reveal that the highest scores were obtained by the strategies reforming water governance policies to prioritize sustainable indigenous water resources (SO1) and establish an independent institution for qanat restoration (WT7). Conversely, educational (ST1), informational (SO3), and research (WO4) strategies concerning the revival of qanats were categorized as intermediate priorities. Lastly, strategies associated with farm-level support policies were assigned lower priorities. Accordingly, it is recommended that policymakers in Mashhad give priority to macro-level and institutional policies concerning qanat restoration, considering the current state of indigenous water resources. Additionally, a continued emphasis on research policies and farm-level initiatives is advised.

Exploring the barriers to implement industrial symbiosis in the apparel manufacturing industry: Implications for sustainable development

Industrial symbiosis, a promising approach for sustainable industrial practices, has garnered attention in recent days for its ability to enhance resource efficiency, minimize waste, and preserve the environment through collaborative exchanges among industries. In emerging economies like Bangladesh, integrating industrial symbiosis in the manufacturing industries offers the potential to balance economic growth with environmental sustainability. However, this integration encounters various barriers that complicate the implementation. Despite research on industrial symbiosis in robust economies, studies on emerging and developed economies are still scarce. To date, no research has yet investigated the barriers hindering the performance of industrial symbiosis in the Bangladeshi apparel manufacturing sector. To address this gap, this study integrates the Bayes theorem and the Best-Worst Method to identify and prioritize barriers to the Bangladeshi apparel manufacturing sector. From extensive literature reviews and expert validation, 17 barriers were identified. Findings reveal the “lack of technology and infrastructure readiness” as the most significant barrier, followed by “lack of inter-company cooperation” and “lack of management support”. Conquering these barriers empowers emerging economies to fortify the apparel manufacturing sector's resilience, resource efficiency, and environmental performance while fostering sustainable development via circular economy practices. This study is expected to guide policymakers and stakeholders in crafting targeted strategies for promoting steady growth and sustainable development in the apparel manufacturing sector of emerging economies like Bangladesh.

Digital transition from industry 4.0 to industry 5.0 in smart manufacturing: A framework for sustainable future

Digital transition to Industry 5.0 (I5.0) has finally arrived after Industry 4.0 (I4.0) was offered ten years ago to help address industry flaws in manufacturing. I5.0 is necessary in contemporary business to achieve competitive advantages for a sustainable future. However, despite the importance of I5.0, the critical success factors (CSFs) for the transition from I4.0 to I5.0 have not been explored extensively in the literature. This study develops a model to investigate potential CSFs for the smooth transition from I4.0 to I5.0. The study ensures systems flexibility, then ranks the identified CSFs based on their influential weights and explores their interrelationships. The study adopts the Bayesian best-worst method (B-BWM) to assess the perceptions of several decision-makers from a probabilistic perspective for reducing information loss. The results from this study reflect that CSFs, such as adoption of advanced technology to create value in I5.0 paradigm, high-powered analytical ability for smart manufacturing, cost and funding in transition from I4.0 to I5.0, aligned organizational strategy in transition to I5.0, and security and privacy to ensure effective digital transformation, contribute significantly towards the digital transformation from I4.0 to I.50 in smart manufacturing in the context of steel manufacturing business environment considering the developing countries. The successful implementation of CSFs can help achieve flexibility in the supply chain, contribute to flexible systems and support managers in framing strategies that help them prioritize for an easy transition towards a sustainable future.