Fuzzy Analytic Hierarchy Process Research Articles

STATISTICAL INTEGRATION OF QGIS AND MULTI-CRITERIA DECISION ANALYSIS FOR SUSTAINABLE RURAL DEVELOPMENT IN BELSAR DEVELOPMENT BLOCK

The definition of “rural areas” varies significantly across regions, making the determination of rurality levels crucial for sustainable development and effective policy design. This study introduces a comprehensive rurality index to rank and categorize villages within the Belsar Development Block of Gonda District, India. Utilizing Multiple-criteria Decision-making Methods (MCDMs) such as Fuzzy Analytic Hierarchy Process (F-AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Weighted Product Model (WPM), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), we developed a robust methodology for this assessment. A thorough sensitivity analysis was also conducted to ensure the reliability of the results. The study identified fourteen critical factors, grouped into five domains: (1) socioeconomic status, (2) education, (3) land use, (4) employment, and (5) healthcare. Based on these factors, villages were ranked and classified into four categories according to their rurality levels, and a detailed rurality map was created with QGIS. The study revealed that the modified F-AHP emerged as the most effective MCDM method for this rurality assessment. Given the systematic execution of this assessment, it can serve as a model for comparative studies in other regions of India and globally, promoting sustainable rural development through informed decision-making.

Exploring Internet of Things adoption challenges in manufacturing firms: A Delphi Fuzzy Analytical Hierarchy Process approach.

Innovation is key to gaining a sustainable edge in an increasingly competitive global manufacturing landscape. For Bangladesh's manufacturing sector to survive and thrive in today's cutthroat business environment, adopting transformative technologies such as the Internet of Things (IoT) is not a luxury but a necessity. This article tackles the formidable task of identifying and comprehensively evaluating the impediments to IoT adoption in the Bangladeshi manufacturing industry. We delve deeply into the complex terrain of IoT adoption challenges by synthesizing expert insights and a meticulously selected body of contemporary literature. We employ a robust methodology combining the Delphi method with the fuzzy Analytical Hierarchy Process to systematically analyze and prioritize these challenges. Using this methodology, we leveraged the combined expertise of domain specialists and subsequently employed fuzzy logic techniques to address the inherent ambiguities and uncertainties within the data. Our findings highlight this clear path. They reveal that among the myriad barriers, "Lack of top management commitment to implementing new technology" (B10), "High initial implementation investment costs" (B9), and "Risks associated with switching to a new business model" (B7) loom most extensive, demanding immediate attention. These insights are not confined to academia but serve as a pragmatic guide for industrial managers. Armed with the knowledge gleaned from this study, managers can craft tailored strategies, set well-informed priorities, and embark on a transformational journey toward harnessing the vast potential of IoT in the Bangladeshi industrial sector. This article provides a comprehensive understanding of IoT adoption challenges and industry leaders with the tools necessary to navigate these challenges effectively. This strategic navigation, in turn, contributes significantly to enhancing the competitiveness and sustainability of Bangladeshi manufacturing in the IoT era.

Prioritizing Criteria for Establishing a Green Shipping Corridor Between the Ports of Sines and Luanda Using Fuzzy AHP

As port authorities and cargo operators seek strategies to reduce carbon emissions while ensuring operational efficiency, some are turning to the concept of green corridors. These solutions aim to establish formalized partnerships among ports, carriers, shippers, and countries. During the process, the stakeholders must consider four priority areas (alternative fuels, bunkering infrastructure, vessel decarbonization pathways, and cargo demand dynamics) from seven angles (environmental, economic, infrastructure, regulatory, operational, technological, and social). This study explores the prioritization of these criteria for establishing a green maritime corridor between two major ports in Portugal and Angola, which would be a significant step toward promoting sustainable global trade. Utilizing the fuzzy AHP, this research analyzes all these factors and their associated sub-criteria derived from a comprehensive literature review and consultations with stakeholders from the Ports of Sines and Luanda. The findings show the dominance of environmental compatibility and economic viability, while social acceptance shows the lowest score. This framework guides the decision-making process for developing a sustainable shipping corridor. The results offer valuable insights for policymakers which can guide them in fostering resilient maritime transport routes, accelerating the adoption of decarbonization strategies and playing a critical role in achieving the IMO’s zero-emission targets by 2050.

Optimizing Supplier Selection using Fuzzy MCDM

This study explores the supplier selection problem within technology manufacturing by employing the Fuzzy Analytic Hierarchy Process (FAHP). Data was collected from a technology manufacturing company, and expert evaluations were sought from professionals with over 10 years of experience in the industry. The criteria considered for supplier selection included profitability of the supplier (C1), relationship closeness (C2), technological capability (C3), conformance quality (C4), and conflict (C5). The FAHP results indicated that relationship closeness (C2) ranked highest among the criteria, highlighting its significance in the supplier selection process, while conflict (C5) received the lowest ranking. These findings emphasize the importance of nurturing strong relationships with suppliers and vendors to enhance overall supply chain performance. The proposed FAHP model effectively addresses the complexities and uncertainties inherent in supplier evaluation, offering a robust decision-making tool tailored to the specific needs of technology manufacturing companies

Calculation and Comparative Analysis of Common Fault Scores for Secure Computers Based on Embedded Neural Networks

With the rapid development of computer networks and information technology, government departments, financial institutions, and enterprises are increasingly dependent on software, and software security issues have become a focus of attention. In this context, how to effectively evaluate the security of software has become an important issue for research institutions both domestically and internationally. On the basis of exploring software definition, this paper not only analyzes the security and potential threats of software in computer networks, but also introduces Embedded Neural Networks (ENNs) as an evaluation tool, and combines Fuzzy Analytic Hierarchy Process (FAHP) to deeply explore a new method for software security risk assessment. By utilizing the powerful pattern recognition capability of ENNs, software logs, system call sequences, and other data can be classified and analyzed to distinguish between normal and abnormal behavior. This ability is crucial for identifying security incidents such as malicious software and unauthorized access. ENNs are designed with resource constraints in mind, which can reduce energy consumption while ensuring performance. For software systems that require long-term operation, this means higher security and stability. Practice has proven that combining ENNs with FAHP can more scientifically and effectively evaluate software security. This method not only improves the accuracy and efficiency of evaluation, but also provides a more solid theoretical foundation and technical support for software security protection.

A multi-criteria decision-making model for sustainable selection of coastal protection structures

Although nature-based and hybrid (i.e., a combination of hard and nature-based) solutions provide well-documented benefits to both the environment and people living nearby, they cannot be implemented in all coastal zones. Thus, the selection process of coastal protection structures (CPSs) requires an in-depth and comprehensible assessment with an effective and robust decision-support framework. Existing frameworks in the literature, however, do not focus on the selection of nature-based solutions and environmental and social factors were not considered thoroughly in these frameworks. In this respect, this research aims to develop a decision-support model that guides decision-makers in selecting the most appropriate and sustainable CPSs. The framework was designed in a way that includes a variety of environmental and social factors along with economic and technical aspects. Then, fuzzy AHP and TOPSIS analyses were conducted to determine the relative importance of the selection factors and to select the most appropriate CPS. Factors such as sediment dynamics, geotechnical issues, construction cost, transportation and navigation effects, and technological know-how were among the top-rated factors. TOPSIS analysis results revealed that mangrove forests and artificial reefs were the top two most sustainable CPSs. The proposed framework can minimize the social and environmental impacts of CPSs and expedite the green transition in the coastal environment.

Glacial Lake Outburst Flood Susceptibility Mapping in Sikkim: A Comparison of AHP and Fuzzy AHP Models

Glacial Lake Outburst Flood Susceptibility Mapping in Sikkim: A Comparison of AHP and Fuzzy AHP Models

Evaluation of sustainable hydrogen production technologies by fuzzy AHP analysis with bootstrapping

The concept of a hydrogen economy has received increasing attention worldwide. Hydrogen is a promising energy carrier that contributes to the development of a sustainable and reliable energy sector. Selection of the most sustainable hydrogen production technology (HPT) plays a significant role in promoting a hydrogen economy. However, it is regarded as a multi-criteria decision-making (MCDM) problem with several incompatible evaluation criteria. In this study, we develop an MCDM approach based on Fuzzy Analytic Hierarchy Process (FAHP) for prioritizing HPT alternatives, focusing on Egypt as a case study. Our contributions are twofold. First, to combat the issue of a small number of subject matter experts, a bootstrap method is employed to validate the main criteria weights in FAHP. Second, we conduct a comparative FAHP analysis from a broad perspective of the national experts from a developing country and the international experts from developed countries. Our results show that PV electrolysis is the most preferable technology option from the perspectives of both the national and international experts. Since the FAHP approach proposed is a flexible framework that can be applied to countries other than Egypt, it will support global policymakers and industry stakeholders in their decision making toward a sustainable and efficient hydrogen economy strategy.

An operational risk assessment method for petrochemical plants based on deep learning

The petrochemical industry is an important guarantee for the development of people’s lives, and the operational risk assessment method of personnel in the operation of petrochemical enterprises is the most important. Based on a deep learning algorithm, a new method based on the micro-Doppler effect and fuzzy analytic hierarchy process is proposed to evaluate the operational risk of personnel in petrochemical enterprises. The original monitoring image of petrochemical equipment is invoked, and micro-Doppler analysis is performed based on the original image to identify the activity target of the personnel on the job site and generate the activity map of the petrochemical plant operators. Based on the analysis data of the micro-Doppler effect, the fuzzy function and hierarchical analysis method are combined. The fuzzy theory is introduced into the analytic hierarchy process, and the inherent expert evaluation scores are transformed into fuzzy numbers. It makes the expert evaluation of petrochemical plants more accurate in the subsequent coupling and improves the objectivity of the traditional analytic hierarchy process. The scheme proposed in this paper can monitor real-time operation safety and provide a guarantee for the personal safety of field operators. This method plays an important role in improving the safety of petrochemical plants.

Development of a Comprehensive Safety Evaluation Mechanism for the Highway Bus Industry

A suitable mechanism for evaluating the safety of the highway bus industry is crucial for ensuring public transportation safety in a country. In this study, we have combined indices from the Compliance Safety Accountability (CSA) program of the Federal Motor Carrier Safety Administration (FMCSA) in the US, the safety evaluation of renting buses in Japan, and some existing safety evaluation methods in Taiwan to develop a new safety evaluation mechanism for the highway bus industry. This paper introduces a two-stage decision making approach that includes the use of fuzzy analytic hierarchy process (Fuzzy AHP) and machine learning techniques such as decision trees, support vector machines, and random forests to develop this mechanism. The data used in this research were collected from the internet and directly from highway bus transportation companies. We calculated the safety performance of each company and assigned them different safety ranks. Compared with the causes of accidents in Taiwan, the results of this study showed that work hours, driver fitness, and administrative penalties are the three most important sub-attributes that affect the safety rank of the company.

The effects of global product design on supply chain efficiency and natural resources management

The significance of global product design as a primary factor in determining supply chain efficiency and natural resource management. This study examines the impact of global product design changes on process and supply chain practices. Specifically, it seeks to understand in promoting sustainable practices across global supply chains. This study, utilizes the fuzzy Analytical Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods to evaluate the criteria, sub-criteria, and various significant opportunities influencing sustainable product design and supply chain management. The fuzzy AHP method is used to assess the key criteria and their respective sub-criteria. The fuzzy TOPSIS method is used to rank the opportunities in driving sustainable practices across global supply chains. The results of fuzzy AHP show that innovation and product differentiation (C4), economic viability (C1), and supply chain efficiency (C5) are the crucial criteria. While the results of fuzzy TOPSIS present that supply chain transparency (O2) is the most suitable opportunity, followed by circular economy practices (O1) and policy advocacy and compliance (O6) in adopting global product design on supply chain efficiency and natural resources management. Thus, this study is highly relevant to the governments, industry specialists, and policymakers who aim at enhancing the dynamic business environment and sustainable development.

Prioritizing environmental and policy factors and solutions in sustainable mineral resources management for green growth in China

AbstractIn the wake of the increasing environmental challenges and the urgent need for sustainable development, this study focuses on the complex issue of mineral resource management. Given the growing global demand for minerals and the environmental impact of extraction, the research seeks to uncover the solutions that promote green growth while effectively addressing the ecological consequences of resource utilization in China. In the study, six factors, twenty‐four sub‐factors, and five potential solutions have been identified influencing sustainable mineral resource management and green growth. The study utilizes fuzzy Analytical Hierarchy Process (AHP) to assess these factors and sub‐factors. Following, the study utilizes fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to evaluate and rank five potential solutions for sustainable development. The fuzzy AHP results indicate that environmental impact mitigation, robust policy frameworks, and innovation in extraction techniques are the most crucial factors. The fuzzy TOPSIS findings indicate that technological innovation for sustainability ranks highest, followed by policy‐driven green growth, socio‐economic empowerment, enhanced environmental stewardship, and integrated circular economy approaches. These findings emphasize the critical role of advanced technologies and robust policies in promoting sustainability while addressing the socio‐economic and environmental dimensions of mineral resource management.

Development and evaluation of a novel framework to enhance k-NN algorithm’s accuracy in data sparsity contexts

This paper presents a novel framework for implementing the k-NN algorithm, designed to enhance its accuracy in contexts with sparse data. The framework addresses limitations in the algorithm’s training process by optimizing data structures. It employs composite datasets generated from the initial data using a data-driven fuzzy Analytic Hierarchy Process weighting scheme. This approach is designed to enhance the informational content in the initial datasets, thus reducing the entropy and implementation uncertainty. The framework was evaluated using 75 publicly available datasets and 3 generated datasets, demonstrating significant accuracy improvements across various k-parameter values. The findings were rigorously generalized using non-parametric hypothesis tests; while the resulting sensitivity was assessed by applying different distance metrics. By enhancing informational content, the composite data structures contribute to both accuracy improvements and scalability, particularly in data-sparse contexts. This relationship underscores the critical role of entropy in enhancing the performance of explainable machine learning algorithms, providing a valuable and interpretable tool for transforming data structures in sparse data environments.

Enhancing Economic, Resilient, and Sustainable Outcomes Through Supplier Selection and Order Allocation in the Food Manufacturing Industry: A Hybrid Delphi-FAHP-FMOP Method

In the food manufacturing industry, which is critical to national economies, there is a growing imperative to meet heightened safety, quality, and environmental standards, particularly in the face of supply chain disruptions. This study addresses the gap in literature by integrating sustainable and resilient supply chain theories with risk management and low-carbon principles into a supplier selection framework. Utilizing the Delphi method, fuzzy analytic hierarchy process (FAHP), and fuzzy multi-objective programming (FMOP), we develop a decision-making model specifically calibrated for the food sector. Initially, the study establishes a comprehensive criteria system encompassing quality, cost, delivery, low-carbon, and risk management through a literature review and expert consultation. Subsequently, FAHP is employed to determine the relative importance of each criterion in supplier selection. Furthermore, FMOP is utilized to develop a decision-making model for optimizing supplier selection and order allocation. Validated through a numerical study based on a Chinese food manufacturer, the framework presents a practical tool for food manufacturers, ensuring supply chain stability while aligning with sustainability objectives. This research refines decision making and strengthens the competitive stance of food manufacturers, significantly propelling the industry’s green transformation.

Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation.

Parkinson disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on health care systems. In an effort to support patients with PD, their carers, and the wider health care sector to manage this incurable condition, the focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes prescribing assistive technologies (ATs), which are viewed as a way to promote independent living and deliver remote care. However, the uptake of these ATs is varied, with some users not ready or willing to accept all forms of AT and others only willing to adopt low-technology solutions. Consequently, to manage both the demands on resources and the efficiency with which ATs are deployed, new approaches are needed to automatically assess or predict a user's likelihood to accept and adopt a particular AT before it is prescribed. Classification algorithms can be used to automatically consider the range of factors impacting AT adoption likelihood, thereby potentially supporting more effective AT allocation. From a computational perspective, different classification algorithms and selection criteria offer various opportunities and challenges to address this need. This paper presents a novel hybrid multicriteria decision-making approach to support classifier selection in technology adoption processes involving patients with PD. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) was implemented to calculate the relative priorities of criteria and subcriteria considering experts' knowledge and uncertainty. Second, the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF-DEMATEL) was applied to evaluate the cause-effect relationships among criteria/subcriteria. Finally, the combined compromise solution (CoCoSo) was used to rank the candidate classifiers based on their capability to model the technology adoption. We conducted a study involving a mobile smartphone solution to validate the proposed methodology. Structure (F5) was identified as the factor with the highest relative priority (overall weight=0.214), while adaptability (F4) (D-R=1.234) was found to be the most influencing aspect when selecting classifiers for technology adoption in patients with PD. In this case, the most appropriate algorithm for supporting technology adoption in patients with PD was the A3 - J48 decision tree (M3=2.5592). The results obtained by comparing the CoCoSo method in the proposed approach with 2 alternative methods (simple additive weighting and technique for order of preference by similarity to ideal solution) support the accuracy and applicability of the proposed methodology. It was observed that the final scores of the algorithms in each method were highly correlated (Pearson correlation coefficient >0.8). The IF-AHP-IF-DEMATEL-CoCoSo approach helped to identify classification algorithms that do not just discriminate between good and bad adopters of assistive technologies within the Parkinson population but also consider technology-specific features like design, quality, and compatibility that make these classifiers easily implementable by clinicians in the health care system.

An Integrated Approach to Constraints Theory and Fuzzy Analytical Hierarchy Process (FAHP) in Construction Projects (Case Study)

The research endeavors to harness the benefits stemming from the integration of constraint theory into construction project management, with the primary goal of mitigating project completion delays. Additionally, it employs fuzzy analysis to determine the relative significance of fundamental constraints within projects by assigning them appropriate weights. The research problem primarily revolves around two key issues. Firstly, the persistent utilization of outdated methodologies and a heavy reliance on workforce experience without embracing modern computerized technologies. Secondly, the recurring problem of project delivery delays. Construction projects typically encompass five fundamental constraint types: cost restrictions, time constraints, administrative and legal limitations, technical and design constraints, and environmental constraints. To address these issues, a field study was conducted, focusing on the development of a housing complex in Qa'imaqamiya, Al-Mada'in district, Bismayah. The researchers propose a three-stage methodology to tackle these challenges. In the initial stage, the theory of accelerated activities is applied to all concurrent implementable tasks. Subsequently, the second stage introduces a hierarchical analysis process, known as FAHP, to quantify the importance of each constraint. Finally, in the third phase, fuzzy logic is employed to calculate the projected duration and associated costs for each activity, ensuring project execution and delivery within agreed-upon timeframes. After applying the proposed methodology, there is a reduction in completion time by up to (23%). Research Type: Research Paper

Penentuan Penentuan Prioritas Lokasi Fasharkan TNI AL dengan Metode Fuzzy Analytical Hierarchy Process dan Geographic Information System

Based on the regulation of the Navy Chief of Staff No. Per Kasal/39/V/2009 dated May 26, 2009 concerning the development policy of the Navy towards minimum essential force, namely the preparation of the Navy's combat power is directed to achieve minimum essential force or abbreviated as MEF. According to the Chief of Staff of the Navy Number Kep/1771 / XII / 2013 concerning the Administrative Guidebook for the Standardization of TNI AL Bases (PUM-7.03) that a TNI AL Main Base (LANTAMAL) must have a class A Fasharkan, whose ability is to be able to carry out maintenance and repair up to the depot level for all types of KRI, both ship buildings, machinery, sewaco, electricity and ship armaments. There are many studies of location prioritization using the Analytical Hierarchy Process (AHP) method, but the results have uncertainty or decision uncertainty. So this research was conducted using the Fuzzy AHP method to overcome uncertainty or decision uncertainty. The Fuzzy AHP method is used for calculations to select alternatives. After the alternative is selected, then verification of location feasibility is carried out using the Geographic Information System (GIS) display. From the calculation using the FAHP method, Lantamal IX Ambon was selected as an alternative with a weight value of 0.251630342. While verifying the feasibility of the location using GIS, the Tawiri location was chosen as a more feasible land compared to other land options.

Development of a total quality management framework for the mobile network operations Centre

Telecommunication is one of the most fast-paced and rapidly changing sectors globally. This study develops a total quality management (TQM) framework for mobile network operation centres (NOC) to aid in network surveillance and fault management processes for a 5G transmission network. A systematic literature review was conducted using academic articles in journals and conference papers sourced from the EBSCOHost database. A fuzzy analytic hierarchical process (FAHP) was used as a multi-criteria decision-making tool to determine which criteria and factors are of the highest ranking for the development of the TQM framework. Data collection and control sheets were ranked higher, making them critical to the framework. This shows that, without good data, no problems can be identified, visualised, analysed, and solved and processes will not be improved. Process improvement is ranked last, showing it is of the lowest priority compared to the other criteria. This however, does not imply that in TQM it is of lower priority, as it is needed to keep meeting the needs of the customers. The framework includes how the TQM tools and techniques can be used in collecting, visualising, analysing, solving problems, and improving processes.

Multi-Criteria Decision-Making Approach for Optimal Energy Storage System Selection and Applications in Oman

This research aims to support the goals of Oman Vision 2040 by reducing the dependency on non-renewable energy resources and increasing the utilization of the national natural renewable energy resources. Selecting appropriate energy storage systems (ESSs) will play a key role in achieving this vision by enabling a greater integration of solar and other renewable energy. ESSs allow for solar power generated during daylight hours to be stored for use during peak demand periods. Additionally, the proposed framework provides guidance for large-scale ESS infrastructure planning and investments to support Oman’s renewable energy goals. As the global renewable energy market grows rapidly and Oman implements economic reforms, the ESS market is expected to flourish in Oman. In the near future, ESS is expected to contribute to lower electricity costs and enhance stability compared to traditional energy systems. While ESS technologies have been studied broadly, there is a lack of comprehensive analysis for optimal ESS selection tailored to Oman’s unique geographical, technical, and policy context. The main objective of this study is to provide a comprehensive evaluation of ESS options and identify the type(s) most suitable for integration with Oman’s national grid using a multi-criteria decision-making (MCDM) methodology. This study addresses this gap by applying the Hesitate Fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitate Fuzzy VIKOR methods to assess alternative ESS technologies based on technical, economic, environmental, and social criteria specifically for Oman’s context. The analysis reveals pumped hydro energy storage (PHES) and compressed air energy storage (CAES) as the most appropriate solutions. The tailored selection framework aims to guide policy and infrastructure planning to determine investments for large-scale ESSs and provide a model for comprehensive ESS assessment in energy transition planning for countries with similar challenges.

Analysing the feasibility of agro-based value chain configurations in the rural context with B2B marketing

Purpose This paper aims to advocate for small-scale entrepreneurship with commercial cultivation to earn better profits at the farmer level. This paper explores potential value chain configurations of vertical coordination. Design/methodology/approach The aloe vera supply chain was examined for the study where aloe vera juice is the product. The various configurations are complete vertical coordination, semi-vertical coordination and non-vertical coordination. These were analysed to find feasibility and market prospects based on parameters. The cyclic view of supply chains, comparative analysis and fuzzy-analytical hierarchy process algorithm are presented. Findings The semi-vertically coordinated supply chain is indicated as the optimum setup to initiate value chains. The entrepreneurs in this configuration will be able to sell semi-finished produce to a brand that will process and distribute the final product. This B2B setup will help the farmer to earn better revenues in the post-COVID-19 scenario. Effective marketing with improved execution and innovation will boost the food processing industry. Research limitations/implications This study uses an established methodology to distinguish among alternatives based on underlying factors. The study is based on data collected from stakeholders. The study reflects the most accurate picture of real-world scenarios at the farm and market level in the Indian context. Practical implications This study’s learnings will help understand the organisational approach to serve demand by designing unique business configurations. Adapting the fundamental model with technological intervention will improve supply chain performance and sustainability. The most modern approaches in agri-business, such as subscription models, vertical farming, corporate entrepreneurship and technological intervention, will be covered in future work. Social implications This study will help stakeholders, including the government, decide whether to introduce training skills, subsidy policies, business expansion ideas and feasibility studies. Originality/value The study highlights the strategic role of developing the B2B platform to support customer values and customer ecosystem with better product availability.