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Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.

This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.

The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.

This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.

This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

The aim is to analyze the European eco-management because the global warming has become a topical issue impacting the whole world. Individual countries are trying to minimize all the catalysts of global warming, such as carbon emissions. This paper addresses this issue and analyzes the performance of European eco-management for the purpose of future energy investments being environmentally.

This paper develops a fuzzy decision-making model to study the performance indicators of selected countries based on EMAS III standard. It employs interval type-2 fuzzy DEMATEL to evaluate the performance factors and TOPSIS methodology to assess five selected European countries' performance in relation to eco-friendly, emission and renewable energy.

Eco-friendly energy plays the most critical role in this respect followed by emissions and renewable energy which constitute significant factors. The novelty of this study is identifying significant criteria regarding environmental and energy efficiency of investments and making performance assessments of European countries with a new fuzzy decision-making model. Both expert opinions and datasets are used for the analysis. This paper supports previous research about energy efficiency investments in Europe.

The innovative feature of this study is identifying significant criteria regarding environmental and energy efficiency of investments and assessing the performance of European countries with a new fuzzy decision-making model. The fact that the analysis only concerns the European region is an important limitation. In future analyses, other groups of countries can be examined. Innovations can be made regarding the method applied. In this context, analyses can be done utilizing different fuzzy numbers. Finally, the importance of the criteria can be calculated with other methods such as SWARA.

The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments is done in this manuscript.

Analysis of European eco-management performance was done for environmentally friendly and efficient energy investments. A fuzzy decision-making model is constructed. The paper fills the gap in performance analysis of European eco-management for environmentally friendly and efficient energy investments.

Reverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes. Owing to growing environmental legislations and the development of new technologies in marketing, RL has attracted more significance among experts and academicians. Outsourcing RL practices to third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies due to complexity of RL operations and the lack of available resource. Current sustainability trends have made 3PRLP assessment and selection process more complex. In order to select the 3PRLP, the existence of several aspects of sustainability motivates the experts to establish a new multi-criteria decision analysis (MCDA) approach.

With the growing complexity and high uncertainty of decision environments, the preference values of 3PRLPs are not always expressed with real numbers. As the generalized version of fuzzy set, intuitionistic fuzzy set and Fermatean fuzzy set, the theory of q-rung orthopair fuzzy set (q-ROFS) is used to permit decision experts (DEs) to their assessments in a larger space and to better cope with uncertain information. Given that the combined compromise solution (CoCoSo) is an innovative MCDA approach with higher degree of stability and reliability than several existing methods.

To exhibit the potentiality and applicability of the presented framework, a case study of S3PRLPs assessment is taken from q-rung orthopair fuzzy perspective. The assessment process consists of three sustainability aspects namely economic, environment and social dimensions related with a total of 14 criteria. Further, sensitivity and comparative analyses are made to display the solidity and strength of the presented approach. The results of this study approve that the presented methodology is more stable and efficient in comparison with other methods.

Thus, the objective of the study is to develop a hybrid decision-making methodology by combining CoCoSo method and discrimination measure with q-ROFS for selecting an appropriate sustainable 3PRLP (S3PRLP) candidate under uncertain environment. In the proposed method, a novel procedure is proposed to obtain the weights of DEs within q-ROFS context. To calculate the criteria weights, a new formula is presented based on discrimination measure, which provides more realistic weights. In this respect, a new discrimination measure is proposed for q-ROFSs.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

The aim of this paper states that total productive maintenance (TPM) is an improvement tool which employs the effective utilization of employees in order to enhance the reliability of the equipment in consideration.

This paper identifies and evaluates the factors accountable for the adoption of TPM methodology in manufacturing organizations. Twenty-four factors affecting the TPM implementation are explored and categorized into five significant categories. Afterwards, these identified TPM factors have been evaluated by using a most popular Multi-criteria decision-making (MCDM) approach namely fuzzy pivot pairwise relative criteria importance assessment (F-PIPRECIA).

In this paper, through application of F-PIPRECIA, “Behavioural factor” is ranked first while “Financial factor” the last. Considering the sub-factors, “Top management support and commitment” is ranked first while “Effective use of performance indices” is ranked the last. A further sensitivity analysis indicates the factors that need higher level of attention.

The result of current research work may be exploited by the top administration of manufacturing enterprises for assessing their TPM adoption status and to recognize the frail links of TPM application and improve accordingly. Moreover, significant factors of TPM can be identified and deploy them successfully in their implementation procedure.

The conclusion obtained from this research enables the management to clearly understand the significance of each considered factor on the adoption of TPM in the organization and hence, provides effective utilization of resources.

Quality management barriers have been discovered in construction small- and medium-sized enterprises (SMEs), determining their long-term survival. Despite the recognition of Lean Six Sigma (LSS) as a valuable quality management technique for addressing the barriers faced by SMEs, LSS implementation within the construction SME context is alarmingly low. Therefore, this study aims to investigate the barriers for implementing LSS within construction SMEs and to determine the most effective strategies for overcoming these barriers.

A quantitative research approach was used, and data was collected in two stages: a questionnaire survey with 44 construction professionals and an expert opinion survey with 12 LSS specialists. The collected data was then analysed using the fuzzy TOPSIS method, achieving a higher degree of sensitivity.

The findings revealed the 15 most significant LSS barriers that need to be addressed. In addition, the ten most important strategies to be implemented in overcoming the identified barriers before LSS implementation were discovered and thematised, most notably the hiring of LSS specialists for project monitoring and the formation of a committee for strategic planning through LSS.

Previous research on LSS examined barriers and strategies for SMEs in general, but to the best of the authors’ knowledge, this study is the first of its kind, focusing especially on the construction SME context and involving the unique fuzzy TOPSIS approach.

The digital transition process is an important strategic initiative for manufacturing companies to ensure continued competitiveness. The purpose is to investigate the relationship between firms' additive manufacturing (AM) readiness and product and process innovation and how this process is mediated by firms' make-or-buy decisions regarding performing AM processes internally or buying AM services from external partners.

The paper is based on a questionnaire survey including full answers from 157 small- and medium-sized manufacturing companies.

Results show a positive relationship between AM readiness and both product and process innovation. Results also reveal that firms with higher readiness invest more in in-house AM, which in turn promotes innovation. There was no significant association between AM readiness and the use of external AM services. Nonetheless, buying external AM services is still associated positively with innovation.

Data in the questionnaire survey are provided by single respondents from each company and are only based on Danish respondents.

The results indicate that firms' product and process innovation benefits from higher AM readiness derive from increased investment in in-house AM rather than from increased use of external AM services. This also signifies that firms with lower levels of AM readiness buy external AM services and derive the innovation benefits hereof.

The paper delivers new, empirically found knowledge about how small- and medium-sized manufacturing can improve innovation by both making and buying AM services.

Key success factors (KSFs) of total productive maintenance (TPM) have historically played a vital role in attaining economic and ecological sustainability but have overlooked social sustainability. Hence, this study analyses and ranks the most significant TPM KSFs for attaining social sustainability in manufacturing small and medium enterprises (SMEs).

The research employs a deductive methodology to identify the relevant TPM KSFs and social sustainability indicators and then uses Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the TPM KSFs in order to achieve social sustainability, followed by a sensitivity analysis to assess the methodological robustness.

The findings indicate that the top five TPM KSFs influencing social sustainability are employee health and safety, organizational culture, top management commitment, employee engagement and effective communication and effective workplace management. In addition, the results indicate that effective equipment utilization is the least significant TPM key factor affecting social sustainability.

SME manufacturing managers do not need to worry about all of the TPM KSFs if they only concentrate on the ones that will have the most impact. If managers use the top 5 TPM KSFs as a starting point, they may create customized TPM training programs for their companies. As a result, this will facilitate the efforts of their personnel toward social sustainability.

In the existing literature, little emphasis has been paid to social sustainability and how SMEs may implement these practices. This research adds to the current theory of TPM and social sustainability and sheds light on how SMEs might use TPM to advance toward more socially sustainable operations.

The reverse logistics supply chain (RLSC) for demolition waste management (DWM) is a complex process that inherits significant interdependent risks. However, studies on the RLSC have not explicitly identified the risks of its inter-relationships by disentangling their effects on operational performance. Accordingly, this paper aims to identify and assess the inter-dependencies of the risks in the RLSC to improve quality-related operational performance.

A sequential exploratory mixed-method research approach, consisting of qualitative and quantitative methods, was employed. The qualitative approach involved 25 semi-structured interviews, whereas the 18 subsequent structured interviews were conducted with stakeholders in the entire RLSC as part of the quantitative method. These were used to identify the cause and effect relationships of the identified risks. The data were analysed using thematic analysis, and the Bayesian belief network (BBN) technique was used to develop a conceptual risk model.

In total 20 risks in four RLSC sub-processes, namely, dismantling and on-site process, off-site resource recovery process, marketing of secondary products and residue disposal, emerged. Among cause and effect relationships of identified risk factors, inferior quality of secondary products was found to have the strongest relationship with customer satisfaction. Under-pricing of dismantling job, improper landfill operations and inadequacy of landfill levy are independent risks that initiate other risks down the supply chain. The aggregate effects of these risks affect customer dissatisfaction of the end-product, as well as health and safety risks in on-site, off-site and residue disposal.

This study only identify the cause and effect relationships of the identified risks within the RLSC for DWM operations. It has not targeted a specific construction material or any secondary production, which could be practiced through a case study in future research.

The results encourage the investigation of RLSC process quality by maintaining the relationship between recycler and customer to enable a safe workplace environment. Hence, the role of relevant practitioners and government is inseparable in supporting decision-making. Future research could discuss the impact of those inter-related risks in relation to time- or cost-related operational performance criteria.

This study contributes to the field through presenting the first major study on the identification and assessment of the inter-dependencies of the risks in the RLSC in South Australia. The RLSC process mapping had been identified as a tactical and operational management approach. However, the risk management process is a strategic management approach. Therefore, the integration of both process mapping and the risk management approaches in one platform is germane to construction management research.