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Hospital structures serve to protect and improve public health; however, they are recognized as a major source of environmental degradation. Thus, an effective performance evaluation framework is required to improve hospital sustainability. In this context, this study presents a holistic methodology that integrates the sustainability balanced scorecard (SBSC) with fuzzy Delphi method and fuzzy multi-criteria decision-making approaches for evaluating the sustainability performance of hospitals.

Initially, a comprehensive list of relevant sustainability evaluation criteria was considered based on six SBSC-based dimensions, in line with triple-bottom-line sustainability dimensions, and derived from the literature review and experts’ opinions. Then, the weights of perspectives and their respective criteria are computed and ranked utilizing the fuzzy analytic hierarchy process. Subsequently, the hospitals’ sustainable performance values are ranked based on these criteria using the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution.

A numerical application was conducted in six public hospitals to exhibit the proposed model’s applicability. The results of this study revealed that “Patient satisfaction,” “Efficiency,” “Effectiveness,” “Access to care” and “Waste production,” respectively, are the five most important criteria of sustainable performance.

The new model will provide decision-makers with management tools that may help them identify the relevant factors for upgrading the level of sustainability in their hospitals and thus improve public health and community well-being.

This is the first study that proposes a new hybrid decision-making methodology for evaluating and comparing hospitals’ sustainability performance under a fuzzy environment.

Collaborative networked organisations (CNO) are a means of ensuring longevity and business continuity in the face of a global crisis such as COVID-19. This paper aims to present a multi-criteria decision-making method for sustainable partner selection based on the three sustainability pillars and risk.

A combined analytic hierarchy process (AHP) and fuzzy AHP (F-AHP) with Technique for Order of Preference by Similarity to Ideal Solution approach is the methodology used to evaluate and rank potential partners based on known conditions and predicted conditions at a future time based on uncertainty to support sustainable partner selection.

It is integral to include risk criteria as an addition to the three sustainability pillars: economic, environmental and social, to build a robust and sustainable CNO. One must combine the AHP and F-AHP weightings to ensure the most appropriate sustainable partner selection for the current as well as predicted future period.

The approach proposed in this paper is intended to support existing CNO, as well as individual firms wanting to create a CNO, to build a more robust and sustainable partner selection process in the context of a force majeure such as COVID-19.

This paper presents a novel approach to the partner selection process for a sustainable CNO under current known conditions and future uncertain conditions, highlighting the risk of a force majeure occurring such as COVID-19.

Big data is relevant to the supply chain, as it provides analytics tools for decision-making and business intelligence. Supply Chain 4.0 and big data are necessary for organisations to handle volatile, dynamic and global value networks. This paper aims to investigate the mediating role of “big data analytics” between Supply Chain 4.0 business performance and nine performance factors.

A two-stage hybrid model of statistical analysis and artificial neural network analysis is used for analysing the data. Data gathered from 321 responses from 40 Indian manufacturing organisations are collected for the analysis.

Statistical analysis results show that performance factors of organisational and top management, sustainable procurement and sourcing, environmental, information and product delivery, operational, technical and knowledge, and collaborative planning have a significant effect on big data adoption. Furthermore, the results were given to the artificial neural network model as input and results show “information and product delivery” and “sustainable procurement and sourcing” as the two most vital predictors of big data adoption.

This study confirms the mediating role of big data for Supply Chain 4.0 in manufacturing organisations of developing countries. This study guides to formulate management policies and organisation vision about big data analytics.

For the first time, the impact of big data on Supply Chain 4.0 is discussed in the context of Indian manufacturing organisations. The proposed hybrid model intends to evaluate the mediating role of big data analytics to enhance Supply Chain 4.0 business performance.

Demand forecasting models in companies are often a mix of quantitative models and qualitative methods. As there are so many existing forecasting approaches, many forecasters have difficulty in deciding on which model to select as they may perform “best” in a specific error measure, and not in another. Currently, there is no approach that evaluates different model classes and several interdependent error measures simultaneously, making forecasting model selection particularly difficult when error measures yield conflicting results.

This paper proposes a novel procedure of multi-criteria evaluation of demand forecasting models, simultaneously considering several error measures and their interdependencies based on a two-stage multi-criteria decision-making approach. Analytical Network Process combined with the Technique for Order of Preference by Similarity to Ideal Solution (ANP-TOPSIS) is developed, evaluated and validated through an implementation case of a plastic bag manufacturer.

The results show that the approach identifies the best forecasting model when considering many error measures, even in the presence of conflicting error measures. Furthermore, considering the interdependence between error measures is essential to determine their relative importance for the final ranking calculation.

The paper's contribution is a novel multi-criteria approach to evaluate multiclass demand forecasting models and select the best model, considering several interdependent error measures simultaneously, which is lacking in the literature. The work helps structuring decision making in forecasting and avoiding the selection of inappropriate or “worse” forecasting model.

This paper proposes a new multi-criteria decision-making method, called the vital-immaterial-mediocre method (VIMM), to determine the weight of multiple conflicting and subjective criteria in a decision-making problem.

The novel method utilizes pairwise comparisons, vector-based procedures and a scoring approach to determine weights of criteria. The VIMM compares alternatives by the three crucial components, namely the vital, immaterial and mediocre criteria. The vital criterion has the largest effect on the final results, followed by the mediocre criterion and then the immaterial criterion, which is the least impactful on the prioritization of alternatives. VIMM is developed in two forms where the first scenario is designed to solve one-goal decision-making problems, while the second scenario embraces multiple goals.

To validate the method’s performance and applicability, VIMM is applied to a problem of sustainable supplier selection. Comparisons between VIMM, analytic hierarchy process (AHP) and best-worst method (BWM) reveal that VIMM significantly requires fewer comparisons. Moreover, VIMM works well with both fractional and integer numbers in its comparison procedures.

As an implication for research, we have added the development of the VIMM under fuzzy and grey environments as the direction for optimization of the method.

As managerial implications, VIMM not only provides less complex process for the evaluation of the criteria in the managerial decision-making process, but it also generates consistent results, which make VIMM a reliable tool to apply to a large number of potential decision-making problems.

As a novel subjective weighting method, there exist five major values that VIMM brings over AHP and BWM methods: VIMM requires fewer comparisons compared with AHP and BWM; it is not sensitive to the number of criteria; as a goal-oriented method, it exclusively takes the decision-making goals into account; it keeps the validity and reliability of the Decision-Makers’ (DMs’) opinions and works well with integer and fractional numbers.

Research on the dynamics of Collaborative Enterprises Networks (CEN) lacks consistency and industrial feedback. The purpose of this paper is to present insights and lessons learned from an industrial case study dealing with the practical experience gathered during the creation, alliance development, and business operation phases of a CEN.

The proposed research methodology relies on qualitative approach, using unstructured interviews with the main decision makers in the network. The objective of the interviews is to highlight the most important events in the lifecycle of the network. From the important elements discussed, success and failure key factors are identified.

Through the case study, the authors identify the main success and failure key factors to consider in CENs. Furthermore, relying on the current state of the art, they highlight the main research directions, particularly with respect to the development of modelling approaches capturing the dynamics of these systems.

The identification of the success and failure key factors and their corresponding technologies, systems and human perspectives is aimed at providing links between theoretical models and practical implications to both academics and industrialists. The challenges and developmental areas proposed provide the basis for new models capturing the dynamics and the evolution of CENs.