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The building and construction industry has a significant potential to reduce adverse climate change effects. There are plans to improve the natural resource use and greenhouse gas emissions caused by the buildings by choosing energy-efficient technologies, renewable energy sources and sustainable architectural and constructional elements. This study systematically reviews the patent data for climate change mitigation technologies related to buildings, aiming to detect their relative importance and evaluate each technology in the Y02B network.

The applied approach covers the process of (1) selecting high-impact technology, (2) collecting patent data from the USPTO database, (3) creating a citation frequency matrix using cooperative patent classification codes, (4) linking high-impact patents with analytical network process method, (5) limiting centrality of identifying core technologies from indicators and (6) creating a technology network map with social network analysis.

The study results show that energy-saving control techniques, energy-efficient lighting devices, end-user electricity consumption, management technologies and systems that convert solar energy into electrical energy are core solutions that reduce the effects of climate change. In addition, solutions that will support core technologies and whose effects are expected to increase in the coming years are energy-efficient heating, ventilating and air conditioning systems, smart grid integration, hybrid renewable energy systems, fuel cells, free cooling and heat recovery units and glazing technologies.

Most of the studies on patent analysis have failed to demonstrate any convincing evidence down to the lowest component groups of an entire technology network. The applied approach considers and evaluates each component included in a technology network from a holistic perspective.

This paper aims to clarify the resistance degree of group road logistics to flood disaster resilience. The paper measures the resilience of group road logistics by establishing network structure model. The purpose of this study is to improve the resilience of road log.

This paper adopts Delphi method to collect data, interviews mainly flood management experts and supply chain risk management experts, and then analyzes the data through the network structure model combined with interpretative structure model (ISM) and analytical network process (ANP).

The results show that flood frequency and drainage systems are the main factors affecting the resilience of road transport logistics in urban areas. These research results provide useful guidance for the effective planning and design of urban road construction and infrastructure.

However, the main factors affecting the resilience of road transport logistics are likely to change with the development of factors such as climate, economy and environment. Therefore, in future work, the authors' research will focus on the further application of this evaluation method.

The results show that the impact of flooding on the four dimensions of road logistics resilience varies. This shows that in deciding what intervention measures are to be taken to improve the resilience of the road network to flooding, various measures need to be considered.

This paper provides a more scientific analysis of the risk management ability of the road network in the face of floods. In addition, it also provides a useful reference for urban road planners.

This paper addresses a clear need to study how to build models to improve the resilience of road logistics in flood risk.

Significant developments in the service sector have been brought about by Industry 4.0. Automated digital technologies make it possible to upgrade existing services and develop modern industrial services. This study prioritizes critical factors for adopting Industry 4.0 in the Indian service industries.

The author identified four criteria and fifteen significant factors from the relevant literature that have been corroborated by industry experts. Models are then developed by the analytical hierarchy process (AHP) and analytical network process (ANP) approach to ascertain the significant factors for adopting Industry 4.0 in service industries. Further, sensitivity analysis has been conducted to determine the sensitivities of the rank of criteria and sub-factors to corroborate the results.

The outcome reveals the top significant criteria as organizational criteria (0.5019) and innovation criteria (0.3081). This study prioritizes six significant factors information technology (IT) specialization, digital decentralization of all departments, organizational size, smart services through customer data, top management support and Industry 4.0 infrastructure in the transition toward Industry 4.0 in the service industries.

The potential factors identified in this study will assist managers in determining strategies to effectively manage the Industry 4.0 transition by concentrating on top priorities when leveraging Industry 4.0. The significance of organizational and innovation criteria given more weight will lay the groundwork for future Industry 4.0 implementation guidelines in service industries.

Our research is novel since, to our knowledge, no previous study has investigated the potential critical factors from organizational, environmental, innovation and cost dimensions. Thus, the potential critical factors identified are the contributions of this study.

The Indian micro small and medium scale enterprises (MSMEs) are potential suppliers to the original equipment manufacturers (OEM). The implementation of sustainable supply chain practices (SSCPs) enhances the chances of developing a long-term partnership with these OEMs.

A hybrid framework of analytical network process (ANP) and fuzzy logic (FL) is developed for implementing SSCPs in Indian MSMEs. This model has been validated through a case study.

The study has identified several critical success factors (CSFs) for the implementation of SSCPs in MSMEs. This study has observed that Government regulation is the most important CSF for the implementation of SSCP in Indian MSMEs followed by management support and policy framework.

The article presents a mechanism, i.e. an adaptability test that enables the OEM decision-makers to assess the suitability of an MSME for a long-term partnership.

Operational excellence (OpEx) is a direction toward learning and developing an excellent culture in all aspects of an organization. To reach this culture, revolutionizing activities using industry 4.0 (i4.0) technologies might be a significant empowering tool. This study aims to identify the challenges and benefits of both concepts and investigate their interrelationship to be considered in applying industry 4.0 technologies toward operational excellence.

The challenges and benefits of OpEx and i4.0 are identified and finalized by reviewing the literature. The causal relations between the considered factors are extracted using the fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) method. Then, the analytical network process (ANP) is applied to determine the importance and weight of the factors (challenges and benefits of OpEx and i4.0) according to the constructed network.

The findings illustrated a strong network structure between the factors. First, the causal factors included OpEx and i4.0 challenges, while the OpEx challenges also affected the i4.0 challenges. Both group challenges had a significant effect on OpEx and i4.0 benefits. This means that challenges are the causal factors to be considered in the alignment of i4.0 toward OpEx. Among the OpEx challenges, lack of strategic planning and proper infrastructure were the main influential factors. In contrast, lack of government support and undeveloped business models were identified as the main challenges of i4.0.

OpEx and i4.0 concepts are reviewed and their pros and cons are studied. Previous studies determined an interaction among these concepts. However, from a practical viewpoint, the relation between the challenges and benefits of i4.0 and OpEx was studied for the first time for their alignment.

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.

The present situation of COVID-19 pandemic has put the health-care systems under tremendous stress and stringent tests for their ability to offer expected quality of health-care services, as it decides the sustainability and growth of health-care service providers. This study aims to deliver a quantitative framework for service quality assessment in the health-care industry by classifying the health-care service quality parameters into four balanced scorecard (BSC) perspectives.

To determine the service quality for the Indian health-care system, decision-making trial and evaluation laboratory and analytical network process are integrated in a fuzzy environment to contemplate the interaction among BSC perspectives and respective performance measures.

The results indicate “internal processes” perspective assumes the key role within BSC perspectives, while performance measures “nursing staff turnover” and “staff training” play the key roles. The results also signify that “patient satisfaction” is the most vital issue and can be strongly influenced by measures belonging to the “learning and growth” perspective. In “learning and growth” perspective, “staff training” is the most decisive criteria, very highly influencing “patient satisfaction”, highly influencing “profitability,” “change of cost per patient (both in and out patients)” and “outpatient waiting time” while moderately influencing “staff satisfaction,” “bed occupancy” and “nursing staff turnover”. Moreover, “staff training” criteria have a positive influence on “nursing staff turnover.”

The contributions of this study are in two folds in the domain of quantification of service quality for the health-care system. First, it delivers an assessment framework for Indian health-care service quality. Second, it demonstrates an application of the framework for a case situation and validates the proposed framework.

This paper aims to classify journal papers in the context of hybrid quality function deployment QFD and multi-criteria decision-making (MCDM) methods published during 2004–2021.

A conceptual classification scheme is presented to analyze the hybrid QFD-MCDM methods. Then some recommendations are given to introduce directions for future research.

The results show that among all related areas, the manufacturing application has the most frequency of published papers regarding hybrid QFD-MCDM methods. Moreover, using uncertainty to establish a hybrid QFD-MCDM the relevant papers have been considered during the time interval 2004–2021.

There are various shortcomings in conventional QFD which limit its efficiency and potential applications. Since 2004, when MCDM methods were frequently adopted in the quality management context, increasing attention has been drawn from both practical and academic perspectives. Recently, the integration of MCDM techniques into the QFD model has played an important role in designing new products and services, supplier selection, green manufacturing systems and sustainability topics. Hence, this survey reviewed hybrid QFD-MCDM methods during 2004–2021.

The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.

This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.

The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.

Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.

Today the role of industry 4.0 plays a very important role in enhancing any supply chain network, as the industry 4.0 supply chain uses Big Data and advanced analytics to inform the complete visibility. Latest data are available to bring clarity and support real-time decision-making in the entire supply chain that’s why adopting optimization techniques such as lean manufacturing and lean supply chain concept for enhancing the supply chain network of the organizations is a good idea and would benefit them in increasing their cost efficiency and productivity. The purpose of this work is to develop a technique, which may be useful for future researchers and managers to identify and classification of the significant lean supply chain enablers.

In this paper, the authors considered hybrid analytical hierarchy process to find the ranking of the identified lean supply chain enablers by calculating their weightage. Interpretive structural modeling (ISM) is applied to develop the structural interrelationship among various lean supply chain management enablers. Considering the results obtained from ISM the Matrices d'Impacts Croises Multiplication Appliqué a un Classement (MICMAC) analysis is done to identify the driving and dependence power of Lean Supply Chain Management Enablers (LSCMEs).

Further, the best results applying these methodologies could be used to analyze their inter-relationships for successful Lean supply chain management implementation in an organization. The authors developed an integrated model after the identification of 20 key LSCMEs, which is very helpful to identify and classify the important enablers by ISM methodology and explore the direct and indirect effects of each enabler by MICMAC analysis on the LSCM implementation. This will help organizations optimize their supply chain by selective control of lean enablers.

For lean manufacturing practitioners, the result of the study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process, as well as in enhancing the supply chain.

This paper is the first research paper that considered firstly deep literature review of identified lean supply chain enablers and second developed structured modeling of various lean enablers of supply chain with the help of various methodologies.