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This paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Two fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.

The findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.

The developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.

The support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.

There is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

The existing literature expresses a strong need to develop tools that support the manufacturing reshoring decision-making process. This paper aims to examine the suitability of analytical hierarchy process (AHP)-based tools for initial screening of manufacturing reshoring decisions.

Two AHP-based tools for the initial screening of manufacturing reshoring decisions are developed. The first tool is based on traditional AHP, while the second is based on fuzzy-AHP. Six high-level and holistic reshoring criteria based on competitive priorities were identified through a literature review. Next, a panel of experts from a Swedish manufacturing company was involved in the overall comparison of the criteria. Based on this comparison, priority weights of the criteria were obtained through a pairwise analysis. Subsequently, the priority weights were used in a weighted-sum manner to evaluate 20 reshoring scenarios. Afterwards, the outputs from the traditional AHP and fuzzy-AHP tools were compared to the opinions of the experts. Finally, a sensitivity analysis was performed to evaluate the stability of the developed decision support tools.

The research demonstrates that AHP-based support tools are suitable for the initial screening of manufacturing reshoring decisions. With regard to the presented set of criteria and reshoring scenarios, both traditional AHP and fuzzy-AHP are shown to be consistent with the experts' decisions. Moreover, fuzzy-AHP is shown to be marginally more reliable than traditional AHP. According to the sensitivity analysis, the order of importance of the six criteria is stable for high values of weights of cost and quality criteria.

The limitation of the developed AHP-based tools is that they currently only include a limited number of high-level decision criteria. Therefore, future research should focus on adding low-level criteria to the tools using a multi-level architecture. The current research contributes to the body of literature on the manufacturing reshoring decision-making process by addressing decision-making issues in general and by demonstrating the suitability of two decision support tools applied to the manufacturing reshoring field in particular.

This research provides practitioners with two decision support tools for the initial screening of manufacturing reshoring decisions, which will help managers optimize their time and resources on the most promising reshoring alternatives. Given the complex nature of reshoring decisions, the results from the fuzzy-AHP are shown to be slightly closer to those of the experts than traditional AHP for initial screening of manufacturing relocation decisions.

This paper describes two decision support tools that can be applied for the initial screening of manufacturing reshoring decisions while considering six high-level and holistic criteria. Both support tools are applied to evaluate 20 identical manufacturing reshoring scenarios, allowing a comparison of their output. The sensitivity analysis demonstrates the relative importance of the reshoring criteria.

There are a number of decision-making problems encountered by a building design team. This issue is apparent in assessment of building envelope materials and designs in the early design stage. The purpose of this paper is to develope a decision support tool based on a quality function deployment (QFD) approach integrated with a knowledge management system (KMS) and fuzzy theory to facilitate a building design team to simultaneously mitigate the decision-making problems when assessing the building envelope materials and designs for the first instance.

This study engaged a design team comprising three decision makers (DMs) to test the developed decision support tool through a case study of a representative building project. The study employed deductive qualitative data analysis with use of a framework analysis approach to analyze perspectives of the DMs after completing the case study through a semi-structured interview.

A mapping diagram derived qualitatively from the framework analysis suggested that the tool can help mitigate the identified decision-making problems as a whole.

Practical contributions of using the decision support tool include achievement of a more efficient design and construction management, and higher productivity of a project. In terms of academic contributions, this study expands capabilities of a conventional decision support system, KMS, and QFD tool to handle decision-making problems.

Online production and transmission of child abuse material (CAM) is a complex and growing global problem. The exponential increase in the volume of CyberTips of CAM offending is placing information processing and decision-making strains on law enforcement. This paper presents the outcomes of a project that reviewed an existing risk assessment tool and then developed a new tool for CAM triaging and investigative prioritisation.

Using a mixed method approach, the authors first explored the capacity of an existing risk assessment tool for predicting a police action. The authors then used these findings to design and implement a replacement CAM decision support tool. Using a random sample of CyberTip alert cases from 2018, the authors then tested the efficiency of the new tool.

The existing risk assessment tool was not fit for CAM triaging purposes. Just six questions from the old tool were found to be statistically and significantly associated with law enforcement agents achieving a police action. The authors found that an immediate threat of abuse/endangering a child, potential case solvability, CAM image assessment, chat assessment, criticality and some weighting for professional judgement were significant in being associated with a police action. The new decision support tool is more efficient to complete and achieved a 93.6% convergence of risk ratings with the old tool using 2018 case data.

This research is unique in its development of an evidence-based decision support tool that enhances the ability of law enforcement agents to objectively and efficiently triage and prioritise increasing numbers of CyberTip alerts.

When introducing new health technologies, decision makers must integrate research evidence with local operational management information to guide decisions about whether and under what conditions the technology will be used. Multi‐criteria decision analysis can support the adoption or prioritization of health interventions by using criteria to explicitly articulate the health organization's needs, limitations, and values in addition to evaluating evidence for safety and effectiveness. This paper seeks to describe the development of a framework to create agreed‐upon criteria and decision tools to enhance a pre‐existing local health technology assessment (HTA) decision support program.

The authors compiled a list of published criteria from the literature, consulted with experts to refine the criteria list, and used a modified Delphi process with a group of key stakeholders to review, modify, and validate each criterion. In a workshop setting, the criteria were used to create decision tools.

A set of user‐validated criteria for new health technology evaluation and adoption was developed and integrated into the local HTA decision support program. Technology evaluation and decision guideline tools were created using these criteria to ensure that the decision process is systematic, consistent, and transparent.

This framework can be used by others to develop decision‐making criteria and tools to enhance similar technology adoption programs.

The development of clear, user‐validated criteria for evaluating new technologies adds a critical element to improve decision‐making on technology adoption, and the decision tools ensure consistency, transparency, and real‐world relevance.

This chapter focuses on techniques and technologies to aid groups in making decisions, with an emphasis on computer-based support. Many office workers regularly meet colleagues and clients in virtual meetings using videoconferencing platforms, which enable participants to carry out tasks in a manner similar to a face-to-face meeting. The development of computer-based platforms to facilitate group tasks can be traced back to the 1960s, and while they support group communication, they do not directly support group decision making. In this chapter we distinguish four technologies developed to provide support to group decisions, clustered into two main traditions. Technologies in the task-oriented tradition are mainly concerned with enabling participants to complete tasks to solve the group's decision problem via computer-supported communications. Group Decision Support Systems and social software technologies comprise the task-oriented tradition. Alternately, in the model-driven tradition, participants use computers to build and use a model that acts as a referent to communicate, mostly verbally, about the group's decision problem. System modeling and decision-modeling technologies constitute the model-driven tradition. This chapter sketches the history and guiding ideas of both traditions, and describes their associated technologies. The chapter concludes with questioning if increased availability of online tools will lead to increased use of group decision support technologies, and the differential impact of communication support versus decision support.

Design-Build (DB) is known as the alternative for Design-Bid-Build in the Malaysian construction industry. For DB projects, it is critical to adopt effective decision support tool to ensure the execution of a systematic decision-making technique. This study aimed to examine the impact of a decision support tool for novice decision makers to reject or adopt DB for their construction projects.

Literature review and qualitative input from experts identified several key-selection factors pertaining to critical success factors and design-build drivers. This resulted in the development of Decision Support Tool for Design-Build (DST-DB). A quasi-experiment, which involved 382 novice decision makers in the construction industry, was conducted to test the DST-DB quantitatively. The participants were required to compare two construction projects using DST-DB and traditional decision-making methods. Multivariate analysis was performed to analyse all collected data.

The quasi-experiment data suggests that DST-DB enables significantly higher usability, likelihood, precision, confidence and satisfaction rate when compared to the traditional decision-making process. The pre- and post-surveys indicated that the DST-DB is effective in improving decision-making performance through selection factors of client-briefing, maximised resources and sharing expertise. The participants also agreed that DST-DB is easy to use and helps them to gain better understanding of the decision-making process for construction projects.

This research contributes to the existing body of knowledge through the impact of DST on the decisions of novices. The novice decision makers found that DST-DB is practically adaptable and comparatively effective for decision-making process than traditional decision-making methods. This contributes to the practical application of construction companies to provide DST-DB training to the fresh graduate employees to enhance their competencies in the decision-making process.

The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.

The purpose of this paper is to design a system deployment model that integrates case-based agent technique to develop an eco-responsibility decision support tool for greening educational institutions toward environmental responsibility.

Data were collected through questionnaires distributed among a statistical population that comprised practitioners across educational institutions in Malaysia that implement green practices. The questionnaire measured the feasibility of the developed tool based on factors derived from the literature. Accordingly, descriptive, exploratory and factor analysis approach using statistical package for social sciences (SPSS) was used to test the feasibility of the developed tool.

Results from descriptive analysis confirm the tool is feasible based on mean values that range from 4.1619 to 3.6508 on a five-point scale, indicating that the tool is effective in sustaining educational institutions going green. Besides, results from exploratory analysis verify the reliability of the tool based on the acceptable Cronbach’s alpha reliability coefficient score higher than 0.7 and Kaiser–Meyer–Olkin value being above 0.5. Finally, results from factor analysis reveal that the developed tool is usable, efficient, helpful, flexible and credible and supports educational institutions in going green at 88.44 per cent of the total variance, suggesting that the respondents are satisfied with the tool.

The sample population in this study comprises only practitioners from educational institutions in Malaysia. Theoretically, this research provides feasibility factors and associated items that can be used in evaluating developed information systems.

Practically, this study develops an eco-responsibility decision support tool to facilitate green strategies and provides information on how practitioners in educational institutions can improve green growth.

This study presents how case-oriented agents aid educational institutions in going green for environmental responsibility. Socially, this research provides the strategies for green practice improvement in educational institutions toward environmental responsibility.

The eco-responsibility decision support tool provides a Web-based platform for promoting ecological protection by supporting the measuring of practitioners’ current green practices for environmental responsibility. Thus, research findings from this study are expected to help decision-makers generate useful insights into environment-friendly strategies to be implemented in educational institutions. Lastly, the statistical tests adopted in this paper can be used to gauge the feasibility of information system application in future.

The purpose of this paper is to identify key project delivery method selection factors to assist water industry decision-makers in selecting the most appropriate delivery method for their water treatment plant projects.

The selection factors were identified by compiling and validating key project delivery selection factors across various industries through an extensive literature review and two industry expert workshops. This resulted in the development of a web-based decision-support tool to facilitate project delivery method selection within the water industry.

The research effort led to the identification of 13 key project delivery method selection factors (seven primary factors and six secondary factors) for water treatment plant projects. These factors were utilized to develop EXPRSS-TP, a pioneering web-based project delivery method decision-support tool for the water industry.

A project delivery method selection process is typically an informal process that may range from days to weeks at a time. Based on this work, the assessment can now be completed in about one hour and provides decision-makers with the most favorable delivery method for their project. And with the new tool that encompasses the new knowledge, not only is the decision reached at an accelerated pace, EXPRSS-TP also documents the entire selection process, allowing for a written and retained record of this key decision and its procedure.

This paper contributes to the exisiting body of knowledge by identifying key project delivery selection factors across numerous industries, assessing and combining them, and finally incorporating them into one comprehensive process. EXPRSS-TP improves the traditional project delivery method selection process and provides evidence-based project delivery method selection recommendations.