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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.

Online product ratings play an important role in the decision-making process of consumers, which are not only sources of information used by consumers to understand the function and quality of a product or service but also sources of information used to find desirable products. The purpose of this paper is to develop a decision-based method for supporting the purchase decisions of consumers based on not only the online product ratings but also the actual product attributes.

First, two types of utility values are designed to measure the preference of the consumer based on either online ratings or actual product attributes. Then, the traditional TOPSIS method is adopted to achieve a comprehensive value by integrating the two types of utility values so that all of the alternative products can be ranked. Further, a product selection support system prototype is designed and developed to support the purchase decisions of consumers.

To help consumers select desirable products efficiently, it is necessary to develop a product selection method based on the online ratings of alternative products and consumer expectations.

The research shows that the proposed method can not only support consumers’ purchase decisions based on a large number of online product ratings but also help manufacturers to find out consumers’ demands or requirements on products so as to facilitate the design of new products or the improvement of products. On the basis of the proposed method, the developed system prototype is helpful for consumers to select desirable products.

To support the purchase decisions of consumers, a new decision-based method for selecting desirable online products is proposed.

This paper aims to propose and present a decision-making support method for poorly structured problems in schools, using the example of one of the most important and difficult decisions that principals face: terminating a teacher's employment.

A literature review was conducted, along with observations and interviews with 30 experienced principals, in order to identify decisions made by Polish principals. In order to sort non-programmable decisions according to their importance and difficulty, data were collected from 377 principals. Opinions from two groups, consisting of 22 and 25 experts, respectively, were collected in order to identify and order a set of criteria for making a specified non-programmable decision.

The four following areas of school activities were distinguished: finances, teaching and care, internal processes, and development of organisation. Within these categories, 126 decisions made by principals were identified, 96 of which were non-programmable. One of the most difficult and important non-programmable decisions was related to the termination of a teacher's employment. In order to support decisions regarding a teacher's dismissal, 44 criteria with different importance levels were identified.

Principals can use the method proposed in this paper, as it increases the rationality and objectivity of making a dismissal decision. The method can also be adapted for other difficult non-programmable decisions.

The expert opinion method might be useful for solving poorly structured problems in the management of educational institutions. As far as it can be ascertained, no previous empirical studies have identified and ranked the most important and difficult non-programmable decisions facing principals.

The paper provides an overview of research published in the innovation and operations management (IOM) literature on 15 methods for cost management in new product development, and it provides a comparison to an earlier review of the management accounting (MA) literature (Wouters & Morales, 2014).

This structured literature search covers papers published in 23 journals in IOM in the period 1990–2014.

The search yielded a sample of 208 unique papers with 275 results (one paper could refer to multiple cost management methods). The top 3 methods are modular design, component commonality, and product platforms, with 115 results (42%) together. In the MA literature, these three methods accounted for 29%, but target costing was the most researched cost management method by far (26%). Simulation is the most frequently used research method in the IOM literature, whereas this was averagely used in the MA literature; qualitative studies were the most frequently used research method in the MA literature, whereas this was averagely used in the IOM literature. We found a lot of papers presenting practical approaches or decision models as a further development of a particular cost management method, which is a clear difference from the MA literature.

This review focused on the same cost management methods, and future research could also consider other cost management methods which are likely to be more important in the IOM literature compared to the MA literature. Future research could also investigate innovative cost management practices in more detail through longitudinal case studies.

This review of research on methods for cost management published outside the MA literature provides an overview for MA researchers. It highlights key differences between both literatures in their research of the same cost management methods.

To complete the general multi‐criteria decision‐analysis process and extend it to the process of requisitely holistic generating ideas and developing them into innovations. Also, in terms of dialectical systems thinking, to recommend the use of individual and group methods and software supporting this process.

In 1974, Mulej invented and introduced the “Dialectical System” concept (DS), and in 1998 Mulej and Kajzer fortified DS with “the Law of Requisite Holism.” The concept was well‐verified in practice, but lacked the support of quantitative versions of systems thinking. In this paper, we eliminate that deficiency: we complete and extend Belton and Stewart's general multi‐criteria decision‐analysis process to a process of generating ideas and developing them into innovations.

Methods and software supportive of creativity can help generate ideas. Multi‐criteria decision‐making (MCDM) methods can be used to complement intuition, verify ideas, and support their development into innovations. The point is in reaching the requisite holism with only a requisite effort by applied systems thinking and innovation.

Quantitative methods are considered necessary and helpful, but are not sufficient conditions for innovations or for holism.

This paper demonstrates that the modern operations research methods can help strengthen innovation and holistic thinking capacity much more than traditional ones.

The innovative aspect of this paper is that it extends a general multiple criteria decision‐analysis process to the process of generating ideas and developing them into innovations. It combines the Dialectical Systems Theory and the MCDM methods, which provides an interesting new synergy.

The information technology (IT) outsourcing (ITO) decision is a key issue in strategic and operational management and is considered a main critical factor influencing successful organizational performance and technology assessment. The purpose of this paper is to propose a framework combining value-focused thinking (VFT) methodology and the FITradeoff method to support decisions regarding ITO.

This study answers the following questions: How to identify, in a structured way, the objectives that should be considered in an ITO decision process in a changing environment? How to identify new IT services to be outsourced from the established objectives? The VFT method is recommended in this case to structure and model the problem. Moreover, the FITradeoff multicriteria method was considered in this study to support the evaluation and prioritization of IT services in an ITO decision

The framework provides promising results for ITO decisions. This study revealed that a lack of strategic and fundamental objectives is a critical issue in making ITO decisions. Although the VFT methodology is subjective in nature, it promotes a better understanding of goals and values, and FITradeoff allows the decision-maker to make comparisons of consequences based on his/her preferences.

The recommendations of the study are restricted to the case study and cannot be generalized. In addition, applying the method requires attention in determining the criteria used for outsourcing IT.

The strategic analysis of ITO provides a holistic view of the current situation since (i) the VFT methodology enables the IT manager to generate new alternatives to assist future decisions and (ii) FITradeoff has been demonstrated as a suitable option to evaluate ITO decisions.

To the best of our knowledge, this is the first paper to utilize a problem structuring method (VFT) to identify criteria and alternatives based on the organizational values and objectives. This is integrated with a multicriteria method that uses partial information and may consequently lead to fewer inconsistencies (FITradeoff) in the context of ITO.

The purpose of this paper is to perform an extensive literature review in the area of decision making for condition-based maintenance (CBM) and identify possibilities for proactive online recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a framework for proactive decision making in the context of CBM.

Starting with the manufacturing challenges and the main principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision making are analysed and their potential relevance to CBM is identified. Then, an extensive literature review of methods and techniques for the various steps of CBM is provided, especially for prognosis and decision support. Based on these, limitations and gaps are identified and a framework for proactive decision making in the context of CBM is proposed.

In the proposed framework for proactive decision making, the CBM concept is enriched in the sense that it is structured into two components: the information space and the decision space. Moreover, it is extended in a way that decision space is further analyzed according to the types of recommendations that can be provided. Moreover, possible inputs and outputs of each step are identified.

The paper provides a framework for CBM representing the steps that need to be followed for proactive recommendations as well as the types of recommendations that can be given. The framework can be used by maintenance management of a company in order to conduct CBM by utilizing real-time sensor data depending on the type of decision required.

The results of the work presented in this paper form the basis for the development and implementation of proactive Decision Support System (DSS) in the context of maintenance.

Over the last three decades, construction projects have increasingly been delivered through alternative delivery methods. As a result, many owners have a range of delivery methods to choose from and aim to use the right one for each of their projects. Researchers have developed several tools and decision-support processes to facilitate this selection procedure. The purpose of this study is to review and discuss differences and common themes across selection tools developed by academic researchers and project owners.

The study reviews prominent selection processes and tools used for infrastructure projects by conducting an in-depth literature review and using the content analysis method to elicit findings on the methodologies and criteria presented in the literature.

This study presents three principal findings. First, findings show three common themes emerge within the selection criteria—characteristics, goals and risks. Second, while academic studies most commonly suggest employing multi-attribute analysis, this study reveals that, in practice, selection tools most frequently employ a staged or gated evaluation based on the type of criteria and their importance to the decision. Finally, this review further highlights the importance of institutional context in decision-making.

This work contributes to the body of knowledge by providing guidance to practitioners and opening new directions for researchers around the way selection criteria are categorized in the relevant literature and the institutional context considerations when structuring or evaluating a selection process or tool.

Urban logistics pooling is seen as a serious alternative to imposed urban consolidation centers. However, such strategies are quite new in urban distribution and merit to be evaluated using adapted methods that take into account the group decision nature of resource pooling. This chapter aims to propose, via an experimental collaborative decision support method, to define a grid of indicators and a reference situation database to measure the sustainable performance of urban logistics pooling systems.

The proposed methodology combines a systematic literature analysis of Key Performance Indicators and a group decision support method to choose a suitable set to define a dashboard. First, we identify the main sustainability indicators from an overview of the literature, and class them into the categories of the 4As Sustainable Transport vision (i.e., Awareness, Act and shift, Avoidance, and Anticipation). Then, a group of 20 experts is solicited for an iterative experimental group decision-making method to converge to the concordance of a set of indicators.

The method allowed us to define a hierarchic dashboard agreed by all experts with seven main indicators and nine secondary indicators. Moreover, the experts signaled the need of defining a unified basis of comparison to estimate initial situations. To do this, we proposed a database of urban routes from the French Surveys on Urban Goods Transport.

The proposed dashboard is an example, and to provide a more unified one, the experience has to be iterated using different groups of decision-makers.

This method has the advantage of proposing a dashboard agreed by all involved stakeholders. Therefore, this chapter shows the patterns to reproduce it since the method is able to be replicated in any context of group decision in urban logistics.

The originality of the chapter arises on the use of an experimental group decision method using a group with a majority of practitioners, and to validate it by consensus.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.