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In this paper, a review of materials selection methods is presented. It initially discusses the importance of materials selection in various fields of scientific study particularly in engineering design. Various tools and methods for the selection of materials are reviewed. These include materials handbook, materials data‐bases, materials selection charts, artificial intelligence systems, and other computeraided materials selection systems.

This study aims to analyse how the materials selection courses of engineering undergraduate programmes can be better aligned with the United Nations Sustainable Development Goals (SDGs).

Initially, a content analysis was performed in 39 materials selection course descriptions from 40 engineering undergraduate programmes of Brazilian higher education institutions, and subsequently, Delphi method procedures were conducted with professors that teach or have taught the course and are knowledgeable in the subject of sustainability.

Considering the analysed course descriptions, it was shown that most of the materials selection courses do not consider or present little emphasis on sustainability aspects. Regarding the Delphi method, eight items were evidenced to consider sustainability aspects in the analysed courses.

This study contributes to the debates about sustainability insertion in engineering undergraduate programmes. More specifically, the findings presented consolidated information that professors and coordinators can use to align materials selection courses with the SDGs better.

Evaluations of grey systems and systems with subjective judgements are always like an impasse for science and companies. Especially, calculations of the problems which include various units are really difficult situations. The purpose of this paper is to propose a grey analytic hierarchy process (G-AHP) for engineering and managerial problems with grey systems to make more clear and objective decisions.

Proposed G-AHP approach is applied to project manager selection for a software project of an energy company. The application includes three different units as year, score and assessment. Six engineers are evaluated with 25 criteria in the application. Weights of the factors and assessments are done by three top managers of the company as pairwise comparisons. Other data in the decision matrix are obtained from the personal information and exam results of engineers.

Final weights of the criteria and evaluations of engineers are all done with the proposed G-AHP. Obtained results of G-AHP are also compared with grey “VlseKriterijumska Optimizacija I Kompromisno Resenje” results as a validation of the calculations and proposed approach. Final results of the applications are ranked for the evaluations and comparison. All results of the case study are concluded with the effectiveness and applicability of the proposed G-AHP method both for this study and other fields of science, engineering and management.

This study provides to evaluate and interpret grey systems with different units and subjective judgements for science, engineering and management more clearly and objectively in an easier way.

This paper aims to explore the requirements of organizational knowledge management initiatives using requirements engineering techniques, identifying the optimal techniques configuration and serving as a management tool for knowledge engineers.

The method is selection attributes. Knowledge management enablers are characterized and mapped with the coverage capabilities of requirements engineering techniques, using the attributes of the elicited object and a box-plot analysis. The information is gathered from 280 references, 32 companies and 16 experts in requirements engineering.

Requirements of organizational knowledge management initiatives are got optimally by combining interviews, use cases, scenarios, laddering and focus group techniques. The requirements of structure and processes are more complex to identify, while culture requirements are the best covered.

Knowledge management enablers are analyzed according to the current studies and comprehension of engineering techniques.

Knowledge engineers need to consider the coverage capabilities of engineering techniques to design an optimal requirement identification and meet the objectives of organizational knowledge acquisition initiatives. Requirement engineers can improve the requirements identification by a staged selection process.

The requirements of knowledge management initiatives that impact the community can be identified and traced to ensure the knowledge objectives. Requirements related to culture and people, like shared values, beliefs, and behaviors, are also considered.

To the best of the authors’ knowledge, this is the first study about formal requirement identification of knowledge management initiatives in the organizational context, providing the optimal configuration. A novel staged process is proposed for requirements engineering techniques selection, analyzing the enablers at component level and identifying the attributes associated with the elicited object.

The majority of engineering failures occur because of surface initiated effects such as wear, corrosion and fatigue. Surface treatment/coatings (T/C) offer the potential to prevent failure and often to reduce cost. T/C selection is an important part of design for manufacture (DFM) spectrum and has been a very elusive activity to formalize. With the advent of knowledge‐based systems, provision of T/C selection assistance to the designer early on in the design process has become a practical reality. Reports the development of the TESS (Tribologically Engineered Surface Selection) expert system, which is concerned with the field of Surface T/C and their selection for optimum engineering design solutions. Introduces the T/C selection methodology and also describes the system developed. A logic‐based AI language, Prolog, has been used for the system development and versions of the system have been developed to run on the workstation and PC platforms. The initial work on the system was carried out with SERC support. Further developments are under way in this and other related design for manufacturability areas in this integrated design decision support approach.

A key task in the material handling system design process is the selection and configuration of equipment for material transport and storage in a facility. Material handling equipment selection is a complex, tedious task. However, there are few tools other than checklists to assist engineers in the selection of appropriate, cost‐effective material handling equipment. This paper describes the development of an intelligent material handling equipment selection system called MHESA (Material Handling Equipment Selection Advisor). The MHESA is composed of three modules: a database to store equipment types with their specifications; a knowledge‐based expert system for assisting material handling equipment selection; and an analytic hierarchy process (AHP) model to choose the most favorable equipment type. The concept proposed in this paper can automate the design of material handling equipment selection system, and provides artificial intelligence in the decision‐making process.

In today's societies, work environment and customers' expectations change on a daily manner. Consequently, it is crucial for companies to find a way for adapting themselves to new requirements. For this purpose, reengineering projects have been introduced and evolved in different companies with different responsibilities over the past decades. However, the risk associated with these projects is inevitable and is a huge obstacle on the way of their implementation. This study, in line with previous studies, contributed in this context by proposing a new methodology for selecting suitable processes and adopted best practices candidate for business process reengineering (BPR). The proposed methodology aims to achieve lower risk and higher probability of success for BPR projects.

This objective is achieved by integration of the concept of portfolio selection problems (PSP) into the organizational decision making concerning BPR project. A model for selection of most appropriate reengineering scenarios, which is a combination of processes and best practices, is adopted and proposed. This model by putting additional constraints on risks associated with a BPR project and increasing its return identifies the most prosperous portfolio of scenarios for a reengineering project. The proposed model is tested step-by-step through a case study in order to validate its outcome and justify its practicality.

In this paper, a new methodology is proposed containing a model as a managerial tool for conducting more successful reengineering projects. The applicability of the methodology is tested in one of the largest metallurgical laboratory and research centers of Iran. Four strategic processes were selected and several best practices customized, after screening all processes of the case study. Accordingly, in total, 15 different scenarios were explored for the reengineering project in which four of them identified by the model as the processes with the highest possibility of success through the BPR project.

This methodology suggests a novel way to benefit from PSP for process selection problems by putting additional control on implementation risk of reengineering project. While the urge of using reengineering project exists within the current companies, the high level of risk of these projects is considered as a huge obstacle in conducting this project. This study, by proposing a new method, aims to address this issue as well as point to the practicality of integrating PSP model in organizational contexts.

In recent years, there has been a growing awareness that most decision makers are not only concerned with the financial implications of information technology/system (IT/IS) projects, but they are also concerned with other objectives such as competitive advantage, market share and future growth. Nowadays, multi‐criteria decision‐making methods are gaining importance due to their inherent ability to judge different alternative scenarios for possible selection of the best alternative. This paper provides a decision‐making framework for senior executives when selecting innovative IT/IS projects. The proposed framework is based on the multi‐criteria utility theory (MCUT) combined with information economics principles to select IT/IS project(s) based on “business value” and “risk” criteria. MCUT has the advantage of taking into consideration the decision maker’s preferences in the form of utility functions defined over a set of tangible and intangible criteria. To illustrate the application of the proposed framework, a hypothetical case study is provided, where input elicited from four engineering professionals is used to develop utility functions for a predefined set of selection criteria.

The purpose of this paper is to compare and contrast research on collaborative information seeking (CIS) and expertise seeking (EXS) to identify focal themes, blind spots, and possibilities for cross-fertilization.

Existing research was reviewed. The review consisted of a content analysis of 70 (CIS) and 72 (EXS) studies with respect to the context, scope, process, and setting of CIS and EXS, supplemented with a bibliometric analysis of the references in the reviewed studies.

In CIS, the context is a group of actors collaborating on a shared task. In EXS, the information need is held by an individual but resolved by consulting other people. While the typical scope of EXS studies is source selection, CIS studies mostly concern the consultation of the sources and the use of the obtained information. CIS and EXS studies also attend differentially to the information-seeking process. Only 4 percent of the references in the reviewed studies are cited in both CIS and EXS research. The author concludes that, at present, CIS and EXS are different discourses about similar issues.

Increased interaction between CIS and EXS will advance research in both areas and prevent duplication of effort. Topics for future research are identified. It should be noted that the findings are limited to the 142 studies reviewed.

By analyzing CIS in the context of EXS, and vice versa, this study provides a fresh look at the information-seeking research that attends to collaboration.

The purpose of this paper is to focus on the use of analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) to select an optimum maintenance strategy for a textile industry.

The relative importance of multiple evaluation criteria and the extension of the TOPSIS are prioritized using AHP. The TOPSIS method is applied to compensate for the imprecise ranking of the AHP in the selection of a maintenance policy mix.

An efficient ranking of alternatives can be achieved for maintenance strategy selection through the combination of AHP and TOPSIS.

The paper highlights a new insight into multi‐criteria decision‐making techniques to select an optimum maintenance policy for a process industry with the use of a case study.