Sustainability, as a concept, is permeating most of today’s human activities, including higher education. The increased importance put on sustainability depends largely on the increased awareness of the huge environmental, social, and economic challenges that humanity is currently facing. As is the case with most complex themes, the route towards the application of appropriate actions starts with enlightenment developed within education, where different engineering programs form important subareas. To address this, CDIO Syllabus 3.0 in general, but optional standard 1 in specific, does now to an even greater extent handle sustainability issues. This paper presents a framework that is built upon several key concepts that are strongly related to education for sustainable development (ESD) at the university level, such as key sustainability concepts (as defined by UNESCO), sustainability development goals (as defined by the United Nations) and constructive alignment (as defined by Biggs and Tang). The framework is applied to two engineering master’s courses where sustainability concepts and development goals are integrated and constructively aligned in the learning outcomes, teaching and learning activities, and assessments. Through the analysis of the two courses concerning sustainability, the framework is shown to provide a means for the analysis of how sustainability is currently incorporated in a course, highlight what possible teaching/learning shortcomings exist, and help identify actions that can be taken to overcome these shortcomings. The objective of the framework is thus to support course managers in the development of appropriate actions related to sustainability.

Manufacturing relocation decisions are complex because they involve combinations of location modes like offshoring or reshoring, and governance modes like insourcing or outsourcing. Furthermore, the uncertainty involved in the decision-making process makes it challenging to reach a right-shoring decision. This study presents a hybrid fuzzy-AHP-TOPSIS model to support generic relocation decisions. Industry experts were involved in a pairwise comparison of the competitive priorities’ decision criteria. A meta-synthesis of empirical studies is used to generate theoretical relocation scenarios. The presented hybrid model is used to rank the relocation scenarios in order to identify the most pertinent alternative. The resiliency of the solution is presented through a sensitivity analysis. The results indicate that the proposed hybrid model can simultaneously handle all the main relocation options involving governance modes. Based on the input data in this study, the competitive priorities criteria quality, time and cost are shown to have a strong impact, whereas the sustainability criterion has a weak impact on the choice of relocation option. The research presented in this paper contributes to the research field of manufacturing relocation by demonstrating the suitability of the hybrid fuzzy-AHP-TOPSIS model for relocation decisions and the resilience of the results. Furthermore, the research contributes to practice by providing managers with a generic relocation decision-support model that is capable of simultaneously handling and evaluating various relocation alternatives.

Hybrid education is a complex combination of simultaneous face-to-face and online teaching. This model of teaching comes with a wide range of benefits, primarily being able to offer the same content to a wider audience. Hybrid education became an effective form of teaching during the COVID-19 pandemic. In these post pandemic years, the benefit of hybrid education can still be utilized, allowing for improved flexibility in teaching schedules, engaging students in interactive learning, bringing online students closer to the teacher and face-to-face students, and offering education to students who could not otherwise participate. However, with all the benefits of hybrid education, there are some significant challenges which restrict the implementation or hinder the full potential of hybrid education. Some key challenges are student engagement from the online students with the teacher as well as with other students, technological requirements, physical classroom set-up, education of the teachers, and time investment in re-structuring courses. In this article, we review the challenges of hybrid education, strategies to address these challenges focusing on implementation and effectiveness, as well as evaluating student feedback from students at Jönköping University that have been a part of hybrid education. 

The pandemic has forced teaching as well as as the Software Development industry, to be performed remotely. The educational institutions are therefore facing the situation of training and steering the students in, not only complex work, but also in remote-based work and it processes. Specific challenges here relate to project work with larger groups of developers, with testing, and integration of technical components for complete solutions, but the psychosocial factors come into play as well. This paper considers the situation that has arisen as a consequence of the pandemic and regards how project-based courses should be adapted to ‘The New Normal’. In focus is a course in Software Engineering, where a large-scaled project shall be developed remotely. Representatives from IT-companies act at the course remote, and at specific occurrences. The course is observed by the teachers to see its outcome, as well as different aspects on attitudes towards future remote work. Interviews and surveys regarding attitudes of students, as well as involved company representatives are presented, where the focus is on process, productivity, work environment, interest in remote work, as well as social aspects. The main findings, based on the surveys, motivates hybrid solutions for university courses, to meet the corresponding companies’ way of future working style.

One of the final courses, if not the last course at university level in Sweden, and especially within the engineering programs, is a thesis course where the students demonstrate their accumulated knowledge and skills. One, or sometimes two students, identifies a topic of interest within his/her main field of study and is guided through the process by a supervisor. Preferably the supervisor has a lot of experience, both within the main field of study and as a thesis supervisor. Many times, however, the latter is not always the case. Hence, some form of recording of the supervisory process would be of importance, to be able to assess the supervisory competence of the supervisor. Through this recording, potential weak supervisory spots can be identified, and a special focus could be put on these.

In literature several supervisory models have been proposed over the years. The goal of this paper is to demonstrate, through a case study, how three of these models can be successfully combined to a hybrid model around the supervisory process building on grounded theory. A combination of the three models together identifies the individual supervisory process of a thesis supervisor. An analysis is then performed, and weak spots in the supervisory process of a novice supervisor can thus be identified and addressed. The results presented in this paper are based on a case where an experienced thesis supervisor was observed during asupervisory session. Hence, the case forms a baseline of what a “good” supervisory session looks like. By applying the hybrid supervisory model on a novice thesis supervisor, possible weaknesses in the process can be identified.

As both students and teachers are involved in a one-to-one teaching-learning activity during the thesis process, CDIO standards such as number 8 (active learning) is important from the students’ point-of-view, but especially standard number 10 (enhancement of faculty teaching competence) is of high importance as the competence of the novice supervisor, or the lack thereof, becomes evident and can be appropriately addressed through especially designed activities.

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Reshoring can be regarded as offshoring in reverse. While offshoring mainly has been driven by cost aspects, reshoring considers multiple aspects, such as higher quality demands, faster product delivery and product mass-customization. Where to locate manufacturing is usually a purely manual activity that relies on relocation experts, hence, an automated decision-support system would be extremely useful. This paper presents a decision-support system for reshoring decision-making building a fuzzy inference system. The construction and functionality of the fuzzy inference system is briefly outlined and evaluated within a high-cost environment considering six specific reshoring decision criteria, namely cost, quality, time, flexibility, innovation and sustainability. A challenge in fuzzy logic relates to the construction of the so called fuzzy inference rules. In the relocation domain, fuzzy inference rules represent the knowledge and competence of relocation experts and are usually generated manually by the same experts. This paper presents a solution where fuzzy inference rules are automatically generated applying one hundred reshoring scenarios as input data. Another important aspect in fuzzy logic relates to the membership functions. These are mostly manually defined but, in this paper, a semi-Automatic approach is presented. The reshoring decision recommendations produced by the semi-Automatically configured fuzzy inference system are shown to be as accurate as those of a manually configured fuzzy inference system. 

This paper presents a decision-support system for reshoring decision-making based on fuzzy logic. The construction and functionality of the decision-support system is briefly outlined and evaluated in a highcost environment contemplating six specific decision criteria, namely cost, quality, time, flexibility, innovation and sustainability. A major challenge with fuzzy logic solutions has to do with the construction of the fuzzy inference rules. In the relocation domain, the fuzzy inference rules represent the knowledge and competence of relocation experts and they are usually created manually by the same experts. One obstacle is that the complexity of the fuzzy inference rules increases with the number of decision criteria. To overcome this complexity issue, this paper presents a solution whereby the fuzzy inference rules are automatically generated by applying one hundred reshoring scenarios as input data. The reshoring decision recommendations produced by the fuzzy logic decision-support system are demonstrated to be close to those of human reshoring domain experts.

Testing of a software system is a resource-consuming activity that requires high-level expert knowledge. Methods based on knowledge representation and reasoning can alleviate this problem. This paper presents an approach to enhance the automation of the testing process using ontologies and inference rules. The approach takes software requirements specifications written in structured text documents as input and produces the output, i.e. test scripts. The approach makes use of ontologies to deal with the knowledge embodied in requirements specifications and to represent the desired structure of test cases, as well as makes use of a set of inference rules to represent strategies for deriving test cases. The implementation of the approach, in the context of an industrial case, proves the validity of the overall approach.

Testing of a software system is a resource-consuming activity that requires high-level expert knowledge. In previous work we proposed an ontologybased approach to alleviate this problem. In this paper we discuss the lessons learned from the implementation and application of the approach in a use case from the avionic industry. The lessons are related to the areas of ontology development, ontology evaluation, the OWL language and rule-based reasoning.