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.

PurposeThe 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/approachTwo 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.FindingsThe 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/implicationsThe 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 implicationsThis 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/valueThis 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 investigates the possibility of increasing the interpretability of fuzzy rules and reducing the complexity when designing fuzzy rules. To achieve this, three novel fuzzy logic concepts (i.e., relative linguistic labels, high-level rules and linguistic variable weights) were conceived and implemented in a fuzzy logic system for reshoring decision-making. The introduced concepts increase the interpretability of fuzzy rules and reduce the complexity when designing fuzzy rules while still providing accurate results. (C) 2019 Elsevier Ltd. All rights reserved.

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 ...

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 pa ...

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.

In order to reach an acceptable level of confidence in the quality of a software product, testing of the software is paramount. To obtain "good" quality software it is essential to rely on "good" test cases. To define the criteria for what make up for a "good" test case is not a trivial task. Over the past 15 years, a short list of publications have presented criteria for "good" test cases but without ranking them based on their importance. This paper presents a non-exhaustive and non-authoritative tentative list of 15 criteria and a ranking of their relative importance. A number of the criteria come from previous publications but also from discussions with our industrial partners. The ranking is based on results collected via a questionnaire that was sent out to a limited number of randomly chosen respondents in the Swedish software industry. This means that the results are more indicative than conclusive.

The work presented in this paper demonstrates an evaluation procedure for a real-life application ontology, coming from the avionics domain. The focus of the evaluation has specifically been on thr ...

Testing of a software system is resource-consuming activity. One of the promising ways to improve the efficiency of the software testing process is to use ontologies for testing. This paper presents an approach to test case generation based on the use of an ontology and inference rules. The ontology represents requirements from a software requirements specification, and additional knowledge about components of the software system under development. The inference rules describe strategies for deriving test cases from the ontology. The inference rules are constructed based on the examination of the existing test documentation and acquisition of knowledge from experienced software testers. The inference rules are implemented in Prolog and applied to the ontology that is translated from OWL functional-style syntax to Prolog syntax. The first experiments with the implementation showed that it was possible to generate test cases with the same level of detail as the existing, manually produced, test cases.

This paper presents an ontology which has been developed to represent the requirements of a software component pertaining to an embedded system in the avionics industry. The ontology was built based on the software requirements documents and was used to support advanced methods in the subsequent stages of the software development process. In this paper it is described the process that was used to build the ontology. Two pertinent quality measures that were applied to the ontology, i.e. usability and applicability, are also described, as well as the methods used to evaluate the quality measures and the result of these evaluations.

With the advent of eMaintenance, there have been many newpossibilities and opportunities to increase the productivity ofindustrial systems, yet decrease resources and administrativecosts. To accomp ...

Workplace innovation (WI) is important to provide betterwork opportunities and increase productivity. WI at the individual tasklevel concerns the structure of individual work tasks. A number of sur ...

Workplace innovation (WI) is important to provide better work opportunities and increase productivity. WI at the individual task level concerns the structure of individual work tasks. A number of surveys have been done that measured WI at the individual task level, however they paid little attention to work environment, in particular to supportive technology. This paper presents the case study of WI in two Swedish organisations with focus on the alignment of ICT and the individual work tasks. We carried out seven interviews of workers at different levels of job and in different sectors. The qualitative data analysis identified four themes: business processes, working roles, data sources, and technology. The analysis was facilitated by constructing BPMN (Business Process Model Notation) diagrams for the identified business processes. We discovered that the supportive technology in the organisations is adequate but downright traditional. We argue that technology is an important factor and enabler for WI. Finally, we present an architectural model that provides a direction for future work on WI taking ICT as the basis.

Testing is a paramount quality assurance activity in every software developmentproject, especially for embedded, safety critical systems. During thetest process, a lot of effort is put into the gen ...

The field of fault tolerance in computer science and engineering has been thoroughly investigated over a long period of time. A great number of different approaches have been presented on means for improving fault tolerance under certain error conditions in computerized systems. One important area that has introduced computers in order to enhance productivity, flexibility and economy, is manufacturing systems in order to acquire computer-integrated manufacturing (CIM). Using computers in a manufacturing system introduces new sources of difficulties, as well as providing new possibilities for overcoming erroneous situations that might disturb production. The aim of this paper, is to describe how the use of different configurations for a manufacturing system can improve fault tolerance. One specific erroneous situation which may occur in CIM is the partitioning of a network. This situation can be handled satisfactorily by using the suggested manufacturing system configurations. Additional improvements to fault tolerance can be achieved through the introduction of data buffers and material buffers, This approach is described in this paper.

The paper describes a series of protocols that increases the ability to automatically cope with failures in a computerized manufacturing system. In order to achieve better automatic failure solutions the behavior of a subsystem must be more precisely defined regarding the possible failure modes. This is called to give the subsystem a stronger failure semantics. Then, regarding the systems specific failure semantics, an algorithm to cope with the corresponding failures can be given. In this paper material-driven systems, so called Push systems, are considered. To get more precise failure resolution protocols with one or two acknowledgment messages from the receiver are defined. A protocol with acknowledgment messages from an intelligent material transportation network is also described.

When an error occurs in a manufacturing cell it sometimes means that the whole cell has to be emptied and then re-initialized before production can continue. Obviously, this is not an optimal approach. Instead, an automatic restart as close as possible in state and space to the one where the error first occurred should be strived for. One way of achieving this is by using the concept of restart points, that is, production states from which a cell can continue its work after the occurrence of an error, sometimes transparently. By restarting from such restart points a lot of actions do not need to be taken. Nonetheless, the restart points have to be identified. This paper presents a control system architecture (known as CHAMP) that, among other things, enables the fast restart of a machining cell after the occurrence of different error types through the use of restart points. The different error types that can be handled are described and the necessary actions to obtain fast restart are presented.

This paper shows how the database model of the CHAMP control system architecture must be updated, at times after and at times before different actions in the system. The reason for this is to reach a safe state after a fault from which point the control system can make a restart. CHAMP is a control system architecture for manufacturing cells that enhances flexibility.