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The integration of Artificial Intelligence (AI) in manufacturing is often pursued as technology push. In contrast, this paper looks upon the AI-human interaction from a viewpoint that considers both to play an important role in reshaping their individual capabilities. It specifically focuses on how humans can play an important role in enhancing AI capabilities. The introduced concepts are tested in an industrial case study of vision-based inspection in production lines. Furthermore, the paper highlights the need to consider relevant implications for work design for AI integration. The contribution can be of practical value for system developers and work designers in how to target at the design stage the human contribution in AI-enabled systems for production environments.

Purpose: To facilitate the design of viable business models by proposing a novel business model design framework for viability. Design: A design science research method is adopted to develop a business model design framework for viability. The business model design framework for viability is demonstrated by using it to design a business model for an energy enterprise. The aforementioned framework is validated in theory by using expert opinion. Findings: It is difficult to design viable business models because of the changing market conditions, and competing interests of stakeholders in a business ecosystem setting. Although the literature on business models provides guidance on designing viable business models, the languages (business model ontologies) used to design business models largely ignore such guidelines. Therefore, we propose a business model design framework for viability to overcome the identified shortcomings. The theoretical validation of the business model design framework for viability indicates that it is able to successfully bridge the identified shortcomings, and it is able to facilitate the design of viable business models. Moreover, the validation of the framework in practice is currently underway. Originality / value: Several business model ontologies are used to conceptualise and evaluate business models. However, their rote application will not lead to viable business models, because they largely ignore vital design elements, such as design principles, configuration techniques, business rules, design choices, and assumptions. Therefore, we propose and validate a novel business model design framework for viability that overcomes the aforementioned shortcomings., Journal of Business Models, Vol 3 No 2 (2015): Journal of Business Models

The goal of this paper is twofold: i) to design a viable business model for community owned solar farms that will be setup in the north of the Netherlands. ii) To present the findings from this case study, and to propose generalisations that are relevant for the development of artefacts that can be used to facilitate the design of viable business models in a business ecosystem setting. USE 2015

Modern computer-algebra programs are able to solve a wide range of mathematical calculations. However, they are not able to understand and solve math text problems in which the equation is described in terms of natural language instead of mathematical formulas. Interestingly, there are only few known approaches to solve math word problems algorithmically and most of employ models based on frames. To overcome problems with existing models, we propose a model based on augmented semantic networks to represent the mathematical structure behind word problems. This model is implemented in our Solver for Mathematical Text Problems (SoMaTePs) [1], where the math problem is extracted via natural language processing, transformed in mathematical equations and solved by a state-of-the-art computer-algebra program. SoMaTePs is able to understand and solve mathematical text problems from German primary school books and could be extended to other languages by exchanging the language model in the natural language processing module.

Many industries base their innovations on product platforms. Platforms have predefined modularity with standardized interfaces. However, product platforms provide significant challenges to engineering industries that rely heavily on R&D, such as microlithography systems. Developing these systems depends on advanced technologies, that are still immature. Therefore, predefined modularity with standardized interfaces cannot be assumed. However, although platforms in the classical meaning cannot be achieved, this paper argues that function platforms offer a feasible alternative. In this paper, a longitudinal case study is described at a large engineer-to-order firm delivering microlithography systems. The study analyses the platform concept in practice together with the emerging use of function platforms. It studies how function platforms are developed and used, and specifically what happens in the stages before the development of a physical platform; the phase where interim (functional) modules are defined. It considers the process of forming the architecture of the functional modules and examines the potential of reusing the functional clustering principles. The paper focuses on methods that model and exemplify function platforms. This study broadens our understanding of function platform management. It exemplifies the dynamics of managing reuse and commonality in R&D based engineering industries. The evidence from the case study shows that function platforms allow nuanced design freeze, since the platform describes the functional elements that will remain stable within the development of a product family. Therefore, function platforms enhance change management of the functionality foreseen. Also, defining an up-front function platform helps to sustain the functional decoupling of the physical product architecture. The paper finalizes with conclusions and implications for industry.

Berichte zur Polarforschung = Reports on Polar Research