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The purpose of this paper is to investigate how necessary changes in a manufacturing system can be determined based on a new product specification. It proposes a formal modelling approach, enhancing the utilization of changeability of a manufacturing system given a set of changes in a product.

To develop the proposed modelling approach, a design science research method is used to iteratively frame an issue, develop a solution and evaluate it in a relevant environment. Evaluation is carried out through a case study.

A stepwise method is introduced, facilitating the creation of a model describing the relations between product characteristics within a product family and the changeability of a manufacturing system. Limitations of each manufacturing system module are evaluated to determine permittable changes in the product domain. This establishes clear relations between product attributes and manufacturing capabilities. Through this, users receive feedback on which parts of the manufacturing system must change, depending on changes in product attributes.

Testing has been carried out in an academic learning factory setting. Products and processes are thus less complicated than an industrial setting. The system used for validation is highly modular by design.

The proposed approach could be used during product development, when determining characteristics and variety of new products, evaluating the consequences of changing the solution space. This implies a shorter time-to-market and lower product costs.

Faster product development and shorter time-to-market would give manufacturers increased agility to track market needs, and ultimately lead to greater fulfilment of customer requirements.

The current body of literature focus on modelling either products or manufacturing systems. Little literature addresses both, but does not touch on identifying changes within parts of the manufacturing system, nor supports the high changeability proposed in this research.

The purpose of this study has been to evaluate the technology of autonomous mobile manipulation in a real world industrial manufacturing environment. The objective has been to obtain experience in the integration with existing equipment and determine key challenges in maturing the technology to a level of readiness suitable for industry. Despite much research within the topic of industrial mobile manipulation, the technology has not yet found its way to the industry. To mature the technology to a level of readiness suitable for industry real-world experience is crucial. This paper reports from such a real-world industrial experiment with two mobile manipulators.

In the experiment, autonomous industrial mobile manipulators are integrated into the actual manufacturing environment of the pump manufacturer Grundfos. The two robots together solve the task of producing rotors; a task constituted by several sub-tasks ranging from logistics to complex assembly. With a total duration of 10 days, the experiment includes workspace adaptation, safety regulations, rapid robot instruction and running production.

With a setup time of less than one day, it was possible to program both robots to perform the production scenario in collaboration. Despite the success, the experiment clearly demonstrated several topics in need of further research before the technology can be made available to the industry: robustness and cycle time, safety investigations and possibly standardization, and robot and workstation re-configurability.

Despite the attention of research around the world, the topic of industrial mobile manipulation has only seen a limited number of real-world integrations. This work reports from a comprehensive integration into a real-world running production and thus reports on the key challenges identified from this integration.

This paper aims to explore and conceptualize the emotional labor of racial minorities and women in the gig economy.

The paper draws on current research on gig workers' engagement in emotional labor and accounts of unfair practices in the gig economy. It calls out the presence of bias in algorithmic systems used within the gig economy as a means of control and gig worker management. The negative impact of such bias on a specific gig worker demographic is theorized.

Through a review of research on the nature of work in the gig economy, the paper suggests an unspoken mandate exists for gig workers to engage in emotional labor practices. Specifically, the nature of gig work in terms of algorithmic control, coupled with gig work dependence, often creates a need for gig workers to engage in emotional labor. This need is heightened for racial and ethnic minorities and women gig workers.

As gig platforms increasingly rely on algorithms for management and gig workers face biases, this paper offers valuable insights that contribute to a comprehensive understanding of the gig economy's impact on worker well-being. The paper discusses the need for more profound research into the nature of work in the gig economy and the life experiences of gig workers.

This paper has significant implications for organizations and gig workers. Understanding the relationships proposed in this paper can inform organizational strategies to enhance the well-being of gig workers and improve their overall work experiences, potentially leading to increased satisfaction and reduced turnover. Additionally, the paper recommends strategies gig workers can utilize to achieve positive outcomes.

The evolving work landscape, driven by the gig economy, necessitates a thorough understanding of new work dynamics. This paper sheds light on the often-overlooked diversity within the gig workforce, mainly focusing on the unique experiences of women and racial and ethnic minorities.