Concept for the cost prognosis in the industrialization of highly iteratively developed physical products

With the ongoing technological progress and increasing global competition, companies are facing a continuously changing market environment. Due to the volatility of the market, rapid product adjustments and shorter product life cycles are required. Changing customer requirements are rarely taken into account, leading to inventions that do not make the transition to innovations. Highly iterative product development poses a possibility to integrate the customer voice into the development process and thus shorten the time-to-market and enable companies to respond to changes in requirements. Within the scope of highly iterative product development methods, cost analysis remains one of the main challenges for companies. Since the scope of development is not known at the beginning of a project, neither development nor industrialization costs can be specified. This, however, is essential for product and process development to meet cost-related customer requirements and for forecasting the production and investment budgets. With existing methods, it is either possible to agree to a fixed development budget and target price or to enable the customer to make changes during development. The concept presented in this paper aims to counteract this challenge. Therefore, existing approaches are analyzed with regard to derived requirements for the transfer from highly iterative and integrated product and process development to agile cost analysis. Influencing factors on product and production process costs are identified based on findings from literature. By aligning the influencing factors and requirements, dependencies between target costs of a product and degrees of freedom of highly iterative product and production process development can be derived and used for the development of a framework for iterative cost analysis. In conclusion, a concept for an agile cost prognosis for the industrialization of highly iterative developed physical products is presented

Data-based identification of knowledge transfer needs in global production networks

Manufacturing companies’ value chains are increasingly distributed globally, which presents companies with the challenge of coordinating complex production networks. In general, these production networks grew historically rather than having been continuously planned, leading to heterogeneous production structures with many tangible and intangible flows to be coordinated. Thereby, many authors claim that the knowledge flow is one of the most important flows and the source of competitive advantage. However, today’s managers face major challenges in transferring production knowledge, especially across globally distributed production sites. The first obstacle to a successful knowledge transfer is to identify what kind of knowledge should be transferred between whom and at what time. This process can take months of information collection and evaluation and is often too time-consuming and costly. Thus, this paper presents an approach to automatically identify at what point knowledge should be transferred. In order to achieve this, the company's raw data is being used to identify which employees work on similar production processes and how these processes perform. Therefore, production processes, which can be compared with each other, need to be formed, even though these processes may be performed at different production sites. Still, not every defined cluster of production processes necessarily requires the initiation of knowledge transfer since performing a knowledge transfer always entails considerable effort and some processes might already be aligned with each other. Consequently, in a next step it is analyzed how these comparable production processes differ from each other by taking into account their performances by means of feedback data. As a result, trigger points for knowledge transfer initiation can be determined

Unintended Side Effects of the Digital Transition: European Scientists’ Messages from a Proposition-Based Expert Round Table

We present the main messages of a European Expert Round Table (ERT) on the unintended side effects (unseens) of the digital transition. Seventeen experts provided 42 propositions from ten different perspectives as input for the ERT. A full-day ERT deliberated communalities and relationships among these unseens and provided suggestions on (i) what the major unseens are; (ii) how rebound effects of digital transitioning may become the subject of overarching research; and (iii) what unseens should become subjects of transdisciplinary theory and practice processes for developing socially robust orientations. With respect to the latter, the experts suggested that the “ownership, economic value, use and access of data” and, related to this, algorithmic decision-making call for transdisciplinary processes that may provide guidelines for key stakeholder groups on how the responsible use of digital data can be developed. A cluster-based content analysis of the propositions, the discussion and inputs of the ERT, and a theoretical analysis of major changes to levels of human systems and the human–environment relationship resulted in the following greater picture: The digital transition calls for redefining economy, labor, democracy, and humanity. Artificial Intelligence (AI)-based machines may take over major domains of human labor, reorganize supply chains, induce platform economics, and reshape the participation of economic actors in the value chain. (Digital) Knowledge and data supplement capital, labor, and natural resources as major economic variables. Digital data and technologies lead to a post-fuel industry (post-) capitalism. Traditional democratic processes can be (intentionally or unintentionally) altered by digital technologies. The unseens in this field call for special attention, research and management. Related to the conditions of ontogenetic and phylogenetic development (humanity), the ubiquitous, global, increasingly AI-shaped interlinkage of almost every human personal, social, and economic activity and the exposure to indirect, digital, artificial, fragmented, electronically mediated data affect behavioral, cognitive, psycho-neuro-endocrinological processes on the level of the individual and thus social relations (of groups and families) and culture, and thereby, the essential quality and character of the human being (i.e., humanity). The findings suggest a need for a new field of research, i.e., focusing on sustainable digital societies and environments, in which the identification, analysis, and management of vulnerabilities and unseens emerging in the sociotechnical digital transition play an important role