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High costs of complex machine tools are especially for small and medium sized enterprises (SME) a huge financial challenge. Therefore, leasing plays an important role in the acquisition of machine tools. In classical leasing the lessee pays a fixed leasing rate and thus creates the inventive to maximize productivity. An acceleration of the wear by continuously high stress or even damages from overload which are not directly visible, reduce the residual value to the disadvantage of the lessor. The lessor has to calculate additional costs for the risks of intransparent usage, because he can not control the stress during the leasing period and the determination of the condition after returning the machine is complex. This results in higher and inflexible cash flows for the lessee and more difficulties in planning the payments due to intransparency for clients and suppliers.
Aim of the project is to develop a stress-based payment model along the entire life cycle of a machine tool and its components with the help of artificial intelligence (AI) and legally compliant blockchain technology. The unique selling point of the approach is resolving the conflict of interest and intransparency from the principal-agent relationship. Therefore on one hand the knowledge about the actual stress of the machine tool plus its components and on the other hand the cause-effect relation between stress and wear is generated. The developed stress factor from this serves as monetary evaluation unit for the pay-per-stress approach and as a foundation from further development of existing business models to intelligent services, which promise long-term competitive advantages of German machine manufacturers over international competitors. The business model Pay-per-Stress thus represents a lighthouse of the program “Smarte Datenwirtschaft”, enabling better process and customer understanding, intelligent product-service offerings and a more flexible SME sector.
The research project is funded by the German Federal Ministery of Economic Affairs and Climate Action (BMWK) within the “Smarte Datenwirtschaft” program. We thank for the opportunity to work on this project.