To monetize the wealth of information derived from networked machines, the industrial machinery manufacturing company is naturally led to carry out a strategic repositioning of its business model from a “transaction- based” to a “service-based” set-up, the latter strongly oriented toward the customer and the intelligent use of information for the management and optimization of risks and processes.
In the Digital Asset Servitization organisational model, the role of the entrepreneur is enhanced as a true cornerstone of business success when sharing and internalising new issues posed by the Service Economy, and is prepared to accept a great challenge: to produce even more excellent machinery (designed to be modular and upgradeable and to last much longer than before) and to manage the new risks and processes brought by Digital Asset Servitization, throughout the useful life of the machinery.
The key concept underlying the Service Economy is that excellent machinery becomes the enabler for creating solutions and services over a multi-year timeframe, thus enabling a revenue model based more on the service components than on the revenue derived from the mere concession in use of the machinery.
In addition, the entrepreneur plays a crucial role in raising awareness in customers toward the adoption of a new service culture, as only with a sharing of this new business model will there be a positive impact on market share and competitive positioning.
Building excellent machines requires excellent ideas and excellent planning/prototyping systems, so it is important to be inclined to constantly facilitate and finance research and development: once again, a company that has adopted Digital Asset Servitization has a recursive revenue stream, which can also be used to plan for the financial coverage of R&D activities.
Thanks to Digital Asset Servitization, the entrepreneur can count on a very large amount of data, generated throughout the useful life of the machinery (from prototyping to scrapping), and the creation of information, through data integration and correlation, becomes a new wealth that can be finalised to different targets (higher revenues, environmental sustainability, circular economy…) that we report, by mere way of example:
- Information related to materials used: today aimed at procurement on the supply chain, tomorrow to populate a database of materials recoverable from scrapping;
- Information related to energy sources used: functional for machinery pricing, but equally useful for a consumption forecast or for redesign initiatives to reduce consumption;
- Information related to the cost of inputs: critical to pricing, but equally useful for initiating competitive quotations on procurement platforms, as much as for exposing financing requests on Supply Chain Finance platforms;
- Information related to machinery user customers: important for managing credit risk on individual machinery, but even more so when correlated with each other, with a view to customer portfolio management and diversification;
- Information related to machinery utilisation: essential for implementing predictive maintenance services, but equally useful for providing trending, which can be used as a thermometer of proper machinery utilisation.