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Today pay-per-use for machines and equipment (EaaS, equipment-as-a-service) has become a successful business model in different industries, especially for office equipment like printers and copier machines, medical equipment, and jet engines.
EaaS frontrunners in industrial manufacturing industries have already applied this business model for new revenue streams, to differentiate in the market place, or to meet the expectation of some of their customers.
Customers benefit from lower whole-life equipment costs, no upfront capital investments, turning CAPEX into OPEX, industry-leading equipment uptime, and a transparent pricing structure. Vendors of the machines and equipment can also benefit from an EaaS model. If done in the right way, it can be an attractive business model for a long-term sustainable revenue stream for manufacturing companies.
Pay-per-use subscription models: Industrial manufacturing examples
More and more industrial manufacturing companies are analyzing this business model for machines and equipment, as well as for software and digital services for their machines. Kaeser (compressors), Heidelberger Druckmaschinen (digital printing machines) and Atlas Copco (mining equipment) are prominent examples for successfully applying this business model for industrial machines and equipment.
While manufacturing companies aren’t planning to replace their traditional business model, and will continue to sell their machines and equipment, many plan to offer equipment-as-a-service as an additional model for selected machines and selected customers.
Challenges in sales and service
Many industrial manufacturing companies who had a deeper look in EaaS as a new business model have realized that it is not easy to provide this new model to each of their customers in a profitable way: It has an impact on most lines of businesses of the company and requires changes along the entire value chain, mainly across marketing, sales, service, and R&D.
Industrial manufacturers need to manage the financial risk of every EaaS case:- Conduct a solid due diligence for every customer case
- Calculate the customer-specific price points, based on a solid lifecycle costing analysis
- Work out smart contracts, considering the specific customer situations
- Define exit criteria
- Analyze each customer case before renewing the contract
To minimize the risks and to ensure a profitable EaaS contract, the vendor needs to monitor each customer case to get the required transparency on the profitability and to clearly understand what needs to be adjusted or changed when the contract will expire and needs a renewal.
All these points could be covered with spreadsheets and manual work. However, when scaling this business model to a larger number of customers, manufacturing companies should consider a proper software support through an EaaS management cockpit in their sales organizations.
The new B2B customer journey: Winning the moments that matter
The B2B customer journey is changing as buyers begin to expect B2C experiences at work. Smart B2B businesses are realizing new growth by adjusting CX.
Aftermarket management: Pay-per-use is revving retention rates
Managing the financial risk is a key point for many manufacturing companies who embark on an EaaS journey, but there are other key topics to be managed: The optimization of the operating costs of the machines and fully automated process for the subscription billing.
Manufacturing companies need the right aftermarket service organizations to provide the required service level agreements for an enhanced asset performance and an efficient service delivery for an attractive cost model to their customers. Predictive maintenance and service powered by IoT technologies is a key point for the optimization of the operating costs of the machines and equipment – an enabler for an EaaS business model in industrial manufacturing.
As always, marketing will analyze the market and competitors, identify potential customers, segment and classify them, and define the competitive solution portfolios for the EaaS offerings (considering machines and equipment as well as consumables etc.).
And of course, R&D need to ensure that the machines and equipment are enabled for an IoT-powered service and predictive maintenance and service, also leveraging latest technologies such as predictive analytics, machine learning, IoT technologies etc.
