{"id":43,"date":"2020-04-17T11:02:14","date_gmt":"2020-04-17T15:02:14","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/service-lifecycle-management\/?p=43"},"modified":"2026-03-26T15:41:54","modified_gmt":"2026-03-26T19:41:54","slug":"asset-as-a-service-for-oems-why-now","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/service-lifecycle-management\/2020\/04\/17\/asset-as-a-service-for-oems-why-now\/","title":{"rendered":"Asset as a Service for OEMs \u2013 why now?"},"content":{"rendered":"\n

It was almost 58 years ago, in October 1962, that Rolls Royce invented its \u2018Power-by-the-Hour’ trademark to support the Viper engine on the de Havilland\/Hawker Siddeley 125 business jet. A complete engine and accessory replacement service offering on a fixed-cost-per-flying-hour basis.[1]<\/a><\/p>\n\n\n\n

This provision aligned the interests of the manufacturer and operator, who only paid for engines that performed well.

In today\u2019s market, discrete product manufacturers are facing growing competition and lower profit margins for their new equipment sales.            <\/p>\n\n\n\n

Much like the appeal of Software-as-a-Service (SaaS) to software\nvendors, Original Equipment Manufacturers (OEMs) can get stable revenue from service,\noften with higher margins than sales of new equipment. <\/p>\n\n\n\n

Research shows that After-sales services \u201ccontribute to about 25 percent of all\nrevenue and 40\u201350 percent of all profits for manufacturing companies.\u201d[2]<\/a><\/p>\n\n\n\n

Also, one McKinsey analysis across 30 industries showed that the\naverage earnings-before-interest-and-taxes (EBIT) margin for aftermarket\nservices was 25 percent, compared to 10 percent for new equipment.[3]<\/a><\/p>\n\n\n\n

Industrial\nOEMs understand their products better than third-party providers, having\npartnerships within their channels, and leveraging collected proprietary data of\ntheir equipment from their installed base, which allows them to best capture\naftermarket value.<\/p>\n\n\n\n

In\nmany cases, however, the OEMs are facing the fierce competition of third-party service\nproviders and part manufacturers who offer less expensive compatible spare\nparts and services.<\/p>\n\n\n\n

When\nit comes to an \u201cAsset-as-a-Service\u201d business model, the motivation of OEMs is\ndifferent; spare parts consumption becomes an expense rather than revenue, and\nso does the downtime of an asset.<\/p>\n\n\n\n

This scenario\nleads to a much better alignment of interests between OEMs and operators.<\/p>\n\n\n\n

For\nexample, with the classic model, a downtime of an aircraft impacts mostly the\nairline operator and service contractor while it brings spare parts revenue to\nthe OEM. In an Asset-as-a-Service model, the OEM is the service provider and\nshares the same impact as the airline operator for downtime.<\/p>\n\n\n\n

While\nOEMs cannot eliminate industry-specific constraints in the aftermarket, they\ncan take steps to increase lifetime penetration, annual services revenue and\nproduct lifetime. The first step involves creating a comprehensive digital\ntool that models the installed base and analyzes individual pieces of\nequipment.[4]<\/a><\/p>\n\n\n\n

Nevertheless, just increasing aftermarket revenue is not enough.\nWe need to maximize operational margin to increase profitability. Hence, OEMs\nmust be capable of  monitoring their\nmargin along with their revenue continuously. <\/p>\n\n\n\n

This process\nis achievable through an effective \u201cdigital twin<\/a>,\u201d depicting the complete process from design through\naftermarket usage.<\/p>\n\n\n\n

With\na closed feedback loop between all lifecycle phases, such as design, production\nand after-market, the \u201cclosed=-loop digital twin\u201d allows continuous design and\nmanufacturing improvements through insight to the real usage. By leveraging\ndata analytics and artificial intelligence (AI) technologies, problems are\npredictable ahead of asset failure and thus reducing the asset\u2019s downtime.  <\/p>\n\n\n\n

Today\u2019s\nIndustrial Internet of Things (IIoT), and data analytics technologies such as artificial\nintelligence, machine learning, allow setting an effective digital twin\nsolution to retain profitability by ensuring effectively of their service\noperations:<\/p>\n\n\n\n