3. Convergence on the outcome economy:
3.2 The emergence of the outcome economy
In the outcome economy phase, companies will shift from competing through selling products and services, to competing on delivering measurable results important to the customer. This is a much more challenging prospect. Among other things, providers will require a deeper understanding of customer needs and contexts in which products and services will be used. Value based on output also entails quantifying results in real time. Both of these requirements have been nearly insurmountable obstacles to scale – until now.
It is the digital age that makes the outcome economy possible. With the proliferation of connected sensors, the physical world is moving online, becoming increasingly quantified and accessible. Similar to the data logs that show web trails, sensory data streams from connected machines contain detailed traces about product usage and customer behaviours. By applying advanced analytics to such data, along with the right external data and domain models, companies can gain a better understanding of interactions among input variables, and optimize what it takes to achieve desired business outcomes.
For example, agricultural companies now have the data necessary to calculate how many bushels of wheat can be produced on a given piece of farmland with a particular mix of seed, fertilizer, water, soil chemistry and weather conditions. By combining analytics software with connected tractors, tillers and planters, they can apply the precise mix of seed and fertilizer to maximize crop yield at harvest. (See sidebar on “Outcome-based Agriculture below.”) Similarly, a building management company can deliver a defined level of energy savings through sensors, controls and software to analyse the data on when and where people work, and thus optimize the lighting and temperature levels required to support them.
Industrial Internet outcomes typically revolve around the product or the business. Product outcomes measure how well a product performs according to its intended purpose. For example, target outcomes might relate to the operations or maintenance of a product (e.g. reliability), or to the savings generated from the use of a product or piece of equipment. In general, product outcomes are fairly straightforward because they typically involve only the product supplier and user. For example, Rolls-Royce’s TotalCare provides a suite of predictive maintenance and repair services for its jet engines, including monitoring engine health, and modifying engines to increase reliability and durability. Customers pay for product reliability. As the product-service provider, Rolls-Royce assumes the entire risk of time-on-wing and shop visit cost.23
Business outcomes, on the other hand, are quantitative measures that address the why behind the buy. One example of business outcomes is from Taleris America LLC. Unlike Rolls-Royce’s TotalCare service, which focuses on the uptime of one product (e.g. Rolls-Royce jet engines), Taleris tackles the larger issue of airline delays and cancellations caused by equipment failures. To accomplish this goal, it focuses on airline fleet optimization far beyond the operational condition of a specific piece of equipment. By servicing the entire fleet, Taleris can impact overall maintenance schedules. This systemic approach means less disruption, lower costs, better spare-parts inventory management and more satisfied travellers.24
The outcome economy will have many implications for businesses. Companies will need more and better data to calculate costs, manage risks and track all the factors required to deliver the promised value. Provider risk will increase, too, as markets move to value based on outcomes, but so will the reward. New financial instruments and forms of insurance will emerge to help enterprises manage the risks associated with guaranteeing outcomes. Pricing practices will also change, as it becomes possible to model the probability of delivering outcomes. Success in this environment will require greater cooperation among businesses than ever before, which will call for a far more connected world, comprising new market ecosystems and technology platforms that can support and serve the Industrial Internet economy.
Workshop Highlights – Munich, Germany, 4 November, 2014
- Software platforms within ecosystems can enable the aggregation and brokerage of data and the collaboration across industries, which can create unexpected business relationships and expertise.
- The supply chain will become more flexible, allowing more on-demand customization and real-time access to information.
- Digital manufacturing will affect the design process of a product, and its lifecycle will be shorter since technology will enable quicker change and modification.
- Production of goods will happen closer to consumption and service delivery will drift further away from consumption as services can increasingly be performed remotely.
- The Industrial Internet will create new complexities and moving parts that will all need to be managed by new positions–leading to the creation of jobs.
- Start-ups are often considered outsourced R&D. Many feel that the start-up environment is stronger in the US when compared to that in Europe.
Case study: Outcome-based Agriculture
One industry at the forefront of the evolution to outcome-based services is agriculture. By connecting farm equipment to geo-location data, agricultural companies and farmers can now coordinate and optimize farm production in ways never before possible. For instance, automated tillers can inject nitrogen fertilizer at precise depths and intervals, as seeders follow, placing corn seeds directly in the fertilized soil. Ultimately, turning such data into actionable insights will improve crop yield to help feed the world’s growing population.
One example of such “smart farms” comes from Monsanto, a multinational agrochemical and agricultural biotechnology company.27 To help farmers increase crop productivity while conserving water and energy, Monsanto purchased Climate Corporation, a company which has used remote sensing and cartographic techniques to map all 25 million farming fields in America by field shape, type of crop, crop yields, soil capacity and other critical metrics.28 By adding Climate Corporation’s data to Monsanto’s data on seed yields, farmers can better understand which seeds will grow best in which fields and under what conditions.
Outcome-based agriculture requires connected ecosystems and platforms. In Europe, the 365FarmNet29 brings together farm equipment makers Claas, Rauch, Horsch and Amazonen-Werke, financial service giant Allianz, chemical company Bayer, seed producer KWS Saat, agricultural software service provider LACOS, agricultural advisory service company Agravis, and the European Global Navigation Satellite Systems Agency. This ecosystem provides farmers with easy access to data and analysis on geo-location, diagnostics, crops, fertilizers, weather and other factors, over smartphones or through direct connections with farm equipment.
Farm equipment manufacturers are also taking an active role in developing their own ecosystems. John Deere is building intelligence into its large combines, tractors and sprayers through sensors that make the machines into mobile platforms. The company is also vying to become a trusted source of agriculture data by forming digital partnerships with companies such as DuPont, Pioneer, Dow Chemical and others to supply precision agriculture solutions to growers. And John Deere and AGCO are working together to connect irrigation systems, soil and nutrient sources, with information on weather, crop prices and commodity futures to optimize overall farm performance.30
While prescriptive agriculture offers many potential benefits, there is also potential for conflict among stakeholders. Many farmers, for example, do not trust companies that offer prescriptive agriculture systems since they fear that the stream of detailed data they are providing on their harvests may be misused. They also worry that these firms could buy underperforming farms and run them in competition, or use the data on harvests to trade against farmers on the commodity markets.31