Three ways data will change the manufacturing business
The digital transformation would be unimaginable without data, and many business leaders consider data to be the fuel that powers it. Technology enablers like the Internet of Things (IoT) and data analytics support the generation of data — which will only continue to grow in the future.
Over the last decade, the increasing individualization of demand and personalized products that meet that demand have made sensing consumer trends more and more important. As a result, the collection and analysis of customer data has become a key priority, providing a competitive advantage for companies that acknowledge its importance.
In addition, internally-generated data (like production and performance data from sensors or PLCs) helps optimize manufacturing processes by applying data analytics to generate predictions — such as about wear and tear on machinery. Using data to enable process efficiency and productivity improvements is great, but it can do more. Company sourced data can actually create new revenue streams.
The digital transformation would be unimaginable without data. Products alone will not ensure the survival of manufacturing companies. They are adding value with complementary services based on the data their products generate.
Three new data-driven business and revenue models
In the future, data will become a product. In recent years, it has become clear that the product business alone will not ensure the future survival of manufacturing companies. Manufacturers are now adding value to their products with complementary services based on the data generated by their products, enriched with external data. However, this is not an endeavor that can be approached haphazardly. These new services must be supported by a carefully constructed business model, since existing product-oriented business models will not adequately support a data-driven business. Let’s look at examples of three new data-driven business models that could change the face of manufacturing.
1. Platform-based business model
This business model is based on a two-sided, or more often, a multi-sided platform. Data is collected and analyzed across value chains and provided to customers willing to pay for the service. The pricing model is mainly based on subscription or pay per use.
Examples of this model are marketing and sales prediction platforms that create predictions on behalf of companies, using data collected from them and the markets. Uber and Airbnb are also using the model, acting as platform operators that combine upstream customers (e.g. drivers or homeowners) with downstream customers (riders and renters).
Automotive OEMs are already experimenting with platform-based models, offering mobility services to customers on a pay-per-use basis. In this case, the physical product – the car – is merely a means to deliver the service.
2. Use-based business model
This model is often integrated into a platform-based model, but is grounded on specific data-driven services that are only paid for when used. It also integrates data that is not available in a platform, such as sensor data from equipment. These types of services could include value stream analysis.
By way of example, John Deere collects the data generated by its products in the field and combines it with external data such as weather data to provide harvesting predictions to its customers. Not only does this enhance customer loyalty, but it may also be a first step in a development where the data becomes more important to the service than the product itself.
In the future, we will see value propositions that are mainly based on data, but it will require increasingly clever algorithms to turn it into predictions and value-added services.
3. Outcome-based business model
Here the data collection and analysis are combined with specific, individual services and knowledge. Payment is based on performance, using specific KPIs such as improvements in equipment availability or reductions in equipment downtime. Pricing models would include pay-per-output or pay-for-performance.
Michelin launched an offering for fleet operators, where it was paid based on fulfilling mileage targets instead of charging per tire purchased. In this case, there is a serious risk of failure because it reverses the traditional notion of caveat emptor or “buyer beware.” If the customer is not fully transparent about how the product is performing, it’s difficult to measure the output and administer the contract fairly for both parties.
Consequently, these models require a great deal of knowledge and experience about the performance of the product and its behavior in the field to define the right performance indicators up-front.
How becoming a data-driven business will change the organization
As demonstrated above, changing from a product-centric to a service-centric company will have large impact on products and services, on the value proposition and the revenue model. It will also substantially change the structure and processes of manufacturing companies. The organization must be able to accommodate the existing manufacturing of products while establishing new structures and processes for data-driven services.
The first step is to define a clear strategy for how data is accessed and used. Existing organizational silos need to be broken up to foster collaboration within the firm. Suitable R&D processes must be established to enable data-driven innovations, along with the freedom to test service ideas. The company management should support the transition to a data-driven business by enabling open communication and providing a suitable budget. Suppliers and partners need to be integrated into the company’s ecosystem.
Finally, one of the most important changes will be in the area of workforce education. In the data-driven world, it will no longer be enough to have brilliant product engineers. Data-driven businesses require skilled data analysts and talented software engineers to explore the new opportunities that data offers, and to implement and maintain cutting-edge solutions such as data analytics and artificial intelligence.