Get to Know Your Install Base
As we look ahead to 2021, we see more and more OEMs investing in installed base visibility. Simply put, if you want to create value in the products you sell, you need to know where these products are, what state they are in, and how they are being used. This becomes more pertinent when intermediaries are selling the product on your behalf. Without adequate visibility, your service delivery will be working blind and your revenue streams will be unpredictable.
Achieving installed base visibility might seem like a simple step. How could you not know what has been sold? Surprisingly, many manufacturers struggle with this either due to poor record-keeping, limited systems reach, or the extensive use of third-parties who further complicate the data collection exercise. Putting the time and resources into capturing a clearer picture of the installed base requires an executive understanding of the value of this exercise. More so, it requires an organization that believes and recognizes that its margin is driven by the aftermarket business and not just the original sale of the product. This is vital. Once this is understood, then it becomes easier to justify the investment.
Data Capture and Preparation is Key for AI Value
Knowing your installed base has far-reaching consequences. It also greatly improves the odds of a positive return on digital investments in mobility, augmented reality, the Internet of Things, or Artificial Intelligence.
Let’s take AI for example. The number of use cases for AI continues to grow, and so does the overall interest in these point-to-point solutions to solve specific problems. We see manufacturers apply learning algorithms to their fault and resolution data to isolate the real service issue and determine the most effective resolution path. Organizations using this type of focused AI solution have greatly improved their first-time fix rates and reduce unnecessary parts shipments. In some areas, these solutions have streamlined the quoting process of add-on work and generated incremental revenue streams.
While specific use cases can be quite impactful, OEMs recognize the strategic value of AI to their organizations. They also recognize that the real value of AI is tied to the quality of data that is currently available in their service organizations. To drive better AI-supported projections and recommendations, organizations will need to continue to improve their data capture and organization, particularly around information on the asset – failure causes, service actions required, part history and more. The development of a will make it easier to record and analyze data for improved efficiency and better performance.
Consider Alternate Data Collection Models
There is a misconception that true visibility into the installed base requires an investment in sensors, real-time condition monitoring, and all the complexities of the Internet of Things. While these do streamline the process and create real-time visibility for improved predictive modelling, there are basic installed base models that can be built within the framework of service and maintenance visits.
For instance, several organizations are relying on their customers to record their assets via self-service portals or applications. Think about it as product registration, but on an industrial scale. More organizations are relying on the digital tools handed to their field service agents to capture pertinent asset information at the point-of-service. If these tools have freed up certain capacity in the calendars of the field technicians, then this additional time can be used to track the assets and installed base on a customer site. In some instances, technicians can capture competitive assets to feed displacement campaigns when the time is right.
There are multiple channels that can be relied on to build a better-installed base. Whatever the channel or combination of channels, it is essential that service organizations begin the capture their installed base and its condition prior to realizing the true value of investments in AI and other digital advancements. This might sound like taking a step back, but it’s actually more like building a data-driven foundation on which these solutions can thrive in order to compete in future.