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The Digital OEM

The Digital OEM combines physical product design, engineering and manufacturing with Software-as-a-Service in order to provide smart, connected products. Artificial Intelligence (AI) and Internet of Things (IoT) are the two key enablers. Taken together, AI and IoT form the Artificial Intelligence of Things (AIoT). The AIoT Framework provides actionable tools and best practices for implementing AIoT.

WHY

The motivation for adopting a digital OEM business model can vary widely. Many incumbent OEMs are seeking ways to build upon their existing business. New market entrants are looking at disruptive new business models enabled by the combination of physical products with AIoT.

While AI and IoT are exciting technical enablers, anybody embarking on the AIoT journey should always start by looking at the "why": What is the purpose? And what are the expected business outcomes? From a strategic (and emotional) point of view, the purpose of the AIoT initiative should be clearly articulated: What is the belief? The mission? Why is this really done?

Digital OEMs - business models

At the core of the digital OEM business model is the physical asset or product. The interesting question is, which new opportunities are arising through the combination of physical products with digital solutions. Examples include:

  • Data-driven business, e.g. building on user-generated data or asset/product performance-related data. Examples include usage based car insurance (UBI), data-driven aftermarket services, or drone-based building facade inspection.
  • Digital add-on services, e.g. an optional autopilot service for an electric vehicle, or cooking recipe add-ons for a smart kitchen appliance
  • Asset-as-a-Service, e.g. car-seat-heating-as-a-service, or the famous "power-by-the-hour" for Rolls-Royce aircraft engines
  • Smart Maintenance, including predictive, preventive and prescriptive maintenance, enabled by deep analytics of asset/machine data via AIoT
WHY: Digital OEM Business Models

The diagram shows key elements of two worlds:

  • The OEM (Original Equipment Manufacturer) is an organization which makes devices from component parts bought from other organizations. This can be a car maker, a manufacturer of household appliances, or a manufacturer of manufacturing equipment, such as robots or laser cutting tools.
  • The suppliers of the OEM are usually referred to as "tier 1", "tier 2", etc - depending on their position in the value chain
  • On the other side, we have the digital ecosystems. Today, large hyper-scalers are dominating cloud-based infrastructure (Infrastructure-as-a-Service, or IaaS) and platforms (Platform-as-a-Service). IaaS includes storage, networking, and virtual compute resources. PaaS includes Internet-based tools and middleware for building applications
  • Software-as-a-Service (or SaaS) are applications delivered over the internet.
  • The digital OEM will combine physical product development with Software-as-a-Service to deliver smart, connected products

Incumbent OEMs - business improvements

Especially for incumbent OEMs, the idea of improving existing business by adding digitally-enabled solutions is attractive. Generating ARR (Annual Recurring Revenue) via digital services is very interesting, since ARR is seen as a more stable and predictable revenue stream. However, the opportunity to improve existing business - and especially EBIT - with digital solutions as a short-term measure should not be underestimated, since unproven, new business models can have inherent risks and realization of new, ARR-like revenues might take longer than hoped for.

Business Outcomes

WHAT

What can be done with AIoT from the perspective of the digital OEM? Usually, the answer is building smart, connected products. These combine physical products with smartness enabled by AI and connectivity enabled by IoT. To build smart, connected products, the digital OEM needs to combine product engineering and manufacturing capabilities with edge and cloud software development capabilities.

WHAT: Digital OEM and smart, connected products

Smart, connected products - enabled by AIoT

Smart, connected products usually combine edge and cloud computing capabilities: Edge computing is anything that happens on (or near) the asset/product in the field. Edge computing capabilities are usually dedicated to a single asset/product, or sometimes a specific cluster of assets/products operating in close proximity. Cloud computing in an AIoT scenario on the other hand can enable insights or functionality which relates to an entire fleet (or "swarm") of assets/products. Consequently, in AIoT we also differentiate between two types of intelligence: asset/product intelligence vs swarm intelligence.

Smart, connected products

Example: Robot vacuum cleaner

A good example for a smart, connected product is a robot vacuum cleaner. These products use AI to identify room layouts and obstacles, and to compute efficient routes and methods. For example, the robot can decide to make a detour vs. switching into the build-in „climb over obstacle“-mode. Another example is the automatic activation of a „carpet boost“ mode. IoT-connectivity to the cloud enables integration with user interface technology such as smart mobile devices or smart home appliances for voice control („clean under the dining room table“).

Example: Robot Vacuum Cleaner

The robot vacuum example will be looked at in great detail in the product design section.

Example: Kitchen Appliance

Another good example for smart, connected products is a smart kitchen appliance. Here, the intelligence could start with data gathered from users of the kitchen appliances, in combination with user-generated ratings. This data could be combined to make targeted recommendations (created via AI), e.g. for cooking recipes. More advanced version of the smart kitchen appliance could also use AI on the product, e.g. for better device control and maintenance.

Example: Kitchen Appliance

Example: Automatic wiper control

In this example, AI is utilizing images from the autopilot camera to determine local weather situation. This is then used to automatically match wiper speed to the intensity of rain or snow. This is how Tesla is doing it, and its an area which is also starting to get the attention of the research community.

What`s interesting about this example is that some Tesla customer have been initially complaining that this is not as accurate as other systems using rain sensors. So Tesla was using their Over-the-Air Update (OTA) capabilities to enhance this function.

Example: Windshield wiper control

Example: Physical product design improvements

Another interesting use of AIoT is for the advanced analytics of product performance, based on data from assets in the field. For example, the team developing the electric motor for the wiper blades from the previous example could use this approach to better understand how their product performs in the field, e.g. at 200 mph on a highway at heavy rain. This information can then be used to improve the next generation of the motor. In this case, it might sometimes not be clear whether we are talking about advanced analytic or real AI (e.g. using ML), but it still an important use case.

Example: Physical product improvements

Example: Smart tightening tool

Another example is the smart tightening tool (e.g. the Bosch Rexroth Nexo cordless Wi-Fi nutrunner). This is the type of tool used by industrial customers, e.g. for ensuring the quality of safety relevant joints.

On the tightening tool, AI/ML can be used to control the proper execution of tightening programs (controlling torque and angel for specific combinations of materials). In the cloud, data from fleets of tightening tools can be analyzed to help automatically detect tightening anomalies, classify these anomalies, and make recommendations for handling of these anomalies.

Example: Smart, connected tightening tool

WHY revisited

Let`s revisit the "WHY" perspective with what we have learned so far about AIoT and the different use cases implemented by Digital OEMs.

Aligning the Product Lifecycle with the Customer Journey

A key feature of AIoT is that it helps with aligning the product lifecycle and the customer journey. In the past, most OEMs lost contact with their products once they left the factory. Although many OEMs try to stay in touch with their customers and support them in the aftermarket, in most cases the customer relationship was based on service contracts, but not a digital one. This is of course changing with AIoT, which enables a much higher level of customer intimacy because OEMs can not learn how their products are used in the field, and how they are performing. The data obtained and analyzed from the products in the field via AIoT can be augmented with other data, e.g. customer feedback from the Internet.

AIoT also gives the OEM the opportunity to react to what he is learning about his products in the field, by constantly updating existing digital features or even creating new ones, deployed via Over-the-Air Updates (OTA). Naturally, OTA in an AIoT setting will have to support updates of both, software and AI models.

WHY revisited: Product LCM and Customer Journey

This topic was actually recently discussed by Uli Homann of Microsoft at the BCW.on session with Microsoft CEO Satya Nadella and Bosch CEO Volkmar Denner. Full video here.

Uli Homann discusses AIoT Cycle

Benefits

The benefits of this approach are manyfold, including shorter time-to-market, improved differentiation, improved sales (including recurring revenues), improved customer experience, and consequently also improved customer loyalty.

WHY: Benefits

HOW

Now let`s take a closer look at how the Digital OEM must go about implementing this with AIoT.

Key design decisions

From the product manager`s perspective, a key question in the future will be - for each feature - whether this feature should be implemented in hardware, software, or AI - or combinations thereof. Implementing a feature in hardware (including HMI, processing, etc.) will have an impact on usability (for example, sometimes it will still be preferable to activate a feature via a physical control), but also on engineering and design complexity. Implementing the same feature completely in software (e.g. as a feature activated via a smart app) can often mean lower cost of delivery (no manufacturing / supply costs beyond the initial development), and also means that the feature can be updated via OTA in the future. Finally, if the feature can be implemented virtually, then the next big question is, whether it should be implemented as a set of hard-coded rules (software development), or as a data-centric AI function which uses inference to make a decision based on its training.

The decision for using AI, Software, or Hardware for a specific feature will have two main implications: First of all, the quality of the User Experience (UX), and second of all the required technology pipeline to deliver the feature.

HOW: Key design decisions

Technical constraints

The decisions for the system design of each feature will also have to take technical constraints into consideration, as shown below.

HOW: Technical Constraints

Considerations for execution and delivery

Finally, the team responsible for executing and delivering on the product vision will have to take all the usual aspects of such an undertaking (starting with the business model, and not ending with technical considerations) and look at them specifically from the perspective of the physical product development, AI, and IoT - and all of this with an agile mindset. How this can be supported is defined in the Agile AIoT Grid.

Agile AIoT GridHOW: Execution and delivery