Business ViewpointUsage ViewpointData/Functional ViewpointImplementation ViewpointProduct ViewpointProduct ArchitectureAIoT Business Viewpoint

Business Viewpoint

The Business Viewpoint of the AIoT Product/Solution Design is building on the different artifacts created for the Business Model. As part of the design process, the business model can be refined, e.g. through additional market research. In particular, the detailed design should include KPIs, quantitative planning, and a milestone-based timeline.

Business Model

The business model usually is the starting point of the product / solution design. The business model should describe the rationale of how the organization creates, delivers, and captures value by utilizing AIoT. The business model design section provides a good description of how to identify, document and validate AIoT-enabled business models. A number of different templates are provided, of which the business model canvas is the most important one. The business model canvas should include a summary of the AIoT-enabled value proposition, the key customer segments to be addressed, the way how customer relationships are build and the channels though which customers are serviced. Furthermore, it should provide a summary of the key activities, resources and partners required to to deliver on the value proposition. Finally, a high-level summary of the business case should be provided, include cost and revenue structure.

ACME:Vac Business Model Canvas

The fictitious ACME:Vac business model assumes that AI and IoT are used to enable a high-end vacuum cleaning robot, which will be offered as a premium product. AI will not only be used for robot control and automation, but also for product performance analysis, as well as analysis of the customer behaviour. This intelligence will be used to optimize the customer experience, create customer loyalty, and identify up-selling opportunities.

Key Performance Indicators

Many organizations use Key Performance Indicators (KPIs) to measure how effectively a company is achieving its key business objectives. KPIs are often used on multiple levels, from high-level business objectives to lower-level process or product-related KPIs. In our context, the KPIs would either related to an AIoT-enabled product or solution.

A Digital OEM which is taking a smart, connected product to market would usually have KPIs which cover business performance, user experience and customer satisfaction, product quality, and the effectiveness and efficiency of the product development process.

A Digital Equipment Operator which is launching a smart, connected solution to manage a particular process or a fleet of assets would usually have solution KPIs which cover the impact of the AIoT-enabled solution on business process which it is supporting. Alternatively, business-related KPIs could measure the performance of the fleet of assets, and the impact of the solution on said performance. Another, typical operator KPIs could be coverage of the solution. For example, in a large, heterogeneous fleet of assets it could measure the number of assets which have been retrofitted successfully. UX and customer satisfaction-related KPIs would only come in if the solution actually has a direct customer impact. Solution quality and the solution development process would certainly be another group of important KPIs.

Vacuum Robot - Product KPIs

The figure with KPIs shown here provides a set of example KPIs for the ACME:Vac product. The business performance-related KPIs are covering the number of robovacs sold, the direct sales revenue, recurring revenue from digital add-on features, and finally also the gross margin.

The UX/customer satisfaction KPIs would include some general KPIs, such as Net Promoter Score (results of a survey asking respondents to rate the likelihood that they would recommend the ACME:Vac product), System Usability Scale (assessment of perceived usability) and Product Usage (e.g. users per specific feature). The Task Success Rate KPIs could include how successful and satisfied are with the installation and setup of the robovac. Another important KPI in this group would measure, how successfull customers are acutally using the robovac for its main purpose, namely cleaning. The Time on Task KPIs could measure how long the robovac is taking for different tasks in different modes.

The Product Quality KPIs need to cover a wide range of process and product related topics. An important KPI is test coverage. This is a very important KPI for AIoT-eabled products, since testing of physical products in combination with digital features can be quite complex and expensice, but is a critical success factor. Incident metrics such as MTBF (mean time before failure) and MTTR (mean time to recovery, repair, respond, or resolve) need to look at the local robovac installations, as well as the shared cloud backend. Finally, the number of support calls per day can be another important indicator for the product quality. Functional product quality KPIs for ACME:Vac would include cleaning speed, cleaning efficiency and re-charging speed.

Finally, the Product Development KPIs must cover all the different development and production pipelines, incuding hardwre development, product manufacturing, software development and AI development.

Quantitative Planning

Quantitative planning is an important input for the rest of the design exercise. For the Digital OEM, this would usually include information related to the number of products sold, as well as product usage planning data. For example, it can be important to understand how many users are likely to use a certain key feature in which frequency, in order to be able to design the feature and its implementation and deployment accordingly.

The quantitative model for the ACME:Vac product could include, for example, some overall data related to number of units sold. Another interesting information is the expected number of support calls per year, because this gives an indication for how this process must be set up. Other information of relevance for the design team includes the expected averate number of rooms services per vacuum robot, the number of active users, the number of vacuum cleaning runs per day, and the number of vacuum cleaner bags used by the average customer per year.

Quantitative Plan

For a Digital Equipment Operator, the planning data must at its core include information about the number of assets to be supported. However, it can also be important to understand certain usage patterns and their quantification. For example, a predictive maintenance solution used to monitor thousands of escalators and elevators for a railroad operator should be based on a quantitative planning model which includes some basic assumptions not only about the number of assets to be monitored, but also about the current average failure rates. This information will be important to properly design the predictive maintenance solution, e.g. from a scalability point of view.

Milestones / Timeline

Example Milestone Plan