Business Model Design: Difference between revisions

Line 73: Line 73:


The AIoT Framework proposes an AIoT business model canvas derives from Osterwalder, but adding another area to specifically highlight the impact of AIoT on the other elements, including value proposition, customer relationships, channels, key resources, key activities, cost and revenue structure, etc.
The AIoT Framework proposes an AIoT business model canvas derives from Osterwalder, but adding another area to specifically highlight the impact of AIoT on the other elements, including value proposition, customer relationships, channels, key resources, key activities, cost and revenue structure, etc.
<imagemap>
File:2.1-x-Canvas.png|1000px|frameless|center|AIoT Business Model Canvas


rect 4 4 3558 1632 [[https://miro.com/app/board/o9J_lJmp474=/|Details on Miro Board]]
[[File:2.1-x-Canvas.png|1000px|frameless|center|AIoT Business Model Canvas]]
 
desc bottom-left
</imagemap>


=== <span id="SolutionSketch"></span>AIoT Solution Sketch ===
=== <span id="SolutionSketch"></span>AIoT Solution Sketch ===

Revision as of 10:18, 15 April 2021

Digital OEMDigital Equipment OperatorHybrid ModelsAIoTAIoT FrameworkBusiness Model DesignAgile AIoT GridCo-CreationTransformation ProgramsBMI

Business Model Design

The development and validation of a - more or less - detailed business model is usually the first step on the journey towards developing a new smart, connected product. The business model describes how an organization creates, delivers, and captures value [1]. The St. Gallen Business Model Navigator [2] defines four dimensions for a business model ("What, how, who and value"): What do you offer to the customer? How is the value proposition created? Who is your target customer (segment)? How is revenue created?

StGallen Business Model Navigator

Usually, business models evolve over time. The assumption is that business model innovation is an iterative and potentially circular process. An initial concept design phase is followed by a detailed design, before going into implementation.

Business Model Innovation

Many Internet-based business models are constantly re-evaluating and adapting their business models, utilizing the flexibility of cloud and DevOps to do so. However, for business models based on physical assets, this is typically not as easy: Design and manufacturing of physical assets has much longer lead times. And once the assets are manufactured, sold and deployed in the field, any alteration of their physical configuration becomes very difficult if not impossible. As shown in the figure below, products based on physical assets usually experience a sharp drop in their ability to change after the Start of Production (SOP).

Business Model Challenges

Smart, connected products are providing an opportunity to address this issue, at least to a certain extend. First, software features can be used to re-configure some of the features of a physical asset. For example, a car manufacturer can now allow customers to add battery capacity or seat heating even after the car has been delivered to the customer. Second, Over-the-Air (OTA) Updates can be utilized to add new software functions (including AI model updates) to the asset after it has been deployed to the field.

The following will look at tools and best practices for developing AIoT-specific business models for smart, connected products.

IoT Business Model Patterns

The area of business model patterns for the Internet of Things is well researched and documents. For example, the St. Gallen Business Model Navigator [2] defines a number of patterns summarized in the table below. These patterns generally are based on the assumption that they combine physical assets with digital services.

IoT Business Model Pattern Desciption
Physical Freemium Physical products are sold with free digital services, e.g. a mobile app to control the product. This pattern allows for additional revenues via premium services.
Digital Add-on Physical products are sold relatively inexpensive. Customer can then purchase digital add-ons, e.g. by unlocking additional features of the product.
Digital Lock-in Use of physical products is protected via sensor-based, digital handshake, e.g. to limit compatibility, prevent counterfeits, etc.
Product as Point of Sales Physical products offer digital sales and marketing services, e.g. an object carrying digital advertising.
Object Self-Service Physical products can autonomously place orders online. For example, a heating system automatically orders oil to refill the tank, based on fill level and oil market prices.
Remote Usage and Condition Monitoring Physical products transmit data about their usage, status, or environment, e.g. a photo copy machine with a pay-per-use model

A great example of a 'Digital Add-on' is BMW's announcement to make seat heating available on demand. Two factors make this interesting:

  • Physically producing many different, custom configured variants of a car could be nearly as expensive as producing a single, mass-manufactured variant
  • Being able to up-sell this feature to customers especially in winter could significantly increase the total number of seat heating options sold in total

AI Business Model Patterns

The area of business model patterns based on AI in the context of IoT is not (yet) widely researched. The diagram below describes the most common patterns.

AI-enabeld Business Models

AIoT Business Model Templates

The following is introducing a set of templates for AIoT business models. As far as possible, these templates are re-using existing, well established business model templates, adding the AI and IoT perspective to them. These templates should be seen as guidance, and can be adapted in a flexible way to best fit the needs of your individual AIoT business model.

The following discussion will be based on the smart kitchen example, which is shown below.

Example: Smart Kitchen

The complete Smart Kitchen Business Model has been documented in Miro. It can be accessed HERE, in case you can`t read some of the details in the diagrams below.

AIoT Business Model Canvas

The business model canvas is probably one of the most established tools in the business model community. There are a plethora of variations, with Osterwalder representing the classic, and the Lean Canvas the one probably most established in the agile development community. The basic idea of the business canvas is that - instead of writing a detailed and lengthy business plan - the key information typically found here is summarized in a canvas on a single page. Sometimes, the canvas also serves as the executive summary.

The AIoT Framework proposes an AIoT business model canvas derives from Osterwalder, but adding another area to specifically highlight the impact of AIoT on the other elements, including value proposition, customer relationships, channels, key resources, key activities, cost and revenue structure, etc.

AIoT Business Model Canvas

AIoT Solution Sketch

The first template is the so-called AIoT Solution Sketch. The idea is to provide a very simple canvas which helps visualize the key functional elements of your solution, mapped to either the field (including EDGE functionality) or the backend (e.g. in the cloud). This simple yet expressive format is especially useful for reviewing and discussing the intended functional scope with management stakeholders.

AIoT Solution Sketch

AIoT Use Case Mapping

The AIoT Use Case Mapping can be used to clarify in how far one of the typical AIoT Use Cases can best be supported by utilizing AI and IoT together. An example is given here.

AI Value Proposition - Smart Kitchen Example

AIoT Customer Journey Map

Customer (or User) Journey Maps are a common User Experience (UX) tool. There are many shapes, sizes, and formats available. The general idea of a journey map is to help understanding and visualizing the process that a person goes through in order to accomplish a specific goal.

Ignite AIoT proposes a format for a customer journey map which has the key user interactions with the asset at the top, e.g. asset purchasing, asset activation, asset usage and service incidents. Depending on the complexity, each of these steps could be detailed in a map on its own. Below this, the template provides space for the following:

  • Touchpoints: What touchpoints is the customer actually using in order to interact with the solution or the asset?
  • Doing: What is the customer actually doing?
  • Thinking/Feeling: This covers the emotional side of the journey
  • Opportunities: What opportunities from a business model point of view can be found here?
  • Key AIoT Features: What features / capabilities from an AIoT point of view are utilized here?

Note that this template is focusing more on the high-level journey, including business model aspects. A more detailed, UX-focused version of this is introduced later in the Solution Architecture.

AIoT Customer Journey

AIoT Value Network Modelling

Value Networks are another tool in the business model toolbox. Again, a wide variety can be found. The basic idea of a value network is to help explore and design the relations and the value exchange between a solution / product / company and the different stakeholders (see example here).

Ignite proposed a value network template which focuses on people (usually described via roles) and corporate stakeholders (e.g. partner companies). In addition, another type of node in the network should be the intelligence embedded in the AIoT solution (e.g. as asset intelligence or swarm intelligence). This is important because in an AIoT solution this will usually be an important provider of either information / intelligence or revenue / value.

AIoT Value Network Modeling

AIoT Business Case

Another key element of the business model is the business case, including the financial perspective on costs and revenues, as well as the strategic contributions.

Direct ROI

The direct ROI for an AIoT solution must typically take into consideration the asset-related as well as the service-related costs and revenues. On the cost-side, the differentiation between capital expenditures and operational expenditures (including unit and operations costs). On the revenue-side, the business case must differentiate between upfront revenues and recurring / subscription revenues. Ignite AIoT proposes to combine these perspectives in the template shown below.

AIoT Business Case

Strategic contributions

In addition to the direct ROI of the investment, many AIoT solutions also provide strategic contributions to a higher-level business case. For example, an AIoT solution for car seat heating on-demand could be part of the overall business case of the car. A summary of typical strategic contributions by AIoT solutions is shown in the diagram below.

AIoT Strategic Contribution to Business Case

AIoT Business Case Validation

Validating the AIoT Business Case in the early stages as much as possible will save you from costly surprises further down your AIoT journey. The business case validation should include both sides, costs and revenues.

Validating assumptions made about revenue in the business model is of course tricky. The best way forward are typically interviews with potential customers, to validate not only their willingness to purchase the intended products and services, but also their price sensitivity.

Furthermore, one should also not underestimate the importance of validating the cost side of the business model. This is especially important for an AIoT-enabled business: While virtual, cloud-based business can scale very well on the cost side, with any business which is involving physical assets or products, this is different. The physical products will have to be manufactured, distributed and supported. A thorough investigation of unit costs / marginal costs should be performed as early as possible, and ideally validated by getting price indications from potential suppliers as early as possible. The AIoT Sourcing BOM introduced in the section on Sourcing and Procurement can be a very helpful tool.

In addition to IoT-related costs (especially hardware and costs for telecommunication), the AI-related costs should also not be underestimated. Especially the data labeling can be a cost driver - don`t forget that this will not only cause costs for the initial data labeling, but most likely require continued labeling services throughout the entire product life cycle.

AIoT Cost Estimation

In general, IT-centric business cases have a tendency to focus more on the initial costs, and not the Total Cost of Ownership (TCO). Over a five year lifespan, initial development costs will most likely be only 20% of the TCO[3].

Proof of Concept

Most investors require some kind of proof along the way, which provides evidence for the feasibility of the investment proposal (this applies both to corporate investors as well as private equity investors). AIoT-based solutions are not different in that perspective. Except that it can sometimes be much more difficult and expensive to run a Proof-of-Concept (PoC) for an AIoT solution: Today it is usually very easy to create a lightweight and affordable PoC for a pure software project (e.g. using simulation or mock-ups). However, as soon as hardware development and/or asset customization is involved, this can become much harder, depending on the hardware and asset categories.

Consequently, the following should be clearly defined for any AIoT-related PoC:

  • Duration & effort
  • Scope
  • Resources
  • Success criteria

Investment Decision

In today`s agile and digital world, most investment decisions are staged - meaning that partial investment commitments are made based on the achievement of certain milestones. However, there is usually a point in time for any innovation project where it transitions from the exploratory phase towards the scaling phase with much higher budgets. Each organisation is typically following its own, established investment criteria. For the project manager, it is often important to keep in mind that these criteria are usually a mixture of hard, ROI-based criteria, as well as the strategic perspective. This is why the business model should address both perspectives, as stated above.

AIoT Investment Decision

References

  1. Business Model Generation, Alexander Osterwalder, Yves Pigneur, Alan Smith, and 470 practitioners from 45 countries, self-published, 2010
  2. 2.0 2.1 The Business Model Navigator: 55 Models That Will Revolutionise Your Business, Oliver Gassmann, Karolin Frankenberger, Michaela Csik, 2014
  3. Distribution of Cost over the Application Lifecycle - a Multi-case Study, Ruediger Zarnekow, Walter Brenner, 2005

Authors and Contributors

Dirk Slama.jpeg
DIRK SLAMA
(Editor-in-Chief)

AUTHOR
Dirk Slama is VP and Chief Alliance Officer at Bosch Software Innovations (SI). Bosch SI is spearheading the Internet of Things (IoT) activities of Bosch, the global manufacturing and services group. Dirk has over 20 years experience in very large-scale distributed application projects and system integration, including SOA, BPM, M2M and most recently IoT. He is representing Bosch at the Industrial Internet Consortium and is active in the Industry 4.0 community. He holds an MBA from IMD Lausanne as well as a Diploma Degree in Computer Science from TU Berlin.


Heiner Duffing.jpg
HEINER DUFFING, ROBERT BOSCH GMBH
CONTRIBUTOR
Heiner has more than 25 years experience in purchasing and partially business development in various business areas (Steel, Automotive, Consumer, Renewables) and countries.Strong focus has been to find market innovations and develop start-up suppliers/products to reliable serial partners, including the negotiation of fitting contracts.Currently he leads the Purchasing of Software and Engineering Services for Bosch products. He holds a degree as Diplom-Wirtschaftsingenieur from TU Darmstadt.


Kim Kordel.jpg
KIM KORDEL, BOSCH.IO
CONTRIBUTOR
Kim Kordel is a senior business development manager for new IoT business at Bosch.IO. In her former position as an IoT business consultant and trainer for IoT business models at Bosch.IO she developed and taught methodology for building IoT business models. With this methodology she developed new digital business for internal and external customers. Kim also co-initiated and set-up the Bosch Startup Harbour, the incubation program for external startups for Bosch. Now Kim is responsible to establish new IoT business for the energy domain.