The Nexus of Forces in Action – Use-Case 17: Smart Buildings and Home Appliances



This use-case addresses the optimization of human machine interfaces of private households such as the TV control menus, in terms of customization, personalization, and product and service feedback. The key stakeholders are companies in the white goods and brown goods markets, software companies, and accessory (e.g., programmable remote controls) companies.

Primary Industry Sectors

Consumer electronics

Business Value

Tracking usage, user intentions, and user preferences enable analytics while increasing customer engagement. User, group, and corporate behavior change adjustment incentives. Tracking energy usage, building environment awareness, user intentions, and user preferences enables analytics while increasing customer engagement, cross-selling, and up-selling.

Key Business Functions

Understanding customer needs, customer personalization

Primary Actors

Multi-media/TV viewer, home appliance manufacturer, building and facility environment

Secondary Actors

Home/building media delivery system (e.g., set-top box) provider, digital TV programming, product portfolio sales and marketing planning, associated building appliance service provider, home insurance

Machine Actors

Set-Top Box (STB), smart TV, customer preferences and behavior analytics, usage and viewing preferences and behavior data aggregation and analytics, social networks integration, Electronic Program Guide (EPG) analytics, mobile smart phone EPG/webstore service app, satellite cable networks, Internet broadband networks, buyer and seller history analytics, cloud platform (private, hybrid, public, community), digital media content

Key Technologies

IoT, cloud, big data

Main Scenarios

Understanding Customer Needs

Companies can learn from unsuccessful searches of specific TV control menus or from surveys with a very small number of multiple-choice questions. If several customers demand without success for a menu design, which includes function X, a new product requirement can be derived. Hence these items aim at identifying requirements for upcoming generations of TVs (and potentially other devices as well).


Customers can download fitting menus thus personalizing their product while also supporting user-centric development of better-designed products. The collected information will be evaluated and shared among the manufacturing and service network related to smart TVs. But smart TVs are just one example of such a personalization approach to products. The scenario ranges from apps for the kitchen oven to a network analysis of smart washing machines in order to identify the regionally optimized water hardness.

A smart TV can serve as an example of optimization of human-machine interfaces, in terms of customization and personalization. Due to the fact that some components are seldom used, interfaces (control menus) for these elements/functions can be excluded from the actual (already purchased) product at will. This downgrading reduces complexity for the customer. Excluded interfaces that are needed again can be reintegrated by the user through an upgrade process. In order to enable upgrading and downgrading for a TV, the product needs to be modular, especially from the software perspective. Frequent upgrading and downgrading of interfaces raises the question of opening up the design process for menus in general.

One way to open up the process is to establish an open platform where people can download, offer, and share their own interface designs and product-feature clusters. The product-feature clusters may be created through an analysis of average usage behavior (“empirically designed” interfaces) or by a group of enthusiasts (non-professionals). In the latter case each menu can be defined, structured, and interconnected as needed by the author of the interface. Search functions and filters may allow the customer to identify downloadable designs for different purposes. This way, the platform serves as an interaction point for product personalization (i.e., platform-to-customer).

Key Data

Master Data

EPC, asset manufacturing usage guide, rules of access and use of the asset guide (e.g., when customer has registered details with supplier or third-party information provider), conditions of use

Current Observations Data

Link to context of asset specification and actual asset configuration set-up (to enable user usage context-driven advice and guidance information), current asset product content specification status and version

Historical Data

Older versions of asset guide, multi-language/country location versions of asset guide

Query Data

Availability of current asset guide, specific component within asset information query, associated information on asset; e.g., nearest supplier of spare parts or existing product replacement (online or physical offline), dynamic control command of smart appliance from mobile app (tablet, smart phone, smart TV)

Action Taken Data


Real Business Examples

Siemens Connectivity

Lets you control home appliances through smart technology. (See the Appliances Online video.)

Additional Considerations

Existing Interoperability Standards

Wi-Fi home and building network

Comments on Context

In order to trigger innovation on the IoT, we need to track and count not only the identifier, type and version of a physical product which is known as Product Lifecycle Management (PLM), but also its usage in all of its lifecycle phases which is known as Quantum Lifecycle Management (QLM). Here, the usage of the home appliance can be tracked in terms mentioned in the scenarios above.


The home appliance has registered its specification to an open available site for access and download.