Dormakaba is a global company present in 130 countries with more than 15.000 employees from different regions and areas. With more than 150
years of experience in the field of building hardware has a turnover of 2.5 billion € in FY 20/21.
They produce and provide building components for doors and access point. The scope ranges from simple accessories like door closers and hinges up to complete access control systems, entrance solutions and high security boarding gates.
Dormakaba will conduct the test of a typical renovation projects of an hotel.
A lot of hotels want to renovate their existing room locks. They want to move from old-fashioned metal keys to digital locks that can be operated by mobile phones or key cards. Hotels have several floors with dozens of doors on each of them. By renovating these, new features can be added to the door locks, and this is the opportunity to change the process of assessing and designing them. Within the methodology and approach designed for this project, dormakaba will use openDBL to collect the data in the building and upload it into the internal design and sales system. This will enable the definition of a new process to design, build and install its door locks. The workflow above shows the new openDBL process that will be validated and finalized after our test.
The use of openDBL in defining the specification creates for many industry playersseveral business opportunities:
1. Data collection on site. It’s a task that can be commercialized by companies. Companies could offer this as a standalone service for customers or other companies. For example: even larger companies don’t have the manpower to serve each customer’s request to collect data in the building. That could be externalized to 3rd party companies. In addition, for companies like dormakaba that do this as part of their service offering, this opens new ways to monetarize and use data. New insights and services could be offered based on the data collected.
2. Creation of the central data model. This model can be utilized to display captured data from components installed in the building. An important value proposition is the task of data cleaning, aggregation and filtering before transferring it to openDBL. Here, the openDBL could define a common standard that industry partners could rely on to integrate their data. This will give the option to interlink data from different sources to create added value services to the customer/user of the building.
3. Automatic alerting. Triggered by changes in KPI or data, actions could be automatically initiated. For example, Public Authorities can receive alerts in case of fire or break-in attempts.
4. Improved facility management. Based on the data in the openDBL, optimized maintenance plans could be created and executed. Application of modern AI techniques would allow to predict maintenance needs and identify possible hazards before they occur.