With the modular approach to a data-based business strategy
Goal
Development of a company-wide data strategy and data platform to optimize processes, strengthen customer loyalty, develop digital innovations and optimize production processes.
Solution
The basis was a modular approach developed by prodot, which wasindividually tailored to the company'scurrent level of digitalization maturity. The data strategy developed and itsimplementation now form the basis forsustainableimprovements in key business areassuch as product development, customer service and sales, through access to customerservice data and by analyzing operational data in support.
Step 1
From idea to impact: data strategy in five modules
Starting point for data visioneering:
Objectives - identifying potential
Based on the objectives, measurable and strategically relevant goals were defined:
- Customer loyalty: Avoidance of unplanned system downtime through condition-based maintenance and predictable replacement of components as well as a portalthatallowscustomers to independently evaluatethe overall status of their systems .
- Production: Optimizing the ability to plan delivery dates for systems by connecting to supplier data and monitoring supply chains.
- Product development: Evaluation of usage behavior and recognition of recurring patterns in the operation of mobility systems, identification of type- and usage-related increased wear and tear on the systems and improvement ofadditional services.
- Service innovation: provision of digital twins for the provision, analysis and mapping of service-relevant data, use of artificial intelligence to implement predictive maintenance .
Step 2
Data discovery: taking stock of the data landscape - analyzing structures
In the Data Discovery module, prodot examined the sustainability of the existing datastock.
As a result, the existing machine data was incomplete,unstructured and fragmented, stored, managed and used in different systems. This silo formation resulted in inadequate and inconsistent data integrity. The lack of data consistency prevented all relevant data from being used by the individual departments across the board.
The customer received documentationafter completion of the data discovery module . This included
- a detailed list of the technologies used and their scaling options
- Transparent presentation of the license and operating costs incurred
- Recording of all existing data with its sources, data volume, forecast data growth and current data quality
- Responsibilities for data maintenance and data usage
- Identification and classification of business-critical and personal data
Step 3
Data strategy: networked data usage in the company
The data strategy module defined the technology, data architecture, time and budget planning and change management, among other things .
A proof of concept was used to ensure that the identified risks and hurdles were avoided. All of these components were incorporated into the definition of a transformation roadmap with appropriate resource planning.The measures defined in this were
- Establishment of a company-wide data platform
- Introduction of a structured data governance process
- Systematic collection and provision of existing data
- Development of new data sources
- Introduction of an IoT module to upgrade existing products
- Implementation of digital, automated user surveys
- Providing a data-based decision-making basis for the development of additional digital services
Step 4
Data strategy implementation: Implementing the data strategy in technology and practice
In cooperation with the mobility manufacturer's project team, the steps defined in the transformation roadmap were iteratively validated and implemented. A new data architecture wasestablishedso that optimized real-time data is available to all stakeholders for operational use. In addition, the planned platform for company-wide consolidated data collection and use was developed and implemented .
Change management measures have increased the acceptance of everyone and thus ensured the effectivenessof the initiative .
Step 5
Data value creation: strategic roadmap for measurable improvements
In the final step,further value-added servicesweredeveloped .
A predictive maintenance tool makes it possible to adapt the maintenance and servicing of the systems to the actual operating conditions. This leads to fewer unplanned outages and maintenance work can be planned more efficiently .
A customer portal for improved support is provided to make customer data centrally available to all parties involved. Service calls can be planned and carried out in a more targeted manner, as all digital components are stored in the platform as digital twins and error messages are displayed on a component-specific basis .
Special dashboards are availablefor customers in order to providereal-time insights into workload peaks or the running timesof their systems.
The R&D department also uses real-time operating data for the further development of existing and new products .
Added value through strategic use of data
Concrete results for the customer
- Better adherence to delivery dates thanks to greater data transparency in production and the connection of suppliers' ERP interfaces
- Optimization of the supply chain makes it possible to produce on-demand and reduce storage space
- Reduced downtime of systems at the end customer
- Significantly shorter response times in the event of operational problems
- Real-time data enables a faster response to malfunctions
- Optimization of personnel requirements
- Improvement in service quality
In product development, real-time data from IoT sensors and digital twins enables the development of new generations of machines based on customer requirements such as frequency, duration and intensity as well as the environmental parameters in which the machines are operated, such as temperature, humidity or vibrations .
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Data Driven Strategies
Harnessing data to unleash potential
With prodot as your partner for your individual data strategy, you can rely on experience, methodology and practical orientation. Together, we turn unused data sources into measurable success.
Find out more about how we can take your data to a new level.
