AI-based service ecosystem for services in the age of Industry 4.0
This project encompasses a cluster of different save costs due to delivering services utilizing an AI-based service platform. The project deliverables enable small and medium enterprises (SMEs)
Keywords:
service
AI platform
AI for SME
service models
servicemeister
AI Service Platform
Submitted by:
Hauke Timmermann, Service-Meister Project, eco Association of the Internet Industry (https://international.eco.de/)
Context
New service tasks and business models lead to an increased need for service staff to maintain or improve mechanical operations, reducing costly downtime of machines. Especially in the environment of Industry 4.0, many machine suppliers and plant manufacturers no longer simply sell their machines, but offer them as part of so-called MaaS (Manufacturing-as-a-Service) or subscription models. Up to now service is being offered and billed on the basis of operating hours – restricting transparency in the service process.
27% of German companies are already using AI in services and customer support – but in 5 years this is expected to rise to 69 %. Networked sensors allow remote access to machine and environmental status data, leading to an optimized management of transparent service and maintenance operations.
Business benefits
Business benefits are due to the transparency gained in the service process. Networked sensors (wired or wireless) allow remote data access that enable the creation of forecasting models to deliver targeted services leading to overall cost reduction and efficient service delivery.
The expectation is that economic advantage can be generated for SMEs by using Reference Architectures and AI-as-a-Service platforms, made available via the open service master platform. This will create a low-threshold access to AI technology and a service ecosystem leading to reduced overall costs for service suppliers and at customer sites.
Solution or innovation detail
Two important goals in the project are:
a) to create leapfrog innovations from which other companies can learn and which they can then also use for themselves, and
b) to also achieve platform innovations in Germany and Europe.
Actually, Germany has been leading AI development and research for 50 years, but so far no large-scale AI Platform implementations have been developed.
The platform is designed to support the needs of SMEs offering services also to their customers. A broad scope of different service demands in industry is incorporated in the project by “speed boats”, representing different use cases in different industries. Some other these are shown below.
■ Krone
Environment Screening: Keeping an eye on water levels from a distance, determining discharge rates, and identifying problems.
IoT services like this are increasingly popular as climate change demands smart water management solutions. If more rain falls in winter and there is only occasional heavy precipitation in summer, this can push wastewater pipes to their limits. The consequence of extreme weather conditions: flooding and high water.
■ Wuerth
Material Supply in Industry: Accelerate service processes
Condition Monitoring and Surveillance: detect faults remotely
These use cases focus on predictive maintenance – based on data supplied by networked tools. AI procedures analyse the devices’ service reports and IoT data.
■ OGE
Service Management: Detect anomalies in gas pipe lines, forecast service requirements
Open Grid Europe operates its own Competence Center, which is tasked with detecting anomalies in the data streams of all 850 gas leakage sensors.
■ Trumpf
Efficient planning of service calls, automatic diagnosis of machine data
Machines get enabled to independently diagnose and analyse problems, transfer results to a cloud platform where they get evaluated. Maintenance tickets can be automated and information can be used in a continuous learning and improvement process. This increases system availability and reduces maintenance costs.
A standardised 100% digital service lifecycle serves as the basis for extending the various functionalities from the speedboats to the platform.
This process creates generic services, modules and blueprints that can be made available to SMEs and ensures the scalability of the solution.
The platform can be operated as a GAIA-X compliant federation and ensures that digitally sovereign partners can exchange and share their data and create new business models.
Ecosystem
The Service Meister platform is designed to provide AI systems and components that cover the entire service process – in a 360-degree view. Service technicians must be able to access the information in various working conditions to ensure a good user experience and barrier-free access to the relevant information and solutions.
The ecosystem of Service-Meister Use Cases encompasses
- ■ Service staf
- ■ AI service platform operator
- ■ Terminal Provider for service operators
- ■ AI solution support centers
- ■ Machine & equipment supplier needing maintenance
- ■ The end user: factory operator
Business model
New business models are being developed to monetize targeted services based on reduced downtime of machinery, reduced risks of environmental hazards and total cost of ownership in the supply chain engaged.
Public available deliverables can be downloaded from the project web page after the project end, in 2023
Further information and references
https://www.servicemeister.org/das-konsortium/
Abendroth, J., Riefle, L., & Benz, C. (2021). Opening the Black Box of Digital B2B CoCreation Platforms: A Taxonomy. In Proceedings of the 16th International Conference on Wirtschaftsinformatik (WI). https://www.researchgate.net/publication/348326720_Opening_the_Black_Box_of_Digital_B2B_Co-Creation_Platforms_A_Taxonomy
Riefle, L., Eisold, M. & Benz, C. (2021). Industrial Corporation’s Transformation into a Digital Platform Provider: A Case Study on Enablers. In Proceedings of the 23rd IEEE Conference on Business Informatics (CBI). https://www.researchgate.net/publication/354339339_Industrial_Corporation’s_Transformation_into_a_Digital_Platform_Provider_A_Case_Study_on_Enablers