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Scalable data models for interoperability in the system development process

Benefit from our expertise in the field of industrial AI and start your AI development with our extensive project experience and preconfigured and customizable shop-floor AI-tools. Our free checklist gives you a comprehensive overview of what you need to consider for the successful implementation and use of AI.

Start with industrial AI

Integrating AI applications into industrial processes and managing software deployment and maintenance is a complex and often costly endeavor. Traditional software development paradigms often fall short when big data becomes a critical factor. In addition, successful implementation requires a high degree of coordination and collaboration between multiple teams. The development of application-specific models for tasks such as prediction, anomaly detection or classification usually requires collaboration between domain experts, data science, data engineering, infrastructure and application development teams.

Achieving a meaningful ROI with AI solutions

When evaluating AI use cases, it is important to focus on scalability, taking into account feasibility as well as effort and potential risks. To address the multiple aspects of scalability, we use established process frameworks for data analytics and AI projects such as CRISP-DM (CRoss Industry Standard Process for Data Mining) and TDSP (Team Data Science Process) as foundational models that are enriched with customized extensions for specific requirements.

Evaluation framework for AI use cases

AI business scenario

Consideration of aspects such as ROI, development costs, availability of experts, restrictions on brownfield sites and technical gaps.

Data requirements

Taking into account aspects such as data basis, development costs, data labeling, environmental influences and signal significance.

Data modeling

Consideration of aspects such as preselection of ML methods, hyperparameter optimization vs. parameter reduction.

Use of AI

Consideration of aspects such as the availability of documented IT processes, access to the solution architect’s network and alternative component agreements.

Operation and maintenance

Aspects such as the setup of the monitoring, the maintenance costs of the solution, the integration of relevant system sources and the traceability of the predictions within the system environment are taken into account.

Download our free checklist for the introduction of AI

What you can expect from the checklist:

A comprehensive framework to assess scalability of AI use cases
33 key factors for implementation, derived from process frameworks such as CRISP-DM (CRoss Industry Standard Process for Data Mining) and TDSP (Team Data Science Process)
Specification of requirements for data, data modeling, AI deployment, maintenance and operation

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“Addressing the dual challenge of achieving industry-standard quality and scalable implementation is key to unlocking the full potential of industrial AI. This questionnaire, developed in collaboration with inpro, serves as a powerful tool to address scalability at every stage of the AI development process.”

Boris Scharinger, AI Strategist at Siemens Digital Industries

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AI-supported analysis of production data

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AI-supported analysis of production data

In times of growing variant diversity, increasing quality requirements and limited expert resources, traditional analysis methods are reaching their limits. An AI-supported analysis of production data helps companies to detect patterns and indications of causes.

Innovation partnership: Accenture and inpro

Accenture and inpro are combining their digitalization and innovation expertise to support industrial companies, particularly in the automotive sector. By using technologies such as AI, data analysis and evaluation or automatic image recognition, quality problems in the manufacturing industry, for example, are to be identified more quickly and resolved sustainably.

AI-Expertise

Scalable data models for interoperability in the system development process

Start with industrial AI

Achieving a meaningful ROI with AI solutions

Evaluation framework for AI use cases

AI business scenario

Data requirements

Data modeling

Use of AI

Operation and maintenance

Download our free checklist for the introduction of AI

What you can expect from the checklist:

Discover more

AI-supported analysis of production data

AI-supported analysis of production data

Innovation partnership: Accenture and inpro

Innovation partnership: Accenture and inpro