The document discusses various industrial AI use cases, with a primary focus on predictive maintenance, quality inspection, manufacturing process optimization, and supply chain enhancement. It highlights real-world applications, such as Deutsche Bahn's predictive analyses for rail failures and Nissan's AI platform reducing downtime. Additionally, it covers technologies like automated optical inspection and AI-driven cybersecurity, illustrating the broad impact of AI in accelerating efficiency and safety in manufacturing.

1. AI UseCases

2. Predictive maintenance

3. Predictive maintenance
• The largest use case for industrial AI is “Predictive Maintenance”.
Predictive Maintenance makes use of advanced analytics (e.g.,
Machine Learning) to determine the condition of a single asset or an
entire set of assets (e.g., a factory).
• The goal: Predict when maintenance should be performed. Predictive
maintenance usually combines various sensor readings, sometimes
external data sources, and performs predictive analytics on thousands
of logged events. Predicting the remaining useful life of an asset using
supervised ML is the most common technique in Predictive
maintenance

4. Predictive maintenance
• One of the biggest challenges with Predictive Maintenance is the
elimination of data imbalances as there is often not enough failure
data for all the assets. Data is called imbalanced, when failure events
represent less than some specific required share of the dataset. To
make accurate predictions on the data, such imbalances have to be
eliminated first. There are 2 main methods to achieve balanced data:
data sampling and cost-sensitive learning algorithms.

5. Predictive maintenance
• Example 1: Deutsche Bahn, the German railway operator, leverages
data from railway switches to predict failures, thus decreasing
unexpected delays at scale. Source: Konux Case Study
• Example 2: Nissan runs an AI Predictive maintenance platform to do
RUL prognostication on 7,500 assets. The company claims an
unplanned downtime reduction of 50% and a payback period of < 3
months. Nissan scaled the solution from 20 critical assets to
thousands without increasing the workload of the on-site PdM team.
Source: Manufacturing.net

6. Quality inspection & assurance

7. Quality inspection & assurance
• Automated optical inspection is a technique where a camera
autonomously scans the device under test for catastrophic failure
(e.g. missing component) and/or quality defects (e.g. fillet size or
shape or component skew). Computer vision is the foundation of
optical inspection. Once the images are recognized, semi-supervised
ML is the most effective technique to classify images into failure
classes. The main benefit of this use case is cost reduction, and the
main potential beneficiaries are large manufacturing facilities, where
a small reduction in scrap or test time can yield very large savings.

8. Quality inspection & assurance
Example: A major German automotive company is introducing a deep
learning-enabled system for quality control. This system is fully
integrated into the flow of the final assembly process and thereby gets
rid of the need to have a separate testing area in a controlled
environment. The solution is expected to soon replace conventional
camera booths that are used today and according to the OEM will
result in “immense savings”.

9. Manufacturing process optimization
• Perhaps the most obvious but still one of the most difficult to implement AI
use cases is automated manufacturing process optimization. One
implementation of this optimization is through Autonomous machines or
robots.
• The idea behind those autonomous assets is that they replicate
monotonous human tasks in the manufacturing process, thus saving costs.
Before being put into production, the autonomous machines/robots
perform the same task over and over again, learning each time until they
achieve sufficient accuracy. The reinforcement learning technique is often
used to train robots and autonomous machinery. Under this technique, a
robot can relatively quickly teach itself to do a task under the supervision
of a human. The “brains” of such a robot/machine are usually neural
networks.

10. Manufacturing process optimization
• Example: ABB is investing $150M to build an “advanced, automated
and flexible” robotics factory in Shanghai. In this plant, ABB will
manufacture robots using robots. According to ABB, these robots will
have autonomous and collaborative elements. The robots’ autonomy
is built with the use of AI and digital twin. Source: ABB.

11. Supply chain optimization

12. Supply chain optimization
• 8% of all industrial AI implementations are improvements to
industrial supply chains. Using AI tools to improve inventory
management is one of the key applications.
• Predictive inventory management leverages predictive analytics for a
variety of inventory-related tasks including to reduce inventory
planning time, minimize inventory cost, optimize repairments, and
find optimal reorder points. For these tasks, techniques such as time-
series analysis, probabilistic modeling (Markov and Bayesian models)
as well as simulations (e.g., Monte-Carlo simulation) are most
commonly used.

13. Supply chain optimization
• Example: Continental has built software to predict the optimal points
for tire changes on its fleet. The underlying model predicts the overall
running mileage and compares it to the baseline, to generate actions.
By that, Continental is reducing its stock of tires, also improving safety
on the road.

14. AI-driven cybersecurity & privacy

15. AI-driven cybersecurity & privacy
• AI-driven cybersecurity & privacy relates to aspects such as cyber
threat detection. It typically involves observing the network
infrastructure and detecting threats of cyberattacks in real time. It
also often includes such activities as network traffic analysis, endpoint
detection and response, malware sandboxes, etc. AI-powered cyber
threat detection is often part of a larger cybersecurity solution that
also uses a number of prevention measures (e.g., firewalls).

16. Automated physical security, Data
management

17. Automated physical security
• Surveillance & physical threat detection entails real-time surveillance
of manufacturing sites or workers in order to automatically detect
physical security threats and/or potential safety hazards.

18. Automated data management
• With data often stored in multiple systems and multiple places, it is
hard to access and analyze the data quickly and holistically. Therefore,
some industrial companies start to employ data management
solutions that perform tasks such as data acquisition, data filtering,
data cleaning & integration, etc. in real-time.

19. Smart assistants

20. Smart assistants
• Voice assistant is one of the examples of smart assistants in
manufacturing settings. Integrating voice assistant technology into its
real-time industrial monitoring systems allows workers to gain
insights without coding the explicit commands or printing long status
reports.

21. AI-driven research & development
• Automated component design is the leading use case in AI-driven
R&D.
• The goal: Letting software independently develop dozens of different
designs in short periods, given a set of predefined constraints. The
optimal design is chosen afterwards. For this task, digital twins and
simulations often complement the AI techniques.

22. Autonomous resource exploration
• Especially relevant in Mining & Quarrying and Oil & Gas industries,
autonomous resource exploration is a technique of analysis and
processing of the massive volume of images (e.g., radar, satellite or
drone images) to detect the optimal point for resource extraction. AI
can be especially useful in the detection of captivities in difficult-to-
access areas, such as the ocean subsurface or mountains.

23. Thank you.
We look forward to working together.
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