Optimization of Maintenance Schedule
Predicting machine failure before it happens to avoid downtime and reduce maintenance costs. Prediction happens based on historical and real-time sensor feeds, vibration, voltage, pressure, temperature, historical failure incidents. AI can also recommend optimal time for intervention and best actions to avoid failures.
ROI Example
- Reduce maintenance costs by 20.5%
- Reduce unplanned downtime by 30% to 50%
- Extend the lifetime of aging assets by 20%
- Achieve 27.5% up to 50% in energy savings
Data Needed
- Historical and Real-time Sensor Data per Machine. Examples: Temperature, Pressure, Vibration, Rotational Speeds, Current, Chemical Properties.
- Historical failure incidents and maintenance work orders.
Automated Visual Quality Inspections
Deep Learning models can be used to detect and localize the defects in images creating a visual inspection system. This system can notify employees about defective parts at inward material reception and on production lines. Automating quality inspection reduces cost of production by saving time and increasing throughput, increases employee safety and improves the quality of the products.
ROI Example
- Annotation of Labelled Defect Images (Ideally Tens of Thousands).
- Model Training on annotated Datasets.
Data Needed
- Historical and Real-time Sensor Data per Machine. Examples: Temperature, Pressure, Vibration, Rotational Speeds, Current, Chemical Properties.
- Historical failure incidents and maintenance work orders.
Optimization of Production Plan Schedule
It can analyse Production Scenarios using Sales Forecasts / Pipelines and create valid and optimal plans/schedules that maximizes yield while improving other important production outcomes.
Optimize Product Transitioning/Sequencing (switching from manufacturing product A to product B on the same line), cycle length, while minimizing the costs of transitions and maximizing product availability for customers (taking demand into consideration) – all whilst minimizing inventory on hand.ROI Example
- 10-20% improvement in OTIF planning and scheduling performance
- 25% improvement in Ontime Delivery & customer satisfaction
- Reduced Delta between Sales and Production
- 15% reduction in rolling inventory levels
Data Needed
- Historical Production Yield data
- Historical Production Schedules used
- Order Booked
- Forecast
- Inventory schedules
Optimization of Production Process
It can analyse inputs from diverse sensor inputs (Temp, Pressure, Boiler, Humidity, Clean Room etc) and create valid and parameters to increase Production rate, maximizes yield while reducing costs.
A Trained model can detect complex correlation between these parameters and production rate. Further an generate optimal configuration to improve overall equipment effectiveness.ROI Example
- 25 – 30 % production yield improvement.
- 5-10 % reduction in production costs.
- 5- 15 % improvement in Equipment uptime.
Data Needed
- Historic Production Data per Product: yield, parameter values per each production step, total time taken.
- Historic Sensor data: Boiler Load , voltage, pressure, temperature.
- Historic Maintenance data per Machine: work orders, failure.
Predictive Maintenance
Predicting supply chain machine failure before it happens to avoid downtime and reduce maintenance costs (e.g. trucks, vessels, airplanes). AI can also recommend optimal time for intervention and best actions to avoid failures.
Business Impact: reduce down time for vehicle and machines, improve safety, decrease spent time on unplanned maintenance, increase customer and driver satisfaction.ROI Example
- Reduce Vehicle Downtime by 15%
- Reduce related delays by 10-15%.
- Reduce Idling times in Service Centres by 15-18%.
Data Needed
- Historical Vehicle Telemetry and Real-time Sensor data (e.g. vibration, voltage, pressure, temperature)
- Historical failures, dates of repairs (work orders), current status for machines and cargo carriers.
Demand Optimization
Forecast demand to ensure that the delivery network is operating at optimal capacity and meet demand during peak time. Use factors like historical sales, Seasonal, demographics, time of the year, and more.
Business Impact
Demand sensing & Supply planning. Optimize Procurement orders based on forecasts. Increased sales with reduced inventories. No missed sales or stock outs.
ROI Example
- 20 to 30% Reduction in Forecasting Errors of Supply Chain Networks
- Up to 35% Reduction in Lost Sales due to inventory out-of-stock situations
- 10 to 40% Reduction in Warehousing Costs
Data Needed
- Historical Product Sales per stock point
- Product Attributes (e.g. price, category)
- Historical Promotions & Marketing Activities (optional)
- Supply Chain Assets data: trucks, routes , drivers, schedules
- Other data sets (optional): Seasonal, competition offers etc
Design Optimization
Take the guesswork out of your design process.
Generative Design uses AI to come up with multiple design variations for products or parts. This leads to faster generation of design options than would be developed through manual design, which leads to faster product development times and more creative choices.
Business Impact
Generative design is capable of simultaneous exploration, validation, and comparison of thousands of design alternatives. These reduces wastages and helps in sustainability.
ROI Example
- An auto company could create 150 new design ideas for a seat bracket. 40% lighter and 20% stronger than the original part
- A design house was able to create a new design that reduced the battery case weight by 40%
Data Needed
- Designs for Products or Parts of interest
- Images for Products and Parts.
