- B
- Blockchain
- C
- Customer Churn
- Counterterrorism
- Cybersecurity in the Public Sector
- Credit Card Fraudulent Transactions
- Credit Default Rates
- Conversion Modeling
- Claim Payment Automation Modeling
- Claim Development Modeling
- D
- Drug Delivery Optimization
- Disease Propensity
- Digital Wealth Management
- Direct Marketing
- E
- Estimating Sepsis Risk
- F
- Finding Duplicate Customer Records in Your Database
- Fraud detection
- Finding New Oil and Gas Sources
- Fraudulent Claim Modeling
- G
- Google AdWords Bidding
- H
- Hospital Readmission Risk
- I
- Inventory Forecasting
- Insider Threat in Public Sector
- Insurance Pricing
- L
- Loyalty Program Usage
- Life Insurance Underwriting
- M
- Multichannel Marketing Attribution
- Modeling ICU Occupancy
- N
- Next Best Offer
- Next Best Action
- P
- Product Personalization
- Q
- Quality Assurance
- S
- Supply Chain Management
- View global site search results

Quality Assurance
Problem: Identifying and Eliminating Product Defects and Accidents
Quality assurance is essential to keep your business running smoothly and efficiently. Workplace accidents and defective materials used in production are two of the most common challenges that businesses need to address.
Workplace accidents can obviously be devastating for workers and employers alike. Keeping a safe, trusted environment is key to boosting workplace morale, increasing productivity and earning your business a good reputation.
When it comes to products, your business can’t afford to have defective materials in the manufacturing process. Defective materials create waste and increase downtime. Furthermore, if your customers end up with defective products, it can hurt your company’s brand and impact everything from sales to marketing.
Fortunately, there are new tools for you to help improve your quality assurance.
Solution: AI and Quality Assurance in the Manufacturing Industry
AI can help manufacturers significantly improve the efficiency and effectiveness of identifying defects of all kinds. Maintenance engineers can prevent defects from getting past their screening procedures, while reducing the resources required and having the ability to make predictions not only on tabular data but also on images.
Similar to the way humans learn to identify patterns, AI learns the patterns in materials to classify which ones are defective. This capability augments the role of your maintenance engineers and enables them to focus on delivering quality products in a safe workplace environment.
With improvements in the usability of AI, maintenance engineers can leverage their subject matter expertise to evaluate how the model makes its predictions. They can then monitor and manage these models to respond to service interruptions.
Why DataRobot: AI Quality Assurance Done Right
With a decade of experience and results-driven ROI, DataRobot knows the importance of quality assurance in manufacturing. We’ve worked with hundreds of businesses in dozens of industries to improve quality assurance practices, helping businesses construct better workplace environments and products alike.
To be a successful manufacturer in today’s competitive marketplace, you need to incorporate AI into your processes. Integrating AI with DataRobot to your existing quality assurance practices is now easier and more streamlined than ever before. Don’t get left behind and risk losing millions on bad products or dangerous workplace conditions. Upgrade to machine learning quality assurance with DataRobot.
Modernize Your Demand Forecasting, Supply Chain, and Predictive Maintenance
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