Optimizing Production Processes and Enhancing Data-Driven Decision Making
15 March 2022 | 09:00 - 10:00 PST
Manufacturers are continuously improving their processes and systems to maximize production, efficiency, agility and safety within their operations. AI promises to be a game-changer at every level of the value chain for the manufacturing sector, by enabling predictive maintenance, reduced downtime, 24/7 production, improved safety, lower operational costs, greater efficiency, quality control and faster decision making.
This Virtual Roundtable Discussion will explore best practice strategies to identify AI use cases, select appropriate technology and implement at scale.
Learn From Our Panel of AI Experts
Director IT/OT Resiliency & Support,
Stanley Black & Decker, Inc
Key Topics Include:
- Artificial Intelligence as a critical component of production optimization: How can technology enhance real-time visibility and decision making, mitigate risk, enhance quality, reduce anomalies and minimize down-time?
- Identifying and prioritising core business use cases: Evaluating business problems to determine if AI and emerging technology is a suitable solution to solve them
- Creating the roadmap for the AI and Analytics Strategy: Fundamentals required for the successful adoption and implementation of AI within Manufacturing
- Connecting people, process, technology and assets: Avoiding common failures by embedding an AI and data-driven culture and governance
- Strategies to maintain reliable and accurate data acquisition, management and governance
- Operationalizing and scaling technologies: Approaches to drive the adoption of AI and scale projects throughout the organization
- Use cases: Improving, planning and detecting faults through AI and intelligent, self-optimizing machines that automate production processes
Vanti is the only scalable AI product for Manufacturers.
Vanti’s SaaS solution enables manufacturers to identify, explain and prevent defects early in their production processes. Using data directly from the shop floor, to train, deploy and maintain ML-based models in production.
The self-service platform doesn’t require any data science knowledge and models can be trained and validated within just a day. So scaling from one line to the entire site or multiple locations is possible within weeks rather than years.