Stanley Black & Decker scales AI solution with flexible industrial data infrastructure

Stanley Black & Decker is a leading supplier of automotive fastening solutions such as riveting and spot welding. The Stanley data team has built an AI solution to predict potential failures and improve operational efficiency for its fastening equipment. This solution takes data in from equipment on the plant floor and uses AI to generate world-class predictive maintenance results.

When Stanley began to scale its AI solution, one of the key challenges was to scale its edge data pipelines. Since data is collected directly from automotive assembly lines, Stanley has to meet the stringent IT requirements from its customers, work with various different network protocols, overcome unreliable and frequently-changing data schemas, and support up to 5000 fastening robots per plant. Stanley partners with Prescient to build a flexible and scalable edge data infrastructure.

Prescient’s distributed low-code data software collects, cleanses, validates, transforms, and engineers data from Stanley customer’s fastening equipment directly at the factory floor. Prescient has extensive experience working with edge data and on-premise IT requirements, and it enables Stanley to install production-grade edge data pipeline at new sites within one week. Prescient’s solution offers the flexibility to support nearly any network protocol or data schema, which is well-suited for automotive customers who have very different network and data requirements.

"Prescient knows what it takes to unlock analytics and AI at the edge.” says Dr. Amir Kashani, Director of AI and Digital Product Development at Stanley Black & Decker, “They are a major enabler for discovering insights from complex, distributed data at the edge which can take every product to its next level.”