What factors are causing the increased error rates in Ness Digital Engineering's AI-powered predictive maintenance solution for manufacturing clients?

Introduction

Increased error rates in Ness Digital Engineering's AI-powered predictive maintenance solution for manufacturing clients pose a significant challenge to the product's effectiveness and customer satisfaction. To address this issue, I'll employ a systematic approach to identify, validate, and resolve the root cause while considering both immediate and long-term implications.

Step 1

Clarifying Questions (3 minutes)

• I'm noticing the focus on "increased" error rates. Has there been a sudden spike or a gradual increase over time?

Why it matters: This helps determine if it's a recent change or a long-term trend. Expected answer: A sudden spike in the last month. Impact on approach: A sudden spike would suggest a recent change or event as the cause.

• Given the AI-powered nature of the solution, I'm wondering about the training data. Have there been any recent changes to the data sources or models used?

Why it matters: AI performance is heavily dependent on its training data and models. Expected answer: No recent changes to data sources, but a model update was pushed last quarter. Impact on approach: This would focus our investigation on the recent model update and its potential effects.

• Considering the manufacturing context, are these errors occurring across all types of equipment or specific to certain machinery?

Why it matters: This helps narrow down whether the issue is systemic or specific to certain use cases. Expected answer: Errors are more prevalent in newer, more complex machinery. Impact on approach: This would guide us to investigate how the solution interacts with newer, more complex systems.

• I'm curious about the error detection process. Are these errors being caught by the system itself or reported by clients?

Why it matters: This helps understand if it's a performance issue or a perception issue. Expected answer: A mix of both, but more client reports recently. Impact on approach: This would suggest investigating both system performance and client communication/training.