Responsible AI FAQs for copilot template for factory operations on Azure AI (preview)
Important
Some or all of this functionality is available as part of a preview release. The content and the functionality are subject to change.
Copilot template for factory operations on Azure AI (preview) is a starter solution in Microsoft Cloud for Manufacturing. It uses the capabilities of natural language interfaces to tackle manufacturing scenarios like resolving quality issues, conducting root cause analysis, and more. In this article, you find answers to some frequently asked questions (FAQs) about the copilot template for factory operations and how Microsoft uses your data responsibly.
What is copilot template for factory operations?
Copilot template for manufacturing offers a natural language interface that generates Domain Specific Language (DSL) queries, tabular results, and summaries related to the manufacturing Operations Management domain.
What can copilot template for factory operations do?
We have standardized our default schema using ISA95 industry entities (especially Person, Equipment, and Process). We preload sample metadata/data into our system for demo purposes. We also provide APIs allowing customers to create their own custom entities and ingest their own data. DSL queries are the primary way that customers retrieve data out of manufacturing data solutions. The AI copilot template takes a natural language question/command (for example, "How many employees are there?") and converts it into the equivalent DSL. The DSL is then run to retrieve the data and summarize the results.
What languages does copilot template for factory operations support?
Copilot template for factory operations is currently supported in English language only. Support for other languages becomes available based on market demand and the availability of Azure OpenAI in those regions. We plan to localize the solution to other languages where Azure Open AI is present and based on customer asks.
How can customers use copilot template for factory operation?
The primary purpose is to:
Simplify data queries: Copilot template for factory operations enables easy, SQL-free data queries through conversational UX, broadening data access across roles
Enhance responsiveness: Copilot template for factory operations acts as a crucial aid for managers in handling shop floor issues like quality problems or downtime, supporting efficient root cause analysis.
Streamlining communication: Copilot template for factory operations facilitates the summarization of analyses for cross-team sharing, improving collaboration and problem-solving speed.
This tool is essential for modern, agile manufacturing operations, promoting informed decision-making and team collaboration. It enables factory staff to perform root cause analysis (RCA) and improve overall equipment efficiency (OEE). They can ask questions in natural language to obtain data results, which can then be used to identify and rectify issues on the factory floor. Every output should undergo human review before use.
How was copilot template for factory operations evaluated? What metrics are used to measure performance?
We evaluated the system by running golden test queries for which we already have the DSL query. The data results for the actual DSL query were compared to the one generated using the copilot template for manufacturing. With these results, we computed a consolidated F1 score for the NL question based on Column and Row precision and recall.
What service does copilot template for factory operations use?
The copilot template uses the Azure Open AI service. We have a prompt engineering pipeline for the language models to intelligently construct a DSL based on the Natural Language Query ask.
Where is the data processed as part of the copilot template for factory operations?
The copilot template has all the data processing within the customer Azure tenant based on the managed-on behalf of model.
What are the limitations of copilot template for factory operations? How can users minimize the impact of these limitations when using the system?
A known limitation of copilot template for factory operations is lack of generalization. We supply golden queries (NL, DSL pairs) and documents to the system as examples in the prompt. If these don't provide adequate context to the query, copilot doesn't generate an appropriate response.
Users can mitigate this limitation by formulating their natural language queries more descriptively and supplementing them with relevant golden queries and detailed documents for better context.
What operational factors and settings allow customers to use copilot template for factory operations effectively and responsibly?
For an effective use, users should add relevant golden queries and detailed context documents.
Additionally, our workflow allows you to use copilot template effectively and responsibly. We check the intent of the question and abort if the question is unrelated to manufacturing. The query also aborts if it doesn't find any relevant entities.