01
The modules provide data, but each analysis must be started manually
OEE, notifications, statuses, processes, and parts histories contain relevant information that must be reviewed individually as part of day-to-day operations.
Manufacturing Insights · AI Module
Automatically analyze production data. Identify relevant issues first.
The Manufacturing Analytics Copilot analyzes event, quality, and process data, prioritizes notable trends, and processes them using AI. Employees save time that would otherwise be spent troubleshooting and can use that time to focus on targeted improvements.
- Autonomous Module Analysis
- AI-Generated Data Stories
- Context-Aware Analysis Chat
Manufacturing Insights Add-on
Dieses Modul beantwortet eine konkrete Produktionsfrage auf der gemeinsamen industriellen Datenbasis.
Time lost before the actual decision is made
If valuable module data is available but no one has time to check it constantly
Copilot handles the routine search for relevant anomalies and presents prioritized investigative approaches to the team.
02
Important issues are overshadowed by the daily flood of data
Employees waste time searching and often don't notice recurring discrepancies until they've already had a significant impact.
03
Insights reach the right people too late
Without automatic processing, samples remain hidden in analysis views or must be explained again for every meeting.
Automated Analysis with Expert Review
From Insights Modules to Prioritized and Verifiable Anomalies
Copilot automates analysis and data preparation. The decision regarding the cause and the appropriate action remains transparently with the team.
- 01
Merge Module Data
Copilot uses the structured plant and analysis context from the activated Manufacturing Insights modules.
- 02
Conduct an AI-assisted investigation
It checks data for relevant anomalies, patterns, and correlations without requiring each analysis to be initiated manually.
- 03
Prioritizing Problems
Anomalies are organized by relevance and presented along with supporting data and visualizations.
- 04
Track
Actions are derived from the deviations found, and problems are resolved. A self-learning system takes feedback into account for the future.
Automatically identify. Present clearly. Explore together.
We view the Manufacturing Analytics Copilot as an assistant that lightens the load for production staff by flagging potential problems early on.
Which anomalies should your team receive automatically prioritized?
Automatic Analysis
Identify relevant anomalies without having to search through each module individually
Copilot analyzes the data provided by production analysis, message analysis, status analysis, process analysis, and product tracking within the context of the plant. It looks for unusual trends and recurring patterns.
OEE · Alerts · Statuses · Processes · Part Histories
Autonomy with Clear Responsibilities
Automate more of the analysis process. Ensure that production decisions remain transparent.
The innovative core lies not in yet another chat interface, but in the proactive analysis of the existing production context and its verifiable presentation.
Automated Analysis with Visible Supporting Documents
The Copilot can analyze and prioritize data on its own. The resulting data story shows which observations and contexts led to the conclusions drawn.
Prioritization Instead of Autonomous Production Decisions
The module saves time spent on searching and processing data. The responsible teams retain the authority to confirm the cause and decide on appropriate actions.
A common context across all modules
Copilot, Insights modules, Chat, and Data Stories all operate on the same system, data, and permissions infrastructure.
Autonomy and Control
What the copilot handles on its own—and what remains the team's responsibility
What exactly does “automatic analysis” mean?
Copilot can analyze the existing, structured data from the Insights modules for relevant anomalies and patterns without requiring individual user input, and prepare the results as prioritized data stories.
Does the copilot make production decisions on its own?
No. He prioritizes the need for investigation and prepares supporting documentation. The technical assessment of causes, approval, and implementation of corrective actions remain the responsibility of Production, Quality, or Maintenance.
What data can the Copilot analyze?
It utilizes the available equipment and data context from the activated Manufacturing Insights modules, such as OEE and production data, notifications, machine statuses, process paths, and part histories.
Why is a chat feature needed on top of that?
Data Stories proactively highlight prioritized anomalies. The chat feature then allows users to ask follow-up questions, narrow down the results further, and access linked analyses directly.
Start with a clear question about priorities
A limited pilot demonstrates how much time the autonomous analysis saves on searching and processing, and how relevant its data stories are to the team.
- 01Focus
Select a module and a prioritization question
Specify which existing analytics data the Copilot should examine and which type of anomaly it should prioritize first.
- 02Basis
Check the Data and Asset Context
Relevant OEE, notification, status, process, or tracking data is checked for completeness and technical relevance.
- 03Pilot
Testing Data Stories and Chat in Everyday Life
The team reviews automatically generated alerts, asks follow-up questions, and verifies the associated supporting documents.
- 04Scaling
Evaluate Time Savings and the Quality of Prioritization
The search effort, the relevance of the results, and the time required to reach a technical decision are compared before any expansion is undertaken.
30-minute product demo
Which production issues should Copilot prioritize for you?
We'll demonstrate live how the Copilot autonomously analyzes existing Insights data, presents relevant findings as data stories, and answers questions via chat.
Product demo featuring a specific prioritization question