Optimizing Operations with Copilot for Enterprise
CLIENT
A global manufacturing group with more than 3,000 employees and multiple production sites across the United States. The company supplies precision components to leading OEMs and operates under constant pressure to maximize throughput, reduce downtime, and meet strict quality requirements.
CHALLENGE
Operations teams faced data overload.
Machine sensors, ERP reports, maintenance tickets, and supplier communications were scattered across systems. Even simple questions — “Which machines have repeat issues this month?” — required hours of manual checks across spreadsheets and emails.
Plant managers lacked a unified view of production performance and bottlenecks. Engineers lost time compiling reports instead of solving problems. Traditional BI dashboards demanded heavy setup and rarely reflected real-time floor conditions.
Hiring more analysts wasn’t viable, and outsourcing sensitive production data was off the table.
SOLUTION
- The company turned to REW Technology in late 2024 to explore a new approach.
At first, the expectation was that we would recommend more conventional fixes — expanding BI dashboards, hiring data analysts, or building additional reporting layers. But in practice, these options carried high costs, steep learning curves, and wouldn’t solve the day-to-day bottlenecks.
Instead, our team worked through the problem step by step:
We mapped the entire workflow of how operational data moved — from machine sensors, into ERP, into maintenance systems, and finally into reports.
We estimated the cost impact of different approaches: custom AI agents built from scratch, generic workflow automation platforms, or embedded assistants within existing tools.
We compared options for language models and security architectures, balancing accuracy with budget.
The analysis showed that building custom AI agents or rolling out a separate automation platform would increase complexity for the client’s staff and create long onboarding timelines.
By contrast, Copilot for Enterprise provided a way to personalize AI assistance while keeping it inside the systems employees already used — Teams, Outlook, and the ERP. The client’s biggest concern was security, so the deciding factor was that Copilot could run on Azure OpenAI Service fully within their tenant. No sensitive production or supplier data ever left their environment.
In short: Copilot offered the most practical balance of cost control, employee adoption, and compliance.
- Microsoft Teams — the single interface where employees already chat, share files, and coordinate. By embedding Copilot here, managers didn’t have to learn a new platform. They could ask questions in plain English and get data back instantly
- ERP system (SAP) — the backbone for production orders, procurement, and inventory. Copilot tapped into this system to answer questions like “How many parts are waiting for inspection?” without waiting for a monthly report.
- Real-time sensor analytics (Splunk) — machines constantly generate logs: temperature spikes, pressure readings, downtime alerts. Normally these flood engineers’ inboxes. Copilot filtered and summarized them so managers saw only the important anomalies.
- Maintenance system (ServiceNow) — every repair ticket and fault report lived here. By linking it, Copilot could show unresolved issues, track delays, and prevent bottlenecks.
- Azure OpenAI Service — this was the engine interpreting natural language. Instead of engineers writing SQL queries or digging into reports, they could just ask questions in plain English. Everything ran inside the client’s cloud, respecting strict compliance.
- Azure API Management — acted like a secure “traffic controller,” making sure data flowed safely between systems without exposing it externally.
- Microsoft Power Automate — handled repetitive workflows: if a machine fault wasn’t addressed within 24 hours, Copilot could automatically escalate it. This cut down manual chasing via emails.
- Azure Active Directory — ensured that only the right people could access the right data. For example, a plant supervisor could see machine health, but not financial forecasts.
RESULT
Within two months of deployment:
Over 1,800 queries per month answered directly inside Teams.
50% faster access to cross-system operational insights.
12% reduction in unplanned downtime from quicker fault detection.
35% fewer unresolved tickets older than two days.
25% productivity lift on critical lines.
Significant reduction in manual spreadsheets and status meetings.
Internal surveys showed a 40% increase in manager satisfaction with data accessibility. Finance projected more than $300,000 annual savings from efficiency gains alone.
All results are based on ERP and maintenance system data plus internal surveys (Q3 2025).
Interested in learning how Copilot can streamline operations in complex enterprise environments? Contact us through our website — we’ll walk you through the architecture and deployment steps.
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