Key results
50+ hours/week saved
by automating plant and inventory reporting
22% reduction
in unplanned downtime with predictive maintenance alerts
18% improvement
in production schedule adherence
15%
better demand forecasting accuracy, leading to optimized procurement
About the Client
The client is a U.S.-based manufacturer of industrial machinery and components, operating multiple facilities across three states. They lacked real-time production insights and relied on siloed systems that delayed decisions and increased inefficiencies.
Key Challenges
Disconnected data across Manufacturing Execution Systems (MES), ERP, inventory, and HR platforms
No visibility into real-time KPIs for production, downtime, or procurement
Manual Excel-based reporting made insights error-prone and time-consuming
Our Approach
- Centralized data from MES, ERP, and procurement into Snowflake on AWS
- Developed Python-based ETL pipelines and structured data models
- Built actionable Tableau dashboards for production, inventory, maintenance, and workforce
- Deployed Python-based ML models for demand forecasting and predictive maintenance
Implemented Solution
Data Engineering & Infrastructure on Snowflake
- Integrated MES, ERP, procurement, and workforce data using custom Python ETL
- Designed modular data marts with daily refresh and validation layers
- Applied anomaly detection for broken feeds and threshold breaches
Manufacturing Intelligence
- Production Analytics
- Monitor utilization rates, shift performance, and bottlenecks
- Analyze planned vs. actual production in real time
- Track operator efficiency and machine-level output
- Monitor utilization rates, shift performance, and bottlenecks
- Inventory & Procurement Analytics
- Visualize stock aging, reorder levels, and obsolete inventory
- Track supplier performance and procurement trends
- Identify material planning gaps using real-time data
- Visualize stock aging, reorder levels, and obsolete inventory
- Maintenance Insights
- Analyze Mean Time to Repair (MTTR) and Mean Time Between Failures (MTBF)
- Schedule preventive maintenance and monitor overdue work orders
- Set alerting rules for asset failures and delays
- Analyze Mean Time to Repair (MTTR) and Mean Time Between Failures (MTBF)
- Workforce Intelligence
- View absenteeism, overtime, and productivity by department
- Monitor training status, tenure, and labor allocation
- Track retention trends and workforce efficiency metrics
- View absenteeism, overtime, and productivity by department
Predictive Modelling
- Built a predictive maintenance model using historical failure logs and runtime behavior
- Developed a demand forecasting model using time-series production and order data
- Created a workforce attrition model to flag high-risk segments based on tenure, performance, and overtime
Technologies Used
No items found.
Maximize uptime and efficiency with predictive maintenance and real-time manufacturing analytics.
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