Manufacturing ranks among the top three industries contributing the largest share to global big data and analytics revenues.
The applications of manufacturing analytics help empower businesses to predict machinery usage patterns, prevent equipment failures, forecast maintenance needs, and identify opportunities for process and performance improvements.
Check out 20 manufacturing analytics case studies to discover practical applications and strategies for implementation.
Top 20 manufacturing analytics case studies
| Vendor | Customer | Country | Industry | Use Case | Results |
|---|---|---|---|---|---|
Augury | Lindt | Global | Food & Beverage | Order management & risk management | Optimized production lines, reduced maintenance costs, supported engineers to make data-driven decisions, and enhanced efficiency and output. |
Augury | Roseburg | USA | Forest products | Asset management | Achieved 7x ROI in under a year, improved reliability, reduced downtime, and created the groundwork for digital transformation across all 15 facilities. |
Augury | N/A | USA | Infant formula manufacturing | Preventive maintenance, inventory management & automation | Detected $350K worth of system errors, identified opportunities for automating reliability practices, and enabled early part orders to avoid expedited shipping costs. |
Busroot | A1 Bacon | United Kingdom | Food & Beverage | Inventory management | Product output increased 14%, and packaging waste and machine idle time costs reduced. |
FogHorn | N/A | China | EV charger manufacturing | Predictive maintenance & asset management | Reduced false positive alerts from chargers and anticipated maintenance needs effectively. |
Konux | Deutsche Bahn | Germany | Railway infrastructure | Predictive maintenance & asset management | Reduced maintenance costs by 25% and minimized delay-causing failures. |
Mu Sigma | N/A | USA | Food & Beverage | Price optimization | Identified stores that could increase product pricing by $0.25 without impacting sales, resulting in a 2-3% revenue increase. |
Mu Sigma | N/A | USA | Energy | Inventory management | Optimized excess inventory, projected to save $15M-$20M annually. |
PTC | Colfax | USA | Mechanical & industrial engineering | Predictive maintenance & asset management | Reduced service costs via remote monitoring and predictive maintenance, detected patterns and anomalies in applications, and improved asset utilization. |
PTC | SIG | Switzerland | Packaging & containers | Asset management & optimization | Discovered micro-outages, identified energy overconsumption in machines, and increased production line speed. |
Rockwell Automation | Harvest Food Group | USA | Food & Beverage | Asset management | Achieved a cycle count accuracy improvement from 6% to 98%, reduced accounts receivable aging from 5% to 1.6%, and eliminated manual inventory checks. |
Rockwell Automation | The Maschhoffs | USA | Food & Beverage | Predictive analytics & automation | Improved space utilization and extended forecasting capabilities from one to five years through automated updates and modeling. |
Rockwell Automation | National Engineering Industries Limited (NEI) | India | Brownfield Factory | Predictive maintenance & risk management | Enhanced visibility into performance across line, shop floor, plant, and enterprise, created a connected smart plant integrating supply chain data, and avoided unplanned breakdowns with proactive actions. |
Rockwell Automation | Nói Síríus | Iceland | Food & Beverage | Predictive maintenance, asset management & optimization | Prioritized job queues, allowing engineers to focus on performance optimization and reduced unplanned callouts through intelligent job management |
Rockwell Automation | N/A | USA | Polymer & coating materials | Asset management & product development | Achieved a 12% production increase, a 6% reduction in natural gas use per ton, and a 50% decrease in process variability. |
SAS | Kia Motors | USA | Automotives | Asset management, product development, predictive maintenance & end user experience estimation | Forecasted failure rates and maintenance costs, reduced production time, extracted complaint categories to address quality issues, and alerted dealers to optimize future repairs. |
SAS | Siemens Healthineers | Germany | Medical technology | Predictive maintenance | Predicted product failure probabilities, reduced system downtime by 36%, and optimized service technician deployment with advanced parts forecasting. |
Sight Machine | N/A | USA | Paper manufacturing | Predictive maintenance & asset management | Identified hidden relationships between production parameters and quality issues, and created a digital twin of the production process. |
Sight Machine | N/A | USA | Automotives | Asset management & risk management | Built manufacturing data models and implemented Root Cause Analysis (RCA) to uncover the source of quality issues. |
Sight Machine | N/A | USA | Industrial manufacturing | Asset management | Identified $500K in potential savings within three weeks and reduced scrap costs by 30%. |
To explore the manufacturing analytics vendor landscape, check out manufacturing analytics software and vendors.
What are the top use cases of manufacturing analytics?
Manufacturing data, including machine and operator information, can be processed and analyzed for:
Supply chain
- Demand forecasting: Predicts future product demand using data to optimize production and inventory.
- Inventory/asset management: Tracks and optimizes the use of assets and inventory to reduce costs and prevent overstocking or stock outs.
- Order management: Manages customer orders to ensure accuracy and timeliness.
- Maintenance optimization: Uses predictive analytics to schedule equipment maintenance and minimize downtime.
- Risk management: Identifies and mitigates potential disruptions in the supply chain.
Logistics
- Automation and robotics: Improves efficiency and accuracy in manufacturing and warehouse operations through robotics and automated processes.
- Transportation allocation: Optimizes the assignment of transport resources to reduce costs and improve delivery times.
Product development
- Product progress measurement: Tracks the development stages of a product to ensure timelines and quality standards are met.
- End-user experience estimation: Evaluates how a product will meet customer needs and expectations based on data.
Sales
- Price optimization: Uses analytics to determine the best pricing strategies to maximize revenue and market competitiveness.
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