Inefficiencies and unplanned downtime can cost companies millions—automotive manufacturers alone lose over $2 million for every hour of unproductive time.1 Manufacturing analytics solutions help address these issues by turning machine and process data into actionable insights.
Check out the top 10 manufacturing analytics use cases that support digital transformation and align with Industry 4.0 goals such as automation, predictive maintenance, and supply chain optimization.
Top 10 manufacturing analytics use cases
Manufacturing generates data from machines (e.g., robotics, sensors) and systems (e.g., ERP, logistics).
This data can be collected, and analytics can be applied to it for:
Supply chain
| Use Case | Description | Example Usage |
|---|---|---|
| Demand Forecasting | Predict future product demand using historical data. | A consumer electronics firm forecasts increased headphone sales before holidays. |
| Inventory Management | Optimize stock levels and turnover using key inventory metrics. | A retailer identifies slow-moving items to reduce overstock. |
| Order Management | Plan production and inventory based on predicted demand. | An appliance maker adjusts production to fulfill a spike in pre-orders. |
| Maintenance Optimization | Predict and schedule equipment maintenance to avoid downtime. | A factory uses sensor data to service machines before failures occur. |
| Risk Management | Detect anomalies and plan for operational risks using analytics. | A plant spots abnormal heat in equipment, preventing a breakdown. |
1. Demand forecasting
Demand forecasting relies heavily on historical data about supply levels, material costs, purchase trends, and customer behavior. Manufacturers can leverage analytics to:
- Define the types of products to be manufactured in a certain period.
- Identify out-of-stock products.
- Calculate the number of products to be manufactured.
- Forecast sales opportunities.
2. Inventory management
Forecasting demands enable manufacturers to manage their inventory, purchase materials, and optimize storage capacities. Analytics also provides insights about:
- Sales-to-inventory ratio (represents the average inventory over the net sales).
- Days in inventory (the number of days a manufacturer holds their product before selling it).
- Gross margin return on inventory (GMROI) indicates how much gross margin a manufacturer gets back for each dollar invested in inventory.
3. Order management
Manufacturers can leverage predictive analytics to optimize the order management workflow.
This process includes identifying in-demand products, calculating the time required to build and ship each one, and defining the inventory needed to meet the demand for the finished product.
4. Maintenance optimization
Maintenance optimization with analytics helps manufacturers minimize unplanned downtime and reduce repair costs. By analyzing data from machines and operations, businesses can:
- Predict maintenance needs based on usage patterns and machine workloads.
- Detect anomalies in real time to prevent failures before they occur.
- Schedule planned maintenance effectively to avoid disrupting production timelines.
5. Risk management
Implementing manufacturing analytics helps businesses to manage risks:
- Determine recurring errors and prevent repetitive losses: Manufacturing analytics can analyze historical data to identify recurring inefficiencies and provide proactive solutions to reduce downtime.
- Predict insurance needs: Data insights help forecast risks like equipment failure or accidents, guiding businesses to optimize their insurance coverage.
- Monitor machinery and operator work: Real-time tracking allows businesses to monitor machinery performance and worker activities, ensuring prompt responses to potential issues.
- Identify real-time fails and system anomalies: Analytics can detect deviations from normal operations, such as machinery malfunctions, to prevent significant failures or accidents.
- Plan risk management strategies: Analytics insights enable businesses to create risk management plans, addressing vulnerabilities and ensuring operational resilience.
Sales
6. Price optimization
Leveraging analytics can help manufacturers understand the real price of a product based on the prices of materials, the cost of operations, and the machines and tools used or purchased for manufacturing.
Additionally, manufacturers can leverage data about competitors, marketing automation trends, consumer behavior, and purchase history to optimize prices accordingly.
Analytics can also help set dynamic prices based on demand, supply, competition prices, and subsidiary product prices.
Example: A tire manufacturer adjusts prices weekly based on rubber costs and competitors.
Logistics
| Use Case | Description | Example Usage |
|---|---|---|
| Automation and Robotics | Find automation opportunities by analyzing production time and cost. | A beverage firm installs robots after finding high packaging labor costs. |
| Transportation Allocation | Plan vehicle use and delivery routes using historical and real-time data. | A logistics team reroutes trucks due to sudden road construction. |
7. Automation and robotics
Analytics can provide an overall view of a manufacturing process, operation costs, and the number of operators and hours spent on a product.
Large manufacturing firms can leverage these analyses to uncover automation or robotization opportunities, which can reduce the time and cost of launching certain products.
Check out to learn more about logistics and automation.
8. Transportation allocation
Manufacturers can leverage analytics on:
- Historical data: This data is used to predict transportation time and vehicle requirements to deliver products to businesses or consumers.
- Real-time data: This data is used to analyze the impact of unplanned transportation events such as labor strikes or road works.
Product development
| Use Case | Description | Example Usage |
|---|---|---|
| Product Progress Measurement | Estimate timelines and spot delays in product manufacturing. | A smartphone firm adjusts launch due to chipset delays. |
| End User Experience Estimation | Analyze feedback and behavior to improve product features and timing. | A startup delays launch to add voice control after user feedback. |
9. Product progress measurement
Analytics can estimate the production process and product launch timeline based on historical data about similar products, materials, machines, tools, and allocated production employees.
It can also identify potential errors or pitfalls and create a roadmap for subsequent procedures.
10. End user experience estimation
Product development teams can leverage analytics on product features, consumer behavior, and comments on online platforms, as well as competitor products. The aim is to estimate why end users buy certain products, when to launch similar products, and which features require optimization.
What other technologies are used in manufacturing?
Some of the technologies leveraged today by manufacturers include:
Robotic process automation (RPA)
RPA is a type of software capable of replicating human interactions with computers in order to automate repetitive processes.
Manufacturers can leverage RPA for supply chain management and stock optimization.
Check out the benefits and use cases of RPA in manufacturing for more.
Artificial intelligence
AI in manufacturing has numerous applications, including:
- Digital twins and digital twin of an organization: Creating virtual replicas of physical assets or entire organizations to simulate, monitor, and optimize performance in real time.
- Augmented reality: Integrating digital information into physical environments to assist in assembly, maintenance, and training tasks.
- Demand forecasting: Analyzing historical data and market trends to predict future customer demand accurately, optimizing inventory and production.
- Generative design: Leveraging AI algorithms to explore multiple design iterations based on specific constraints, creating innovative and efficient manufacturing solutions.
- Quality assurance: Using image recognition and data analysis to detect defects, ensure compliance, and maintain product standards.
- Process optimization: Optimizing manufacturing processes by analyzing data to identify inefficiencies.
- Intelligent automation: Integrating AI, machine learning, robotics, and IoT to automate end-to-end processes, enhancing efficiency, quality, and adaptability in production operations.
What is manufacturing analytics?
Manufacturing analytics is the practice of capturing, cleansing, and analyzing machine data in order to predict their future use, prevent failures, forecast maintenance requirements, and identify areas for improvement. The aim is to improve efficiency, automate traditional manufacturing processes, and reduce costs.
Manufacturing data includes all structured and unstructured information collected manually or by using software from machines and humans during every stage of production until a product is launched on the market.
Figure 1: Big data analytics for manufacturing processes.2
Comments
Your email address will not be published. All fields are required.