Industry 4.0 and the Industrial Internet of Things (IIoT) are driving digital transformation in manufacturing. Manufacturing organizations are now using analytics generated from real-time production data to make better, faster decisions and drive automation across the enterprise.
Industrial equipment and machinery connected with sensors and edge devices generate large volumes of data and send it to cloud-based analytics systems that can provide actionable insights, improve decision-making, and drive significant process improvement. What are the top benefits and objectives of manufacturing analytics, and how can it be applied on your shop floor, warehouse, or factory?
What is Manufacturing Analytics?
Manufacturing analytics uses machine, operational, and system data to manage and optimize production, including critical functions such as planning, quality, and maintenance.
Manufacturers are still using traditional, fragmented methods for collecting data, with employees manually collecting and recording data for the machines on the factory floor. These methods are usually time-consuming, error-prone, and prone to bias. Connected sensors and devices can reduce manual data collection and documentation labor. And with the advent of advanced analytics software and algorithms, you can deliver actionable, real-time insights.
Automated data collection is driving advanced analytics in manufacturing, unlocking several use cases that range from simple monitoring and health checks to predictive maintenance and process automation.
Top 4 Manufacturing Analytics Use Cases
Real-time production data is transforming the manufacturing industry and enabling several advanced use cases:
1. Fault Prediction and Predictive Maintenance
Preventive maintenance has been around in the manufacturing industry for decades. Unplanned breakdowns are less likely to occur when you use manufacturing analytics for preventive maintenance. You can leverage real-time analytics to do more than prevent breakdowns. You can accurately predict the likelihood of a breakdown and the moment it will occur, which is called predictive maintenance. Technicians can perform repairs at the optimal time and source spare parts in advance, reducing overall downtime, increasing productivity, and reducing costs.
2. Inventory Management and Demand Forecasting
Complete control of the manufacturing supply chain allows for better inventory management. Therefore demand forecasting becomes critical for manufacturers, but it can be very complex. End-to-end supply chain insights can be used in conjunction with real-time shop floor data to manage purchasing, inventory, and logistics. You can generate accurate demand forecasts that identify trends that would go unnoticed previously.
With a better understanding of the time and effort involved in developing products, scheduling production runs, and achieving profitability, manufacturers can make more accurate estimates of the raw materials required to optimize production.
3. Price Optimization
Cycle times play a significant role in product pricing. Calculating the precise time for product development and the associated costs allows for accurate cost models and optimized pricing strategies. Setting prices too low reduces profitability, while setting them too high could affect demand. Manufacturing analytics can help you ensure that prices are set correctly and help manufacturers optimize their production standards to ensure accurate cycle times.
4. Warranty Analysis
Warranties often follow a one-size-fits-all approach that brings uncertainty and presents unexpected challenges. You can collect information from active warranties and use manufacturing analytics to make more informed decisions regarding improving, changing, or introducing new products, thus minimizing the possibility of failure and reducing costs.