IDC projects that the global big-data analytics market revenue will exceed $274B in 2022. Manufacturing will be the industry with the highest growth rate for analytics. Manufacturing analytics are essential steps in manufacturing’s digital transformation. This is required to support the fourth industrial revolution, industry 4.0. It aims at automating traditional manufacturing processes, reducing costs, and improving efficiency.
What is manufacturing analytics?
Manufacturing analytics is the art of capturing, cleaning, and analyzing machine data to forecast their future use, predict failures, identify areas for improvement, and forecast maintenance needs. Manufacturing data is all information, both structured and unstructured, that has been collected by machines or humans from every stage of production to the point when a product goes on sale.
What are the use cases of manufacturing analytics?
The data generated by manufacturing processes is large.
Machines: robotics, sensors, actuators, IoT devices, etc.
Operators: ERP, sales, logistics, etc.
These data can be collected, and analytics can then be applied to them.
Demand Forecasting is heavily based on historical data regarding supply levels, material prices, purchase trends, and customer behavior. Analytics can be used by manufacturers to:
- Define the products that will be manufactured during a given time
- Identify out-of-stock products
- Calculate the product number to be produced
- Forecast sales opportunities
Forecasting can help manufacturers manage their inventory, buy materials, and optimize storage capacity in a data-driven way. Analytics provides insight about:
- The ratio of sales to inventory, which is the average inventory over net sales
- Days in inventory (the number of days that a manufacturer keeps their product before they sell it)
- Gross margin return on inventory (GMROI), which shows how much gross margin a manufacturer receives for every dollar it invests in inventory.
Predictive analytics can be used by manufacturers to improve order management. This includes identifying the products that are in high demand, calculating the time it takes to make and ship each product and determining the inventory required to meet the demand.
To analyze data from various manufacturing machines, tools, and devices as well as data about operations, and the machines they require, it is possible to:
- Based on the time spent using it and the operations performed, you can predict when your machine will need maintenance.
- Detect anomalies that are related to or could result from machine failure.
- Plan for machine repairs, replacements, and fixes to prevent downtime.
Analytics allows manufacturers to monitor and manage their risks using data.
- Prevent repeated losses by identifying recurring mistakes
- Predict your insurance needs
- Monitor operator and machinery work in real-time
- Identify system anomalies and real-time failures
- Plan risk management strategies
Automation and robotics
Analytics can give a complete view of the manufacturing process, costs, and labor hours. These analyses can be used by large manufacturing companies to identify automation and robotization opportunities that can help reduce the time and costs of launching products.
Manufacturers can use Analytics to:
Historical data: To predict transportation time and vehicle needs to deliver products to consumers or businesses.
Real-time data: To analyze the effects of unplanned transport events, such as strikes or roadworks,
Product progress measurement
Based on historical data regarding the same or similar products, machines, and tools, as well as the allocated workers for production. Analytics can give an estimate of the production process, the time it will take to launch the product, any errors or pitfalls, and create a plan for the next steps.
End user experience estimation
Analytics can be used by product development teams to analyze consumer behavior, product features, comments on online platforms, and competitor products to determine why customers buy certain products and when similar products will be launched.
Analytics can be used to help manufacturers determine the true price of a product. This is based on the cost of raw materials, the cost of manufacturing, and the tools that were purchased or used in manufacturing. To optimize prices, manufacturers can also use data on competitors, market trends, and consumer behavior to determine the best price. Analytics can be used to help determine dynamic prices based on supply and demand, the price of the competition, and prices for subsidiary products.
Also read: 9 Best Tools Help in Supply Chain Management
What other technologies are used in manufacturing?
Manufacturers are using some of these technologies today:
Robotic process automation (RPA)
RPA software is capable of replicating human interactions and automating repetitive tasks. RPA can be used by manufacturers to optimize stock and supply chain management.
AI can be used in many areas of manufacturing, including:
- Digital twins and twins of an organization
- Augmented reality
- Demand Forecasting
- Generative design
- High-quality assurance
- Process optimization