Getty Images/iStockphoto
Importance of data analytics in manufacturing and use cases
Data analytics can help manufacturing leaders make more informed decisions when planning for the industry's future. Learn about analytics' importance.
Manufacturers' equipment and operations data can provide crucial insights into how leaders can improve company processes.
Data analytics tools examine information and then highlight important findings for leaders, like patterns in the data. Companies can use manufacturing data to improve assembly line performance, reduce organizational waste and improve revenue.
Learn more about how manufacturers can use the important information from data analytics to succeed in the industry.
4 use cases for data analytics in manufacturing
Data analytics has various applications in manufacturing. Here are several.
1. Equipment maintenance
Failure of any equipment can quickly bring an entire production line to a halt, so preventative maintenance is essential.
Predictive analytics can examine manufacturing machines' data to discover whether equipment is likely to break down. Engineering teams can then address any issues with the equipment to prevent problems before they happen. Combining equipment analytics with a maintenance schedule helps ensure the technology on a production line continues to work.
2. Quality control
Manufacturing equipment includes built-in sensors that identify products with potential defects. Suppliers can use data analytics to set baselines for products and ensure the system will reject items that fail to meet those thresholds.
Setting these baselines improves quality control and reduces the risk of defective products being sold to customers.
3. Production line planning
Manufacturers must react quickly to changes in the marketplace. When customer demand grows, production lines must ramp up quickly so inventories stay stocked.
Data analytics helps manufacturers stay on top of developments. Forecasting models can predict customer demand and ensure that manufacturers have allotted enough employees and raw materials to meet that demand.
The analytics tools can optimize product availability and boost revenue.
4. Inventory planning
Keeping up with customer demand is vital, but manufacturers must also avoid producing too much stock.
Data analytics can track sales and inventory levels across the supply chain and adjust replenishment levels based on this information. Enabling these capabilities will help manufacturers avoid tying up too much capital in inventory and running production lines unnecessarily.
5 benefits of using data analytics in manufacturing
Using data analytics in manufacturing can help companies improve their operations in various ways. Here are some of those benefits.
1. Reduced time-to-market
Data analytics can identify trends in customer purchases. Manufacturers can use that information to quickly design, prototype and build new products.
Launching new products ahead of competitors is important in any industry. Analytics can help manufacturers learn about new buying patterns more quickly so they can act on them.
2. Improved prototyping and testing
Analytics can help manufacturers with prototyping because the data can give insight into, for example, the features customers will value the most by drawing on sales data. Analytics can also use financial data to estimate the maximum amount of money that employees can spend on product creation.
Once employees have created a product prototype and presented it to customer focus groups, analytics can synthesize the most helpful feedback from the groups and share it with employees so workers can act on the customer evaluations.
3. Reduced risk
Many types of products must meet stringent manufacturing requirements. Product recalls can be expensive, and faulty manufacturing can expose a company to legal penalties or fines.
Data analytics can help manufacturers comply with health, safety and other frameworks by flagging issues before the products are sold to customers.
4. Optimum staffing levels
Production lines rely on employees to run smoothly. Data analytics can help manufacturers predict the number of employees they will need so the company avoids a workforce shortage.
These capabilities are especially important as a manufacturer nears its peak season, as companies often temporarily increase their workforce and labor shifts to meet increased demand. Analytics can provide insight into the best number of employees to schedule for each shift based on past staffing levels and product requirements.
5. Plan for the future
Manufacturing data can also help organizational leaders create long-term strategies and business goals.
Analytics can give insight into the marketplace and its trends, enabling manufacturing leaders to create plans for coming years based on revenue targets and customer demand.
Paul Maplesden creates comprehensive guides on business, finance and technology topics, with expertise in supply chain and SaaS platforms.
