Manufacturers’ device and operational data can provide important insights into how executives can improve their business operations.
Data analytics tools examine information and then highlight important insights for business leaders, such as patterns in the data. Companies can use manufacturing data to improve assembly line performance, reduce organizational waste, and increase sales.
Learn more about how manufacturers can leverage the important information from data analytics to succeed in the industry.
4 Use Cases for Data Analytics in Manufacturing
Data analytics has many applications in manufacturing. Here are some of them.
1. Equipment maintenance
The failure of one piece of equipment can quickly bring an entire production line to a halt, so preventative maintenance is essential.
Predictive analytics can be used to examine data from manufacturing machines to determine if equipment failure is likely. Engineering teams can then address any equipment issues to prevent them before they occur. Combining equipment analytics with a maintenance plan helps ensure that a production line’s technology continues to function.
2. Quality control
Manufacturing facilities have built-in sensors that identify products with potential defects. Suppliers can use data analytics to set baselines for products and ensure the system rejects items that do not meet these thresholds.
Establishing these core values will improve quality control and reduce the risk of selling defective products to customers.
3. Production line planning
Manufacturers must respond quickly to market changes. When customer demand increases, production lines must be ramped up quickly to keep inventories full.
Data analytics helps manufacturers stay on top of the latest trends. Forecasting models can predict customer demand and ensure that manufacturers have enough employees and raw materials to meet that demand.
The analysis tools can optimize product availability and increase sales.
4. Inventory planning
Keeping up with customer demand is crucial, but manufacturers also cannot build up excessive inventory.
Data analytics can be used to track sales and inventory levels throughout the supply chain and adjust replenishment levels based on this information. By enabling these features, manufacturers can avoid tying up too much capital in inventory and running production lines unnecessarily.
5 Benefits of Data Analytics in Manufacturing
Using data analytics in manufacturing can help companies improve their operations in a number of ways. Here are some of those benefits.
1. Shortening time to market
Data analytics can help identify trends in customer purchases. Manufacturers can use this information to quickly design, prototype and build new products.
In any industry, it is important to bring new products to market before the competition. Analytics allows manufacturers to learn about new buying habits more quickly and respond accordingly.
2. Improved prototyping and testing
Analytics can help manufacturers with prototyping because the data can provide insight into what features customers will value most, for example, based on sales data. Analytics can also use financial data to estimate the maximum amount employees can spend on product creation.
Once employees have created a product prototype and presented it to customer focus groups, analytics can summarize the most helpful feedback from the groups and share it with employees so they can act on customer reviews.
3. Reduced risk
Many types of products must meet strict manufacturing requirements. Product recalls can be expensive, and faulty manufacturing can subject a company to legal penalties or fines.
Data analytics can help manufacturers comply with health and safety regulations and other regulations by uncovering problems before products are sold to customers.
4. Optimal staffing
Employees are needed to keep production lines running smoothly. Using data analytics, manufacturers can predict the number of employees needed and avoid staff shortages.
These features are especially important as a manufacturer approaches its peak season, as companies often temporarily increase their workforce and work shifts to meet increased demand. Analytics can provide insight into how many employees should best be scheduled for each shift based on past staffing levels and product requirements.
5. Plan for the future
Manufacturing data can also help business leaders develop long-term strategies and business goals.
Analytics can provide insights into the market and its trends and enable manufacturing leaders to make plans for the coming years based on sales goals and customer demand.
Paul Maplesden creates comprehensive guides on business, finance and technology topics and has expertise in supply chain and SaaS platforms.
