6 use cases for big data in manufacturing

1. Demand forecasting

Every manufacturer must balance supply and demand, and demand forecasting enables supply chain leaders to predict the amount of items that their company will need to produce in the future to meet customer needs.

Demand forecasting relies on these types of big data:

• Consumer behaviors. Consumer behavior data reveals whether customers are changing their spending and consumption habits.
• Marketplace trends. Marketplace trends are the overall factors affecting the marketplace, such as competitors or economic changes.
• Historical sales data. Historical sales data is information about the performance of product lines, including numbers sold, revenue and profit margins.
• Marketing and promotional activities. Marketing and promotional activities data includes changing sales volumes due to promotional efforts, peak seasons and similar factors.

Demand forecasting helps optimize inventory levels, maximize product availability and reduce waste.

2. Supply chain optimization

Manufacturers are just one part of a complex and interdependent supply chain. Using big data to understand what's happening upstream and downstream can help every member of the supply chain work together better.

Big data can help supply chain leaders with the following tasks:

• Understanding downstream inventory capacity to optimize the flow of products.
• Planning logistics routes to speed up delivery.
• Sharing logistics data across the supply chain to improve supply chain visibility.

Using big data to optimize the supply chain helps manufacturers speed up deliveries and reduce costs. It also improves efficiency and enables manufacturers to make supply chain-related decisions faster.

3. Quality control

Defective products frustrate customers, negatively affect profit margins and lead to waste. Analyzing production line data can help manufacturers identify defects before the items are sent to customers.

Production line data includes the following information:

• The performance and tolerances of production line equipment.
• Individual product inspection data.
• Defect trends, risks and issues.
• The root causes of manufacturing defects.

This data can help supply chain leaders improve quality control.

4. Production line improvement

Big data can help manufacturers improve their in-house operations, which can lead to reduced costs and reduced waste.

Manufacturing big data can include the following information:

• Potential blockages throughout the production process.
• Number of resources, such as equipment and employees, and ways in which the manufacturer is currently using each.
• The locations of raw material and parts.

Supply chain leaders can use this information to improve operations. For example, gaining insight into potential future problems in the manufacturing process can help supply chain leaders prevent those issues, which will reduce waste and extra costs from production delays.

5. Customer feedback analysis

Some of the most important manufacturing data comes from customers. Gathering and analyzing customer feedback can help supply chain leaders resolve consumer issues and develop new products.

Customer data can help employees carry out the following tasks:

• Analyzing customer complaints and reviews and identifying any consumer issues.
• Identifying patterns in customer returns data, such as common reasons for returns.
• Identifying ways to improve existing products further, based on customer reviews.
• Finding ideas for potential new products based on customer feedback.

6. Pricing optimization

A major part of a supplier's plans is pricing strategy. Supply chain leaders can analyze big data such as marketplace trends, consumer behavior and competitor pricing to ensure their company will achieve a profit margin while still attracting customers with its prices.

Supply chain leaders and other leaders who work with them on pricing can use the information they've learned from big data to implement pricing strategies such as testing different price points for products and introducing dynamic pricing, which enables manufacturers to update prices in real time based on their production line inputs and operational costs.

Paul Maplesden creates comprehensive guides on business, finance and technology topics, with expertise in supply chain and SaaS platforms.