July 16, 2024

This paid piece is sponsored by Eide Bailly LLP.

A version of this insight first appeared on EideBailly.com.

Better data drives better business decisions. When measured and leveraged effectively, data can be a powerful tool for optimizing manufacturing operations; however, many manufacturers need more resources to access and analyze their data efficiently.

Becoming data-driven consists of three critical steps: capturing, analyzing and turning the data into actionable insights. We’ve outlined everything you need to know to get started.

Step 1. Capture the data

Examples of data that can be collected and analyzed by manufacturers include:

• Production
• Machine and equipment
• Supply chain
• Inventory
• Financial

To gain actionable insight from your data, you must identify your manufacturing company’s most valuable key performance indicators. While your KPIs will differ between departments, all should be driven by the organization’s strategic objectives, which means leadership must be at the helm whenever there is a data and technology initiative.

Step 2. Analyze the data

Disparate systems create data silos. By pulling and centralizing data into a data warehouse, all the information you collect, including sales, accounting and inventory data, is synthesized into a single source of truth that every department can access. This eliminates data silos and results in more accurate reporting.

Data analysis then takes the information from your data warehouse and uses computational algorithms, including artificial intelligence, to identify problems. Visualizations of the data help you understand the current state of your organization and lead your team to action.

Step 3. Use the data

After collecting your data and organizing it into something everyone can understand, it’s time to turn insight into action. Consider a consumer goods manufacturer operating in a highly competitive landscape. The business wants to increase production capacity and reduce production costs as part of its strategic operating plan for the upcoming year.

By looking at production data such as OEE, machine downtime, cycle time and production yield, leaders can identify bottlenecks that slow production and limit capacity. They can dive into supply chain data such as performance, lead times, delivery times and material cost to explore ways to increase efficiency in their processes.

With the help of AI, they can use predictive analytics and what-if scenarios to gain insight into different courses of action. These actions may include investing in new equipment, optimizing their supply chain, automating tasks or reducing waste.

Becoming a data-driven manufacturer

When you align your business goals with your data, you gain valuable insight that keeps your organization agile and at the forefront of the manufacturing industry. But becoming data-driven starts with strategy.

Your data strategy must be unique to your business. Eide Bailly’s experienced data professionals can help define your key goals and objectives, organize your data for actionable insight and take your manufacturing business to the next level.