Leveraging real-time production data: Q&A with Adam Mullen

Maximizing production data in real-time. That’s about as important to maintaining uptime and minimizing downtime as you can get. It’s also critical to ensure cost accuracy, to make the most of resource utilization and improve decision-making.

So, we brought in an expert, Adam Mullen, to get into the nitty-gritty of this important area of manufacturing, Industry 4.0, and digital transformation.

What follows is our Q&A with Adam:

What are some of the biggest challenges manufacturers face when trying to accurately calculate standard production costs?

One of the biggest challenges is the complexity of manufacturing itself. Standard production costs rely on estimates for materials, labor, and overhead, but those inputs can change frequently due to supply chain disruptions, wage fluctuations, or energy price shifts. If manufacturers don’t have timely and accurate data, their cost estimates can quickly become outdated, leading to pricing errors and budgeting miscalculations.

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Another major issue is data fragmentation. Many manufacturers still rely on spreadsheets, disconnected systems, or manual tracking methods, which can create inconsistencies and errors in cost calculations. Without a centralized source of truth, it’s difficult to get a clear and reliable picture of actual production costs.

Additionally, many companies struggle with variability in their production processes. Differences in machine efficiency, worker productivity, and raw material quality can all impact final costs, making it challenging to rely on a single standard cost. If these variables aren’t accounted for accurately, manufacturers risk setting prices too high or too low, which can eat into profit margins or make them less competitive in the market.

How can real-time production data improve cost visibility and help manufacturers make better financial decisions?

Real-time production data gives manufacturers a clearer, more immediate view of their actual costs, rather than relying on historical estimates. When manufacturers can track material usage, labor hours, and operational expenses as they happen, they can identify cost trends and variances early, rather than uncovering them after financial reports are finalized.

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For example, if a material shortage causes a spike in procurement costs, real-time data allows finance teams to adjust forecasts accordingly. Similarly, tracking machine downtime in real time can help pinpoint inefficiencies that drive up per-unit costs. Without this level of visibility, manufacturers may be making decisions based on outdated or incomplete information, which can lead to pricing errors, unnecessary spending, or missed cost-saving opportunities.

By continuously monitoring production data, manufacturers can adjust pricing, budgets, and resource allocations proactively. This level of cost visibility is essential for staying competitive in an environment where material and labor costs are constantly changing.

What role does data integration play in ensuring a more complete and accurate view of production costs?

Data integration is essential because production costs aren’t determined by a single factor—they’re influenced by everything from raw materials and labor to equipment performance and supply chain logistics. If a manufacturer’s data is spread across multiple, disconnected systems, it’s nearly impossible to get a true, up-to-date picture of costs.

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When financial, production, and supply chain data are integrated, manufacturers can track costs in a more holistic way. For example, a sudden spike in production costs might not be caused by inefficiencies on the plant floor, but rather by a delay in raw material deliveries that forced the company to pay for expedited shipping. Without an integrated view, these cost drivers might be overlooked, leading to incorrect assumptions about where cost issues originate.

Integration also enables greater automation and machine connectivity—both key to reducing manual data entry, minimizing human error, and improving the quality and consistency of cost information.

When data flows seamlessly across systems, manufacturers can rely on a single, consistent version of cost information rather than trying to piece together numbers from multiple sources. This not only improves financial reporting but also enables more informed decision-making.

Beyond cost accuracy, how does leveraging real-time production data contribute to operational efficiency and resource optimization?

Cost accuracy is just one of many benefits that come from leveraging real-time production data. When manufacturers have access to up-to-the-minute insights, they can make faster and smarter decisions that improve overall efficiency.

For example, real-time data allows manufacturers to reduce waste by identifying production bottlenecks or inefficiencies as they occur. If a machine is running slower than expected or producing excessive scrap, immediate visibility into that issue allows for quick corrective action—rather than discovering the problem days or weeks later when reviewing reports.

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Real-time data also helps manufacturers optimize labor and equipment utilization. If demand spikes unexpectedly, real-time insights can help balance workloads across shifts or reallocate resources where they’re needed most. This helps avoid overstaffing in some areas while leaving other critical operations understaffed.

Energy usage is another area where real-time data has an impact. By monitoring energy consumption patterns, manufacturers can adjust operations to minimize costs—such as running energy-intensive processes during off-peak hours.

Ultimately, leveraging real-time production data leads to a more agile and responsive manufacturing operation, reducing costs, improving productivity, and enhancing overall profitability.

What are some best practices for manufacturers looking to transition from fragmented cost data to a more connected, data-driven approach?

Transitioning to a more connected, data-driven approach requires a combination of technology, process improvements, and cultural shifts within the organization. Here are a few key best practices:

• Start with a data audit. Before making changes, manufacturers need to assess where their cost data currently resides, identify gaps, and determine how different systems interact (or don’t). Understanding where inefficiencies exist is the first step toward fixing them.
• Invest in system integration. Whether through an ERP, MES, or other centralized platform, manufacturers should work toward consolidating their data into a single, unified system. This eliminates silos and ensures decision-makers have access to accurate, up-to-date cost information.
• Standardize data collection processes. A key challenge in fragmented cost tracking is inconsistency in data entry and reporting. Establishing clear protocols for how cost data is recorded, validated, and updated can help ensure reliability.
• Leverage automation where possible. Manual data entry and reconciliation are not only time-consuming but also prone to errors. Automating data capture—whether through sensors, barcode scanning, or connected machinery—can help improve accuracy and reduce administrative burden.
• Foster a data-driven culture. A successful transition requires buy-in from employees at all levels, from plant floor workers to executives. Encouraging teams to use data in daily decision-making and providing training on how to interpret cost insights will make the shift more effective.

By following these best practices, manufacturers can move away from outdated, disconnected systems and toward a smarter, more cost-effective way of managing production expenses.