A manufacturer’s factory data often contains overlooked insights that could make the company more profitable, safer and increasingly productive. Enterprises that excel in manufacturing data analysis could become more competitive and growth-focused in a challenging industry. Which advantages can leaders anticipate by capitalizing on information collected from their assembly lines, industrial equipment and employees?

1. It Can Reduce Injury Rates

All manufacturing data collection and utilization plans must have well-defined goals. Many manufacturing decision-makers want to gather internal data to determine how often workers get injured and why.

Then, safety managers or other specialists can review the information to determine which tasks, processes, employees or shifts have the most dangerous or undesirable associated trends. Employee wearables often collect factory data automatically and send it to the cloud for later review. One example is a backpack-like exosuit fitted with sensors to detect a worker’s motion in real time. The innovation provides customized robotic assistance after analyzing the data.

Statistics indicate the product reduces employees’ back strain by about 40% during their shifts. This solution also gathers data about potentially risky movements, such as bending or twisting. Those specifics help managers coach their staff and provide practical ergonomic tips.

Although safety wearables’ information can inspire process improvements, it also aligns with regulatory requirements. As of July 2023, the U.S. Department of Labor’s Occupational Safety and Health Administration (OSHA) began requiring companies in numerous high-risk industries — including manufacturing — to release injury and illness records publicly. The most recent statistics included information from more than 375,000 organizations with at least 100 employees.

2. It Can Support Digital Transformation Efforts

A growing number of manufacturing data collection options provide leaders with plenty of choices when they explore how to create or improve analysis-driven workflows. A 2024 study showed voice-directed picking technologies were especially popular and have achieved substantial growth in only a couple of years. As of 2022, 21% of respondents were using those information-collecting tools to accelerate their workflows. However, the total increased to 37% in 2024.

Experts also assert that manufacturers who want to implement artificial intelligence (AI) in their factory workflow should look beyond complex AI systems. Those are not inherently unhelpful, but high-quality data is essential for successful outcomes.

In such cases, data analysis goes beyond trend tracking and requires leaders to scrutinize internally collected information and find duplicate records or other mistakes that could cause inaccuracies.

As leaders develop digital transformation plans, they must consider whether their current assets could collect or process data with few or no modifications. Some tech products support several hundred communication protocols, so it is easy to make them work with existing equipment.

The involved parties must also decide what data they want to collect, how often and why. Ironing out those specifics allows them to maximize their efforts and expand them over time.

3. It Can Solve Known Problems

Industrial producers may know about issues within their companies but feel they need help to solve them. That was the situation for a food manufacturer that wanted to scale digital solutions across numerous sites. Factory data showed the company had sustained up to $5 million in losses at one location, due partially to its reliance on fresh ingredients.

Leaders pinpointed waste as a primary problem to solve but needed to find its main sources. Manufacturing data analysis played a significant role in the investigations since those involved installed more than 6,000 sensors to track how goods moved throughout the facility. They also began using software to optimize manufacturing and ensure workers adhered to best practices. That change tackled energy waste, but executives wanted to improve processes for each line worker.

Data indicated speed losses and microstops were among the most significant losses, worsened by constant product changeovers that prevented staff from optimizing their speed and quality-related outcomes. Switching to a new product required workers to start from scratch.

However, a new manufacturing data collection-based system solved that problem by gathering historical machine information and analyzing it to recommend equipment parameters for each product. Additionally, tablet installations for the machines allowed workers to be proactive by  taking pictures of new equipment issues and sending them to maintenance teams.

4. It Can Automate Tasks

One 2022 study revealed that manufacturers can save 1.6 million productive hours annually by upgrading single processes. Figuring out which to focus on requires understanding production necessities that are most error-prone, time-consuming, or need targeted improvements.

Some producers use data analysis to determine the most appropriate tasks to automate. Similarly, technologies such as machine learning tend to perform better with greater exposure to high-quality information.

In one example, an automaker implemented a digital production platform to enhance its manufacturing data analysis strategy. The company’s production footprint encompasses more than 120 sites and 200 million incoming components arriving daily to make 11 million vehicles annually.

A cloud-based analytics tool supported the business in making its output more sustainable, efficient and high-quality. It also opened opportunities to apply automation to reduce manual tasks. Specific production steps require applying 25 country-specific labels with thousands of variants to components before automobiles leave the factory.

Workers used to apply and inspect the labels manually. However, part of the company’s data-driven upgrades involved an AI system to automate those steps, significantly increasing speed and accuracy.

Making the Most of Factory Data

These examples show that creating a thorough manufacturing data analysis plan can pay off. Manufacturing professionals should follow numerous best practices to get similar results.

First, they should decide what type of data they will collect and from where. Next, they must establish information-cleansing procedures to eliminate low-quality content that could skew statistics or mislead executives.

Keep staff well-informed of how data usage will change within the factory and how the new procedures may affect their work. Give employees ample time to adjust and tell them how to get supervisor support if needed.

Finally, decision-makers should treat factory data analysis issues as ongoing opportunities. Rather than fixating on an end goal, they must stay motivated with the potential for continuous improvement that streamlines internal processes and impresses external stakeholders.