In a world where the macroeconomic environment is uncertain, margins are tight and competition is fierce, manufacturers can no longer afford to operate without visibility into their factory floors.
The average manufacturer loses more than 800 hours of production each year due to equipment downtime — that’s over 15 hours per week, according to Forbes. This downtime translates to an estimated $50 billion in annual losses for the industry. And while large manufacturers bear the brunt of these costs, SMEs are not immune. In some cases, an hour of downtime can cost up to $150,000, according to Siemens.
So, what’s the solution?
For a growing number of manufacturers, the answer lies in digital transformation — and more specifically, manufacturing data collection. By collecting real-time data from their machines, production teams can gain the insights they need to reduce downtime, maximise productivity, and make smarter decisions. In this article, we’ll explore:
Manufacturing data collection is the process of capturing real-time performance data from machines and production lines. This includes data points such as machine uptime, downtime, cycle times, operator inputs, and more.
The goal? To turn operational data into actionable insight — helping manufacturers move from reactive firefighting to proactive decision-making.
Traditionally, this type of data might have been collected manually on paper or spreadsheets. Today, modern tools such as IoT sensors, machine monitoring platforms, and cloud-based dashboards make it easier than ever to automate and scale data collection, without major infrastructure overhauls.
When implemented effectively, manufacturing data collection delivers measurable value across your operations. Here are the top benefits:
Data allows manufacturers to see exactly when and why machines stop, enabling faster response times and better root-cause analysis. In the UK and Ireland, engineers spend an average of 38 hours per week on maintenance — nearly 20 of those are unscheduled. Better visibility into downtime trends can reduce this dramatically.
Aerospace manufacturer AVPE Systems used FourJaw’s platform to increase factory uptime by 30%, after identifying unexpected stoppages and taking action.
By uncovering bottlenecks and low-performing machines, manufacturers can make targeted improvements that lead to greater throughput with the same resources.
Fernco, a global manufacturer of plumbing solutions, used machine analytics to gain deeper visibility across teams and production lines. This new insight helped improve decision-making and factory floor communication.
Downtime is expensive — whether from machine failure, changeovers, or idle time. The Manufacturer’s Industry in Motion report found that UK businesses can lose between £1,700 to £7,500 per hour in downtime costs.
By tracking when machines are running (and when they’re not), businesses can quantify lost time and take steps to recapture it — leading to real, measurable savings.
AMS, an Irish manufacturer, unlocked over €30,000 in annual savings and a 19% uplift in machine utilisation using FourJaw’s analytics tools.
Aerial view of AMS 400,00 square feet facility in Little Island, Cork.
Data collection supports lean initiatives by identifying waste (e.g., excessive downtime, underused equipment, long changeovers), and helping teams set realistic targets based on facts — not gut feeling.
When machine operators and supervisors have access to real-time data, they become part of the improvement process. They can flag issues, take ownership, and contribute to performance gains.
Vernacare, a global healthcare manufacturer, reduced machine downtime by 20% and eliminated production backlogs, thanks to improved visibility and engagement from the factory floor to the top floor.
Starting small is key. You don’t need to build a smart factory overnight — you just need to capture the right data and use it effectively. Here’s how:
Are you trying to reduce downtime? Increase output? Improve OEE? Setting clear goals will help you choose the right tools and metrics.
Start by tracking machine states: e.g. running, stopped, and idle. More impactful measures would be the likes of downtime reasons, cycle times, or quality metrics.
Look for a system that’s easy to deploy, doesn’t require complex integrations, and provides real-time visibility.
FourJaw’s plug-and-play MachineLink hardware, for example, can be installed on virtually any machine — in less than 20 minutes.
The best data in the world won’t help if your team doesn’t understand or trust it. Provide training, share dashboards openly, and empower your operators to log downtime reasons in real time and ensure they understand why they’re doing it. Communicate how they will benefit to get better buy-in.
Use manufacturing dashboards and reports to uncover trends, measure improvement, and inform daily production meetings or weekly reviews. Think of data collection as the first step toward supporting a continuous improvement project.
Digitalisation in manufacturing is accelerating fast. According to Gartner, 80% of manufacturing CEOs increased tech investments in 2023 in response to economic and workforce pressures. A separate study by Foundry found that 89% of manufacturers have either adopted a digital-first strategy or plan to.
If you’re not already capturing data, the best time to start was yesterday. The second-best time is now.
Manufacturing data collection is no longer a ‘nice to have’. It’s a foundational step in building a more efficient, agile, and competitive factory.
The benefits are clear: less downtime, better utilisation, faster decisions, and empowered teams. And with solutions like FourJaw, it’s now easier and more affordable than ever to get started — whether you’re a small workshop or a global operation.