Big data and manufacturing go hand in hand because so much of what goes on in a manufacturing business is measurable and able to be optimized. Data streams are increasing in size, relevance, and number as most manufacturers flock to the powerful capabilities, predictions, and insights that shop floor data can unlock for their businesses.
But the true value of data can only be accessed if manufacturers can successfully develop a culture and deploy an infrastructure that supports the automated real-time collection of manufacturing shop floor data. They’ll be able to experience rapid value creation and immediately improve productivity. They’ll also enable even more advanced use cases based on higher volumes of data, more data sources, and automation.
In a word: visibility. By collecting shop floor data, manufacturers are better able to measure, understand, and optimize production. With the right shop floor data in front of them, to make decisions that are rooted in fact. This is why it's incredibly important to have accurate, real-time production data democratized for all stakeholders.
This is all part of a journey of advancement for manufacturers. As they become more "data-mature," they can begin to drive even more advanced use cases with their data beyond simple data collection and visibility. We call this the "manufacturing analytics journey" and explore it further in a comprehensive eBook on manufacturing analytics.
Because so many manufacturers still don’t collect data (or only use manual systems), deploying solutions that allow you to monitor and collect manufacturing shop floor data in real-time is a major competitive advantage. Not only can this help win customers over, but it also creates a leaner operation rooted in effective data-driven decision-making.
The benefits of shop floor data collection are multi-faceted for modern manufacturers. Although not an exhaustive list by any means, adopters of data-driven strategies can see benefits in the form of:
|Using Data to Drive Efficiency: Learn how Mayville Engineering, a contract manufacturer, leveraged production data to drive a bottom line impact, increasing their uptime by 15% and efficiency by 20%. Read the full story.|
Without shop data collection and shop data input, there would be no production tracking. A cumulative piece count of first quality pieces or other broad stand-alone aggregate numbers doesn’t constitute production tracking. The data must have enough data points to analyze the rate of production, efficiency, overtime and shift differentials, maintenance costs, etc.
And that analysis must be able to look at the workflow of orders across the shop floor at the holistic level. Even if it is manually collected, data entry errors are common. Accurate data is essential to track production if the goal is process improvement, efficiency, or cost savings. Types of data collection for production tracking may include:
Shop floor data collection is essential for production tracking. Manual data entry is possible, but there are severe drawbacks, including human error, data entry errors bias, and lack of reliance on real-time. A shop floor insight makes for better decision-making. Production tracking software uses real-time data collection and powerful analytics to look at the production system and eliminate these drawbacks.
With so many potential data sources, manufacturers often use a variety of data collection techniques. Some of these include:
MachineMetrics utilizes many different methodologies for shop floor data collection from machines. How we go about accurate data collection is entirely dependent on the unique features and limitations of each machine on a shop floor. Age, control, make, and model all factor into a machine’s capabilities and shop floor performance.
We consider the information from the control of the machine to be the best source of production data in delivering a shop floor insight. Still, we also realize the importance of human context. Machine data alone can’t tell the full story of production performance, so we layer on data from operators logged via a tablet interface at each machine.
This gives operators and managers a clearer picture of production performance. Not only will they know the amount of downtime experienced, but they’ll know the reason why machines were down.
Not only will there be an accurate scrap rate benchmarked, but they’ll know the common reasons for scrap parts. They’ll also eliminate extremely tedious tasks, improve the performance of valuable fixed assets, and realize a higher ROI with a cost-effective rollout.
This added layer of operational data provides a comprehensive solution. It gives manufacturing leaders the additional shop floor insight data to tweak processes, eliminate non-productive activities, and make decisions that positively impact productivity.
Manual shop floor data collection won’t get you to the level you need to compete in modern manufacturing. You can’t manually collect the shop floor data types and levels needed to realize the benefits we’ve discussed thus far. It would be impractical for your employees to spend such large amounts of time manually tracking part counts, downtime reasons, and other shop floor data points. The raw shop floor data would also be nearly unusable and severely delayed.
It would take even further effort from data-savvy employees to manually compact the shop floor data collection by calculating key metrics, developing reports, and sharing the shop floor data collection with all stakeholders.
A system that can automatically collect, contextualize, and share manufacturing information so that your employees can focus on making decisions is a far better scenario.
Requirements you should look for in a system built for manufacturing data collection include:
Learn how you can use MachineMetrics plug-and-play solutions to easily collect production data and get instant visibility and control of the shop floor.
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