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 manufacturers flock to the powerful capabilities, predictions, and insights 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 data. If they are able to do so, they will not only be able to experience rapid value creation and immediate productivity gains, but they can build upon their success to enable even more advanced use cases based on higher volumes of data, more data sources, and automation.
In a word: visibility. By collecting data, manufacturers are better able to measure, understand, and optimize production. With the right data in front of them, it becomes a lot easier to make decisions that are rooted in fact. This is why it's incredibly important to have accurate, real-time production data that is 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.
Considering the fact that so many manufacturers still do not collect data, or only use manual reporting, it can be a major competitive advantage to deploy solutions that allow you to monitor and collect manufacturing data in real-time. 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 collecting data 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:
A customer of MachineMetrics displays a prominent production dashboard over the shop floor for operators and managers to know where they stand when it comes to production goals as well as to quickly identify when machines are down or falling behind.
With so many potential sources of data, it follows that manufacturers often use a variety of data collection techniques.
Some of these include:
A pareto chart from MachineMetrics displaying the top reasons for machine downtime events.
MachineMetrics utilizes many different methodologies for collecting data from machines. How we go about 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.
We consider the information from the control of the machine to be the best source of production data, but we also realize the importance of human context. Machine data alone cannot tell the full story of production performance, so we layer on data from operators that is 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 will know the reason why machines were down. Not only will there be an accurate scrap rate benchmarked, but they will know the common reasons for scrap parts.
This added layer of operational data gives manufacturing leaders the additional insight to tweak processes and make decisions that will positively impact productivity.
Manual data collection will not get you to the level you need to be in order to compete in modern manufacturing.
You simply cannot successfully manually collect the types and levels of data needed to realize the benefits we’ve discussed thus far. Not only would it be impractical for your employees to spend such large amounts of time manually tracking part counts, downtime reasons, and other data points, but the raw data would be nearly unusable in its current state, not to mention severely delayed.
It would take even further effort from data-savvy employees to manually compact all the collected data by calculating key metrics, developing reports, and sharing the data to all stakeholders.
A system that can automatically collect, contextualize, and share manufacturing data 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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