There is a festival at your local park, and you are hungry. There are three food trucks. The first truck has a long line-up and you think, wow, they must have great food but how long is it going to take to get that food? I’m hungry now. The second truck doesn’t have anybody waiting in line and you think, nope, no one likes that food so I’m not going to waste my money. The third truck has a shorter line up than the first and you decide that’s your truck, decent food, in a decent amount of time. You made a snap decision.
However, while you are sitting on the bench eating your okay cheeseburger, you watch what’s really going on. The long line up was for a pizza truck and they cook and serve two pizzas at a time. Sometimes the line up is long but then it dissipates quickly after that gooey pizza is dished out, and wow, you could have really gone for a slice of that! If only they had some sort of sign to tell you how long a batch of pizza takes, then you could have estimated how long you would be waiting based on how many people are in line. But uh oh, some people are taking two or three pieces! What is the average wait time?
And then a bell rings over at the empty truck and the server shouts out a number and a group steps up to claim their deep-fried cheeseburgers on a stick. Now you’re thinking, ah, I would have liked to try that! I’m glad I didn’t stand in line while the fryer was doing its thing, but maybe they should somehow post where you are in the queue? I could have been next in line and wouldn’t know it!
So, your snapshot or “real-time” decision was an adequate success for now, but next time you want to do better, you want to plan based on your observations. Then it dawns on you, each truck does things differently and you need to compare average wait time based on each method. It would require you to watch the lines over time to build up historical data.
After dinner you want to work off those calories. You want to go for a run and make use of the outdoor training centre. You google the estimated calorie count for a greasy burger and use your sports watch to set your goal. You run two laps of the park and you do your reps of push-ups and triceps presses; yet your watch hasn’t thrown confetti up to say, “Congrats, you’ve burned those calories”.
What now? How many more bicep curls do you need? Should you run another lap? Tapping your watch isn’t helping because it doesn’t have the on-board data you need to update your goal. You get frustrated and you go for a slice of the pizza you wanted in the first place.
Each of your decisions have been foiled by using a single point of data that has been developed out of context of surrounding data. This is the biggest downfall of IIOT devices on the shop floor.
Clearly defined data is the basis for good decisions, whether choosing a food truck or building a truck. What is clearly defined data?
“Some important properties of data for which requirements need to be met are:
- definition-related properties
- relevance: the usefulness of the data in the context of your business.
- clarity: the availability of a clear and shared definition for the data.
- consistency: the compatibility of the same type of data from different sources.”
How does 10in6 Production Reporting give you better data than IIOT devices alone?
10in6 achieves data relevance.
10in6 achieves data clarity.
10in6 does collect data from the individual assets on you shop floor so you obtain the stand-alone data for that asset, just as an IIOT device does. WE GO FURTHER. We drive the most relevant signals from your shop floor PLC to a concentrator PLC where the data is placed in context with surrounding data. 10in6 takes that data and associates it with shift information, product information and other asset information. Your data is transformed beyond machine data and into data that is in the context of your business.
IIOT devices format data according to their internal data structure and it is up to you to clean and define it. The 10in6 data model is defined by a data template incorporated at the shop floor PLC level. We also apply a layer of redundancy to ensure that your machine data is clean and clearly defined within this template before it is brought into the 10in6 Database.
10in6 achieves data consistency.
10in6 uses the same data collection process for every asset you require. 10in6 doesn’t rely on external devices or communications packages that can be a point of communication failure or controlled by proprietary languages. We develop the data through consistent layers of proven technology. Shop floor data moves from the ladder logic within the PLC template up to the SQL Server database, and is displayed in a familiar MS Excel experience. Our solid technologies produce solid data.
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