Manufacturing Challenges Blog Series: Reporting

Welcome back to the Manufacturing Challenges blog series. I hope you enjoyed part one of the series, “Audits,” from Doug Fair, InfinityQS COO. In this second entry to the series, we’ll discuss how and why reporting can be challenging for some manufacturers, and how InfinityQS can help ease the pain.
 
Reporting is not a perfect science. Let’s start with that. And it can be a painful thing for manufacturers. It can be unreliable. A report can provide information that is not specifically what you (depending on your position within the organization) might need. And, often, reports do not “slice and dice” the data in ways you might need.
 
So, it’s not a cut and dried thing. Reporting is not just “data comes into the system and a report that fits your needs is spit out of the system.” It just isn’t. Allow me to explain…
Manufacturing and monitoring

Smoothing the Data

I like to tell the following story about reporting because I think that the way we go about reporting in the manufacturing world is essentially flawed.
 
When the operator is looking at data on the factory floor, they're looking at individual data points. And they're responding to each signal that comes into the control charts or from a sensor or some other feedback system. What operators like about this is that it’s an opportunity for an immediate response: to correct whatever the issue might be, or to just let it be because it's okay. A quick decision.
 
In another part of the organization, the supervisor is looking for shift reports. So, the operator gathers all those individual data points—the data points they were managing during their shift—and rolls them up and gives the supervisor anything and everything they ever wanted: the average, the mean, the standard deviation, how many points were out-of-spec, and a whole host of summary statistics; they may even make a histogram of that shift and send that in with the shift report. The end result is they’ve got an eight-hour summary, or “smoothing” of the data, so to speak.
Reporting

And Up it Goes

Now, let’s say, the supervisors must send monthly reports “upstairs.” They're taking all the shift reports from the floor and averaging them up. Then, at the corporate level, they are doing quarterly reports because they must satisfy the folks above them: the board, the stockholders, and the like. These folks are looking at things on a quarterly basis. As you start taking the exact same amount of data and rolling it up—and then up again—you are “smoothing it out.”
 

To Corporate

I worked on the corporate side for quite a while, and we would see these quarterly reports—the results of the managers and executives looking at blips on a spreadsheet that were, quite literally, just a couple of pixels higher than the last quarter. And then business decisions would be made based on that!
Tip of the Iceberg
The corporate folks know that all they're seeing is the tip of the iceberg and something major had to happen downstream or upstream; however, as they're looking at it, they really have no idea what caused that line to blip those couple of pixels. So, this means that even if the data were all accurate, just the nature of rolling things up (and up again) and averaging and smoothing things out and coming up with 12-month moving averages and things like that, is overly concealing…it’s truly smoothing out the data. As a statistician, I know that all the fine details of each data point are just ripe for misdiagnosis.
 

Keep it Rough

Depending on your audience (and your position) you're going to generate different types of reports, right? It's just the nature of organizations. As you move up the food chain, the people looking at the reports have less and less time (and reason) to look at all the individual data points—they just don’t have the bandwidth. So, you need to come up with some means of conveying meaningful data, of rolling the data up so the corporate folks can make better informed business decisions…without smoothing things out.
Manufacturing reporting

Another Story

Okay, so the best way for me to explain how not smoothing things out can be the best thing you do is by telling another story. A guy walks into a bar…
 
No, not that one. Okay, here goes. I knew a person who got a new job for a manufacturing company—in the quality group that generated reports and distributed them. He was what I would call a “forward thinker,” meaning that he worked outside the box, was always coming up with new ways to handle things, that sort of thing.
 
Anyway, he starts his new job and the first thing he does is check the reports that are currently (and probably always have been) going out to corporate. He does a double-take and immediately thinks “I don’t know if this stuff in the reports is any good or not.” He’s skeptical.
 
So, he tells his people, “Don’t send out this report. Just don’t send it. Let’s wait and see who calls.”
Sharing Shop Floor Information

The Response

When he did get a few calls, he asked questions that helped him get to the heart of things: “What do you need in this report? What parts of the report do you use? What parts work for you?” With their feedback, he refined the report, which began as multiple pages (and about an eighth of an inch thick), down to just a few pages. Users of the report were very happy.
 
Essentially, he stopped smoothing the data. Rather than roll up the data that had already been rolled up—and thus create a warmed-over average of an average that was of no use to anyone—he instead pinpointed the specific pieces that the users of the report found helpful and concentrated on those. He delivered what the users wanted and needed.
Quality Check

InfinityQS Helps in So Many Ways

How does InfinityQS help manufacturers with the challenge of reporting? So glad you asked. First, the less human intervention there is with the data as it is being collected and compiled, the more reliable that data will tend to be. That means electronic data acquisition systems…and sampling strategies.
 
Sampling strategies are interesting. And necessary for many manufacturers. Why? Because sampling strategies are regimented, not based on whims like an operator simply saying, “It’s the end of my shift; it’s time to collect my data.”
 
A sampling strategy does a better job of picking up the true personalities of the population of the data—as opposed to just what happened in the middle or at the end of a shift.
Quality Intelligence in Action

Tagging Data

When it comes to the data model, InfinityQS’ quality intelligence systems shine. Tagging the data appropriately is key to solid data collection. At a minimum, every piece of data is tagged with a time stamp, as well as the employee, feature, part, and process to which it is tied.
 
By tagging data with the part, process, feature, and time stamp, you enhance the level of analysis that can be performed on the data—ways in which you can compare, slice and dice, and roll up the data.
 

Keep it Simple

Another way in which InfinityQS adds value to your reporting efforts is by keeping the data collection interface as simple as possible. Your reporting is really only as good, and reliable, as your data collection. The person who is involved in data collection doesn’t want to have to worry about confusing instructions, an ambiguous interface, or difficult sequencing of the data. Those issues can add time to their data collection that they just don’t have.
Data Collection
 
For example, let’s say you’ve got a part for which you need to conduct ten different tests…and the engineer who established those checks never really had to measure that part—they just laid sampling instructions out alphabetically, or something equally expedient. You then send that sequence of tests down to the shop floor and the operator gets one look at it and blanches. “This is crazy,” she says. “This adds 15 minutes to my data collection time just because of the way I have to orient the parts to take the measurements…This can’t be right!”
 
Resequencing the checks according to how the work is performed saves a lot of time and makes it easier for the operator. It also makes the data more reliable. Cutting back on the number of times the operator needs to manipulate the instrumentation during testing enables them to move through the checks faster and smoother…and happier.
Quality Intelligence 

Quality Intelligence Takes the Pain Out of Reporting

Reporting can be challenging, and painful, for manufacturers. You want to be able to slice and dice the data any way that suits your needs. You want to avoid smoothing the data when reporting to supervisors and corporate personnel. And you want your data to be as reliable as possible—so your reports are as accurate as possible. Great reports will help your organization make decisions that will transform the operations.
 
InfinityQS quality intelligence systems take the pain out of reporting. Sampling and standardization go a long way toward making reporting an easy task that doesn’t take up all your time.
   
Read other parts of our Manufacturing Challenges blog series:
Steve Wise
By Steve Wise
Vice President of Statistical Methods
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