The purpose of this blog post is to elaborate on 8 tangible ways that quality engineers can improve quality documentation design to boost inspector efficiency, reduce errors and improve overall data quality.
At Prescience, we work with customers that require or suppliers who fill a lot of as-built documentation. 99% of the time this is a manual process. We always try to challenge both customers and suppliers to make the switch to digital as-built documentation, because it brings so many benefits.
I recently wrote a blog post about how manual quality paperwork can kill productivity. Because the process is so manual it scales poorly and is extremely difficult to manage. Acknowledging that digital documentation workflows is the logical next step, the post argued that quality engineers should exploit technology to make life easier for inspectors.
In short, you should aim to make documentation that is; 1) easy for inspectors to work with, 2) actively prevents errors, and 3) requires as little cleansing and consolidation as possible. Here are 8 tangible tactics to improve documentation design:
Step 1: Clean up existing documentation design
When reassessing the existing quality documentation the first step should be to make a radical cleanup. Divide data points into the following categories:
This part is essentially about making a quick cost/benefit analysis to see which data is worth the effort it takes to collect. And remember effort equals money (real money!).
NOTE: If the quality department performs this exercise they will likely rank the data more important than it actually is. So look over their shoulder and be critical!
With the cost/benefit analysis in place simply delete the “waste-of-time” data points. Similarly, delete half of the nice-to-have data points unless you find a compelling reason to keep more of them.
I know this may seem like a brute approach, and it is. But it is necessary. Without this exercise, the documentation will grow because quality engineers invent new business-critical and need-to-have data points without ever deleting the ones that have become nice-to-have or waste-of-time.
Step 2: Align documentation design to fit data collection
The static format of paper forms means that they are often structured with the end-user in mind. Consequently, inspectors have to jump back and forth between forms. As a result, inspectors lose focus, which increases the risk of omissions and mistakes.
A key benefit of digital documentation is that we can separate data collection from the subsequent data presentation. As a result, inspectors collect data in one format while end-customers see it in a completely different format.
Typically, it would make sense to structure documentation chronologically to fit the timing of data collection. However, another option is to split documentation into separate forms for each key process. Or perhaps the context will dictate a completely different way of structuring the documentation.
This may seem like a no brainer, but take a look at your own quality documentation. Is it organized to accommodate inspectors, or is it organized to suit customer preference?
When documentation is digital it can be optimized for data collection and subsequently be presented to customers as required.
Step 3: Delete duplicate data
When reviewing manual documentation, I always see a lot of duplicate data. This is typically because document headers repeat serial and item number information.
I acknowledge that duplicate data has some merits in paper documentation. However, it still annoys me because duplicating information is a waste of time. But more importantly, duplicating and copy/pasting data is a KEY source of errors.
Inspectors should only enter data once, period!
Duplicate data should be easy to spot when you go through steps 1 and 2.
Step 4. Remove master data
Master data is static data that is descriptive and will likely never change. As with duplicate data, master data should be eliminated from the quality documentation entirely. Registering known information makes absolutely no sense.
Instead, the documentation should include no more than a reference to the item number and perhaps a drawing revision. Any additional information, such as; item description, category, weight, dimensions, color, and similar item properties should be maintained separately.
Then link master data to the quality documentation when it is handed over to the customer.
At best, capturing master data inside the documentation is just a waste of time, but most likely inspectors will occasionally register false master data information. So in conclusion, quality engineers should separate master data and quality data.
Take a look at your documentation. Does it contain known or repetitive data that can be classified as master data? If the answer is yes, then extract it and maintain it separately.
Step 5. Use less signatures and dates
Manual signatures and dates are also a source of frustration for me. I really don’t see the value. OK, there can be exceptions where signature and date is a legal requirement. However, inspectors often fill dates retrospectively (using inconsistent formatting!) and paste an image of somebody else’s signature. I ask again, what is the point?
Remove signatures and dates that are not strictly required!
If documentation requires signature and date, there are more credible alternatives. Prescience, for example, automatically logs user id and timestamp for all data registrations. This makes it easier to see who submitted the data and when it happened. In my opinion, this is more credible and useful than free text dates and images of signatures.
Step 6. Make sure documentation design uses appropriate input controls
Using the appropriate input control is actually quite important. It can make life easier for inspectors, eliminate errors and reduce the need for post-processing significantly.
Input control limitations are one of the many caveats of paper documentation; ultimately the pen dictates the input. Oppositely, digital forms offer rich input controls that significantly increases the odds of getting good data. Applying text, number, checkboxes, and dropdowns correctly seems pretty straight forward. However, I am constantly amazed at how poorly designed some documentation is.
Take an example. An inspector has to specify whether paint thickness should be 150, 175 or 200 microns. Given a text input control, the inspector can write whatever he wants. Given a numerical input control, we are at least sure a numerical value is provided, but the value could be 160, which is invalid. Finally, if we provide a dropdown control with 150, 175 and 200 options, it is only possible for the inspector to provide a valid answer.
Similarly, DateTime data is often captured in text input controls. As a result, inspectors use dots, slashes, and dashes at random. Similarly, some inspectors type YEAR, MONTH, DAY while others type DAY, MONTH, YEAR creating a lot of formatting inconsistency.
Using carefully selected input controls will reduce errors and invalid data. As a result, you also reduce the need for post-processing the data (cleansing, transforming and modeling). This is a MUST if you plan to use the data for quality performance measurement. Our experience shows that any performance measurement relying on manual data processing provides less reliable output and tends to die quickly because people don’t have the time to consistently prepare the data day-after-day, week-after-week.
Step 7. Use default answers to save time.
Some data points feel like a waste of time because the answer is the same 99,9% of the time. If this information cannot be deleted because it is business-critical or need-to-have consider to pre-fill the data point with a default value.
With pre-filled values, inspectors can quickly glance over the data points, but only have to actively modify something, if this case belongs to the 0,01%. This potentially saves the inspector a lot of time accumulated over days, weeks and months.
While some paranoid quality engineers may claim this approach is dangerous, I simply consider it part of the reality. Most suppliers I have visited, already use a template with the obvious data points prefilled. I know this is a risk, but if suppliers blindly fill these data points anyway, why not make the process a bit smoother?
Step 8. Use a single template for all customers
I often see inspectors struggling with many different data template formats. Typically, because customers A, B, and C each have their own format (that is better than their competitors’). As a result, the data collection process is difficult to standardize and confusing to inspectors.
Why not merge all customer documentation requirements into a single standardized template. A digital template that is the superset of all customer data needs. But hang on… won’t we collect more quality data than necessary? Yes, possibly. But consider the benefits of standardizing the entire quality documentation workflow.
Similarly, consider the added value of supplying more data than the customer asks for. Will this have a positive impact on your customer relationship? If the customer insists on receiving less data, simply cherry-pick the data you need from the superset. No harm done.
Quality documentation design greatly impacts how inspectors work. Poorly organized and repetitive documentation means inspectors are wasting time, lose their focus and make mistakes. Oppositely, well structured and carefully designed documentation leads inspectors to save time (and money) and reduces the number of errors.
By switching to digital quality documentation, quality engineers should be able to make significant improvements. And because version control is much easier to control with digital forms, quality engineers should make documentation design optimization an ongoing activity.
Ultimately, having a good quality documentation design is critical to secure high data quality and consistency. When done right, improved documentation design drives cost out as inspectors waste less time and become more efficient.
I hope you enjoyed the blog post and feel inspired to take a look at your own quality documentation. If I missed something, let me know in the comments, so I can include your feedback in an updated version of this blog post.
In my next blog post, I will look into how digital workflows improve data collection.