OK, here’s a new tack: Me, a lifelong engineer/teacher, acknowledging that sometimes improving production rates through training is NOT the best thing to do.

Yes, I know, I have written countless articles prodding management to train their staff, all the while touting the improvements that can be obtained in production as a result. I have routinely thrown around various quotes regarding training. For example, one company owner might ask, “What if I train my people and they leave?” to which I would reply, “What if you don’t train them and they stay?” Someone else might say, “I am not going to pay for training my staff,“ or “Training costs too much money,” to which I would reply, "If you think the price of education is expensive, try the price of ignorance. You’ll pay for ignorance every day for the rest of your life."

However, a recent project challenged my way of thinking. I was working for a very large municipality on a difficult CADD implementation. As I touted how the software and related training would improve production on projects, I was told right up front that improved production wasn’t desired by the staff.

This group of designers, surveyors, planners and construction staff seems to be conscientious. They show up to work on time, have a good work ethic and follow directions well. So what could be the problem?

My research showed that their workflow is grounded upon a decades-old philosophy of essentially doing all of the work manually with little to no automation. Yes, they use AutoCAD. They also have some design/surveying solutions, but they do not use them much. Their fieldwork is done with total stations, which is good news; however, the data collected is drawn by hand. Literally, the points are hand-connected in CADD. I thought this idea went out decades ago, but I guess no one will be shocked when I say it is still occurring far too often in our business.

Of course, this is where I prodded them to improve: Use the software at their behest to automate dreary hand computations and eliminate typographical errors as well as manual input and drafting. Field-to-finish surveying software is one of the most productive tasks that can be automated. Yet these professionals insisted that they “had more control” if they hand-connected the dots.

Data is processed into a surface so that contours can be drawn, but then adjustments are made to the polylines, not the source data for corrections. Of course, that blows the surface model. Profiles are hand-calculated and hand-drawn in the CADD system. Geometry is hand-computed and hand-drawn in CADD, as well.

And this is where I got into trouble. I recommended that the profiles be pulled from the surface model. What a concept! This group vehemently pushed back and resisted my advice, and I must admit I was shocked at this defense of time wasting. So, as an experienced consultant, I backed off to consider their argument. As I did, I inquired further as to the cause of their dismay at my suggestions.

What I found was that sometimes the systems and processes that are governing workflows and methodologies take precedence over incremental production improvements. Either I never quite encountered this before or didn’t catch it in the past, but it came as an important realization to me this time.

The production in this large city could be improved radically if we could give it a top-down makeover, but that was not in the cards. What the city has managed to accomplish, though—and this is the crux of their argument—is a workflow that works.

Here is what I found:

• The current manual methods are understood by everyone;
• Learning curves are minimized for new or transferred staff;
• The tasks being performed have decades of background data to support them;
• The city has a plethora of metrics to refer to;
• Deadlines can be very closely estimated based on experience;
• Estimates of project costs can be very closely approximated;
• Staff can be held accountable for the tasks ongoing fairly easily;
• Supplies, support and resources can be closely monitored and maintained;
• Many upgrades, the introduction of new solutions and bleeding edge technologies are minimized or avoided; and
• This workflow works for the system and its processes.

This last item is really where I learned my lesson. The city has a process that has endured for decades and is closely monitored by the taxpaying public, which funds this outfit.

Consider the following. If the survey branch improved its production by, say, developing its field-to-finish to produce automated surfaces, that improvement, in turn, would allow for profiles to be pulled automatically, which would certainly benefit that branch. However, the data would then be turned over to the design group, which is fundamentally unprepared to receive such data. The design workflow is not set up to use digital surfaces. It isn’t trained in the theories of modeling, the errors that can occur or the troubleshooting methods. So, the survey department would gain productivity, but the receiving branch would be hampered, and liabilities could skyrocket.

Likewise, if the design branch were to automate its work and produce digital data for construction, this advance would actually hamper the construction division from moving forward if it isn’t prepared to handle the influx of technical data. And so on and so forth.

From my analysis, I found that everyone must be in the mode of using technology for it to actually benefit the organization. Each proverbial cog in the wheel must understand the technology, its theories and applications. Additionally, the technology must be integrated into the organization’s workflow. Further, the data emanating from one department must be delivered in such a manner that it is conducive to the receiving processes. And the dataflow, the workflow and all of the participants must fall into a predictable, verifiable, repeatable system that everyone understands and in which everyone can co-exist.

Until this is accomplished, these departments are 100% correct—improving production piecemeal can actually harm the agency’s ability to produce solutions. While some tasks would improve, others could be irreparably harmed.

So now we need to look for ways to fix the system, bring it up to date, develop justification for improvement, obtain funding and move forward.


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