An experienced Tekla Structures user can be exceptionally fast and still spend a surprising amount of time performing work that does not require their experience.
That distinction matters.
Tekla Structures productivity is not simply a measure of how quickly a skilled detailer can operate the software. It is a measure of how much valuable work gets completed for the human effort invested.
A detailer may become extremely efficient at selecting objects, navigating views, repeating drawing operations, entering information and executing familiar sequences. Years of experience can turn inefficient processes into remarkably fast habits.
But making a repetitive process faster does not necessarily make the underlying workflow efficient.
For detailing managers, fabrication businesses and experienced Tekla users, the more valuable question is therefore not:
“How can our detailers work faster?”
It is:
“Which parts of our Tekla workflow still require skilled human time when they don't need to?”
Anyone who has watched an experienced Tekla user work understands what proficiency looks like.
Actions happen quickly. Shortcuts become automatic. Selections are made almost instinctively. Experienced users develop personal methods for moving through repetitive operations faster than someone unfamiliar with the software could reasonably follow.
That expertise is valuable.
But it can also hide process inefficiency.
Consider a repetitive operation that takes an experienced user only 20 seconds.
Twenty seconds sounds insignificant.
If it occurs 100 times during a project, however, that is more than half an hour.
If several detailers repeatedly perform the same operation across multiple projects, the economics begin to change.
The important metric is therefore not simply time per action.
It is:
Time per action × frequency × number of users × number of projects.
That is where apparently insignificant workflow friction can become expensive.
Productivity loss is not necessarily caused by one obviously inefficient operation.
Often it is distributed across dozens of small interactions throughout a working day.
Potential friction can exist wherever users repeatedly:
None of these automatically represents a problem.
The question is how often the operation occurs and how much skilled time it consumes cumulatively.
Trimble itself recognizes this principle within the Tekla ecosystem. Tekla Open API supports recording and running user-interface actions, creating automation tools for frequently needed objects, interacting with model and drawing information, integrating Tekla Structures with other software and creating new functionality.
The capability to automate is therefore well established.
The harder question is deciding what is actually worth automating.
Short repetitive operations are particularly easy to overlook.
Suppose an operation takes 30 seconds.
A detailer performs it 40 times per day.
That is 20 minutes.
Across five detailers, it becomes 100 minutes of labor each working day.
Across 220 working days, that hypothetical operation represents more than 366 labor hours per year.
That does not mean software could eliminate all 366 hours.
It doesn't mean the operation is necessarily suitable for automation.
And it certainly doesn't mean Fab Intel Tech is claiming to save that amount of time.
It demonstrates something more important:
Frequency changes the economics of small tasks.
This is why workflow analysis should look beyond the obviously time-consuming operations.
The largest cumulative opportunity may be hiding in something everybody considers too small to worry about.
Before deciding whether something should be automated, measure it.
For any repetitive process, establish:
How often does it actually occur?
Once per project?
Several times per drawing?
Dozens of times per day?
Frequency is one of the biggest determinants of whether a small inefficiency matters.
How long does the operation take an experienced user?
Use real measurements where possible rather than estimates.
A manager may think something takes ten seconds when users know it routinely takes a minute.
The opposite can also be true.
How many people perform substantially the same process?
A workflow improvement affecting one person has one economic profile.
The same improvement deployed across ten Tekla users has another.
Does the process follow reasonably consistent rules?
Automation works particularly well where inputs, decisions and outputs are sufficiently predictable.
If every occurrence requires a completely different judgment, the automation opportunity may be considerably weaker.
This may be the most important question.
Does the task genuinely require an experienced detailer's knowledge?
Or does an experienced detailer perform it simply because somebody has to?
Those are very different things.
Repetitive manual work can create another cost beyond time.
Where the process involves repeated selections, inputs or transfers of information, inconsistency can potentially create additional checking or correction.
Automation may therefore be worth evaluating for consistency as well as speed.
Ask experienced users how they currently make the process faster.
This can be revealing.
A sophisticated workaround may demonstrate that users have already identified a workflow problem and independently attempted to solve it.
The workaround itself is evidence worth examining.
One of the best sources of workflow intelligence is likely already sitting in front of Tekla every day.
Experienced users know which operations irritate them.
They know which processes require unnecessary repetition.
They know which actions they have reduced to muscle memory.
They know where shortcuts, macros, custom components and personal methods have accumulated around a recurring problem.
Instead of asking:
“What features would you like?”
a detailing manager might get more useful information by asking:
“What do you find yourself doing over and over again that requires almost no thought?”
Then:
“How often do you actually do it?”
And:
“What would happen if that operation took one action instead of five?”
That moves the discussion from feature requests to workflow economics.
A strong candidate for Tekla workflow automation will usually exhibit several characteristics at the same time.
It is:
Frequent. The process happens often enough for small savings to accumulate.
Repetitive. The user repeatedly performs substantially similar actions.
Predictable. Clear rules determine what should happen.
Low in judgment. The process consumes skilled labor without requiring much of the skill you are paying for.
Measurable. You can establish a reasonable before-and-after comparison.
Stable. The underlying workflow is not changing every few weeks.
The more of these characteristics a task exhibits, the more worthy it becomes of investigation.
That still does not mean it should automatically be automated.
It means it has earned a closer look.
The fact that something can be automated does not mean that it should be.
Poor candidates can include tasks that:
There is little value in spending significant resources automating a five-minute task performed twice a year.
There is also danger in automating an inefficient process before asking whether the process itself should exist.
Automation can make a good process faster. It can also make a bad process happen faster.
Process evaluation comes first.
There is another distinction worth making.
Productivity software should not automatically be viewed as a mechanism for squeezing more work from detailers.
Experienced Tekla users represent valuable technical knowledge.
Using that knowledge to repeatedly perform predictable operations that software could potentially handle is not necessarily an efficient use of the resource.
The better objective is:
Remove low-value repetition so experienced people can spend more time on work requiring experience.
That can mean more attention available for detailing decisions, coordination, checking, problem solving and project delivery.
The value is not simply faster clicking.
It is better allocation of skilled human attention.
Tekla Structures already provides an ecosystem capable of significant customization and automation.
Trimble's Tekla Open API allows applications to interact with model and drawing objects, automate routine activities, create frequently needed objects, integrate with other software and add functionality.
That means the technical question is often not:
“Can Tekla be automated?”
It can.
The operational question is:
“Where would automation create enough value to justify using it?”
That is a fundamentally different conversation.
And it is the conversation detailing and fabrication businesses should have before evaluating any productivity application.
Fab Intel Tech approaches Tekla Structures productivity from the workflow side of the problem.
The starting point is not:
“What software can we build?”
It is:
“Where is experienced human time being spent on repetitive work that software could reliably perform instead?”
Fab Intel Tech develops purpose-built applications for Tekla Structures intended to address specific areas of repetitive workflow friction.
That distinction matters.
The objective is not to replace the knowledge of an experienced detailer. It is to identify processes where that knowledge is being consumed unnecessarily by repetitive operations.
As Fab Intel Tech's applications are introduced, individual productivity claims should be evaluated against the specific workflow involved, the frequency with which it occurs and measurable before-and-after results.
There is no universal percentage that accurately describes every detailer, every company or every project.
The correct question is always:
What does this particular workflow cost today, and what changes when unnecessary steps are removed?
Yes. Tekla Structures supports automation and customization through mechanisms including Tekla Open API. Trimble documents the ability to automate routine tasks, create automation tools for frequently needed objects, work with model and drawing information, integrate with other software and create additional functionality.
Start with processes that are frequent, repetitive, predictable and require relatively little human judgment. Measure their actual frequency and duration before deciding. The most annoying task is not necessarily the most economically valuable task to automate.
Begin with the current workflow. Measure how long the task takes, how often it occurs, how many users perform it and how frequently the relevant projects occur. Then compare the realistic time or cost reduction against the cost of acquiring, implementing, learning and maintaining the automation.
Yes, when the operation occurs frequently enough. An action taking only seconds may be economically insignificant in isolation but substantial when repeated hundreds or thousands of times. Frequency and scale determine whether the opportunity matters.
No. Infrequent, highly variable or judgment-intensive work may not justify automation. The development and maintenance burden also needs to be considered. Automation should solve a measurable workflow problem rather than exist simply because it is technically possible.
The next major improvement in Tekla Structures productivity may not come from making experienced users work faster.
They may already be exceptionally fast.
The opportunity is to examine what they are being asked to do repeatedly.
Measure the workflow.
Identify the repetition.
Separate work requiring expertise from work merely consuming expertise.
Then determine whether the repetitive portion can be simplified, standardized or automated.
Because the most expensive unnecessary click in a Tekla workflow isn't necessarily the one that takes the longest.
It is the one your skilled people have to make again and again.
Fab Intel Tech develops purpose-built productivity applications for Tekla Structures, focused on reducing repetitive workflow friction and allowing experienced steel-detailing professionals to spend more of their time where their expertise creates value.
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