Why Data Type Choices Matter to Your Bottom Line
I'm a procurement manager at a mid-sized automotive parts supplier. I've managed our automation budget—roughly $180,000 annually—for six years now. When I'm not negotiating with vendors, I'm tracking every invoice in our cost tracking system. And here's something I've learned the hard way: the data types you choose for your Allen-Bradley PLC program can cost you thousands in hidden programming time and maintenance.
This isn't about which data type is 'better.' It's about total cost of ownership. When I compared our 2023 spending across 12 projects, I found that teams using structured data types (UDTs, arrays, strings) consistently had 25-30% lower debugging and revision costs than those sticking with basic types (BOOL, SINT, INT, DINT, REAL) alone.
So let's break this down. In this article, I'll compare two approaches to data management in Allen-Bradley PLC programming: basic data types vs. structured data types. We'll look at three dimensions: programming speed, system scalability, and resource optimization. Each comparison will have a clear winner—but not always the one you'd expect.
Dimension 1: Programming & Debugging Speed
Basic Data Types: The Traditional Approach
When I started in this industry, every piece of data was a DINT or a BOOL. Simple. Predictable. But here's where it gets expensive: a single machine might have 200+ discrete tags. Finding a specific sensor's value meant scrolling through a flat list. One engineer on our team spent an entire afternoon hunting for a 'conveyor_speed_03' tag buried among 500 others.
Structured Data Types: The Modern Alternative
Then we switched to user-defined data types (UDTs). Instead of flat tags, we created a 'Conveyor' UDT with sub-elements: speed, status, fault codes. Suddenly, that hunting game disappeared. One line of logic could reference 'Conveyor_03.Speed' instead of 'conveyor_speed_03.' Our average debugging time dropped from 4 hours per fault to 1.5 hours.
I assumed structured types were overkill for small projects. Didn't verify. Turned out even a 50-tag program benefits from grouping. A lesson learned the hard way when our junior engineer spent 3 hours tracing a single bit that should've been part of a status UDT.
The Verdict
Structured data types win for programming speed—hands down. But here's the catch: initial setup takes longer. Creating the UDT structure adds 1-2 hours upfront. After that, it's pure savings. If I'm tracking total time across a project lifetime, structured types pay for themselves by the second revision cycle.
Dimension 2: System Scalability & Maintenance Costs
Basic Data Types: The Growth Problem
Here's a scenario I've seen three times: a line expansion happens. The original program used basic types. Now the maintenance team needs to add 30 new tags. They find the global tag database, add rows, update the HMI tags, and test. Simple enough—until the next expansion. After 5 expansions, the tag database is a mess. Duplicates. Mismatched naming. One plant spent $12,000 (in lost production time) tracking down a tag naming inconsistency that caused a line stoppage.
Structured Data Types: Built for Growth
With UDTs, adding a new conveyor is cheap. You instantiate the existing UDT, rename it, and connect it. The tag database stays clean. When we expanded our main assembly line in 2023, the structured approach cut our integration time by 40%—saving about $6,000 in engineering hours.
Looking back, I should have insisted on UDTs from year one. At the time, the project manager argued it was 'too complex for a simple line.' It wasn't.
The Verdict
Structured data types are cheaper for scalable systems. If your facility grows, or if you have more than 3 machines, structured types reduce total cost of ownership by 18-22% over 5 years (based on our internal tracking). But if you have a single, static machine with 20 tags? Basic types are fine.
Dimension 3: Resource Utilization & Hidden Costs
Basic Data Types: The Memory Trap
Every Allen-Bradley PLC has finite memory. A CompactLogix 5380, for instance, has 2MB of memory. Use DINTs for everything (a common bad habit), and you waste 3 bytes per tag when a SINT would do. Multiply that by 500 tags, and you've consumed 1.5KB of unnecessary space. Not critical—until it is. I've seen a project hit the memory limit because of inefficient data type choices, forcing a $2,500 upgrade to a larger controller.
Structured Data Types: Optimized Footprint
UDTs don't automatically save memory, but they force you to think about data size. When we standardized on SINTs for status flags and DINTs only for actual numeric values, our memory usage dropped 15%. Plus, structuring data means the controller processes it faster—less scanning overhead. Our average scan time improved from 12ms to 9ms after refactoring.
According to NFPA 79 electrical safety standards, proper data organization is also a maintenance requirement. A messy tag database can lead to incorrect troubleshooting—and that's a safety risk and a cost risk.
The Verdict
Basic types can actually be more memory-efficient if used carefully (use SINT instead of DINT). But structured types win for overall resource optimization because they force better habits. If your team is disciplined, basic types are fine. If not, structured types are the safer bet.
How to Choose: A Cost Controller's Decision Matrix
After all this comparison, here's my framework for deciding:
- Choose basic data types if: your program has under 50 tags, the system is static (no expansion), and you have one maintenance engineer who knows every tag by heart. Total cost advantage: minimal for basic, but lower startup cost.
- Choose structured data types if: your program has over 100 tags, the system will be expanded, or more than one person will maintain it. Total cost advantage: 15-25% lower lifetime cost, especially on projects over $50,000.
One more thing: don't assume mixing is bad. I've found hybrid approaches work best. Use UDTs for major subsystems (conveyors, robots, stations), but basic types for simple I/O. That's where we settled after comparing 12 projects over 4 years. It's a compromise—but in procurement, the best deals are usually compromises.
And if you're still unsure, start a simple 30-tag project with UDTs. Test both approaches. Track your time. You'll know within a week which saves you money.
