Six Sigma and the Single Tag

By Pat King

Jan. 30, 2006—We often read or hear about how one RFID system is better than another because the first can read 500, 600 or even 1,000 tags concurrently. This is a point RFID equipment marketers are constantly making in print and at conferences. The most frequently referenced applications are luggage at airports and the holy grail of reading, an entire shopping carriage as it rolls past a portal. There is a small problem with all of this, though, and it pertains to read reliability.

The inability to read multiple tags reliably is why Wal-Mart and the U.S. Department of Defense have focused on reading only the pallet tag rather than the tags of all cases on a given pallet, and on reading the case tags only when the boxes are on a conveyor. They are subscribing to singulation—making sure only one tag is in an interrogator's read field at a time—and not knowing it.


RFID vendors still drone on about their systems' ability to read hundreds of tags simultaneously. Unfortunately, RFID is being promoted as a confidence game, and we are naively rushing to a sad ending.

Why? Are we inherently stupid? I think not. Is it because we lust for automation and advances in technology? Perhaps, to some degree, but it's not the only reason. Then what is going on?

Most of the world has begun to take bar codes for granted. This is the first part of the problem. When you fail to understand the base solution we live with and how it actually works and doesn't work, you set yourself up to be conned and duped.

Let me take you back for a moment. The bar code actually dates back to antiquity, but the really old part is not pertinent. We will fast-forward to the late 1970s and early 1980s. In that period, companies imagined a new printing market based on item identification. They created symbols such as black and white lines or bars that varied in thickness, width and/or pitch. They standardized them and called them Code 39 and Code 128. They convened U.S. and ISO standards groups, all along making sure they could sell more and more labels with these new bar codes.

Well, they forgot one thing—namely, how the heck do you read a bar code? The early solution was with a camera or line scanner/wand. The first cameras were costly boxes, and the handhelds were boxes with handles. They were ugly and costly, and they were unreliable because cameras had limitations regarding such things as ranges for depth-of-field, depth-of-focus, contrast, dynamic range, etc. The early industrial cameras tended to capture too much or too little information, or the lighting was wrong, or the image was fuzzy, and so forth. Most people rejected bar codes as ever being useful. The wand worked OK, but was very manual and labor intensive.

The big technological breakthrough came with the invention of the galvo-laser reader. This device sent out a single scanning beam that ran across the surface of a bar code, reflecting back any laser light not absorbed. The black absorbed the light, and the white reflected it back to the galvo-mirror optics. Suddenly, you could reliably scan a single bar code. This laser scanner, which we take for granted today, continued to improve in the usual ways: smaller, lower cost, improved performance, etc. As a result, you can plug one of these modules today into almost any electronic device and reliably read a bar code.

So why don't bar codes work, and why do we passionately want to replace them? Well, bar codes do have some flaws. These flaws are normally either the first or the second slide in most recent PowerPoint presentations at conferences. They include line-of-sight requirements, distorted/soiled or damaged bar codes, lost bar codes and, finally, the inability to write additional data to a bar code. Most of those are described as being solved by RFID within the next two slides at the same conference.

These days, we are so enamored with RFID's potential that we lose sight of one important fact: When we receive a data read, it must have Six Sigma reliability. Manufacturers attain Six Sigma in many of their applications today. They still want to improve but cannot go backward in terms of data reliability.