Time not tide waits for no man.
And so it is for mobility in supply chain management. Once primarily the domain of laser barcode scanners and barcode printers, (with a smattering of voice technology), mobility in supply chain management is exploding in multiple directions. A key driver in this evolution is all the new hardware options now available for mobilizing supply chain processes. From ruggedized tablets to smart phones to imaging cameras to the “internet of things”, today’s supply chain managers have multiple options for mobilizing their processes, with the combined goals of increasing productivity, increasing customer satisfaction, and/or lowering cost.
Let’s take a look at this trend, and apply a little Bayes’ analysis to try and quantify its development.
Let’s start with a simple question. What is the probability that new hardware technology, (aka tablets, smartphones, imagers, etc), significantly displaces the sale and/or deployment of traditional barcode scanning technology in the first half (1H) of 2014? We want to know if it is a SIGNIFICANT cut, and we want to stick to the 1H of 2014 to increase the relevance.
To use a Bayes’ approach, we need to start by defining an a-priori probability to our question. What do we think is the probability that tablets, imagers, etc, will have a significant negative impact on the traditional barcode scanning deployment by 1H 2014? This is going to be a subjective guess, and everyone will likely have their own opinion. For the sake of argument here, let’s assign this a probability of 1/4, or 25%.
Whether you agree with this number or not doesn’t matter. We’re going to “tweak” it using a Bayesian approach, and it is the process of tweaking we want to evaluate. You can change the numbers to your own heart’s content later.
Given our specific question regarding supply chain mobility, and our assigned a-priori probability, we now want to look around for any evidence that might support, or detract from our guess. This evidence will be used to tweak our probability. So, what kind of evidence can we find regarding the adoption of new mobility devices in supply chain operations? And more specifically, what evidence can we find regarding all these new device’s impact on the barcode scanning market?
Let’s start with the available media outlets. DCVelocity released an article in Nov. 2013, that describes some specific use cases of tablet and smartphone adoption by JBHunt and others in supply chain operations. Likewise, VDC published a study in Sept. 2013, forecasting a decline in sales in barcode scanners through 2017. The basis for this decline in barcoding CAGR emphasized the adoption of imagers and consumer devices?. A little digging will turn up these, and a number of other other indicators that address our question. But just how valuable is all this Evidence with regard to our prediction?
There are two questions we need to ask to use this evidence in a Bayes’ evaluation. They are;
- What is the probability that these journals would report this information in Q2/Q3 of 2013 IF barcode scanners ARE going to fall off a cliff in 1H 2014? And,
- What is the probability that they would be report this information in Q2/Q3 of 2013 IF barcode scanners are NOT going to be significantly displaced in 1H 2014? In other words, what is the probability these are just some timely releases, BUT there’s not going to be any huge conversion from barcoders to new hardware in 1H 2014?
For the first question, what is the likelihood that DCVelocity and VDC, who make a living by timely reporting and forecasting, would be releasing this information now IF barcode scanners will be significantly displaced in 1H 2013? We should probably expect there is a very high likelihood that we would see these reports at the end of 2013, if we’re on the cusp of a major technology transition early 2014.
Actually, we should probably expect to see these, and a lot more. Let’s set this probability very high say, 95%.
Ok, now what about that second question. We’re not suggesting that new hardware devices will not have any impact. We’ve been witnessing the adoption of non-barcode devices in supply chain ops for a few years now. What we want to ask though, is how likely is it that we see these reports come out late in 2013, BUT barcode scanning isn’t really all that negatively impacted in 1H2014. (Any more say, than it was in 2013.) That’s an interesting question, and bears a little thought. (And btw, it is this thought problem that is really the major benefit of using Bayes’ analysis.) Consider this.
Even if the barcode market is NOT significantly displaced in 1H2014, there is still a pretty high likelihood that we would see these reports. Why? Many reasons come to mind, but here are a few.
- Publish or perish
- Industry journals and syndicated research organizations have editorial calendars to meet. Therefore, even if barcoding was NOT negatively impacted by a LARGE amount 1H2014, there’s still a good bet that we’d see these articles published.
- We’ve been witnessing the evolution of new hardware form factors for several years now.
- Although interesting as a data point, this isn’t new news. Tablets have been out for several hardware generations now, and have been available ruggedized for a while. Imagers have been around for years. The point being, even if we do NOT see a large negative impact next year, we would probably still see these reports.
Given that, it’s probably safe to assume these reports would be published even if there is no HUGE displacement of devices in 1H2014. Let’s put that likelihood at 75% to suggest it’s still likely, but not as likely as would be IF we were on the verge of a major swing event.
Okay, now we have a question with a priori probability. We have some new evidence, (….read some news…), and we’ve guessed how important, or impactful, this news is given our question. What would Bayes tell us should be our NEW probability for our question?
I’m not going to detail the mathematics here. You can find that elsewhere. (See  for example.) But, just running the numbers yields,
P[ Significant decline | Evidence by reporting ] = P[ Evidence | Significant decline ] x P[ Significant decline] / ( P[ Evidence | Significant decline ] x P[ Significant decline] + P[ Evidence | NOT Significant decline ] x P[ NOT Significant decline] ) = (.95 x .25) / ((.95 x .25) + (.75 x .75)) = .2375 / ( .2375 + .5625 ) = .297
Based on our analysis, we now feel that there is a 29.7% likelihood that new hardware technology will significantly displace barcode scanners in 1H2014. Up from 25% originally. Not a great big jump, is it? Interesting, but probably not great enough for us to start ringing the fire alarm …..yet….
Let’s broaden our question, and now ask, “What if……..”
Say we’re a vendor selling autoID technology and services. What we really want to know is, what would we consider REALLY impactful evidence. In other words, what would we need to see to convince us that barcode devices are about to suffer a SIGNIFICANT displacement in 1H2014. Something more tangible than a[nother] headline like, “Mobility Devices EXPLODING in 2014!!!”
Here, Mr. Bayes lends us a big hand. The answer to our question comes down to looking at the ratio between the probability our evidence occurs IF there is a significant displacement to barcode devices 1H2014, versus the probability that our evidence would occur if there was NOT going to be a significant displacement in 1H2014. In other words, we would need to see some evidence, or some event, such that,
P[ Evidence occurs given that there IS a significant hardware displacement ] ------------------------------------------------------------------------------ P[ Evidence occurs given that there is NOT a significant hardware displacement ]
was a very BIG number. And, the bigger the better.
So, what kind of Evidence can we imagine that would fit that bill?
Suppose it comes to light that a major supply chain company has contracted to deploy a significant amount of new tablets and imagers to modify their operations? To use the parlance of the media, what if we discover that someone has developed the “killer supply chain app” for tablets and/or imagers, and also that some major player has contracted to buy and deploy it? Whatever the app, (….omnichannel anyone?…), the key is that we find evidence that some player has adopted it…..for actual money….
Major players in supply chain management do NOT adopt new technology on a whim. That is a direct route to the unemployment line. Big players have planning committees, and strategy initiatives, and lots of checks and balances to ensure any new technology adoptions provide significant benefits, and do so for a looooong time. Given this, we can now play the following game.
The likelihood that we see a major player announcing barcode hardware displacement early in 2014, IF we’re witnessing a significant displacement of mobility hardware should be fairly high. In other words, if the adoption of new hardware technology IS taking a drastic uptick, then we probably would expect to see a report of a major player adoption. Let’s say this likelihood is 80%.
But actually, the second question becomes more important. If new mobile device hardware was NOT going to displace barcode devices in 1H2014, would we really expect to see a major player announcing this type of adoption? I would argue that it would NOT be likely. Again, not unless someone wants a quick trip to the unemployment line, OR someone is exceptionally visionary and/or knows something that absolutely nobody else knows….. Therefore, let’s assign this likelihood a very small amount say, 5%.
Ok, so what does our new probability become?
P[ Significant barcode device decline | Evidence a major player announces new technology deployment ] = P[ Evidence | Significant decline ] x P[ Significant decline] / ( P[ Evidence | Significant decline ] x P[ Significant decline] + P[ Evidence | NOT Significant decline ] x P[ NOT Significant decline] ) = (.8 x .25) / ((.8 x .25) + (.05 x .75)) = .2 / ( .2 + .0375) = .842
Or, 84.2% !!! So, given THIS evidence, our probability shoots up from 25% to 84.2%, or 3.36 times as high. And that, as they say, is pretty significant.
Applying Bayes’ reasoning to the problem of new mobility adoption in supply chain management provides a method for focusing analysis, and even generating quantifiable numbers to compare. Yes, it can seem a bit contrived, but I would argue it’s better than just holding up a finger to test the wind, or blindly trusting “everything you read in the news”. Also, the process of reviewing and considering “evidence” is valuable in facilitating management dialogue. If you are a vendor or other provider in supply chain autoID, I would recommend you go through this exercise yourself, use your own numbers based on your perspective and market position, and debate your own prediction. Again, it’s not the final numbers or even the probabilities that are primary. It is the process of analysis, and the dialogue around the assumptions that hold the value.
Using Bayes’ process here, we’ve developed a couple of useful insights. We’ve identified how much weight we’re willing to assign to commonly reported information on mobility technology adoption. We’ve also highlighted what we would WANT to see as evidence to convince us that 1H2014 is the BIG ramp-up for new mobility hardware in supply chain. To summarize, we don’t find reporting from standard media outlets as important as other market events we could imagine.
What do you think?
Ok, now it’s your turn. What do you think will be the impact of new mobility devices on the barcode device market in 1H2014? What Evidence would you consider significant as an indicator in this evolution? And, what about Bayes’ analysis? Any ideas on how to make it more useful, or specific to mobility in supply chain management? Let us know!