In Part I, we began this series with a reference to a recent article in StoreFront BackTalk on how a recent snafu made by Home Depot during a recent upgrade to its online website on February 1st Left Home Depot Customers Running in Circles and chasing their tails due to incomplete planning and testing. While this was breaking news for some analysts and bloggers, given that it likely won’t even make a blip on Home Depot’s bottom line when all is said and done, for reasons discussed in the post, it isn’t something Home Depot needs to be concerned about. However, as discussed in the second post of the series, Home Depot does have serious technology-related problems in the doctor‘s view — problems that it may not even be aware of which are only going to amplify as time goes on. And these problems are very serious because, as discussed in the third post of the series, they are likely resulting in dissatisfied customers every day in every one of the 2,200 stores across North America. And when you consider that it would only take 3 dissatisfied customers per day per store (which seems entirely feasible in the doctor‘s view) to create 2,200,000 dissatisfied customers over the course of the year, the unnoticeable drops in the bucket become a rip current that could cause some serious damage.
So what’s the problem? As discussed in the last post, it is SARS, short for Storefront Automated Replenishment Systems, which, to the doctor‘s understanding, they have rolled out to the store level across each and every North American store over the past year or two. Advertised by vendors as the ultimate solution to stock-outs and lost sales, as the system is supposed to automatically place purchase orders and replenish inventory at just the right time to insure an item is never stocked out and that the optimum quantity is always on hand, it is sold as a retailer’s dream when, in fact, it is actually a nightmare in disguise. As explained in the last post, these systems only work in a perfect world, but there ain’t no perfect world, and they inevitably break down due to imperfections in the system, incompleteness in the knowledge, and inadequacies of the human operators (including programmers, administrators, and users).
You see, like traditional Automated Replenishment Systems (ARS), also known as Automatic Ordering Systems (AOS), SARS assumes:
- Initial inventory counts are correct
for each and every product in the store.
- POS-based inventory updates are regular and correct
preferably, on a regular, daily, basis.
- Damaged merchandise is removed from inventory promptly
and removed from the system just as promptly.
- The replenishment model is accurate
and takes into account weekly, monthly, and seasonal variations in demands
- The world of tomorrow never comes
because the model on which the inventory demand is modelled is supposed to repeat cyclicly with no change, ever.
But they are not Xanadu. And, in the doctor‘s view, the source of SARS is the same as that of the Kubla Khan because:
- A significant number of inventory counts are always wrong … and this number only increases with time.
There’s a reason retailers typically have all-night inventory counting marathons on a regular, often quarterly, basis. Damage, theft, loss, and human error results in a large number of products having an inventory count that is off.
- Software is buggy and even the internet is not infallible.
Errors in the POS system can result in the odd transaction not being included in the summary sent to the inventory system, the update file being cut off, or incomplete transmission. Plus, a poorly timed communication failure can result in the POS system thinking the transmission is complete when part of the file was lost.
- Even if it is removed from inventory, it’s often not removed from the system!
A junior associate may remove the item from the shelf, but forget to update the system. This will cause the inventory counts to get wildly out of whack over time.
- The replenishment model is typically a randomly chosen best-fit model on available data.
And depending on how much data is chosen, that model could change wildly.
- The arrow of time dictates that tomorrow always comes.
Next Monday will not be the same as this Monday. Next February will not be the same as this February. And as soon as an unplanned promotion occurs on an unexpected item, something wildly different will occur.
In other words, at the store level, SARS does not work — at least not in an automated fashion. Thus, if Home Depot wants to turn things around, or at least insure that things get pointed in the right direction before it needs to turn things around, in the doctor‘s view, it needs to (partially) abandon SARS at the store level and go back to ARS at the (local) distribution centre level where, when done properly, ARS can be tuned to work like a charm. How? That will be discussed in the next post.