Daily Archives: February 21, 2012

It Starts By Remembering that an ARS is Used to Carry a LoAD, Not a Store. (HD Part V)

Last week we discussed the recent snafu made by Home Depot during a recent upgrade to its online website on February 1st that Left Home Depot Customers Running in Circles and chasing their tails due to incomplete planning and testing. We noted that, despite the fact that it was breaking news for some analysts and bloggers, it is not something Home Depot needs to be concerned about. However, as discussed in following posts, Home Depot does have some serious technology-related problems in the doctor‘s view — problems that it may not even be aware of which, if left unchecked, may only amplify as time goes on. However, as discussed in our last two posts, the problem has an easy solution, and it starts by rolling back SARS and retrenching to ARS at the Local Area Depots.

Done right, an Automated Replenishment System (ARS) is the foundation for next generation inventory management. Just ask any inventory management software vendor (including SYSPRO, that was reviewed here on SI in this post and this other post). However, done right requires that the implementation follow a few simple rules

  1. Forecasting at the item level is only done across a geography
    Trying to forecast item demand at the depot, and especially at the store, level is like trying to forecast the performance of an individual stock. It’s more or less impossible. For an item that moves erratically, a single purchase can shift the entire demand pattern. However, just like the performance of a broad mutual fund across an industry will be consistent over time, so will a forecast across aggregated demand across multiple Local Area Depots and the stores they serve.
  2. Forecasting at the depot level is only done across a short time span
    Again, while the demand for many depots will be predictable with reasonable accuracy, the demand at a single depot will not be predictable with reasonable accuracy over a long period of time. So, the more fine-grained the forecast, the shorter the term the forecast must be for.
  3. Forecasts can always be overridden and adjusted by the depot category manager

    No algorithm is intelligent. Not even close. An expert, who knows of a(n upcoming) promotion, change in local tastes or fashion, or trending feedback on product quality will always have the upper hand on certain items at the local level. Thus, the system should allow the local category expert to override each and every forecast and pull rule as circumstances dictate. As the rules get more fine-tuned, the need for this will decrease over time, but there will always be a special situation.
  4. Forecasts and Rules are reviewed and updated on a regular cycle
    As per our last few posts, the forecasts and rules assume at the minimum a predictable, if not a perfect, world — and the world is never predictable. The system will have to be adjusted as time goes on.

Furthermore, if the retailer insists on maximizing use of the system across each and every one of its locations, including retail stores, then the ARS must be restricted to suggest mode. In other words, there’s nothing wrong with using the rules engine and forecast models to suggest what inventory should be ordered, when, and in what quantity, but the order for any item must be presented for review by the appropriate category or department manager, and corrected if required. Thus, there should be human involvement at the end of each and every order cycle. If the category or department manager knows that only certain items have been presenting (potential) issues, the manager must be able to quickly review the suggested orders for those items, make any mods, and then accept the entire order for submission. In other words, if the system has been doing a good job on the widget category, the category manager shouldn’t be forced to review the widget category on every order, but should always have the option just in case she has an inkling. However, if there has been a continual stock-out of sprockets, the category manager should be able to up the order, and, if necessary, change the rule at the local level (subject to review of the depot manager or category manager if required).

And it must be extremely easy for anyone to report an error in inventory count or historical data at any location so that model, and forecast, is corrected as soon as possible. Without accurate data, the system will never work.

That’s the secret to a successful implementation of an Automated Replenishment System — don’t set it and forget it and don’t force it where it doesn’t fit. Monitor and adjust it continually, and as time goes on, it will get more and more accurate with less and less adjustment for any items with steady trends, leaving forecasters free to focus on fashionable or seasonal items in an effort to truly minimize stock-outs and maximize sales.