Category Archives: Inventory

Open Up Your Supply Chain With E2Open

Today is the official launch of E2Open‘s new Collaboration Center, E2Open Version 8.0. The focus of this release are their new supply dashboards with real-time KPIs, predictive analytics and exception notifications designed to allow an organization to manage its global trading network across multiple supply tiers.

E2Open was founded in 2000 with the vision to provide supply chain managers visibility into their entire supply chain network — beyond just the first tier of suppliers because problems often start with your suppliers’ suppliers and your suppliers’ suppliers’ suppliers. Getting visibility into a late shipment or raw material shortfall as soon as it happens gives an organization time to find an alternate supply or alternate go-to-market strategy, as opposed to finding out the day after your supplier was supposed to ship. Since then, E2Open has gone through multiple versions of its platform and its E2open Business Network (8 to be precise) and now offers solutions in Collaborative Supply Planning, Demand Management, Logistics Visibility, Order Management, Inventory Management, and B2B Managed Services with a customer list that includes Blackberry, Dell, FoxConn, Hitachi, Motorola, and Seagate to name a few.

However, today we are only going to focus on its new collaborative platform and its supply management dashboards to be precise. Why would I do such a thing, especially since I repeatedly claim that Dashboards are Dangerous and Dysfunctional in full agreement with Robert D. Austin? Because the reason they are dysfunctional is that they lull you into a false sense of security when you see a lot of green. As I said in SI’s now classic post:

a dashboard can not tell you how well you’re doing … the best it can do is capture the data it’s been programmed to capture, roll-up the metrics it’s been programmed to roll up, and do the built in calculations of efficiency based on those roll-ups.

As a result, even if it tells you that 90% of spend is “on contract”, that doesn’t mean it is. It won’t tell you that 10% of spend has been misclassified under the wrong code and is being reported as on-contract when it’s really, really not. The truth is that:

a dashboard can only provide an upper bound on how well you’re doing, and this is useless. Reporting that my efficiency is at most 98% when it is in fact 92% is useless and unactionable.

However, if the goal is reversed from trying to tell you how well you are doing, and giving an inaccurate upper bound, to how poor you are doing, and give an accurate, minimal lower bound, it becomes useful. And if you can then define metrics such as inspected orders, reviewed invoices, verified shipments, etc. and report on the uninspected orders, unreviewed invoices, and unverified shipments (etc.), then you not only know everything that’s wrong but how many dark corners could be holding problems waiting to materialize but where to look when the problems you know about have been solved.

And that’s why E2Open’s new dashboard, developed in HTML5 and available through your browser, is useful. Not only does it provide deep, near real-time insight into your global supply network, with data aggregated across the multiple tiers of your supply network as fast as the platform can get access to it (which is real-time if the suppliers are using a modern supply management system with real-time query / export capability or once a day if the supplier is still on an old ERP/MRP that does a daily export in CSV to a secured FTP directory), but the drill-down dashboard can be configured to display whatever KPIs and metrics you want, however you want.

You can choose the standard indicators that show that 98% of your orders are expected to ship on time, based upon tier-1 and tier-2 suppliers shipping their components and raw materials on time, or you can invert it and show that 2% of your orders are late. Every metric can be reversed and you can filter what is displayed. So, if you want, you can set it up to show ALL RED and just show you

  • all the problems the system has identified that need an investigation and/or resolution and
  • how many records, products, shipments, etc. have not been manually reviewed, tested, verified as this will tell yo exactly where problems could be lurking and, if the count is high, where more oversight might be required to prevent new problems.

It’s not the standard configuration, but it is supported — and the ability to razor sharp focus into issues two levels down into your supply chain within 24 hours of your supplier’s supplier reporting a delay is fantastic. And, unlike most “dashboard” products, they support the creation of multiple public and private “dashboard” pages, at different levels of visibility and granularity, to allow each user to track all KPIs, metrics, and issues relevant to them. It’s not trying to be a one-size fits all solution because E2Open recognizes that, in supply chain, one size does not fit all.

Furthermore, 90% visibility at each tier is possible very quickly as they have done over 400 ERP / MRP / Supply Chain system integrations to date and can on-board suppliers on all of the major platforms very quickly. And they even have the ability to do trending and predictive analytics to identify where problems might occur — which is useful when you know that somewhere in a certain data blackhole there is likely an issue but are unsure where to start.

E2Open’s new release is worth checking out. The platform strives to give you a single version of the truth across your supply network and does a good job at doing it. And the inventory management / collaborative forecasting drill down capability is just as detailed as some of the best inventory solutions on the marketplace.

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.

However, There Is Still Time To Turn Things Around. (HD Part IV)

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.

Save Yourself an Hour — There’s Only ONE Real Driver Behind Inventory Costs

There’s only one real driver behind inventory costs.

Inventory. No inventory, no inventory costs.

Now you can skip the first ten minutes of the over-promoted webinar coming up three weeks from two days ago. (Deliberately confusing.)

As for proven, practical techniques for controlling inventory, there’s only two of those you really need to know.

  1. Don’t buy what you don’t need and
    You’d be amazed at the cost avoidance you’ll realize.
  2. Don’t buy more than a moderate buffer beyond what you expect to need by the next replenishment cycle. (Altering the quantity every order if need be.)
    Then inventory doesn’t build up beyond an expected level and storage costs don’t escalate out of control.

Ten more minutes saved. As for smart use of technology to manage inventory data — upstream and downstream — and to improve forecasting, there are three key points:

  1. get sales updates at least as frequently as orders are made,
    forecasts will always be more accurate with recent data
  2. be sure to factor in upcoming marketing or (predictable) market events expected to make an impact,
    so you won’t be surprised by a rapid spike or drop in demand and
  3. put the tool in the hands of an expert.
    Forecasting is art and science. You need an artist who knows how to select the right model and use the tool properly or you’ll be repeating the i2/Nike fiasco all over again. (Don’t get the reference, Google It.)

Okay, twenty more minutes saved. Now on to procurement and transportation tactics to reduce inventory build up. This is where it could get interesting, but it could also get quite obvious. If you review the six key points from above, you will reduce inventory build up if you:

  1. don’t order product you don’t use
    no inventory, no build up — the best way to cut inventory costs is to control demand
  2. don’t order more product than you expect to sell or use within the next two replenishment cycles
    as more than a moderate amount of buffer can add exorbitant cost
  3. re-run the forecasts before each replenishment cycle
    as downward projections must result in an inventory reduction and
  4. don’t forget the expert
    as this is one application where the tool alone isn’t enough

You can certainly get much more advanced than this, but is it worth it? The most successful organizations follow the 80/20 rule. They apply 20% of the effort to get 80% of the savings and then move on to the next low-hanging fruit big savings opportunity. Inventory management is as old as the Procurement profession, and best practice inventory management and forecasting hasn’t improved that much over the last decade. Returns are diminishing and at some point you have to wonder if it’s worth it when there are so many other opportunities on the table for cost reduction and avoidance. You can disagree, but the most successful Supply Management organizations use cost reduction waves (and implement multiple cost reduction strategies) and only go after the last 20% if the effort is really worth it. With raw material and fuel costs rising rapidly, unless you’re using a third party storage facility that is significantly over-billing (and this is almost as common as office supply vendors replacing cheap contract SKUs with expensive off-contract SKUs when the products reach end of life) or maintaining a buffer that is much too high, inventory costs are no longer a significant portion of lifecycle costs for many products.

That’s the doctor‘s view. Leave a clearly defined different one if you wish.

TMS Requires 100 Million, Does ERP Require 1 Billion?

A recent article over on Logistics Management that put[s] the spotlight on ERP had a great quote from Ben Pivar, Vice President and North American Supply Chain Lead for Capgemini regarding Transportation Management Systems (TMS) Pivar says that the economics of installing a TMS package on a client server, for example, doesn’t really work until you have nearly $100 million in freight spend and that’s why on-demand is so popular in that space.

SI has to agree. Unless a firm has tens of millions in freight spend, the costs of installation, maintenance, and usage tend to dwarf the benefits of using a TMS system. However, what’s even more important to note is that enterprise ERP (from a top vendor) is, on average, at least five, if not (usually) ten times, more expensive to install, integrate, maintain, and use than TMS. This would seem to indicate that the economics of traditional ERP don’t make sense unless your company has 1 Billion in spend, or at least 1 Billion in revenue. In other words, unless you’re a member of the Fortune 2000 or Global 3000, traditional end-to-end on-premise enterprise ERP is probably not for you. And it would appear that Oracle, one of the largest players, tends to agree. Why do you think it has advertisements stating it has 98% of the Fortune 500? It’s not just because the Fortune X, it’s target market, provide it with its biggest deals. It’s because Oracle also understands that unless a company has reached a critical mass, given the cost of the system, the company won’t get the advertised return (which is a key to keeping the company as a high-paying customer year after year).

However, every organization needs a good transaction store and data repository as analysis is key to supply management success. So what does this mean if you’re not one of the lucky ones? Don’t look at a a tradtional on-premise end-to-end ERP from a big boy. Look at either a newer, smaller, slimmed down offering from a smaller player, possibly based on an open-source solution (like Compiere), a suite from a provider that maintains its own transaction store, or a newer, slimmed down, SaaS offering from a traditional provider that can integrate with some BoB solutions in the cloud and offer an effective hybrid solution. Just don’t go for the billion-dollar solution, because your organization likely won’t get a return from the millions it will cost.