Technology Wars 2: The Search for More Profits

Almost a year ago I wrote an article entitled, “What does ‘demand-driven’ really mean?” in which I outlined a view of a supply chain driven end-to-end by real-time (or near real-time) demand feedback. My recollection of this writing was triggered today by an article that appeared today on the Financial Times website: “Technology: Smarter software helps minimise discounting.”

In the FT (Financial Times) article, Claer Barrett writes:

“As retailers grapple with falling consumer spending and rising costs, the smart use of technology is proving a valuable weapon.

“Creating a point-of-sale linked supply chain is the latest tactic that larger retailers are employing in order to manage inventories and minimise discounting.”

Among other things, Barrett discusses how the entire supply chain—from the retail all the way back to the manufacturer—is being forced to cope with greater and greater uncertainty. At the same time, Barrett correctly points out that today’s “consumer is more empowered than ever before” via online shopping and price-comparison options.

Barrett’s discussion of the matter leads directly to another topic on which I have written here a number of times—namely, market segmentation. [Click here for more.] Retailers everywhere are learning to collect and leverage high volumes of point-of-sale data, mostly through the proliferation of loyalty programs. [Note: I just checked my pockets. I must be a member a more than dozen loyalty programs ranging from pet supply stores to gas stations and more.]

Between a rock and hard place

Even with improved ability to segment the market and identify buying trends and patterns, the whole supply chain is still caught between the “opposing problems of excess inventory and stock shortages,” as Barrett puts it. Barrett, however, is far too gentle, I think. The horns of the dilemma should really be stated as

excess inventory versus stock-outs.

Almost everyone who has had responsibility for managing inventories of any kind knows exactly what I’m talking about. Being short on stock (low inventories) does not on whit of damage. But being out-of-stock means

1. Lost sales of the out-of-stock goods
2. Lost sales on other goods that may have been purchased by customers seeking the out-of-stock item(s)
3. Potentially, customers lost temporarily or even permanently to competitors

As I have stated elsewhere, the value of losses resulting from out-of-stock conditions—if calculated at all—is almost always vastly understated.

However, on the other end of the spectrum, even though the supply chain suffered out-of-stocks on (almost always) the most popular items, they are almost never able recoup the profits on those items for which they are overstocked.

No.

In fact, chances are they will have to liquidate their overstocked item at or below the price they paid for them. Hence, Barrett’s reference to finding ways to “minimise discounting.”

The key to creating more profits is a “demand-driven” supply chain

My article on a demand-driven supply chain suggests technology that is within the reach of almost every retailer today—not just the big-box merchants. But it requires management to seek two things that they are presently overlooking in far too great a degree;

1. The true cost of out-of-stocks to their operations and to the entire supply chain
   
2. The return-on-investment available to them for building a truly connected and collaborative supply chain

If you are a mid-market retailer, distributor, wholesaler or manufacturer, do not delay in pursuing the discovery of ways to create for yourself a sustainable competitive advantage even in a very challenging economy.

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Further reading: Dynamic Buffer Management (DBM)

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Richard D. Cushing is a senior solution architect at RKL eSolutions in Lancaster, PA.

Simpler is better: Dynamic Buffer Management (DBM)

Somehow, in the dark recesses of the past, someone came up with the idea that we should (at least in our minds) segregate our regular stock (inventory quantities) from our “safety stock” as if there were some difference between the two. “Safety stock,” APICS and others suggest, is to cover “variations” in lead-time or demand, while our “regular stock” is to cover “normal demand”—whatever that is. But for most businesses today, variation in demand is the rule, and not the exception. Furthermore, isn’t it true that our whole stock quantity is really what we want to manage—not some isolated portion of our stock that we describe logically as “safety stock.”

Simpler is better. Our whole stock quantity should buffer the system (read: the whole enterprise) from losses in throughput (read: profits).

For years I have worked with small-to midsized enterprises (SMEs), many of which I first touched when they were in transition from entrepreneurial to enterprise in nature. When I found them, they generally knew very little about their inventory. Oh, sure: they knew in a general sense which items were profitable and which were not. They also had a general handle on which items in their inventory were the “fast movers” and which were “the dogs.” Nevertheless, when it came to managing their inventory quantities they almost all struggled with the all too common problem of being sold-out of some items (and thus incurring losses of potential sales and profits) while, at the same time finding that they were overstocked on dozens of other items (so that they were simultaneously incurring high carrying costs and lower cash flows as a result). The problem was, from month to month, it was almost never the same items that were sold-out versus over-stocked. They could never predict what quantities were going to sell, so they couldn’t predict what quantities to stock.

Constraints management (Theory of Constraints) suggests—as I said above—that our whole stock of any item (taken in total) should serve one purpose: to buffer the system from losses to throughput. Now, it is not the purpose of this present writing cover all of the various details of a full Dynamic Buffer Management solution. The simplicity of Dynamic Buffer Management (DBM) is what makes it so appealing. The following is a real-life application of DBM in action.

The raw data we have on our example SKU looks like this:

We have just two months of data from 2007, full years’ data from 2008 and 2009, and a partial year for 2010. Note that demand in 2008 was fairly stable, ranging between 72 and 220 units per day. However, demand is 2009 become wildly erratic—ranging from just 1 unit per day to 389 units per day. Over the entire recorded history for this SKU, we find the following statistics:

If we graph these data, the results look like this:

Now, it’s nice to know that a third-order polynomial curve fits pretty nicely with a six-period moving average of these data, but most SMEs do not have a staff statistician available to them to help analyze all their inventory history in order to determine how to set parameters like stock levels, safety stock, reorder points, line points and more. Nor, do they have confidence that statistics will necessarily serve them better than their intuition has in the past.

What they are looking for is something SIMPLE, RELIABLE, EASY TO UNDERSTAND and EFFECTIVE. Dynamic buffer management is all of that.

Let’s imagine that we are at the end of year 2008 and we want to set up DBM for year 2009. We’re going to do so based on our 2008 history.

The first thing we need to know is: how big should our starting buffer be for this item?

Well, it ain’t rocket science! Establishing a starting buffer quantity requires the knowledge of a few facts because it is more important to be “approximately right” than to be “precisely wrong.” No matter how much precision (read: time, energy and money) is put into calculating a “precise number” for the size of the buffer (or any other business ‘forecast’ number) that number will end up being “precisely wrong” 99.999 percent of the time.

So, to find an “approximately right” number for the starting buffer is more important than finding a “precisely wrong” one. In our example, we used the following formula:

Starting Buffer Size = average period consumption over the Last 12 months + (safe replenishment time in days * average consumption/day * 2 * paranoia factor)

Some of these numbers are arbitrary:

1. “Safe Replenishment Time” is nothing more than a “safe” estimate of the time it would take to replenish the item under normal circumstances. Almost anyone working in purchasing or replenishment or manufacturing can pick that number for items with which they work day-in and day-out. If one says, “Five,” and another says, “Eight,” then use eight. It’s that simple.
2. The number “2” used in the formula is also arbitrary. It is nothing more than an additional safety factor to cover unusually high demand or unusually slow delivery. In a moment you’ll see why it is not terribly important in the long run.
3. “Paranoia Factor” is our third arbitrary number. This value is used to cover management’s concern about things like:
1. “Our inventory will skyrocket” – so let management set a paranoia factor of less than 1.0 on some items
2. “If we run out of this item, we lose sales on other things, too! – so increase the paranoia factor
3. “This is a high-margin item and we don’t want to lose a single sale” – so make the paranoia factor larger

For our example, we calculated a starting buffer size of 11,954 base on a paranoia factor of 1.000. Let’s watch what happens using the actual consumption figures from year 2009.

Now, let’s see how DBM helps us out:

• Period 1: We just stocked up to almost 12,000 units and in period one we had the worst month ever! We sold only 23 units! Have we done the right thing here?!?
  Even though it seems like we have plenty of stock, we follow our basic rule: Whatever we consume, we replenish. So, we place a replenishment order for 23 units.
  
  At the end of the period, our “Buffer Status” = 99.81 percent. We have almost a full buffer.
• Period 2: Things return to normal now. We consume 3,315 units, we get our replenishment supply of 23 units, and we end the period with a buffer status of 72.27 percent. That’s okay. We really don’t get concerned as long as the buffer remains in the green zone—that is, above two-thirds.
  
  We dutifully place our replenishment order for your consumed quantity—3,315 units.
• Period 3: We consume 2,153 units and get our 3,315 units from our replenishment order. True to form, we order replenishment for the 2,153 units, and we end with the buffer solidly in the green at 81.99 percent.
• Period 4: Wow! We consume 7,903 units; get our replenishment of 2,153 units and our buffer status ends up in the red zone. The red zone is a buffer below 33.33 percent full. [NOTE: Here I’m going to play along with some anomaly in Excel’s failure to calculate and apply conditional formatting correctly. We’re at 33.89 percent and this should be “Yellow,” but it’s not. Excel says it’s “Red,” so we’re going to call it “red.” Close enough!] We take no immediate action other than to note that this is the FIRST PERIOD in which our buffer has fallen into the red zone.
  
  We place our standard order to replenish period consumption.
• Period 5: We have another great period for this item. We consume 8.476 units; get our replenishment order for 7,903 units, and end the period for the SECOND PERIOD IN SUCCESSION in the red zone. The buffer reached 29.09 percent.
  
  Other than placing our replenishment order, we take no specific action.
• Period 6: We’re hit with record sales and move 11,666 units. Even after replenishment order arrives, we still are sitting near the bottom of the red zone at 2.41 percent.
  
  Since this is the THIRD SUCCESSIVE PERIOD where we have ended up in the red zone for this buffer, we take action to INCREASE THE BUFFER SIZE BY ONE-THIRD. Our replenishment order is now for the 11,666 units consumed PLUS the buffer increase of 3,985 units.
• Periods 7 and beyond: We will continue to monitor and manage the buffer dynamically applying these simple rules…
• THREE CONSECUTIVE PERIODS IN THE RED ZONE, then INCREASE the BUFFER by ONE-THIRD
• FOUR CONSECUTIVE PERIODS IN THE GREEN ZONE, then DECREASE the BUFFER by ONE-THIRD

As you can see, this is a very SIMPLE, YET EFFECTIVE, way to facilitate stock management. There are some other principles that should be understood—such as the fact that the BUFFER actually contains both the stock in the warehouse and what is in-transit (or, in manufacturing, if a make-item) and is due within one “Safe Replenishment Time” period.

This is so simple!

Most inventory systems could do this with relatively minor tweaks. It is really just managing inventory by “max stock level”—when quantities fall below the maximum stock level, replenish back to the maximum stock level—with some kind of data view (perhaps even using Microsoft Excel™) to display the buffer status with action signals.

Let me know what you think.

[Cross-posted at Kinaxis Supply Chain Community.]

The Dangerous Dichotomy—Part 3

[Continued]

The conclusion of the preceding article was that, without doubt, reducing out-of-stock occurrences will tend to increase revenues. Increasing revenues will certainly satisfy the sales and marketing team, who have been mandated by the firm’s executives with doing that very thing. But, the question remains, can actions be taken to reduce out-of-stock occurrences in such a way that will satisfy what should be everyone’s goal of helping the business make more money tomorrow than it is making today?

We believe it can.

Consider a distributor that buys products from Pacific rim suppliers. One line of products produces gross profits of about 80 percent. Of the costs associated with this product line, about 15 percent are the actual product cost (including any taxes and duties). The remaining five percent are the costs per unit of shipping the product by ship from its source to the firm’s distribution centers.

Like the product in the example provided in the preceding article (see “The Dangerous Dichotomy—Part 2”), this line comes in an array of styles (or color or sizes). Some of these variants sell better than others, naturally. However, because the distributor (wrongly) believe that they are stuck with a three-month or longer lead-time to get these products, they feel that they must forecast demand well in advance and place their orders based solely on this forecast.

The three-month lead time consists of the time it takes to produce enough product to fill a container (or meet some other policy-based “cost-saving” arrangement), plus the time for ocean-going transportation, and the time to get it takes to get the items through customs and provide land transportation to the destination distribution centers. But, because the forecast is always wrong, the firm inevitably finds itself in the situation we described in “The Dangerous Dichotomy—Part 2”; that is, they experience out-of-stocks on several of the variants while being overstocked on several other varieties of the product.

The firm is aware that they can ship these items by air—in much smaller quantities, of course. However, doing so doubles the per-unit cost of shipping these products.

When managers hear that simple phrase: “Shipping by air doubles our freight costs,” that is usually all they need to hear. They think of those “slashed margins” and “higher costs” and that is where the conversation ends.

But, consider this: Doubling the per-unit cost of shipping on this product line reduces the margin from 80 percent to 75 percent. Sure, that is, in fact, a reduction in profit margins on this product line.

Now, consider this: Shipping by air forces shipment in smaller batches. The smaller batches in the shipments mean that the manufacturer can produce the batches for shipment in less time—perhaps as short a time as a few days. Shorter lead times mean the original forecast and the original order need only cover the starter stock—the stock to be sold while the firm figures out what styles or colors are going to be the “big-sellers.”

When the “big-sellers” are known, replenishment stock can be ordered and shipped by air, but the firm is likely to actually make more money than they did when they were paying lower shipping costs.

Why?

The reason is simple: At a 75 percent gross margin and a five percent increase in shipping costs—between multi-mode sea-land transportation and air transportation—every additional sale (resulting from reduced out-of-stocks on the popular models) covers the difference in shipping costs for 15 units (i.e., 75 percent gross margin divided by the five percent increase in shipping costs).

Besides the obvious advantage found in the extremely high likelihood of increased profits—despite “doubling your shipping costs” and suffering “reduced margins”—this thoughtful approach has all of the following advantages, as well:

1. Happier and more satisfied customers
2. Less likelihood of customers being lost to competitive sources
3. Fewer lost customers means the firm is more likely to be able to sustain revenues with lower marketing costs
4. A happier and more productive sales and marketing staff—able to spend their time capturing new customers and markets instead of appeasing disgruntled customers who could not buy the product they wanted
5. A happier and more productive organization overall—with less in-fighting and a real sense of success and accomplishment
6. More satisfied management and executive team
7. A far greater opportunity for success in the future

All of these benefits accrue to an organization that discovers “system thinking” (i.e., seeing their organization as a whole, rather than as disconnected pieces and departments). Meanwhile, the firm still caught in “the dangerous dichotomy” is still fighting fires day-by-day and trying to keep the smoldering animosity between the factions from breaking out into open warfare.

Makes you want to give “system thinking” a try, doesn’t it?

What does “demand-driven” really mean?

Recently I stumbled across a whitepaper entitled Demand-Driven Inventory Management Strategies: Challenges & Opportunities for Distribution-Intensive Companies (Fraser and Brandel 2007) prepared by Julie Fraser and William Brandel, principals at Industry Directions, Inc. What I found amazing about this article is that it doesn't really get to the point of "demand-driven inventory management strategies." Instead the focus is on better systems, better data, better use of the data, and better forecasts at the SKU level (rather than at the "product family" or "product category" levels).

Now, maybe I'm an idealist, but when I think of "demand-driven" inventory, I think of an integrated supply chain that functions in such a way that when an end-user takes a unit of product off the shelf at the retailer's store (or whatever model is being used), that action triggers the production of one unit at the manufacturer's plant with a minimum of mid-stream manipulation.

See the accompanying illustration, and let me describe for you my concept of a demand-driven supply chain. We will start at the bottom of the illustration – where the action begins – at the retail store. Ideally, each retail store should stock just enough product to cover one day's sales plus a "buffer" to allow for expected variability in demand.

At the end of each business day, the retailers should transmit to their associated distribution center (DC) the quantities sold of each SKU in the supply chain depicted. It doesn't matter whether the DC is owned by the retail chain or the distributor, the process would and should work the same.

Next, depending upon the agreed replenishment cycle (although daily is ideal), the DCs would prepare replenishment orders to be shipped to the retail outlets. The goal would be to replenish exactly the quantity that was reported as sold (plus or minus any adjustments for seasonality, special promotions, etc.) at each outlet. Meanwhile, the DCs will have reported to their supplying warehouse how many units of each SKU that they have sold (again, plus or minus any adjustments).

Each DC should stock only enough of each SKU to cover the replenishment cycle from the domestic warehouse plus a "buffer" to cover any variability in demand. On its scheduled replenishment cycle – and, again, daily is ideal – the domestic warehouse should ship out replenishment orders to the DCs. In the meantime, if the replenishment cycle is longer than one day, the domestic warehouse will have transmitted daily sales numbers back to the off-shore warehouse, so that the consumer purchase made at the retail outlet is transmitted all the way back to the manufacturer within one business day.

Following the pattern we have discussed already, the domestic warehouse should carry just enough of each SKU to cover variability in demand and supply (lead-time). The size of the "buffer" should include a calculated allowance for disruptions in the supply chain where it is most vulnerable (e.g., overseas transportation, or other). Naturally, since this represents aggregate demand, estimates of demand will be more accurate at this level than they will be at either the DCs or the retail outlets. As a result, the domestic warehouse inventories will be larger, but not nearly as large as if each lower level in the supply chain tried to estimate (read: forecast) demand for periods into the future. This approach helps keep inventories to a minimum and makes the whole supply chain more responsive to changes in demand.

Likewise, the foreign port warehouse should carry just enough stock of each SKU to cover variability in demand from the warehouse(s) on the opposite shore plus any variability in lead time from the manufacturing plant which, as we shall see, should be near zero.

Since estimates of demand variability will be most accurate at the level that demand is most highly aggregated, the manufacturer is the most reasonable place to keep the largest "buffer" of inventory for the SKUs in the supply chain. The manufacturer should carry enough stock of each SKU to cover production lead time variability. (Typically, this buffer length should be only three times the actual production time for each SKU. That is to say, if a day's aggregate supply of SKU #1001 can be produced in a single day of production, then the buffer for SKU #1001 should initially be set for three days. Then production should be scheduled in batches as small as is practical for the SKU.) Generally, production should be scheduled at the manufacturing plant based on producing the actual demand reported via the supply chain plus enough to fill any "holes" created in the buffer created by unusual demand in a prior period.

Now, that's what I call a "demand-driven" supply chain. It is do-able and it makes life better for everyone. Here's why:

• Lower inventories everywhere
  
• Reduced write-offs due to obsolescence
  
• Lower inventory carrying costs all across the supply chain
  
• Manufacturing is more responsive to changes in market demand – not separated from real feedback by weeks or months
  
• Fewer lost sales due to stock-out (And the value of lost sales are almost always under-estimated in supply chain calculations simply because they are done by "averages," but the items most likely to suffer stock-outs are the most popular selling items, not average performers.)
  
• Reduction or elimination of expediting costs across the whole supply chain
  
• Dramatic reduction in overstocks (and about 73% of companies report overstocks simultaneously with expediting for items that are running short), which leads to less price-cutting to liquidate unneeded inventories
  

©2010 Richard D. Cushing

Works Cited

Fraser, Julie, and William Brandel. Demand-Driven Inventory Management Strategies: Challenges & Opportunities for Distribution-Intensive Companies. White paper, Boston, MA: Industry Directions, 2007.