Dynamic Buffer Management (DBM) for the Supply Chain

Dynamic Buffer Management (DBM) for Inventory and the Supply Chain on Prezi

Here is the presentation I made to the RKL eSolutions ERP User Group in Lancaster, PA, on Friday, 11 May 2012. Please contact me directly via the link below if you would like a copy of the accompanying white paper, as well.

Increased supply chain confidence through simplicity

Traditional approaches to inventory management and replenishment divide inventory stocks into two portions:

1. Working stock – the inventories designed to cover daily demand
   
2. Safety stock – the inventory quantities designed to cover variation in supply or demand or both
   

Years of statistical analytics and software development have been focused on improving the ways in which lead-time, demand and safety stock values are calculated. So much, in fact, that most of the people who use supply chain management, inventory management, or replenishment software frequently do not even understand what the software is doing, how it is doing it, or why it works or does not work.

Some years ago I was consulting a firm and, in the course of the business, reviewing how they went about their inventory management and replenishment. They had software that did inventory management and that included replenishment calculations.

So, we were sitting together and he was describing to me what he was doing on his computer. He said, “Here’s the ordering screen. It shows historical demand here [pointing], and the recommended order quantity here [again, pointing]. And, I don’t know exactly what this number is for [pointing], but if I think the system is suggesting that I buy too much or two little, I can adjust this number until the suggested order quantity lines up with what I think it ought to be.”

Well, of course, what the system was doing was exponential-smoothing of demand and the value he was adjusting was the value of alpha in the formula.

What I refrained from asking him (only by biting my tongue) was, “If you are going to simply adjust the system’s findings to your intuition, why use the system at all?”

The moral is: Systems that are not understood—and most complex systems are not understood—are also not trusted. Especially if they frequently—or even, regularly—produce what are perceived to be unreliable results.

The artificial divide

The artificial subdividing of stock quantities into “working stock” versus “safety stock,” and adding complexities around the factors used to calculate the one value versus the other provides no added value. In fact, the complexity actually leads to less reliability because the users frequently do not know how to set the input parameters effectively. Not to mention the fact that the parameters that are effective today may not—in fact, likely will not—be effective tomorrow or next week.

The fact of the matter is, in most cases, the only awareness of the division between “working stock” and “safety stock” quantities is found in the software itself and those that may be intimately acquainted with the software and its configuration. The people on the warehouse floor typically do not know when they have made an incursion into “safety stock.” They don’t know that the first 41 units they picked for order number 8789089 were from “working stock,” and the last nine units were taken from “safety stock.” And, they should not care.

Even the managers frequently have no visual signal that an incursion has been made into “safety stock.”

Inherent simplicity

Employing Theory of Constraints (ToC) Dynamic Buffer Management (DBM) makes life easier to understand for those responsible for inventory management and replenishment (read: supply chain managers). The buffer size (for any given item in any given stocking location) is a single number. (Let’s say, 1,000 units.)

The formula for setting the initial buffer size is simple and easily understood. Typically that formula is something like this:

Initial Buffer Qty = [Average Daily Demand] * [ToC Replenishment Days] * [2] * [Paranoia Factor]

The only factor that really needs any kind of explanation is the “Paranoia Factor.” This is merely a multiplier selected by intuition and based on senses of the criticality of an item. An item might be critical because it is used in the production of 800 other items; or because the majority of your customers all buy this item; or because one hugely important customer relies upon you for this item; or dozens of other reasons.

Once the initial buffer size has been calculated and set, the buffer is divided (mathematically) into three “zones.” The top third is called the green zone, the middle third is called the yellow zone, and the bottom third is called the red zone.

Going forward, the DBM system simply monitors for conditions at each replenishment cycle and adjusts the buffer size according to rules. The rules are typically:

1. Too Much Green – The item has been found in the green zone on three consecutive replenishment cycles; therefore, reduce the buffer size by one-third.
   
2. Too Much Red – The item has been found in the red zone on two consecutive replenishment cycles; therefore, increase the buffer size by one-third.

It’s that simple. No complex formulas for calculating and managing variability in demand or supply.

On top of that, supply chain managers can have simple visual signals as to the status of their buffers. A simple view of the inventory data (by location) can readily provide red light, yellow light, and green light indicators for the buffer status in any stocking location for any item. No math and easy to equate to action:

• Green light – no action required
• Yellow light – take note, perhaps investigate critical factors like larger-than-normal orders or orders pending for critical customers
• Red light – consider expediting measures, if necessary

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NOTE: There are more options available with DBM, such as identifying and managing SDCs (sudden demand change items—like seasonality), managing Virtual Buffers (between stocking locations, such as warehouse-to-warehouse replenishment, or broader supply chain visibility and collaboration). It is not the intent of this article to exhaust the applicability of DBM.

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RKL eSolutions, LLC is in the process building a cloud-based solution to help you manage your inventory in just such a way—using Dynamic Buffer Management and the Theory of Constraints. Contact me or fill out the contact form here if you would like more information.

What’s wrong with EOQ?

Economic Order Quantity (EOQ) EOQ is essentially an accounting formula that determines the point at which the combination of replenishment costs and inventory carrying costs are the least. The goal being to minimize both the ongoing costs of carrying inventory and the expenses involved with replenishing inventory.

The basic EOQ formula looks like this:

As you can see, this formula attempts to balance (simultaneously) the following factors related to the business expense linked to holding and replenishing inventory:

1. Usage rates – how many are sold or consumed over a period of time (one year in the basic formula)
2. Cost of replenishment – how much it costs the firm to replenish a single inventory item (SKU) from the point of recognizing the need for replenishment through putting the quantities back on the shelf
3. Carrying costs – all of the costs and expenses related to storing and handling of the inventory quantities held

Let us take a look at how these factors interact in a practical example:

In our example, we have an item that has a cost of $25 per unit, and the average daily demand is five (5) units. For this firm, the cost of replenishment is slightly above average—sitting at $30 per PO line processed for inventoried goods.

Observe what happens to the EOQ on this item as the cost of carrying inventory moves through the range from five percent (5%) to 40 percent.

When inventory carrying costs are very low compared to the cost of replenishment (five percent and $30, respectively), EOQ recommends big orders. In this case, each order would support more than 75 days of average demand.

On the other end of the spectrum, when carrying costs are quite high (40 percent) relative to the cost of replenishment, EOQ suggests smaller inventories (as the result of smaller orders) and the order cycle is slashed to almost one-third its former value (now, just over 26 days).

Underlying assumptions

The assumption being made in the construction of the EOQ formula is that the cost of carrying inventory is linear. That, at a five percent rate, a one dollar decrease in inventory on-hand will lead to a five cent reduction in carrying costs to the firm. Similarly, at a 40 percent carrying cost rate, a one dollar decrease in inventory on-hand will lead to a 40 cent decline in carrying costs.

Unfortunately, the linear relationship assumed by the EOQ formula simply does not exist.

When calculating the cost of carrying inventory, a large number of factors are generally considered:

1. Warehouse space rental (or equivalent)
2. Utilities expense
3. Property tax expense
4. Maintenance expenses on the warehouse and warehouse equipment
5. Inventory write-offs/write-downs
6. Other inventory shrinkage
7. Financing expenses for the warehouse, the equipment, and the inventory itself
8. Insurance expenses on the warehouse and the inventory
9. Labor expenses related to warehouse operations

When inventory is reduced $1,000 in a warehouse with a calculated 25 percent carrying cost, what are the likely real impacts on expenses for carrying inventory?

1. Warehouse space rental (or equivalent) – no change
2. Utilities expense – no change
3. Property tax expense – no change
4. Maintenance expenses -  no change
5. Inventory write-offs/write-downs – possibly some change, but not necessarily at the same “average” rate
6. Other inventory shrinkage – same as above
7. Financing expenses for the warehouse, et al -  no change
   Financing expense on the value of the inventory – some change possible
8. Insurance expenses on the warehouse, et al – no change
   Insurance expenses on inventory – some change
9. Labor expenses – no change

In short, only three of the nine items involved in calculating the cost of carrying inventory would likely change based on $1,000 reduction in inventory. That’s because increases or decreases in the volume and dollar amount of inventory held in a warehouse operations produce relatively large but non-linear changes operating expenses.

As inventory grows, changes like adding a second shift in the warehouse, acquiring additional warehouse space, or adding manpower to handle increased volumes happen incrementally. The EOQ formula has no way to account for these non-linear changes to operating expenses. Therefore, your EOQ decision-making my be entirely off the mark for success and increased profits.

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What’s the answer?

To manage your inventory quantities, I would highly recommend the application of Dynamic Buffer Management. [Click on the link and read the article there.]

To deal with non-linear changes in your enterprise—decisions that may lead to major changes in inventories (increases or decreases)—you need a broader formula that considers your system (your enterprise) as a whole. That would be this one:

Where,

• ROI = Return on Investment
• delta-T = Change in Throughput 
• delta-OE = Change in Operating Expenses
• delta-I = Change in Inventory or demand for other Investment

This formula would cover changes like adding a second shift (change in Operating Expenses) or building a new warehouse (change in Investment).

Think about. Contact me if you need further clarifications.

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.]