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.]

Taking the Easy Way (Down and) Out

In a LinkedIn group discussion today, many people were offering advice regarding how to save a small business that has been struggling due to the recession.  There has been no shortage of advice.  However, one comment today really stuck out to me. Here is what the contributor had to say:

A company is making 1 million a year.
From that it makes 10,000 profit (1%).
Each sale yields 25% return - i.e. if you sell 1,000 250 is profit.
To double its profit it can:
1. Reduce costs by 10%
2. Increase sales by 40%
Do the math(s). Which is easier?

Now, perhaps this example was intended to demonstrate what a clearly bloated and, likely, wasteful company really looks like. After all, the firm is grossing $250,000 on $1 million in revenues, but net profits are only $10,000 (1%).  That means that the firm is spending $240,000 (99%) on “expenses.”

If this is true – that the company really is bloated and wasteful – then, by all means, the quick and easy way to making more money is to “reduce costs by 10%.” It may even be likely for a $1 million revenue company that is spending $240,000 in expenses that $24,000 could be cut out and not do a bit of damage to the firm’s ability to survive and thrive.

The real state of things

For better or for worse, most small businesses today do not have a profit-and-loss statement that looks anything like that – at least not in terms of being bloated and wasteful. Most of the SMBs (small-to-mid-sized businesses) that I encounter are already running a pretty tight ship. There is no extravagance left in the firm’s operating expenses and, typically, they have already cut back on staffing so that many of the folks in the organization are working long hours and have taken on multiple duties so that fewer people are needed to keep things running. These organizations do not have any “fat” left to trim away. If they seek to cut expenses by even five percent (5%), it would mean cutting away “muscle and bone” – the strength that has allowed the organization to survive until today.

Cost-cutting may have gotten here

If management in such organizations are trapped in cost-world thinking, it could be that cost-cutting is what helped bring them to the brink of destruction, as it is. Here is how cost-world thinking can take a executives and managers astray and lead them to make decisions that are damaging to the organization:

Misleading allocations of overhead expenses

Using the figures offered by the contributor to the discussion (above), this company believes it has a gross profit of 25% ($250 for every $1,000 in revenues). Let us say that this is being calculated in the following (traditional) manner:

Cost Classification	Cost Amount
Raw materials	$250.00
Direct Labor	$100.00
Allocation of indirect costs and overhead	$400.00
Total Calculated Cost of Product	$750.00

For the sake of simplicity, let us say that each “widget” sells for a price of $1,000, so we have the following:

	Amount
Unit Revenue	$1,000.00
Unit Cost (incl. allocations)	$750.00
Calculated Gross Profit per Unit	$250.00

Also, let us assume that, due to the recession, this company also has excess capacity at this time.  (Otherwise, how could their operating expenses possibly be $240,000 on revenues of $1 million?)

Opportunity knocks

Now, one of this firm’s salespeople comes back from a long discussion with a potential new customer in Europe. This firm wants to buy up all the remaining capacity at the firm. That means 1,200 units. However, they are only willing to pay $650 per unit.  What should the company do?

Far too many executives caught up in cost-world thinking would turn this offer down. They would say, “We can’t take a loss of $100 per unit and ‘make it up’ in volume! That’s crazy!”

But, let us look at what is really happening. The company already has excess capacity. It could produce the additional 1,200 units without investing in any new facilities or equipment. Furthermore, it would not add to operating expenses, because no additional back-office staff would be required and no overtime is expected to meet the new demand. So, here is a contrast between cost-world thinking and reality:

COST-WORLD THINKING	Amount
Unit Revenue	$650.00
Cost-world Cost	$(750.00)
Gross Margin per Unit	$(100.00)
Number of Units Sold	1,200
Gross Profit from Offer	$(120,000)
Gross Profit from Current Operations	$250,000
Total Gross Profit	$130,000
Operating Expenses	$(240,000)
Net Profit	$(110,000)

Throughput Thinking

THROUGHPUT THINKING	Amount
Unit Revenue	$650.00
Truly Variable Costs (TVCs) (Raw Materials)	$(250.00)
Throughput per Unit	$400.00
Number of Units Sold	1,200
Change in Throughput from Offer	$480,000
Throughput from Current Operations	$250,000
Total Throughput	$730,000
Operating Expenses	$(240,000)
Net Profit	$490,000

Escaping from cost-world thinking

Here is a simple formula to help rescue firms from making the error we have illustrated above:

Where ROI = Return on Investment,
delta-T = Change in Throughput, where T = Revenue less Truly Variable Costs (TVCs),
delta-OE = Change in Operating Expenses, and
delta-I = Change in Inventory or Investment

In this case, we have determined that the change in OE = zero, and for simplicity’s sake, we have also assumed that the change in Inventory or Investment is zero (or negligible).

Essentially, when looked at properly this offer to “sell below cost,” actually increases the firm’s net profit by $480,000 with virtually zero investment. (In a real situation, some change in inventory is likely, but the effects would still be small.)

I trust this sheds new light on your business situation. Contact me at rcushing@GeeWhiz2ROI.com if you’d like to have help getting a better view of your business and how to make more money.

©2010 Richard D. Cushing

The New ERP – Part 12

Jeepers! Creepers! Where'd you get those numbers?

In the last post, I said that the management team in our unnamed example company had estimated the following for a change proposed in their warehousing operations:

• Change in Throughput (delta-T) = $0
  
• Change in Operating Expenses (delta-OE) = $168,942
  
• Change in Inventory/Investment (delta-I) = $75,000
  

Let us now take a look at a relatively quick way to get to numbers such as these without suffering paralysis by analysis.

One shorthand way to estimate changes in OE is the use of the value of a typical or average FTE (full-time equivalent) in the department or area being affected. This effective and rational method allows us to create estimates of potential savings (or added costs, if that be the case) even if no actual employees are going to be laid off or hired as a result of the proposed change. Here is how: While firms seldom layoff employees as a result of process improvements – and we highly endorse such acts of employee retention – if the firm is focused first and most importantly on increasing Throughput (T), the organization will actually experience these savings over time by being able to support growth in Throughput of 30%, 60% or even 100% or more without adding to Operating Expenses by forestalling the hiring of additional personnel.

So, here's how our example company's team calculated FTE values for their warehouse operations:

By extrapolating from this calculated FTE value ($49,686), here is how the management team took the next step to estimate annualized savings from the proposed changes in the warehouse and picking-shipping operations:

At this point, our example management team also made an arbitrary decision. They established a preliminary investment budget of $75,000 to cover the cost of technologies to provide (at a minimum) the three critical functions of:

1. Integrated bar code printing
   
2. Integrated ASN processing, and
   
3. Paperless (or near paperless) picking and shipping operations
   

Since our management team has no predisposition for an Everything Replacement Project (traditional ERP), they have many options open to them. They are focusing solely on Extended Readiness for Profit – my radical new approach to ERP. Therefore, they could take advantage of any one or more of the following courses of action:

• Develop integrations between existing bar code applications and their inventory management software. Most of the better bar code applications on the market today already provide APIs (application program interfaces) and ODBC (open database connectivity). These capabilities would help keep the cost of development reasonably low and permit relatively easy and low-cost changes as demands on the organization change over time.
  
  
• Purchase and integrate an EDI (electronic data interchange) engine with their existing inventory management and sales order processing software in order to generate and deliver ASNs (advanced shipping notices) for them. This project would have to be coordinated with the solution chosen to handle the paperless picking and shipping operations, however.
  
  
• Acquire a paperless shipping and manifesting application and leverage its APIs to integrate it with the firms existing inventory and sales processing application. They might even hit the jackpot and discover a shipping and manifesting solution that includes ASN processing as part of the package, or has already been successfully integrated with their existing inventory and sales processing application.
  
  
• Roll their $75,000 budget into a large project that may be part of replacing an existing inventory or sales processing application.
  

If they choose the last option, they should still predicate their purchase and implementation budget on the sum total of all measurable improvements and savings anticipated from all outcomes when compared to their Current Reality Tree (CRT). They should not arbitrarily throw money into the budget "kitty" based on some vague feeling that "more technology" or "newer technology" will automatically make the organization more profitable or stop losses. That is to say, never substitute a traditional ERP (Everything Replacement Project) for a focused and measurable Extended Readiness for Profit (the New ERP) program.

[To be continued]