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It's All about FLOW!

Posted by RDCushing May 26, 2017

As I write this, the Memorial Day weekend is in the offing. There will be lots of folks hitting the road to go various places. Most of them will be taking freeways, the Interstate system, and major highways wherever possible on their journey.

Long stretch of empty highway
Why is that?

It’s all about FLOW!

When we are intent on getting to our destination, what is relevant to us is one KPI (key performance indicator): the number of miles we can cover in the shortest amount of time. That is: Throughput!


We avoid things that disrupt the FLOW!

We avoid stoplights like the plague. THROUGHPUT is what matters to us. How many miles pass beneath the wheels of our vehicle in pursuit of our goal (read: destination).


Even our mileage improves!

Even though we may not be paying that much attention to our efficiencies (that is, miles-per-gallon), we have this knowledge that when FLOW is increased (that is, average miles-per-hour) and when blockages to FLOW are decreased (that is, stop signals and the like), our net efficiency is dramatically improved.


Almost without our thinking about it, as we focus on FLOW and avoid disruptions to FLOW, our efficiencies are improved.


The same is true with your supply chain!

By the way, precisely the same thing is true in your supply chain.


If you will focus on FLOW, two things tend to happen auto-magically:


  1. More of your GOAL is achieved (you will make more money, cash flows improve, customers are happier)
  2. Your EFFICIENCIES improve (when properly measured in terms of system Throughput/Operating Expenses [T/OE])


Next time you’re driving on a freeway, you should think about this. Then, give us a call or contact us if we can help you FOCUS on FLOW and improve your supply chain.


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While the quantity of information is increasing [vastly, every day], the amount of useful information almost markus-spiske-207946_small.jpgcertainly isn't. Most of it is just noise, and the noise is increasing faster than the signal. There are so many hypotheses to test, so many data sets to mine--but a relatively constant amount of objective truth. [1]


Nate Silver’s insightful book, The Signal and the Noise: Why So Many Predictions Fail—But Some Don’t, highlights for us just how intrinsically fallible our human judgment is, and how our relatively meager range of experience colors our interpretation of the evidence around us—even without our knowing it is doing so.


Our instinctual shortcut may deceive us

Silver reminds us that “[t]he instinctual shortcut that we take when we have ‘too much information’ is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest.”


"[But] men may construe things after their fashion / Clean from the purpose of the things themselves," Shakespeare warns us through the voice of Cicero [in The Tragedy of Julius Caesar]. [2]


As a result of our predilection for things familiar and, therefore, deemed safe, we “face danger whenever information growth outpaces our understanding of how to process it.” [3] So, “unless we work actively to become aware of the biases we introduce, the returns from additional information may be minimal—or diminishing,” according to Silver. [4]


Rescued by computers

So, that’s why we use computers! There’s clearly too much data for us to process manually. We use computers to sift, filter, organize, relate and present our big data to us so that we don’t have to rely on our human intellect alone, I hear you say.


But “the numbers have no way of speaking for themselves,” Silver warns. “We speak for them. We imbue them with meaning. Like Shakespeare's Caesar, we may construe them in self-serving ways [to our detriment] that are detached from their objective reality.” [5] Remember! Even our computer programs are designed and developed by humans with inherent biases about which data they will process, as well as how they will process it, filter it, organize it, and present it. In doing so, the data are imbued with meaning by the software developers before you even have an opportunity to take it all in.


Unfortunately, we humans tend to “regard computers as astonishing inventions, among the foremost expressions of human ingenuity…. And we expect computers to behave flawlessly and effortlessly, somehow overcoming the imperfections of their creators.” In fact, Silver opines: “[W]e view the calculations of computer programs as unimpeachably precise and perhaps even prophetic.” [6]


Evidence of our unwarranted faith in computers

I frequently remind my clients of some a simple fact when they are considering typical manufacturing and supply chain management computer systems. I tell them:


Okay. You are about in embark on the implementation of a very complex system. To get and keep this system operating, you will need to supply it with—ultimately—thousands of factors (when all of the SKUs, routings, bills of material, work centers, and more are considered) upon which the computer system will base its calculations. You will be asked for waste factors, work center efficiency factors, set-up times, run-times, lead times, queue times, wait times, consumption rates, and much more. In many cases, the numbers you provide will be averages. In equally as many cases (I believe), the numbers you provide will be best guesses.


All of these thousands of factors that you have fed into the machine will be “crunched” and the system will produce reports like shop floor schedules (to the minute, perhaps) and material requirements plans (for exact quantities that you should produce where, and precisely when). The system will also calculate for you your “costs” to buy or make hundreds or thousands of components and finished goods.


Here is the problem: Even though you fed the system with “averages” and “best-guesses” at the outset, you are now going to actually believe that the data produced in dozens of plans and reports are “precisely correct.” If the system says it cost you $127.19 to produce a unit of SKU 1001, you are going to believe it—and probably act on it as a “fact.” If the system tells you that you need to buy 1,507 units of SKU 4712 into location ‘A’ by 07/19/20XX, you will probably believe it to be precisely correct and attempt to act accordingly.


But, if you supplied the system with averages and best-guesses, the result cannot possibly be as precise as you are going to believe them to be!


Silver expresses this wisdom in a simple sentence:


Technology is beneficial as a labor-saving device, but we should not expect machines to do our thinking for us. [7]


Traditional ERP and MRP systems answer the wrong question

Beginning with our inputs of averages and best-guesses, traditional ERP and MRP systems attempt to very precisely answer the following question:


What should we make, buy or transfer and when should we make it, buy it or transfer it?


But, because the answers to those questions—while precise—can never be assured of being accurate, the real question that systems need to answer is this one:


Given what we know at this moment, how likely is it that our stock buffers, time buffers, and capacity buffers will adequately protect the FLOW of relevant materials in our supply chain? [8]


If we know the answer to that question for each strategically placed buffer in our supply chain, we automatically are able to prioritize actions that will maximize our opportunities to profit from FLOW across our supply chain.


Simply. Effectively. Accurately.


That is the demand-driven way to supply chain improvement. Read the success stories of those who are taking this way by going here.


We can help you get there. Please leave your comments below, or feel free to contact us directly, if you prefer.



[1] Silver, Nate. The Signal and the Noise: Why So Many Predictions Fail - But Some Don't. New York, NY: Penguin Books, 2015.

[2] Ibid.

[3] Ibid.

[4] Ibid.

[5] Ibid.

[6] Ibid.

[7] Ibid.

[8] The production, shipping of irrelevant materials (stuff for which there is no known or foreseeable actual demand at the present time) is a waste of time, energy, money and other resources regardless of how efficiently it may be being done.



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Someone (who shall go unnamed in this article) posted a comment in response to a LinkedIn post entitled Why Your Forecast Accuracy Hasn’t Improved. Here’s what the comment said:firefighting-arny-mogensen-172490.jpg

I think that quite a few of us have given up on pushing for accurate forecasts and resorted to other options such as planning at the sub-assembly level and putting together strategies to allow for more "agility," i.e., firefighting capabilities. I agree… that companies think they can grab additional market share or shelf space by offering more and more options, more varieties of items. This makes planning at the end item, not only inaccurate but, pretty much invalid as soon as the new product pipeline is updated.


I applaud the honesty

Yes. I added the emphases in the quote.


While we have been observing this trend for many years amongst supply chain executives and managers, we have not very often actually heard the confession made explicitly.


I was quite struck with the phrase, “quite a few of us have given up….”


Reading it reminded me of something I read in the book Moneyball some years ago:

Of course, no one in pro sports ever admits to quitting. But it was perfectly possible to abandon all hope of winning and at the same time show up every day for work to collect a paycheck. Professional sports had a word for this: "rebuilding."  That's what half a dozen big league teams did more or less all the time. [1]


By the way, it is perfectly acceptable in the world of professional sports to use the word “rebuilding” to describe your situation even if no actual rebuilding is going on with the team.


Similarly, so it appears, in the world of professional supply chain management, it is perfectly acceptable to employ the term “agility” when one really means “we’re getting better at firefighting.”


And, after having abandoned all hope of “winning” (read: real improvement), what does that say about our supply chain professionals’ willingness to continue to “show up every day for work to collect a paycheck”?


Firefighting as a strategy

Saying that your management “strategy” is to improve your “firefighting” seems, rather, an admission that you have no strategy for real improvement at all.


We agree that it is certainly a strategic move by cities to decide they need to have and operate a fire department. In that sense, firefighting—and even improvement in firefighting methods—is a strategy in support of safety of the citizens.


Nevertheless, I think that analogy breaks down when we consider this scenario: If the fire department in the city of Minneapolis, Minnesota, were to get called every day, every week, or even every month, to put out fires at the same three or four addresses, I am guessing they would take some other action.


I don’t believe the city’s leadership would simply say: “We need to get better at firefighting. We need to be able to respond more quickly and more effectively when these fires break out—again and again and again and again. We need new and improved fire engines, water hoses, and ladders.”


That being said, I am nearly 100 percent certain that the “firefighting” mentioned in the comment in the opening paragraphs above was firefighting in the same few departments, the same few operations, the same few product lines, or the same few warehouses or vendors time after time after time.


Being bold…

I am going to be so bold as to say…

  • If you have “given up” on expecting real improvement in your supply chain operations (internal or extended)…
  • If you have managed to convince yourself that “supply chain agility” is just another term for “better firefighting”…
  • If you have fallen to the point that “better firefighting” is your “strategy”…


Then you need help creating a real and effective POOGI (process of on-going improvement) [2]. There is no doubt in my mind about it.


You might want to begin here by reading some of the numerous success stories of companies that have broken through by applying truly demand-driven [3] principles.


We can help. Please feel free to leave your comments below, or contact us directly, if you prefer.



[1] Lewis, Michael. Moneyball: The Art of Winning an Unfair Game. New York: W.W. Norton, 2003.

[2] Process of On-Going Improvement

[3] Demand-driven does not mean make-to-order (MTO), by the way


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"Our doubts are traitors, and make us lose the good we oft might win, by fearing to attempt."Lonely_Commute-01.jpg

– William Shakespeare, Measure for Measure, Act 1, Scene 4 [1]


"In science, novelty emerges only with difficulty, manifested by resistance, against a background provided by expectation.


"Because it demands large-scale paradigm destruction and major shifts in the problems and techniques of normal science, the emergence of new theories is generally preceded by a period of pronounced professional insecurity."

– Thomas S. Kuhn, The Structure of Scientific Revolutions, 1962 [2]


How our mind works

What Shakespeare said of doubts, in general, and what Thomas Kuhn says of revolutions (or, even, evolutions) in science, is equally true in business.


It is scientifically accurate to say, “This is simply how our human mind works.”


In the Brain Games series, episode 3, entitled “Trust Me,” we are told:


When you see or hear something you recognize, it activates the parietal and occipital lobes in your brain and releases oxytocin, the feel-good molecule. But when confronted with something unfamiliar, the amygdala, your brain's danger detector kicks into gear.


In short, if it’s familiar, we tend to automatically trust it—whatever it is; and if it’s unfamiliar, our “fear factor” takes over without our even really (consciously) thinking about it.


So, even if…

So, even if you hear that a $7 billion company, with on-time shipments ranging in the 90 percent range and an achieved service level greater than 97 percent, discovered that using DDMRP (demand-driven materials requirements planning) allowed them to hold to these same performance levels with an average of 27 percent less inventory, your doubts and fears about the unfamiliar DDMRP might keep you from even looking into it. [3]


So, even if you hear that one of the nation's leading manufacturers of precision-welded fittings used in the oil, chemical and power production pipelines, as well as in water treatment plants, automobile production facilities, and high-rise structures, proclaim that

    • "Demand-driven has set us apart" from our competition; and
    • "Demand-driven has enabled us to compete and prosper in… a very volatile market;” and
    • Using "demand-driven logic, we can determine the right actions to maintain our current market, and position ourselves for future growth;” and
    • DDMRP is "allowing our company to grow quickly and expeditiously during good times," while "enabling us to react smoothly through good markets and bad, with a minimum of pain." It is also "allowing us to focus on the right metrics, [thus] positioning us to be a viable company for years to come." [3]

      Still, your doubts and fears about the unfamiliar DDMRP might keep you from even finding out if you might reap similar benefits in your supply chain.

So, even if another large manufacturer reported that, after implementing DDMRP…

  • Both overtime and excess (expedited) freight expenses have been dramatically reduced
  • Significant production volume increases have been experienced with "much less stress and no heroics"
  • Buying and planning have been "greatly simplified and our signals are based on real pull and priority"
  • Our "execution priorities are stable because our schedules are reliable and based on true [actual] demand-pull"
  • They now happily report that "all resources remain synchronized to the right schedule and market priorities due to the [high] visibility provided by the time… and stock buffers" [3]

    Still, your doubts and fears about the unfamiliar DDMRP could hold you back from “the good you might win” for your company and your supply chain by simply taking a chance to really investigate the options for improvement.


Don’t let doubts be your traitors!



What have you got to lose by discovering what others have discovered in the DDMRP revolution?


It is revolutionary—not evolutionary.


It will make you think differently about your supply chain and, in doing so, help you also achieve success that may be well beyond your present imagination.


Overthrow your fears! Check it out.


We can help.


Feel free to leave your comments below, or contact us directly, if you prefer.



[1] Eagle, Simon. Demand-Driven Supply Chain Management: Transformational Performance Improvement. New York: Kogan Page, 2017.

[2] Ibid.

[3] All of the above statements about actual benefits accruing to companies that have implemented DDMRP (and many more) can be found on the Demand Driven Institute Website.


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John Wilder Tukey (June 16, 1915 – July 26, 2000) was an American mathematician with a Ph.D. in mathematics from Princeton University. He became founding chairman of the Princeton University statistics department in 1965, even though he was dividing his time between his obligations and Princeton and his invaluable work at AT&T Bell Laboratories.


As a scientist and mathematician, you might think he would be all about being “precise.” Nevertheless, he strongly affirmed this:


"Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise." [1]


Getting Exact Answers to the Wrong Question

Traditional ERP systems—regardless of the price paid for them—almost always embed traditional MRP (material requirements planning) for use in manufacturing and supply chain planning and execution.

MRP Planning Screen example.png
In screens similar to the one shown above, traditional MRP attempts to give you exact answers to the wrong question. The traditional MRP process tries to tell you precisely

  • WHAT you should make, buy or transfer
  • WHEN you should make it, buy it or transfer it
  • HOW MUCH you should make, buy or transfer


It does so with the very best of intentions—by the way.


The goal of MRP systems is to keep you supplied with WHAT you need, WHERE you need it, and WHEN you need it.


There are a few problems, however.


One big problem is that MRP doesn’t care if you need to attempt to bend the space-time continuum to meet its calculated plan.


Another huge problem is traditional MRP is dependent upon, typically, tens of thousands of data points, settings, and configurations in the ERP system—and some of those settings were “best guesses” when they were configured. Others were pretty accurate when they were set—six months or six years ago!


The results coming from traditional ERP/MRP planning systems are typically not something upon which the supply chain managers and inventory replenishment planners can execute.


This is precisely why most companies—way over 90 percent of companies—that have access to, and (may) use, traditional MRP, supplement the data coming from the ERP/MRP systems using spreadsheets, homegrown databases, and even whiteboards or clipboards before making final decisions.


The data coming from the traditional ERP/MRP system are very precise—and almost always precisely wrong.


Approximately Right Answers to the Right Question

Demand-driven MRP (DDMRP) is a learning system (meaning it is constantly updating itself with the latest relevant data coming from your ERP system). Its simple, high-effective signals give your supply chain managers, production planners, inventory folks and any others the right answers to the right question.DDMRP BufferStatusBoard.png


The RIGHT QUESTION is this one:


What should our priorities be in order to assure the FLOW of relevant materials in our supply chain right now?


Of course, those signals must lead to the relevant actions, and that is done simply enough. A simple set of signals (such as those in the accompanying figure) tell everyone the required priorities and what they need to know to take appropriate actions.


Whether these SKUs are make items or buy items, it is easy to see that SKU #0060480 is in the RED ZONE with only 2.25% remaining in its (stock) buffer. This buffer requires immediate replenishment and the quantity (to make or buy) 1,247 units.


If all of the RED ZONE matters are attended to, then the YELLOW ZONE SKUs should next be addressed. SKU #0068565 requires replenishment of 4,797 units.


What’s the Difference?

DDMRP doesn’t try to incorporate tens of thousands of variables—with a high probability of error—and then attempt to tell you precisely what you should buy, make or transfer, at precisely what time.


Instead, DDMRP give you simple signals that help you understand…


How well are my various buffers protecting FLOW (read: profits) right now?


If a buffer is in the RED ZONE, it needs immediate attention if it is going to continue to protect the FLOW of relevant materials in your supply chain. The lower the remaining percent of buffer, the more urgent the attention is required. Thus, priorities are easily determined.


Don’t Confuse “Precision” with “Accuracy”

Traditional MRP’s numbers are always precise, but they are seldom accurate. DDMRP’s numbers are far more accurate in terms of supporting the FLOW of relevant materials.


Robert E. Kass, of Carnegie Mellon University, says, “[T]here is a simple and elegant way to combine current information with prior experience in order to state how much is known…. It makes full use of available information, and it produces decisions having the least possible error rate." [2] [Emphasis added.]


Isn’t it time that you and your supply chain found a truly simple and very effective way to help assure the FLOW of relevant materials across your supply chain? (By the way, mass shipments and mass production of goods are not needed at the present time is the FLOW of irrelevant materials and actually hurts the bottom-line of everyone in your supply chain.)


We can help.


Please leave your comments below, or feel free to contact us directly, if you prefer.




[1] McGrayne, Sharon Bertsch. The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. New Haven: Yale University Press, 2012.

[2] Ibid

[3] By the way, there is book entitled Approximately Right, Not Precisely Wrong – Cost Accounting, Pricing & Decision Making on a different topic than I cover here. Nevertheless, you might find it interesting.


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