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Attempting to Answer the Wrong Question

Traditional MRP (material requirements planning) systems fail again and again. As Simon Eagle reminds us, writing in Demand-Driven Supply Chain Management:DDMRP Buffer Calculation.jpg


[D]riving replenishment execution through materials requirements planning (MRP)-dependent demand network with today's high levels of forecast inaccuracy inevitably leads to unbalanced inventories that cause supply chain and production instability, or variability, as schedules frequently have to be amended to prevent service issues. This leads to the development of excessive inventories, excessive lead times and necessitates the use of unplanned capacity. However, adoption of the Demand-Driven Supply Chain Management (SCM) approach, especially in 'make to stock' supply chains, allows planned service levels to be achieved from half the average inventories, with far higher overall equipment effectiveness (OEE) and significantly shorter lead times. [2]


If you work in supply chain management, inventory management, production planning or purchasing, you’ve experienced the reality of this day after day.


The reason driving supply chain execution off MRP and forecasts fails, I believe, is because it attempts to answer the wrong question.


Forecast-driven MRP attempts to tell you precisely what to build or buy, and when. It is always precise and it is virtually always precisely wrong.


Is There a Better Answer?

Writing in the outstanding book The Theory That Would Not Die, Sharon Bertsch McGrayne offers this tidbit regarding Bayesian thinking:


Bayesians could… combine information from different sources, treat observables as random variables, and assign probabilities to all of them, whether they formed a bell-shaped curve or some other shape. Bayesians used all their available data because each fact could change the answer by a small amount. Frequency-based statisticians threw up their hands when Savage [Jimmie Savage] inquired whimsically, "Does whiskey do more harm than good in the treatment of snake bite?" Bayesians grinned and retorted, "Whiskey probably does more harm than good." [1]


NOTE TO READER: If you’d like to have a gentle, non-technical introduction to Bayes’ Theorem, watch this YouTube video from Veratasium.


Keep reading. I not trying to convert you to a statistician or mathematician--really!


Combining information from different sources

There are some key factors to be noted in the statement above. Bayesian thinking allows us to better understand our world, our situation, and make proper adaptations incrementally as new information is made available. It does this by combining relevant information of different types, from different sources, and then guiding incremental adjustments to our thinking.


This really works!


Real-world applications of Bayesian thinking cracked the “unbreakable” German Enigma code in World War II, helped us hunt down Russian submarines during the Cold War, and guided the calculations that provided a sound basis for starting the U.S. Workers’ Compensation Insurance program.


Answering the Wrong Question the Wrong Way

Going back to the Jimmie Savage question in the passage above: Traditional MRP approaches try to use statistics to answer the question:


“Does whiskey do more harm than good in the treatment of snake bit?”


The answer from this approach must be binary. It’s “yes” or “no.” Traditional MRP wants to tell you to act—or not act—based on forecasts and statistical analysis.


Answering the Right Question the Right Way

Demand-driven MRP (DDMRP) as promulgated by the Demand Driven Institute shows us a better way.


Strategically placed and sized buffers are built and maintained by combining relevant information from different sources and treating observables as variables in the maintenance of the buffers themselves. Here is a summary of the kinds of data that are combined into the day-by-day maintenance of buffer sizes and statuses:

  1. Average Daily Usage (ADU)
  2. Demand Variability Factors
  3. Replenishment Lead Time Factors
  4. Planned Adjustment Factors (based of foreseeable future events)
  5. Demand Spikes within a predetermined Spike Horizon
  6. Minimum Order Quantity (where applicable)


Combining these relevant data into the calculation of the buffer size and its current status, DDMRP provides the answer to the right question the right way.


Instead of trying to answer in a binary way, DDMRP can tell you:


“Whiskey probably does more harm than good in the treatment of snake bite.”


More directly, a glance at the status of any buffer tells everyone the answer to the truly critical question:


“How effectively is this buffer—at this moment—protecting FLOW in my supply chain and what actions, in what priority, will probably protect FLOW in my supply chain most effectively?”


Based on this bit of data, and looking at this bit of data across all the buffers in the supply chain, managers can rapid make accurate and timely decisions about execution priorities and quantities.

DDMRP Dashboard NetworkPlanningPriorities.png


As Robert E. Kass, a Bayesian at Carnegie Mellon University says,


"Bayes Theorem…. says there 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." [Emphasis added.] [3]


Decisions with the Least Possible Error Rate

Wouldn’t you like to start making supply chain execution decisions that have the highest likelihood of being right, and with the right priorities to protect FLOW (read: profit)?


Then we suggest that you become truly demand-driven (which does not mean, make-to-order, by the way), and we can help. 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. – Referencing Erickson W.A., ed. (1981) The Writings of Leonard Jimmie Savage: A Memorial Selection. American Statistical Association and Institute of Mathematical Statistics.

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

[3] McGrayne, ibid.


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A thought experiment

Imagine that you are a person who has grown up your entire life in a cave. You have never, ever seen the sun, nor do you know anything about the sun.sunrise001.jpg


However, one day, you walk out of your cave and experience your very first sunrise.


What you would not know is whether this is a one-time event, or something that happens more than once.


About 24 hours later, you would experience your second sunrise, and you might be pleasantly surprised after your falling into darkness at the end of the preceding day.


By the third sunrise, you would probably begin to recognize a pattern—sunrise and sunset. You might be delighted and hope that this pattern continues.


As the days turn into weeks, weeks into months, and months into years, you would become virtually certain that sunset today will be followed by sunrise tomorrow morning.


Now, ask yourself this

Chances are, if you are like 99.44 percent [1] of supply chain executives and managers you have experienced—literally, day-after-day—the repeated cycles of failure in your supply chain.

  • All our forecasts are going to be wrong (you know it and I know it—we just don’t know by how much or in which direction)
  • Wrong forecasts cause our supply chains to waste resources buying, making and shipping the wrong stuff
  • Even the right stuff gets shipped to wrong places
  • Some of the right stuff gets shipped to the right places, but at the wrong times
  • Our inventories become unbalanced—too much of the wrong stuff and too little of the right stuff
  • Customer service levels are threatened
  • Expediting cause us to interrupt schedules and break setups
  • Disruptions consume otherwise valuable capacities
  • Lead times must be extended
  • Virtually all our management attentions are drained away in firefighting—there’s no time, energy or money left to think about improvement


So, ask yourself this:

Why aren’t we as smart as the caveman?

Why don’t we learn from cycles that repeat themselves again and again and again and again?

Next, watch this

I think you will benefit from setting aside less than ten minutes to watch this YouTube video about Bayes theorem.


"If we internalize that something is true, and maybe we're 100 percent sure that it's true, and that there's nothing we can do to change it; well, then we're going to keep on doing the same thing, and we're going to keep on getting the same result. It's a self-fulfilling prophecy…. A really good understanding of Bayes' theorem implies that experimentation is essential. [Emphases added.]


"If you've been doing the same thing for a long time, and getting the same result that you're not necessarily happy with, maybe it's time to change."


Now, think about it


If you’ve been doing the same thing for a long time, and getting the same result that you’re not necessarily happy with, maybe it’s time to change. Maybe it’s time to at least “experiment.”


Begin by reading this great new book: Demand-Driven Supply Chain Management: Transformational Performance Improvement by Simon Eagle. [2]


Start an experiment that might just rescue your company and your supply chain into a huge transformational improvement. Perhaps something more than you have can imagine.


We can help. Contact us to talk about it. Or, just leave your comments below.




[1] I made that number up! But you and I both know it’s not far from accurate!

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



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RubeGoldberg_AlarmClock.jpgWikipedia tell us that Reuben Garrett Lucius "Rube" Goldberg (July 4, 1883 – December 7, 1970) was an American cartoonist, sculptor, author, engineer, and inventor.


He was best known for a series of popular cartoons depicting complicated gadgets that perform simple tasks in indirect, convoluted ways, giving rise to the term “Rube Goldberg machines” for any similar gadget or process. Goldberg received many honors in his lifetime, including a Pulitzer Prize for his political cartooning in 1948 and the Banshees' Silver Lady Award in 1959.


Complexity sometimes start simply enough

Many times our ideas for improvement start simply enough. However, over time, what was once simple evolves—unintentionally—into convoluted complexity.


Right now I am working with two different companies where it seems apparent that technologies intended to aid in the execution of fairly straightforward tasks have become “Rube Goldberg machines.”


However, over time, we see (or, hear) that the description of the requirements evolved along these lines:

  • “We just need it to do this….”
  • “Oh. We need it to do this, too….”
  • “And, this…”
  • “But, not like that…”
  • “And, it should end up with this outcome…”
  • “Instead, it should work around that, and come to this result….”


There is no planning or design. There is just a lot of adding onto, tweaking, and adjusting as goals and concepts change over time.

This rapidly turns what was a simple idea (“We just need it to do this…”) into something very complex and frequently unwieldy.


Complexity more potential points of failure

As Scotty—the faithful engineer on “Star Trek”—once said in an early Star Trek movie, “The fancier they make the plumbing, the easier it is to stop it up.”


This is true. The more complex a computer program or individual algorithm is, the greater the number of potential points of failure that are likely to exist in it.


Complexity leads to higher costs

Complexity in software and other systems almost always leads to higher maintenance costs. This is frequently because

  • The causes of failure are more difficult to diagnose
  • The causes of failure are more difficult to rectify
  • Fixing the failure is more likely to cause some other part of the system to fail or function in a way other than anticipated


As a result, complex systems frequently cost more for each “repair,” and require more repair effort over their lifespan.


Helping companies and supply chains return to Inherent Simplicity

We are working now to help two major clients restore their “evolved complexity” to inherently simple capabilities. We help them

  1. Figure out what the system really needs to be able to do
  2. How to leverage standard code and modules to meet as many requirements as possible
  3. Design modular systems to meet their additional or custom requirements
  4. Manage the development in order to prevent the unintentional evolution of needless and wasteful complexity


While there is an up-front investment to return to inherent simplicity, the long-term savings in operating expenses can be very, very large. Plus, there is frequently improvement gained from just re-thinking what is being done today—along with the how’s and why’s.


Your turn

Do you have systems and processes that have become needlessly complex? A good sign that you do is when your “levers” and “management actions” are no longer producing expected results, or error rates are high and increasing. If what you’re company is best at is firefighting—and you don’t have a fire station as an office—then chances are you are fighting complexity that’s hurting your chances at making more money.


Tell us about it below. Or, feel free to contact us directly, if you prefer.



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The North Carolina State University Poole College of Management Supply Chain Resource Cooperative offers the following definition for order management:maarten-van-den-heuvel-126551.jpg


Order management involves the seamless integration of orders from multiple channels with inventory databases, data collection, order processing including credit card verification, fulfillment systems and returns across the entire fulfillment network. For proper execution the process involves real-time visibility into the entire order lifecycle starting from the placement of order and ensuring that orders (SKUs) are not lost, delayed, or corrupted during the fulfillment process. The system may also comply with and support parcel carriers and provide sophisticated, centralized freight management and tracking/tracing capabilities. Clients, Customer service representatives account managers and suppliers will thus have the ability to track real-time inventory levels for each SKU and inquire about order and shipment status via the web – anytime, anywhere.


An analogy

Let’s consider an analogy. The fulfillment of the demand for fuel made by your car’s engine involves a “fulfillment network.” That fulfillment network includes you—at the tail end—assuring that you have sufficient inventories of fuel in your fuel tank to satisfy the demand.


Also involved in the “fulfillment network” are your local gas stations, bulk fuel depots (perhaps), regional refineries, fuel pipeline and long-distance transport companies, and even the companies that extract the crude oil and explore for underground reserves.


Given this analogy, let’s rewrite the opening sentence of this definition in concrete terms:


Order management for the vehicle owner [to keep it properly supplied] involves the seamless integration of orders from multiple channels [e.g., wife and children who may also make demands for fuel by using the vehicle] with inventory databases [i.e., the fuel gauge on the vehicle], data collection [e.g., keeping track of fuel purchases and mileage of the vehicle], order processing including credit card verification, fulfillment systems [e.g., the gas stations’ purchasing and replenishment operations]… across the entire fulfillment network [read: all the way back to the crude oil producers].


This is clearly nonsense!


What I really need to know

As a driver, I really need to know only two things:BufferPerformanceMeter_Execution.png


  1. How well am I managing my inventory buffer (the fuel level in my tank) in comparison with available supplies (including lead times) and variations in demand?
  2. How well does my supplier do at managing his or her inventory buffer in light of available supplies and variability in demand?


For most American drivers, the second question is (with very rare exceptions) one we rarely worry about. We expect to be able to go to the local gas station and fill our buffer (fuel tank) on demand.


For question number one, we use our fuel gauge to tell us how well our inventory buffer is coping with variability in supplies and demand. We know this at a glance and we use simple metrics (i.e., full tank, half-a-tank, and so forth).


So, take a look at this last figure.

BUFFER as Tank Metaphor.jpg

Strategic buffers

Beginning with my position in the supply chain, I really need to know how well my buffers (stock, time and / or capacity) are performing relative to changes in supply and demand.


If I have concerns about upstream or downstream performance in the supply chain, having my trading partners share information with me about the performance of their buffers can help me take appropriate actions that will protect FLOW across the entire supply chain.


I do NOT need…

What I do not need is all of the complexity that would be attendant with “the seamless integration of orders from multiple channels with inventory databases, data collection, order processing including credit card verification, fulfillment systems and returns across the entire fulfillment network.”


That is overkill! It would be costly and would probably give me no more relevant information that knowing how the neighboring buffers are doing in support of FLOW.


Invest in your supply chain where it supports the flow of relevant information, not a flood of irrelevant data.


Your turn

Give us your thoughts. We would really like to hear from you. Leave your comments below.


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At TechTarget, their definition of supply chain visibility (SCV) is…


…the ability of parts, components or products in transit to be tracked from the manufacturer to their final destination. The goal of SCV is to improve and strengthen the supply chain by making data readily available to all stakeholders, including the customer.


Let’s just think about this for a minute



Let’s say you manage 10,000 SKUs and half those SKUs pass through twelve different waypoints that you track for arrival and departure. That means you will be monitoring 240,000 data points for half your SKUs. Let us hope that the other half of your inventory is easier on you.


By the way, just a reminder: 240,000 data points is very nearly a quarter-of-a-million!


What do you really need to know?

Do you really need to know every arrival and departure of your inventory as it traverses your supply chain?


You don’t.


I will say that flatly: No. You don’t.


What you really need to know is how well the stock buffers, time buffers, and capacity buffers in your supply chain are doing at their task of protecting FLOW.


If your supply chain requires buffers at half of the twelve waypoints, then you need to have visibility into just 60,000 data points—not a quarter of a million!


It still sounds like a lot, though, doesn’t it.


But, what if you could easily see priorities and positions in a clean, easy to comprehend way?


What if your list for monitoring your buffers looked something like this?

DDMRP Dashboard NetworkPlanningPriorities.png


The easy-to-read combination of a color (red, yellow or green) and a single number (remaining buffer) can help you readily determine priorities, even across a multi-echelon distribution or supply chain network.


In the accompanying figure, I can very easily see that…

  • More NJ buffers need attention than any other location
  • SKU #1004 is a priority (RED) in three different buffers (NJ, IL and WA)
  • The buffer for SKU #1002 in WA is top priority with only 7 percent of the buffer remaining, followed by…
  • SKU #1003 in NJ at 9 percent

Wow! That was fast!

No more combing through pages and pages, or screen after screen, of columns, tables or charts.


Simply look at the color and the number and know precise where management attention needs to be focused, and in what priority.


Isn’t it time that you and your supply chain team began to see supply chain visibility as sometime simpler and more achievable than ever before?


It means you can finally become truly demand-driven. No more excuses.


We can help.


Please feel free to contact us directly, or leave your comments below. We look forward to hearing from you soon.



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Reading the sign to the right of the page here is easy.Semasciographic_example3.png


If we asked readers to translate the sign into French, Spanish, English, Germany, Japanese or Chinese, each of the translations would mean the same thing, but the words and sounds would be different.


The sign is said to be semasciographic. Semasciographic writing or communications systems are independent graphic languages not tied to any one spoken language.


The written language of mathematics using Arabic numerals and standard signs is semasciographic.


Semasciographic communications mean that no translation is necessary. Like this sign, while the sounds of the words relaying the meaning of the sign in English, German, or Chinese may sound vastly different to the hearer, the underlying concept or message is plainly understood when heard in the hearer's native (spoken) language.


The language of mathematics is universal, but…

Even though the language of mathematics is universal—that is to say, eight times eight in any language computes to 64—lots of numbers and mathematical formulas used on a page do not necessarily mean you are communicating or are being understood.


For example, as a supply chain manager, if you share applications, spreadsheets, reports, or whatever that are complex like the one shown here, those to whom you are trying to communicate may fail to understand—even if they are in your own office in the same company. What do you think might happen with the intended recipient if he or she is half-a-world away and speaks another language altogether?


The semasciographic language of supply chains

On the other hand, if you provide your supply chain with easy-to-read (and decode) data in the form of colors (red, yellow and green) and a single number (per SKU-location) that conveys simply and effectively how well the buffer (whether stock, time, or capacity) is protecting FLOW in the supply chain, the recipient can probably digest the information and take appropriate action without “a translator” or long conversations.

DDMRP BufferStatus PlanVsProd.png

From the SUPPLY SIDE, those supplying the buffers can know immediately the how well FLOW is being protected at this moment, and how to set priorities for actions when comparing the colors and numbers for any two or more buffers.

From the DEMAND SIDE, those relying upon the buffers can immediately and effectively assess just how well protected FLOW to them is being protected. They can easily determine if they believe they need to take any actions that might be indicated if there is the possibility of a disruption in FLOW from the buffer.


Demand Driven principles have introduced us to the universal language for supply chain management

Like the semasciographic language of the sign above, demand driven MRP’s language is easy to read, understand, and act upon regardless of your native spoken language.


Now it’s your turn…

Wouldn’t you like to be able to communicate status and priorities simply, yet with extreme effectiveness, up and down your supply chain? Wouldn’t you like to be able to see this effectiveness take hold across company boundaries and even in collaboration with supply chains extending around the globe?


You can. And we can help.


Contact us directly, if you wish, or leave your comments below. We would be delighted to hear from you.


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