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All kinds of things have been written about ERP (enterprise resource planning) implementation failures over the past 20 or more years. Most of these articles have focused on failures to deliver a acceptably performing system on time, within the prescribed budget, or of a quality that support ongoing business requirements. And, while all of those failures are significant, what troubles me more (if you have read my writings for long) is knowing how many “successful” ERP implementations lead to no significant improvement in the enterprise’s profit performance.TOC Money Machine.bmp


Our goal is to do everything within my small power to change that for the clients with whom we work. For me, personally, if a client is not more profitable and seeing rapid ROI (return on investment) from its ERP deployment, then the implementation was not successful regardless of its on-time, on-budget status.


Why can I say that?


Because if a company is going to spend $75,000 or $150,000 or $500,000 on any kind of “investment,” then it seems like they should expect a return on that investment. If they are getting no effective return on investment, then it was not an “investment,” it was an expense—plain and simple. All else being equal, if after-tax profits were $X before the implementation and they remain $X after the implementation, then there is no effective ROI flowing from the “investment.”


An article by Daniel Erickson, published on the Web at Manufacturing Business Technology* listed some of the reasons ERP “implementations go all wrong.”


The first reason that Erickson lists is “Lack of buy-in.” In describing this symptom, he says this:


The Captain’s chair should be occupied during this foray into the relative unknown. An engaged CEO will inspire top managers and decision makers to take up command positions “on the bridge” of the enterprise. Create a company-wide awareness of this major undertaking, with a call for “all hands on deck.” Each employee should know the destination (full ERP implementation) and understand the timeline for arrival.

Now, I don’t know about you, but for me, this paragraph does not speak of “buy-in.” For me, this paragraph is all about “command and control.” “Each employee should know the destination…and understand the timeline….”


Now, “buy-in,” for me, would sound something like this:


The executive sponsor of the ERP technology project has engaged a cross-function and vertically-integrated team of leaders from across the enterprise to help determine what needs to change in order to help the company make more money tomorrow than they are making today. Using guided processes that help the team clearly visualize and logically analyze what makes the whole enterprise work—or fail to work—the team has reached unanimity, not only on what needs to change, but also on what the change should look like. From this position, and using logical tools that help them clearly articulate these requirements, they have also been able to get the entire organization—all the way up to the production floor—excited about creating and activating these new solutions. Using these same “thinking processes” (a set of logic-based visual tools and exercises), they have also been able to find the appropriate technologies and locate engaged software vendors and VARs that equally excited about helping the enterprise create a new future and achieve rapid ROI for their planned investment.

What do you think “buy-in” should sound like or look like? Let us know by leaving your comments here.


We look forward to hearing from you.


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* Erickson, Daniel. "10 Often Forgotten ERP Implementation Faux Pas." Manufacturing Business Technology. March 10, 2015. Accessed March 11, 2015.

This is the third installment in a conversation I recently had with one of our clients. (Some minor changes may be made in what is published below to protect the confidentiality of our client.)




As I said earlier, things may have changed dramatically in your workflows since I was last on-site with you. However, given what I knew then, here is how would envision production management and controls (from a high level) in a demand-driven environment using buffers to manage priorities and flows:

Mfg Workflow w Buffers 20151016.jpg

In this diagram, the hopper-shaped (trapezoidal) green, yellow and red shapes represent STOCK BUFFERS (actively synchronized replenishment strategic inventories). In this case, the one buffer symbolizes all of your raw materials inventories.

The semicircular green, yellow and red shapes represent TIME BUFFERS. Typically, a time buffer is created by taking the full time allotment for an activity, and dividing that time by one-third (at least as a starting point, remembering that buffers can and should be adjusted over time to optimize system Throughput). Then, that one-third of the full time is subdivided equally into three zones—a GREEN ZONE, a YELLOW ZONE, and a RED ZONE.

Allow me give you an example: Let’s imagine that you were going to create a TIME BUFFER at a certain control point in your system. And, for simplicity’s sake in our example, let us further say that this is a MTS (make to stock) environment and not MTO (make to order). In such an environment, there may be a whole class of goods for which it might be said, it should take 18 working-hours for a production order to get from the gating operation (release to production) to control point ‘A’.

We would take that 18 working-hours and divide it by three. That would give us a time buffer at control point ‘A’ of six hours (18 / 3 = 6). That six-hour time buffer would be subdivided into three equal zones: a two-hour GREEN ZONE, a two-hour YELLOW ZONE, and a two-hour RED ZONE.

The time at which a particular order should arrive at control point ‘A’ is calculated at the time it is released as being the release time plus 18 working-hours or, with more sophistication, as the next time-slot available in the work already queued up for control point ‘A’. In our example, and to make our math simple, let’s say that the calculated time for arrival of Order 1001 at control point ‘A’ is noon tomorrow. (Order 1001 is an order for a finished good that is scheduled to ship at a specified date and time.)

If Order 1001 arrives tomorrow morning (all preceding steps complete and in good order) between 6:00 AM and 8:00 AM, it will have arrived in the GREEN ZONE and be available to control point ‘A’. No action is necessary.

If Order 1001 has not arrived at control point ‘A’ by 8:00 AM, production managers should be aware (by some simple mechanism usually encapsulated in a visual system supported by a software application) that there is a HOLE in the YELLOW ZONE of the BUFFER for CONTROL POINT ‘A’; however, they would merely deem this as something of which to be aware, but not necessarily take any action.

If Order 1001 arrives at control point ‘A’ at 9:51 AM (between 8:00:01 AM and 10:00 AM), it would recorded as having arrived in the YELLOW ZONE. Life is still good!

However, if Order 1001 has not arrived at control point ‘A’ by any time after 10:00 AM (the beginning of the RED ZONE in this TIME BUFFER), managers should be ALERTED as to a HOLE in the RED ZONE of the BUFFER for CONTROL POINT ‘A’. This should trigger appropriate investigation, but not necessarily remedial action. For example, if a quick look tells managers that Order 1001 is within 10 minutes of wrapping up at the preceding work center or operation, they would likely need to take no action.

Nevertheless, whenever an order arrives at a control point in the RED ZONE or LATE, not only is the time of arrival recorded (as with all arrivals), but this should automatically trigger the mandatory selection for a REASON CODE (and, perhaps, some comments) to attach to the RED ZONE arrival record. This allows for the use of Pareto analysis of all threats to FLOW occurring in the system.

Priorities for action are easily determined and agreed upon


Such a system should present two pieces of vital information:

      1. Buffer Status of every RELEASED customer order at each selected CONTROL POINT
        1. Not due
        2. Arrived early (before the GREEN ZONE start) (datetime)
        3. GREEN ZONE
          1. A hole
          2. Arrived (datetime)
        4. YELLOW ZONE
          1. A hole
          2. Arrived (datetime)
        5. RED ZONE
          1. A hole
          2. Arrived (datetime)
        6. LATE
          1. A hole
          2. Arrived (datetime)
      2. Buffer Penetration – This is a simple percent calculation of the amount of buffer penetration divided by the total buffer size. For example, if an order has a six-hour TIME BUFFER (360 minutes) and it is 216 minutes late, its BUFFER PENETRATION IS 216/360 or 60 percent.


By comparing these two factors, priorities for actions can easily be determined using the following two, very simple, rules:

      1. RED ZONE or LATER (sometimes referred to as the BLACK ZONE) always gets worked on first; then YELLOW ZONE; and, finally, if any action need be take on GREEN ZONE items
      2. The HIGHER the BUFFER PENETRATION (percent), the HIGHER the PRIORITY for action and attention


An immediate comprehension of the ‘health’ of your system in a moment


By using STRATEGIC BUFFERS in this way, with relatively modest data maintenance, every executive and manager can tell in two minutes or less the ‘health’ of the system at any given time. It is simple:

      • IF BUFFERS are dominated by GREENS and YELLOWS, all is well
      • IF BUFFERS are showing significant number of REDS (or BLACKS), then something has gone wrong


No only so, but the PRIORITIES FOR ACTIONS will be very clear to everyone (as indicated above).


If you believe these concepts are worth pursuing, and you don’t feel that the [demand-driven scheduling system we suggested] will get the job done for you—for whatever reason—then there are other options that you do not have to build from scratch.

I would next suggest that you take a look at a product called [another demand-driven production control system we suggested].

I hope this explanation of how strategically placed and dynamically managed buffers can help you manage FLOW simply, yet effectively.




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If you would like to know the names of the truly demand-driven solutions we recommended to this client, drop us a request here.


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This is a continuation of a conversation I recently had with one of our clients. They were talking about buying a new—and costly—APS (Advanced Planning and Scheduling) application. The goal, of course, was to improve their company’s performance.


Let’s pick up in the next step of our conversation. Remember, some minor changes have been made to protect the confidentiality of our client.


In the hope of providing a more accurate and even clearer picture of my concerns, I have taken some time to rework your CRT (Current Reality Tree) from what I learned while working with you and your team some time ago. I recognize that some things have probably changed since I was with you, and I assume you will take those things into account from what you know that I do not know at the present time.

Nevertheless, I think the analysis may raise some cogent points for your consideration as you move forward.

CRT Partial RootsOnly.jpg

As you know, at the bottom of the CRT we find the ROOT CAUSES (provided our CRT accurately represents “reality”). [Note: the accompanying image is only a small portion of the CRT I provided in my correspondence with the client.) Here, in my analysis, I find nine (9) roots, which I have listed in the following table, along with some general comments:




[1] Some materials are damaged during the manufacturing process

Because this contributes to so many UDEs further up in the tree, time spent on Pareto analysis of the causes of such defects and damage would probably have a significant pay-off (if you have not already undertaken this as part of your POOGI)

[9] Production scheduling and resource allocation is generally ad-hoc

It appears to me that you are thinking that you are addressing this root cause through the application of your PlanetTogether Galaxy software. My question is whether this will add complexity without correlated benefits. I will discuss why I have this concern further in this correspondence.

[8] Customer service processing delays sometimes lead to shortened production lead-times (release of order to required ship date/time)

Here again, some amount of time spent in capturing the causes of delays and ongoing Pareto analysis of the causes would probably have some relative immediate payback (if you have not already undertaken this as part of your POOGI)

[7] Company policies allow customers to create “Rush” orders

This may be unavoidable, but should be carefully reviewed in terms of policy and pricing.

[12] Inventory status is not easily correlated to production demand

In a fast-paced, make-to-order (MTO) environment, this is never easy to do. MTO multi-level BOMs/Routings virtually assure that almost every MTO order will show that one or more components are not presently available. This is where TIME and CAPACITY buffers, rather than STOCK buffers, can be a powerful tool in sustaining FLOW.

[15] Some inventory items may be out-of-stock or unavailable in sufficient quantities

See comment on [12] above.

[13] All production demand for all BOM levels is released to production at once (daily)

The virtual “flood” of paperwork that accompanies a daily release probably needs to be looked at in a fresh new way.

[11] A single finished good may require 4, 5 or more work orders (multi-level BOMs / Routings)

While this may be a given, and it correlates closely with [13] above in producing the resulting “flood” or paperwork, innovation may provide a key to reducing the “flood.”

[33] Little’s Law: More WIP = longer lead times

This is a given. The larger your WIP, the longer your lead times. This is all relative in terms of touch time, queue time, and wait time. In one plant we might be talking about six weeks versus three weeks; however, in your plant we might be taking about six hours versus three hours.


On Quality Failures and Rework

I don’t know how big the rework UDE (Undesirable Effect) is in your production environment today, but I recall that it was a relatively BIG ISSUE at the time of my last visit. As you can see from the CRT, regardless of the issue’s present size, it affects lots of areas:


What I do not see is how any APS (advanced planning and scheduling) system is going to account for, or provide support for, any level of rework in your system. The very best you could hope for, I should think, would be to reduce your estimated production capacity at each resource by some allowance for rework. However, that means that all your production schedules will have slack everywhere in the system. This is not, likely, a good approach.

The problem is, the APS cannot possible know what materials and which resources will be involved in rework in any given day or hour of the day.

This is where too much complexity and attempts to plan and control at too finite a level actually makes matters worse and more confusing than the implementation of higher level, simpler signals and controls—which is what [a demand-driven scheduling solution] provides. Time and capacity buffers, and the signals stemming from these buffers, simplify management and provide clearer priorities for action.

Production Scheduling and Resource Allocation

In the message you sent [earlier], part of the “Future State” with regard to “Capacity Planning” was the hope that [the APS]’s capacity planning capabilities would allow you “to determine available capacity” and do “Long-Term and Short-Term Capacity Planning based on accurate schedule dates.” [Emphasis added: there’s that word “accurate” again—that sounds so good in theory.]

My concern is that it won’t work out in reality like it sounds in theory for some of the very reasons I mentioned in my [earlier] message, and those above, as well. In the absence of a huge data maintenance effort, ongoing throughout the production day—day-in and day-out—[the APS]’s finite scheduling mechanisms will remain ignorant of the impact of rework, “busted” schedules due to late-discovered materials shortages, or changes in CCR (Capacity Constrained Resource) capacities resulting from unexpected adjustments and adaptations being made on-the-fly on the shop floor.

Since employees and managers almost always want to do a good job for the company, their ad hoc decisions on the shop floor will virtually always be made in favor of FLOW (read: high levels of customer service), but the finite scheduler in your APS will not be updated with these changes—wise though they may be. This means the time, money and effort you put into having a complex finite scheduler produce a sound theoretical schedule for you by 8:00 AM, may be entire wasted and the shop floor may be running entirely “by the seat of its pants” by 10 AM—and this might happen several times a week!

Advanced Planning and Scheduling Systems and “Murphy”

APS finite schedulers work beautifully in theory, but they do a lousy job of accounting for “Murphy.” And, without constant data maintenance (throughout the day), and the processing of potentially dozens of what-if scenarios, no APS will be able to give you clear priorities for actions when Murphy disrupts the schedule issued earlier in the day. In fact, running a what-if scenario one way may tell you to do ‘A’, while running the scenario a different way may tell you to take action ‘B’, and a third what-if scenario may give you an entirely different action ‘C.’ This is of little use where the need is fast, accurate decision-making. Do you draw straws? What if the managers can’t decide which of the variety of actions to choose?

The simplicity of buffers and signals from buffers—whether they be STOCK, TIME or CAPACITY BUFFERS—provides the entire management team with clear, unequivocal action signals upon which everyone can agree because they know and understand exactly what the signal means and how it is derived. [Demand-driven planning and scheduling systems] essentially provide these kinds of action signals, which is why they are so simple and effective.

Continuing to Read Up Your CRT

As you can see from the CRT [which I had attached to the email], even if you may have some difference of opinion on the logic—because we didn’t build this together (as I’d prefer)—what is happening at the roots of the tree affect everything in the sense that they lead to INCREASES in both TVCs [Truly Variable Costs] and Operating Expenses, and likely reductions in revenues, as well. All of this combines to REDUCE THROUGHPUT (a direct product of FLOW), REDUCE PROFITS, and REDUCE CASHFLOWS across your enterprise.

I’m happy to discuss this matter further with you, but this probably enough (maybe too much) for one message.

            Thanks for bearing with me and these long messages.



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Recently, I had the following straightforward conversation with a client that was thinking about adding more technology into their manufacturing and supply chain mix. In this article, we are picking up part way into the conversation—and some minor details may be changed to protect the privacy of our client.



There is a lot to be considered here. I will try to address a few matters in a coherent way, although there is no way I will be able to answer every possible question that might arise in these emails. (In fact, initially anyway, it is possible that my responses to some matters may actually raise more questions in your mind than they resolve.)

You said: “Costing and time considerations are presently very abstract and not real-world” for you

I wouldn’t be too concerned about that, if I were you. In my opinion, and in the opinion of some other highly respected individuals, much of the needless complexity we find in the business world—and the manufacturing world, especially—is cause by a single misconception. This singular misconception is, unfortunately, a deeply held belief. By that, I mean that this concept—this belief—is held at such a deep level in our being that we almost never consciously challenge it.

That deeply held belief could be stated in any number of ways, but for the sake of this conversation let me put it this way:

Because financial information is essential in defining the purpose of a for-profit enterprise, we hold deeply the belief that financial information will also provide us with the primary means to control the outcomes or results of the enterprise (or, supply chain).

As H. Thomas Johnson, Professor of Business Administration at Portland State University, stated so cogently in his article “Managing a Living System, Not a Ledger” (Manufacturing Engineering, Society of Manufacturing Engineers (SME), December 2006), “Until that idea is abandoned and the practices it spawns cease, there is no reason to believe that ‘lean’ in any of its forms… can improve the long-run performance of businesses or the economy as a whole. This becomes evident when one examines why companies seem unable to match Toyota’s financial performance.” [Regarding Toyota’s financial performance when compared to U.S. automakers, read here.]

If your company’s (or, supply chain’s) goal is to MAKE MORE MONEY, then FLOW must be the objective of all that you do.

Toyota’s operations are very efficient; but they never focus on—or even measure—efficiencies. This efficiency is brought about, not by focusing on unit cost, batch sizes, and individual employee performance. Rather, the inherent efficiency of the Toyota Production System is the result of an irrepressible focus on “continuous improvement through endless rapid problem solving,” and FLOW [Johnson – ibid].

“[T]he Toyota accounting system treats daily plant operations essentially as a ‘black box’ that it does not enter. Accountants, of course, record everything that goes into the plant, and all the production that comes out.” But, the finance department at Toyota never concerns themselves with “costing” of operations or using financial metrics as “levers” for improvement. [Johnson – ibid]

Is there a place for “ongoing measurement of time usage”? Yes.

Time identified as “waste” should be, inasmuch as possible, be eliminated. But this is done by improving the processes and FLOW of manufacturing, not by identifying waste and accounting for it on a case-by-case, work order-by-work order basis. This leads to too much additional non-value-added activity. (The effort to capture and account for the wasted time is to add one non-value-added activity on top of another. This is the opposite direction from where you want to go.)

Is there a place for “time studies”?

Of course. Time studies are valuable for use in forecasting capacity requirements based on change in products or processes.

If you are going to keep a bill of materials (routing) similar to a typical product we reviewed when I was last on-site with you and your team, and you intend to schedule every job at each work center, capturing the starts and completions on each of the operations, then your non-value-added time will necessarily skyrocket. Consider the following:

BOM Indented - Typical.jpg

With a typical routing of eight (8) operations and processing 250 orders per day, you will need 2,000 data entry operations just to capture completion times—double that if you want to capture both start and stop times. If each entry takes an average of 20 seconds to complete—end-to-end—you will lose more than eleven (11) hours (1.4 person-days) per day to the data entry burden. All of this adds nothing to the value of the products going out the door.


Take a good look at the first block of “requirements” that you sent me. These are really prerequisites more than requirements. These are prerequisites that the newly proposed technology will require of you and your team in order to provide the “results” they are predicting in the post-implementation world.

Note the repeated use of the term “accurate.”

      • Identify and integrate accurate work center capacities
      • Accurate
      • Accurate
        • Implying accurate cycle times
        • Implying accurate set up times
        • Implying accurate work center allocations


By the way, for a routing with eight (8) operations, that is at least eight (8) cycle times that must be accurate, because scheduling errors will compound as they run through the routing operations. Then, of course, there are the set-up times and work center allocations that also must be accurate, in order for the schedule produced by the APS (Advanced Planning and Scheduling system) to approach anything near reality—even in theory.

The assumption made by every APS is “accuracy.”

For better or for worse, the APS will schedule according to the data you provide, and it will do so “accurately”—that is, in theoretical compliance with all the data you provided to the system.

However, no matter how accurate you attempt to make all of these inputs, the schedule produced by your APS will NEVER represent what is going to actually happen on the shop floor. This is because the “clairvoyance” component of the APS system is still under development.

Your APS system cannot know that Betty strained her back last night is only working at 72.3 percent of ‘standard’ capacity right now, and that number is falling throughout the day. Nor can your APS know that Joel had a fight with his wife before coming to work and is working at only 81.9 percent of ‘standard’ capacity; or that Mary Jo is feeling really good about herself today because she knows she’s “going gangbusters” (and working at 111.2 percent of ‘standard’ capacity). But all of that is going to change tomorrow, or later today.

In my opinion, an effective demand-driven scheduling system should schedule your CCR(s) [Capacity Constrained Resource(s)] only, and these should be protected by appropriately designed buffers of either stock, capacity or time (or some combination of these). It is your CCRs that limit your company’s ability to make more money.

In a high-volume MTO (make-to-order) environment like yours, where the CCRs may change due to the mix of orders being processed, then it may be valuable (or, even, necessary) to artificially “fix” a constraint to function as your “drum”—to set the pace for operations. This is why simpler is better; it simply required far less data and produces results that are far more accurate in terms of reflecting reality.


Murphy must be accounted for in the design of your buffers, because it is impossible for any APS system to account for Murphy (by definition, attacks of Murphy are random and cannot be built into any scheduling mechanism). As I said, your buffers may be any one of three types: stock, capacity or time.

With a simple approach to scheduling, you will know if your system is functioning on-time and according to the anticipated schedule simply by knowing the condition of all the buffers in your system.

If your buffers are green or yellow, you are in good shape. If any buffer is red, you know immediately know where to focus your attention and, based on buffer penetration, you will know how to prioritize your actions.

That is inherent simplicity at work. You will see some of that in the webinar I recommended to you, I am sure.

I know this is a lot to swallow all at once. And there are probably about 20 more pages I could (attempt) to write to try to convey more depth about some of these concepts. But, it’s too much to attempt now.

I hope this isn’t so poorly written as to leave you more confused than helped. I was just trying to address a couple of points that really stuck out to me.

     Let me know if I’ve left you worse off, instead of better, for having had this conversation this morning.



We hope you found this dialogue interesting. If it struck a chord in something you have experienced or to which you can relate, please let us know. Leave your comments below, or feel free to contact us directly. Thanks.


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Whether in your supply chain or within your own enterprise, the challenges that executives and managers must meet and defeat day after day can be grouped into three broad and generic classes:CRT Why No POOGI.jpg


Unfortunately, the typical ways in which managers respond to the challenges posed by these three classes leaves much to be desired




When our minds are overwhelmed by complexities—like the many hundreds of moving part in our enterprises or our supply chains, and the tens of thousands of interactions between these moving parts that occur every day—we tend to respond by attempting to identify recognizable subsystems within the larger complexity.


If we can identify and rationally isolate subsystems, our hope is that we can define, measure and adjust or tune each subsystem. Our further hope is that our tinkering and tuning of the subsystems will lead to the optimized performance of the larger and more complex system of which it is merely a part.


Our belief that such a path of subsystem optimization will lead to the optimization of the whole is firmly ground in a mechanistic view of our enterprise (or, supply chain). That mechanistic view holds that each subsystem (e.g., department, function, or supplier) is merely a “cog” in the larger “engine” as a whole.


Unfortunately, supply chain and enterprise “systems” are not like machines, at all.


Instead of being mechanical in nature, they are, in fact, complex adaptive systems (CAS).


Complex adaptive systems do not behave like machines. It is not so easy to adjust this and expect a specific outcome in that. Complex adaptive systems, unlike machines, are characterized by the ability to internalize information, learn, and modify their behaviors and actions as they adapt to what is changing in their environment.


Put in more specific terms, it means that department B has already learned what to expect from department A in your organization (your “system”)—including both the standard things the department does, and the quirky things that the department occasionally throws at it. From its learning, it has also adapted itself to those expectations.


If, therefore, you make department A change its behaviors, department B must re-learn and adapt itself to these changes in behavior. Furthermore, the improvement in department A (as you perceive it) may not have the desired effect on the overall system because, even if department B adapts to the change, some change in department C, D or F might effectively null out any “improvement” anticipated from the “improvement” made in department A.


For this reason, as shown in the accompanying Current Reality Tree (CRT), attempts to define, measure and improve subsystems in the absence of a full understanding of the entire system is likely to lead to policies, procedures and changes that actually conflict with the ability of entire system to achieve improvement. Sometimes, changes in one subsystem (read: department, or a vendor in a supply chain scenario) actually creates immediate and direct conflicts in a connected subsystem.




The next big class of challenges faced by executives and managers is uncertainty.


Actually, we deal with uncertainty in a way very similar to the way we deal with complexity. We believe that the way to deal with too much uncertainty is to break it down into increasing levels of detail in hopes of unraveling the mystery that creates the uncertainty in our minds.


For example, if a forecast sales number seems too large (or, too small), an executive is likely to say something like, “Can you give the details behind this number?” As a result, one big number that is seems uncertain, might become 100, or 1,000, numbers. But, because each individual number is smaller, instead of having this gnawing feeling that the original number was off by $100,000, we can now tinker with the much smaller individual numbers—each one being adjusted by $100 here, or a $1,000 there. In the end, we may have only adjusted the larger number by $20,000 or so (20 percent), but somehow we feel more comfortable.


This action, in turn, tends to lead to creating and managing budgets, forecasts and plans in much finer level of detail. Management’s metrics are frequently tied to the detail—if for no other reason than we have the computing power in their hands to do so.


Unfortunately, the smaller numbers and finer detail are all too frequently not large enough to absorb the “noise” level in the system.


Management finds itself swimming in a plethora of data where the actionable data may be nearly impossible to filter out of the noise. Instead of focusing on a single large issue that may be affecting the entire system at a deeper and more profound level, executives and managers get caught up in the minutia, reacting “noise,” instead of real underlying cause-and-effect.




Conflict is the next class of challenges with which managers and executives must deal.


Since conflict avoidance is a rather natural human behavior, managers frequently prefer to merely put out the current fire and restore some semblance of normality, rather than dig into the real core causes of such fires.


How are such issues “put to sleep” again and again (as one of my former mentors used to say)?


All too often it is done through compromise.


Compromise is generally a matter of accepting either the best of the worst or the worst of the best. It is certainly no recipe for anything much beyond mediocrity, but the immediate payoff is that “the fire gets put out.”


However, when root-causes are not addressed, a “fire” suppressed through compromise in one part of the organization, may break out as a different kind of “fire” in some other part of the system. Then, not infrequently, the original compromise action is reversed in favor of a different compromise to quell the current outbreak of fires.


It is not at all unusual to see managers and executives oscillating on an array of conflicts including:

  • “Run larger batches for more efficiency and lower costs” versus “Break those setups now so we can get these other shipments out the door—it’s quarter-end, you know!”
  • “Buy those items in bulk to keep our unit-costs down” versus “We have too much inventory! Why do we have 100,000 of those widgets? We only use about 1,500 a month!”
  • “We need to keep our margins up; no deep discounts” versus “It’s year-end and we need those revenues now! Cut prices or do whatever you have to do to close those big deals before the 31st.”
  • “We need to keep our shipping costs down! No more shipping partial orders without management approval” versus “Look! Our customers are complaining and canceling their orders! Ship those partial orders today before they get cancelled, too!”
  • “Our production payroll is through the roof! No more overtime!” versus “I don’t care whose labor budget we bust! Get those items produced and shipped by the end of Q2 or heads will roll!”

Dealing effectively


By applying the principles first articulated so cogently by Eliyahu Goldratt it is possible to deal effectively with complexity, uncertainty and conflict. Doing so requires finding a way—and applying proper tools—to see your enterprise or your supply chain as a complex adaptive system and not as a machine.


We help companies do this using the Thinking Processes. The diagram above is actually one of the Thinking Process tools—a Current Reality Tree or CRT.


These tools and our training help companies begin to see how their organization—their system—behaves, learns and adapts. The system’s behavior can be modeled in the Thinking Processes toolset.


Learning to use these tools, and being coached while you get started, can help put you and your team on the fast-track to a POOGIa process of ongoing improvement.




We would like to hear from you. Leave your questions or comments below. How are you doing in handling complexity, uncertainty and conflict?


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If your enterprise is designed to make money through the manufacturing or distribution of goods, then there are really only two—that’s right! Two—metrics that truly measure the DDMRP Flow at the Center.jpgperformance of your supply chain:

  1. Return on investment (ROI)
  2. Due-date performance (customer service levels)


Every other metric or KPI employed by the organization should be directed at simultaneously driving these two system-wide metrics higher, in a process of ongoing improvement (POOGI). There should be no “trade-offs” here—as are so frequently claimed by some. In the long-run, there is no need to sacrifice ROI for due-dates, or vice-versa.


So, whose job is supply chain management (SCM) anyway? Is it the job of your designated “Supply Chain Manager”?


Supply Chain Management is Everyone’s Job


Sadly, in too many organizations, the title of “supply chain manager” is just that—a title. This is the man or woman frequently caught in the middle of battles between Sales and Marketing (who want the system to favor FLOW over costs (unless the rising costs will affect their personal incomes via commissions) and Finance (who frequently see their job as “controlling costs”) and Operations (who are frequently measured by Finance based on “cost performance”).


In such cases, the supply chain manager’s job become little more than making sometimes vain attempts to satisfy the “biggest bully” at the present time. Sometimes the biggest bully is Sales and Marketing, pushing operations to get things built, bought and out-the-door. Not infrequently, Sales and Marketing may have an ally in Finance—especially near quarter-end or year-end. At times such as these it’s “Damn the costs! Just get the stuff to our customers so we can get it invoiced!”


Under these circumstances, inventory levels may skyrocket, excess freight costs may explode, and expenditures on overtime may double or triple, but—for the moment, anyway—FLOW is king! Everything is sacrificed as such times to FLOW—including the willingness to “break” set-ups and reduce batch sizes.


However, once this quarter-end or year-end cycle has run its course, the supply chain manager may be bullied with nearly equal vehemence to “slash needless inventories” and “make sure our manufacturing costs don’t get out-of-hand.” “No more overtime,” comes the call from Finance; and “increase batch sizes to improve our efficiency metrics,” Operations is commanded.


The all too frequently occurring picture just painted in the paragraphs above happens more often than most companies would like to admit. Such management oscillation is very damaging to morale and leads to a process of ongoing firefighting instead of ongoing improvement.


That is “supply chain management as everyone’s job” at its worst!


There is a Better Way


We are 100 percent in agreement that supply chain management is, indeed, everyone’s job. However, we also believe that there is a better way to carry out the practice of making supply chain management a cross-functional responsibility.


The process begins with using smarter metrics—metrics that align quickly and accurately with actual consumer demand and the ability to maintain FLOW across the entire supply chain.


Cost-world metrics must be let go. Retire them completely.


Your company really does not want the lowest possible costs! (You can always reduce your costs to zero by merely closing the doors on your enterprise and going home!)


What your executives and managers should be seeking is the lowest level of costs where FLOW is not disrupted. As Toyota has so ably demonstrated over the last 60 years or so is that this is the real road to sustaining profitability on the long term.


Within Toyota’s enterprise, production remains “a black box” to Finance. There are inputs and there are outputs from production, but Finance never meddles with the processes of production in order to “control costs.”


A process of ongoing improvement that maximizes FLOW through the elimination of as much non-value-added activity as possible will automatically produce the result of the lowest level of costs without impeding the flow of goods to customers.


Breaking It All Down


So, let’s quickly think—in a general way—about everyone’s role in managing the supply chain (based on the illustration above):

  1. Finance – Change your metrics and stop trying to manage costs. Finance’s biggest job—at least early on—is believing that if they stop managing “costs” that profits might actually improve.
  2. Sales and Marketing – Sales needs to keep the folks in operations and supply chain roles well-informed regarding anticipated upcoming changes in the market conditions that may affect demand (in a positive or negative way). They can also help reduce artificially induced demand variability by changing the way they offer discounts and incentives to customers and/or salespeople. They also need to be able to supply cogent feedback to Operations when internal buffer indicators (i.e., stock buffers, time buffers or capacity buffers) appear to be drifting unexpectedly. They need to able to help the enterprise figure out what is changing as quickly as possible.
  3. Operations – Operations has the obligation to learn how to measure the right things, while shifting to a more demand-driven system for managing inventories (across the supply chain) and production signals. We strongly recommend moving to Demand Driven MRP or a similar approach.
  4. Quality – The Quality folks need to work diligently on a POOGI that helps the enterprise build quality in, not sort quality failures out of the FLOW. This is the only way to support FLOW effectively.
  5. Planning – Planning should become a process of focusing on scaling anticipated changes in future demand (not forecasting numbers to be used for planning production). We call these estimated scales for anticipated changes Planned Adjustment Factors, and they are usually stated as a percent of change. Furthermore, Planning should learn to trust their buffers and the planning and scheduling of purchasing and production will be triggered and managed based on the status of the buffers—not based on forecasts (which are always wrong).




Supply chain management is everyone’s job. But it is a job that should be carried out cooperatively, systematically, and within a system designed to support a POOGI working toward improving the entire system’s ROI and customer service levels.


It should not be carried out erratically with constant oscillation between “cost-saving” and “get the order out the door” mentalities.


\We help move our clients in the right direction.


Please, leave your comments below. Let us know how you and your enterprise go about the task of supply chain management. We look forward to hearing from you.


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