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Computers and software can make excellent allies in business and supply chain management. However, while certain progress is being made into the realm of artificial intelligence, for the present—at least—the computers we employ for most of our business functions (in ERP, MRP, MRP II, and similar) are not really thinking machines. They don’t think, at all.


They compute, and that’s why they are called “computers.”


The following is a piece of correspondence I recently sent to a company who has engaged us to help them with automation around their manufacturing and supply chain. The focus of the letter was high-level information gathering. But, I want you to pay particular attention to the high-lighted portions of the letter.


Read on.




As I promised, I would like to start with some high-level questions that, I believe, will help guide us toward the best possible outcomes for you—including a rapid return on your investments. So, here we go….

  1. What is(are) your chief business goal(s) in seeking to implement and use [ERP system name - redacted]’s Advance Manufacturing capabilities in your business?
  2. Please list the top 5 business challenges you face, and hope to address, in the deployment of [ERP system name - redacted] Advanced Manufacturing.
  3. What average percent of your present capacity is being consumed by your market today?
  4. Lead Times:
    1. What is the typical lead (PO release to receipt of goods) time range for your raw materials?
    2. What is your typical production cycle time (release to manufacture to completion) for intermediate components?
    3. What is your typical customer tolerance lead time (order placement to delivery) for your finished goods?
  5. SKU-Locations (SKULs):
    1. How many SKU-locations do you presently manage (e.g., a SKU stocked at three different points in your supply chain and under your management is equal to three SKULs)
    2. Are all of your SKULs managed, presently, in [ERP system name - redacted], or by other systems (including custom applications or Excel workbooks)
    3. What Reorder Method is presently assigned to the majority of your SKULs in [New technology - redacted]?
    4. What Cost/Valuation Method is presently assigned to the majority of your Items in [New technology - redacted]?
  6. How do you presently manage inventory and replenishment today?
    1. How do you presently determine the strategic size of your stock buffers?
    2. How do you presently determine the strategic positioning of your stock buffers in your supply chain?
  7. How do you presently drive manufacturing execution? That is, what factor(s) trigger a manufacturing work order?
    1. What determines the quantity of units to be produced on a work order?
    2. What determines when a work order is to be released to the shop floor?
    3. How are manufacturing batch sizes determined (if used)?
  8. Describe in a paragraph or two your existing S&OP processes and cycle.
  9. How do you, or will you, define “work centers” for manufacturing purposes? Why have you chosen that approach?
  10. Are your manufacturing times known and in control? (Note: A process is in control when variation within the process can be attributed to random variation, whereas a process is not in control when variations are large and are not clustered around a statistical mean.)
  11. Do you know (or have you calculated) your typical manufacturing (work center cycle) times for all production steps to be included in your routings? These would include (where applicable) ….
    1. Queue time – the average time a production unit (or job) spends waiting for processing at the work center, or to be moved to the next work center
    2. Setup time – the average time a production unit (or job) spends waiting for the work center to be setup for processing the unit (or job)
    3. Move time – the average time a production unit (or job) spends being moved from the previous work center to the current work center in a routing
    4. Process time – the average time a production unit (or job) spends actually being processed by the work center
    5. Wait times
      1. Wait-to-batch time – the average time a production unit (or job) spends waiting to form a batch for either simultaneous processing or moving
      2. Wait-in-batch time – the average time a production unit (or job) spends waiting in a process batch for its actual processing
      3. Wait-to-match time – the average time a production unit (or job) spends waiting for other components for an assembly operation

        NOTE: These get recorded in the Routing Labor Steps by Work Center and, in turn, are used by the scheduling component for calculating capable to promise times, and other scheduling parameters. If they are not known, not correct, or not in control, the scheduling and CTP outputs will be invalid.

        There are formulas available to calculate expected queue length and/or average cycle times at a work center or line, but other factors may need to be known or calculated to perform these calculations.


One of the things of which I often warn clients when implementing an advance manufacturing environment is this:


Setting up an advanced manufacturing environment requires a very large amount of data (see above), and many companies do not really have all the data they require, and some even are uncertain as to how to gather it. As a result, they populate their manufacturing system with averages and best guesses.

At the other end, an advanced manufacturing systems puts out a huge volume of data, in great detail.

The problem is, the people using the systems frequently forget that the output is based on their input. Even though they may have populated their system with guesses and averages, they will tend to actually believe the outputs as being 100 percent correct. If the system tells them that it cost $187.1655 per unit to manufacture an item, they will believe it. If the system tells them that a CTP (capable to promise) date is 27 days from today, they will tend to believe it, without remembering the guesses and averages that went into those calculations.


Where they do notice is when MRP (material requirements planning) or APS (advanced planning and scheduling) outputs simply don’t make sense. So, as a result, they return to managing execution by the seat of their pants and Excel workbooks (or even whiteboards), and leave just the “accounting” to their manufacturing system.


I would like to help you avoid such a scenario as described above. Let me know how I can best be of assistance to you as we move forward.


Please let me know, as soon as possible… etc., etc.



I mention this in the context of supply chain management especially because, it seems, that there is some considerable thought being given to the concept that ever more complex systems and increasingly complex computing algorithms will ultimately lead us to the Nirvana of supply chain excellence.


But, I beg you to check with those companies that have sought solace (and, improved profits) by spending on more and more complex systems. Ask them what their real ROI (return on investment) has been? Many will probably tell you: “We don’t really have any yet, but we're still hoping for some in the future.”


The concept of inherent simplicity tells us that the more complex a problem appears to be, the simpler the effective solution must be.


“Predictability and information operate in opposite directions.”


As George Gilder writes, “Predictability and information operate in opposite directions.” By this statement he is highlighting the fact that “[I]nformation itself is best defined as surprise--what we cannot predict rather than what we can.”*


Think about this: what the effective supply chain manager needs mostly to be aware of what he or she cannot predict, and to know just how well his or her buffers are likely to protect against the “surprise” in the flow of relevant data. What can be predicted should already be covered and protected.


Finding inherently simple methods to protect and promote FLOW in the supply chain is the way to reach the real optimal state for your supply chain.


We can help.


Feel free to leave your comments below, or to contact us directly, if you prefer. We look forward to hearing from you soon.



* Gilder, George F. Knowledge and Power: The Information Theory of Capitalism and How It Is Revolutionizing Our World. Washington, DC: Regnery Publishing, 2013.



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So, a colleague of mine was asking some questions about how he might help his small-to-midsized business enterprise clients improve their supply chain and inventory management.


Knowing that many of these small businesses have grown organically, and may lack a lot of formal training in inventory management theory, I offered the following in my response.



Steps from Flow to Profit

Here are some thoughts about “better” inventory management that might be helpful.


Let’s start with the basics: What are some of the things the client needs to know about every SKU-Location (SKUL)?


      1. Lead Time
        1. They need to know the actual lead times (not guesses). And they can only get that information is the data in their system is reasonably accurate. For example, a purchase order generated on October 2, that isn’t released until October 10, is only valuable for calculating actual lead times if they know the release date, not the create date. Lead times need to be measured from when the vendor becomes aware of the demand.
        2. If internal lead times (the time from internal awareness of demand and the release of the replenishment order) is significant, it should be tracked independently and managed to as small a time period as possible.
        3. They need to know their lead time variability. This is a simple calculation of ratio between the average lead time for a SKUL and the standard deviation in lead time for the same SKUL. (Statistically, this is known as the CV or coefficient of variation and is calculated as StdDeviation/Mean.) It’s one thing to say the average lead time for a SKUL is “10 days” with a variability of one or two days. It’s a completely other matter to manage inventory for a SKUL with an average lead time of 10 days, where the variability is minus 2 to plus 14 days.
        4. They need to know the difference between full lead time and ASRLT (Actively Synchronized Replenishment Lead Time) for each SKUL. It is only by knowing these factors that they can strategically position their inventory for maximum ROI.
        5. They need to know the range of lead times across their supply chain so they can appropriately manage inventory profiles. A “short” lead-time in one supply chain might be one to three days, whereas a “short” lead-time in another company’s supply chain might be one to three weeks.

      2. Average Daily Usage (ADU)
        1. They need to know the ADU for every SKUL in their system.
        2. They need to know that the ADU is kept up-to-date. In today’s economy, an ADU calculated two months ago may not be accurate for today, especially if it is being factored by long lead times (large multipliers).
        3. They need to be able to identify and quantify sporadic demand. Factoring for sporadic demand is really pretty easy to do, even it T-SQL code. Take the number of days out of the last 365 where demand was non-zero. Divide that number into 365. For example, if a specific SKU had 41 days with non-zero demand in the last 365 days, you would take 365 days / 41 orders = 8.9 days per order (or, an order about every 9 days). We then take the square-root of that number (9^0.5 = 3) and use it in our calculations of “safety stock” or RED ZONE, and the Order Spike Threshold. You can read more about the details in an article I posted here, or read here for an alternative approach to sporadic demand.

      3. Demand Variability
        1. They need to know the demand variability (CV of demand) for each SKUL. Demand variability, like lead time variability, is a simple calculation of the ratio between average daily demand and the standard deviation in daily demand.

          NOTE: These calculations can be quickly and accurately done using Transact-SQL’s standard capabilities for both demand and lead times, provided the data are reasonably accurate.

          NOTE: Like lead times, they need to be able to separate SKUs into “families” by knowing demand variability across their range of SKUs. In one company, a CV in demand of 0.2 might be considered “medium” variability; whereas, in another company, a CV in demand of 0.8 might be considered “medium.” Again, where these factors are used with long lead times (large multipliers), it can make a significant difference in inventories.

      4. Minimum Order Quantities (MOQs)
        1. They need to know the true MOQs for each SKUL. By true MOQ, we mean the smallest economically sensible replenishment order quantity (not a number somebody picked out of their hat because they believe it is “efficient” to use that number).
        2. They need to know (at least internally) the why behind the MOQ. (They want to avoid large MOQs that are based on some calculation of “efficiency” that leads to constant or recurring disruptions to the more crucial—profit-making—matter of FLOW.)

      5. Order Spike Threshold – It is crucial to know what single-day demand quantity for any given SKUL requires special attention. This is the order spike threshold and should be up-to-date so that supply chain planners receive appropriate alerts for demand exceeding the threshold.


Fortunately, solutions like Sage Inventory Advisor automate (using different nomenclature, or without “naming” the concept at all) the consideration of many of these factors. If the users are attempting to use Sage 100 in its native state, they will need lots of assistance if they are ever going to get out of, and stay out of, “firefighting” mode in their supply chain operations.


If they want an even better solution, they should consider Replenishment+, for any number of reasons—the largest reason being a history of producing huge ROI for companies that use it.


In the absence of any external solution, there would be a lot of statistical analysis that would have to be built “from scratch” to help the typical company become anything more than an “also ran” in their industry and marketplace.


Let me know if I can help further. Thanks for your interest and questions.


Thanks for reading. If we can help you, let us know by leaving your comments below, or feel free to contact us directly, if that is your preference.


We would be delighted to hear from you. And don’t forget to use the links below to follow us on Twitter and like us on Facebook. Thanks.



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What happens if we compare the Demand-Driven Operating Model (DDOM) with what a typical ERP (enterprise resource planning) system is able to do employing more or less standard methods for inventory management and replenishment? let’s take a look.


DDMRP buffers and Sage 500 Inventory Replenishment parameters

The Demand-Driven Operating Model (DDOM)

For those of you not familiar with the Demand-Driven Operating Model (DDOM), I would suggest that you review the details and concepts made available to you on the Demand Driven Institute’s website.


For a quick summary, suffice it to say that the DDOM divides each stock buffer into three main zones or segments:

  • GREEN ZONE – basically, the replenishment zone
  • YELLOW ZONE – the working stock (on-hand quantities typically spend most of their time somewhere in this zone)
  • RED ZONE – the “safety” or “warning” zone


Comparisons to typical ERP inventory management methods

It may be a little difficult to make out the connections between the DDOM zones and the traditional nomenclature clearly in the accompanying figure. So, we will try to make the connections and relationships clear in our explanation. Here goes.


Typically, among the available traditional or standard methods found in ERP systems, the min/max method comes closest to the way the DDOM buffers are managed and replenished. Therefore, as you see in the Sage 500 | Inventory Replenishment portion of the accompanying figure (used as an example of a traditional ERP system), the reorder method is set to “Min/Max.”


The min/max method typically means the following replenishment process is followed: when the replenishment position falls below the minimum quantity, a suggested order will be generated to replenish back to the maximum position.


Since in the DDOM, the GREEN ZONE is the “replenishment” zone, then the difference between the maximum level and the minimum level is roughly equivalent to the DDOM GREEN ZONE.


At the other end, the DDOM RED ZONE may be roughly equated with traditional safety stock.


Sitting between the RED ZONE and the GREEN ZONE is the DDOM YELLOW ZONE. Therefore, in traditional ERP parlance, the quantity lying between the top of safety stock and the minimum quantity is equivalent to the DDOM YELLOW ZONE.


Using the values for the “Gateway400C” SKU in the accompanying figure, we have the following calculations:

  • SAFETY STOCK (RED ZONE) QTY = 15 units
  • MIN QTY – SAFETY STOCK QTY (YELLOW ZONE) = 20 – 15 = 5 units
  • MAX QTY – MIN QTY (GREEN ZONE) = 60 – 20 = 40 units


In typical DDOM fashion, we calculate as follows:

  • TOP of RED = 15
  • TOP of YELLOW = 15 + 5 = 20
  • TOP of GREEN = 20 + 40 = 60


So far, so good!


It almost sounds like we can just shift over to the DDOM using our traditional ERP system’s replenishment methods.


But, here’s the problem.


What’s missing in your traditional ERP replenishment system?

First of all, a great many standard ERP systems don’t really know anything about dynamic buffer management. In many of them, the quantities set for safety stock, minimum and maximum levels are fixed until someone changes them—manually.


I can’t tell you how many clients’ offices I have been in, and been told something like this: “Oh, those quantities were set—oh, I don’t know—about six years ago, and the guy that did it doesn’t even work here anymore. I don’t know how they were set or calculated.”


That’s a dangerous position to be in, I believe.


Fortunately, using Sage 500 (as in the example), there are three levels of automation that will help make at least the safety stock portion of the replenishment parameters somewhat dynamic. Those levels are these:

  1. Projected Demand (what the DDOM calls, average daily usage or ADU) may be calculated by extrapolating from historical demand (and may include both past and future demand adjustments)
  2. Projected Lead Time may also be calculated by looking at historical lead times (but only for purchases from the “primary vendor”)
  3. Projected Safety Stock may be calculated by formulas that may include both Projected Demand and Projected Lead Time as factors in the calculation


That’s better than nothing, but it does nothing for dynamically adjusting the minimum and maximum stock quantities. In Sage 500 and many other traditional ERP systems, these must be manually adjusted.


Furthermore, in Sage 500, the calculations listed to make Projected Safety Stock dynamic must be initiated manually, and the calculations only consider historic demand by inventory period. So, if your “Inventory Period” is set to a calendar month (as is quite typical), no changes are recognized until 1) the prior inventory period is closed, and 2) inventory replenishment factors are manually recalculated.


This is a big limiting factor, especially when added to the fact that minimum and maximum quantities are not dynamically adjusted at all.


And, what about the rest of the DDOM?

Given that it is not overwhelming task to automate, in accordance with the DDOM principles, the process of making buffer management dynamic in Sage 500 or many other ERP systems, is it safe to say that implementing the DDOM may be accomplished in this way?


The answer to that questions is both “yes” and “no.”

5 Steps to Building Demand Driven Supply Chains


Yes, that automation may accommodate one aspect of the DDOM. But the DDOM is not merely one aspect.

To be truly effective, the DDOM includes at least five crucial elements:

  1. Strategic inventory positioning – deciding where to place inventories in your supply chain in order to maximize return on investment (ROI)
  2. Establishment of buffer profiles and levels – setting buffer profiles allows SKU-locations that behave similarly to be managed similarly without manually setting each buffer size
  3. Dynamic buffer management – this is the dynamic adjustments to buffer zone sizes based on the latest actual demand information available
  4. Demand-driven planning – this is a whole new approach to planning and execution that is driven by actual demand without excluding the influence of forecasts (where necessary) and without becoming make-to-stock
  5. Visible and collaborative execution – this adds a whole new set of metrics that help keep your whole cross-functional supply chain management team focused on the right priorities all of the time

By enhancing traditional min/max inventory management with safety stock, we may be able to approximate portions of steps 2 and 3, but doing so still leaves us far from becoming truly demand-driven.


Learn more

If you’re interested in learning more about how to become demand-driven, we would suggest getting and reading two new books on the subject:


And we can help you get started. Leave a comment below, or feel free to contact us directly, if you prefer. As always, we would be delighted to hear from you.



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Inventory Turnover

Inventory turnover is the number of times average inventory (typically stated in dollars) is sold and replace over a specified period of time (typically one year).


For example, if your firm has $11 million in average on-hand inventory, and average inventory-related costs of sales is $2.66 million per month, then inventory turnover would be calculated as $11 million / $2.66 million, or 4.1 times per year.


While this number has some value in terms of financial averages and, typically, a higher inventory turnover ratio is better, it provides supply chain managers with almost no guidance for individual SKU management.


Inventory turnover ratios can be calculated on a SKU-location basis, however, and this provides additional information. It is likely that some items will have very high turnover ratios, while others will have very low turnover ratios.


Glenday sieve analyses over a large number of enterprises strongly suggest high inventory turnover ratios will be found on only about six percent (6%) of SKUs in any location. On the other end of the spectrum, nearly 50 percent of SKUs will have relatively low turnover ratios and together account for only five percent (5%) of unit sales. The average inventory turnover ratio, therefore, provides very little guidance to the inventory or supply chain manager.


SKU velocity

While inventory turnover ratios are intended to provide guidance on SKU velocity, it is of little practical use because it provides no clear way to connect SKU management decisions and parameters with the resulting velocity.


Demand-driven supply chain theory suggests that there is a better way. In the DDOM (demand drive operating model), SKU velocity is linked directly to SKU-specific management profile decisions through a metric called the flow index.


The flow index for any SKU-location (stocking position) is calculated within the DDOM as the size of the GREEN ZONE divided by the ADU (average daily usage). For example, a SKU-location with a GREEN ZONE of 70 units and an ADU of 10 would have a flow index of 7.0 (70 / 10).


Calculating the size of the GREEN ZONE

Going into the full details of how to calculate the full DDOM (or DDMRP) buffer size--with its RED ZONE, YELLOW ZONE and GREEN ZONE--is beyond the scope of this article. (If you'd like the full details, please read DDMRP - Demand Driven Requirements Planning by Ptak and Smith.) However, we will talk about the three typical options for sizing the GREEN ZONE in a stock buffer. They are:


  1. Using a fixed order cycle - Sometimes the expected number of days between replenishment orders is fixed. This may be due to a repetitive production cycle (read: Lean RFS - Repetitive Flexible Supply by Ian Glenday and Rick Sather, but don't confuse the RFS "green stream" with the DDOM "green zone"), or for any number of other valid reasons. When the order cycle is fixed, then the size of the GREEN ZONE is calculated simply as ADU * Order Cycle Days.

    For example, if Order Cycle Days = 7, and ADU = 10, then the size of the GREEN ZONE = 10 * 7 = 70.

  2. Applying a Lead Time Factor - For a SKU-location with an ADU of 7, a decoupled lead-time (LT) of 12 days, and a Lead Time Factor (LTF) of 50 percent (indicating coverage of 50 percent of lead-time), the size of the GREEN ZONE would be calculated as ADU * LT * LTF = 10.0 * 12 * 0.50 = 60 units.

  3. Use of a Minimum Order Quantity - If a minimum order quantity (MOQ) is imposed for any reason, then calculation of the GREEN ZONE is always determined by the maximum of any other method or the MOQ. For example, if the MOQ for our SKU-location is 85, then the GREEN ZONE would be set to 85, regardless of the 60 units calculated using a Lead Time Factor, or the 70 units calculated by the imposed order cycle.

    However, if the MOQ were 50 units, then either of the other methods might be applied, leading to a GREEN ZONE of 70 or 60 units instead.


Back to the FLOW INDEX

So, for the SKU-location used in our example above, the FLOW INDEX would be calculated as

  1. 70 / 10 = 7.0 for the fixed order cycle method
  2. 60 / 10 = 6.0 for the LTF method
  3. 85 / 10 = 8.5 for the MOQ method (where MOQ = 85 units)


Unlike the turnover ratio where higher is better, with the FLOW INDEX a lower value is better.


Therefore, if you can serve a higher ADU with the same amount of inventory, your FLOW INDEX improves (goes down). For example, if your ADU increases from 10 to 11.3 units, the FLOW INDEX calculations above change to

  1. 70/11.3 = 6.2
  2. 60 / 11.3 = 5.3
  3. 85 / 11.3 = 7.5


With these calculations, supply chain managers can immediately see the working relationships between their stocking calculations and FLOW. If order cycles can be reduced, or if lead times and lead time risk factors can be reduced, or if minimum order quantities can be reduced, FLOW INDEXES decline. And, provided the net flow is meeting customer demand, improvement is the result.


By the way, you might have noticed that the FLOW INDEX is actual "days" and represents the frequency of replenishment orders (not lead-time). Without going into the DDOM details, with a GREEN ZONE of 60 and ADU of 11.3, you should be placing a replenishment order about every 5.3 days. However, with a GREEN ZONE of 85 (MOQ) and an ADU of 11.3, you would be placing a replenishment supply order about every 7.5 days.


Linking actions with metrics

I think you can see that linking specific actions to the metrics is easier using the DDOM's FLOW INDEX method, than when using the inventory turnover ratio method.


While the inventory turnover ratio is linked to accounting measures and controls (dollars in inventory and cost of goods sold), the FLOW INDEX is linked directly to decisions the supply chain manager considers every day: order cycles, average daily usage, minimum order quantities, and lead times. The supply chain manager or inventory manager is working in familiar territory to drive daily improvement actions.


If you'd like to experience the difference of supply chain management in the demand-driven operating model, we can help.


We would be delighted to have you leave your comments below. However, if you'd prefer, feel free to contact us directly, as well.



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Definition - Trade-Off

Frankly, I’m concerned

I keep reading statements like these in the supply chain management literature:


“Effective inventory management all boils down to a delicate balancing act….”


“Your job as an inventory manager is to strike a compromise between conflicting priorities--and those of your colleagues.”


Wow! That second one is a real stinger!


As an inventory manager, not only are you expected to function managing inner conflict—that is, your own “conflicting priorities”—but you’re also expected “to strike a compromise” between additional conflicts that are part of the domains your colleagues manage.

Trade-offs and compromises

The Merriam Webster online dictionary defines “trade-off” as “a balance of factors all of which are not attainable at the same time.”


So, if you find yourself believing this is the true case for inventory and supply chain managers, here are my questions for you:

  1. Just why is it that your organization is in conflict with itself?
  2. Isn’t there a single, unified goal around which you could unite and end the conflicts and compromises?

Let’s face it

If your answer to the second question above is, “No,” then let’s face it. Your organization and your supply chain will need to resign itself to mediocrity.

Quote Debra Smith on Organizational Conflict



Simply because, as Debra Smith so keenly observed and articulated in The Measurement Nightmare*, if your organization is in conflict with itself, then it really doesn’t make much difference how good your strategies might be. Execution on strategy, when trade-offs and compromises are involved, will almost always lead to mediocrity—except by chance. Trade-offs and compromises are the enemy of excellence in execution.


We don’t believe it

We don’t believe supply chain and inventory managers need to make trade-offs between factors such as costs, speed, service levels, quality, flexibility and reliability.


We believe—and have the evidence to prove—that you really can have it all!


In a conscientiously applied program of DDS&OP (demand-driven sales and operations planning), we are confident that you can truly unite your entire organization around a single goal. And, you will be able to extend that unifying effect up and down your supply chain. You can end conflicting signals and function without all of the constant firefighting and finger-pointing.


In fact, we can help you do it.


Tell us how you’re doing at ending trade-offs and compromises by leaving your comments below, or by contacting us directly, if you prefer. We would be delighted to hear from you.



* Smith, Debra. The Measurement Nightmare - How the Theory of Constraints Can Resolve Conflicting Strategies, Policies, and Measures. Boca Raton, FL: St. Lucie Press, 2000.



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In a SCM World report offered by Kinaxis here, Gartner’s SCM World’s cross-industry learning community, with the help of author Pierfrancesco Manenti and his co-contributors, offers some insights for those supply chain executives and managers trying to make decisions about SCM technologies.


Important and disruptive technologies

Interestingly, the top 5 important and disruptive technologies (according to a poll of more than 1,400 respondents) were, in descending order:

  1. Big data analytics (81%)Graph of poll results on SCM disruptive technologies
  2. The digital supply chain (68%)
  3. The Internet of Things (IoT) (64%)
  4. Cloud computing (58%)
  5. Advanced robotics (53%)

The report also suggests that there is a hunger in the marketplace for technologies. More than half of the respondents (57 percent of 277) thought that—as of today—“the technology is ready and it is the right time to invest” or the technology “has been there for years….”


What kinds of technologies?

The report categorizes SCM technologies in five (5) groups, with subcategories as follows:

  1. Control tower
    1. Supply chain visibility (SCV) and risk
  2. Forecast to delivery
    1. Demand planning and forecasting (DPF)
    2. Supply chain planning (SCP)
    3. Sales and operations planning (SOP)
  3. Order to cash
    1. Logistics and management (LOM)
  4. Procure to pay
    1. Supplier relationship management (SRM)
    2. Spend analysis (SAN)
  5. Design for profitability
    1. Product lifecycle management (PLM)
    2. Smart manufacturing management (SMM)


This seems to cover the full gamut of SCM. And, as reported above, the majority of marketplace respondents say that “the technology is ready.”


So, why the long faces?

If “the technology is ready” for deployment, why the relatively lackluster reception by those who have purchased and implemented these cutting-edge technologies?

Graph of Voice of the Customer poll results


More than 200 respondents were asked about things like:

  • The scale of the impact of deployed technologies on the performance of their supply chains
  • The reliability of the technologies and the vendors behind them
  • Their view of the innovation expressed in the technologies supplied
  • The speed to deployment and benefit
  • The value they received for their investment


The author of the report said, “The results of the VOC [Voice of the Customer] survey were disappointing, as the customers who participated gave vendors’ digital capabilities an average score of only 3.2 on a scale of 1 to 5.” That’s barely above midrange! Where’s the excitement about these new technologies?


What this says to me

Maybe I’m reading something into this report that isn’t there. Maybe I’m missing the point entirely. (You should read the report and decide for yourself.) But, here’s what these results say to me:


Despite the SCM leaders’, managers’ and executives’ sense that “the technology is ready,” their expectations for real and measurable benefit from these technologies are low. They don’t really seem to expect much ROI (return on investment) from their expenditures on the new technologies, and—if “scale of impact” and “value for money” measures mean anything—they are getting what they expected. In other words, they had low expectations going in, and their expectations were met! The got mediocre results.


This says something more to me, too.


It tells me that, while technologies like big data analytics, supply chain digitization, the IoT, cloud computing, and advanced robotic all have importance and will all have their own dramatic effects on supply chains, there is something more fundamental that is not being effectively addressed by any of these highly-promoted technological solutions.


I don’t believe the “average Voice of Customer score” on this report would be a mediocre 3.2 out of 5, if the following had been typical of the results being achieved by the implementation of the new technologies:

  • Sustainable achievement of planned service levels, irrespective of demand patterns, accompanied by
  • Reductions in average inventory of between 30 and 50 percent, along with
  • Cost reductions in the neighborhood of 20 percent, with
  • Planning lead-time reductions of up to 85 percent, all the while
  • Greatly reducing or eliminating the costly and continuous “firefighting,” expediting, and frustration found in most operations and SCM organizations


These (listed above) are typical results for demand-driven supply chains as reported in Demand-Driven Supply Chain Management by Simon Eagle.


Maybe… just maybe, the really important and disruptive technology that is being overlooked by so many supply chain managers isn’t a technology at all, but an entirely new approach to supply chain management. Without the demand-driven approach, the results of other SCM technologies are just mediocre.


How about you? Are you ready to become a demand-driven supply chain leader?


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



CITATION: Manenti, Pierfrancesco, Patrick Van Hull, and Geraint John. In Pursuit of the Right Supply Chain Technology Solution - Mapping the Path to Supply Chain Digitization. Report. Boston: SCM World (Gartner), 2017.


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



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