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2015

On the first day of June, 2012, the International Supply Chain Education Alliance (ISCEA) announce its strategic partnership with the Demand Driven Institute (DDI) to provide DDMRP House.jpgeducation and certification in Demand Driven Material Requirements Planning (DDMRP). IISB (ISCEA International Standards Board) dubbed this program of education and certification the CDDP (Certified Demand Driven Planner).

 

What is Demand Driven MRP?

 

Demand Driven MRP (DDMRP) is a new and improved formal planning method that has been designed and developed to specifically correct the recognized inadequacies and inappropriate rules still dominating traditional and conventional MRP (material requirements planning).

 

Traditional or conventional MRP was first articulated in the 1950s, methodized and systemized in the 1960s, and then promoted commercially in software and systems beginning in the 1970s. Unfortunately, it has not really changed significantly since—even though the whole world of manufacturing and supply chains is almost unrecognizably difference from what it looked like in 40 or 50 years ago.

 

Simply put, the traditional approach to MRP was designed and implemented for a world where yesterday was a reasonably good predictor of tomorrow in most major industries. That, however, is not the supply chain world that we live in today.

 

In 2011, Carol Ptak and Chad Smith published a rewritten and expanded version of Orlicky’s Material Requirements Planning, Joe Orlicky’s original tome on the workings of MRP. The new version, Orlicky’s Material Requirements Planning, Third Edition, defines a new approach to MRP that meets the challenges of what Ptak and Smith call “the New Normal.”

 

The New Normal

 

The New Normal looks like this:

  • Global sourcing and global demand
  • Dramatically shorter product life-cycles
  • Significantly reduced customer tolerance times
  • Increased product complexity and mass customization
  • Huge pressure for reduced inventories
  • Forecasts accuracy failing to improve because improved methods are constantly offset by increased supply chain and demand volatility
  • More product variety than ever before
  • Many long lead time components, despite the shrinking customer tolerance times

 

Does Demand Driven Planning mean Make-to-Order (MTO)?

 

No. Becoming demand-driven does not necessarily mean that you must produce to order.

 

What it does mean, however, is that all production and replenishment orders are triggered by what occurs relative to actual demand in the supply chain. While planning may include forecasts that will lead to adjustments in the size of supply chain buffers—stock, time or capacity—the actual order to produce or replenish will be triggered by actual demand in the system.

 

This whole approach improves FLOW. FLOW, in turn, drives inventories down while driving profits and ROI (return on investment) higher.

 

Why apply Demand Driven methods?

Traditional MRP, ERP (enterprise resource planning), DRP (distribution requirements planning) planning and execution systems rely on detailed forecasts to drive orders for production and replenishment. While such “push and promote” systems worked well in a world where market demand outstripped production capacities (as in the 1950s, 60s and into the early 70s), it has proven to be far less effective in today’s world where manufacturing capacities exceed market demand (except, of course, in some emerging markets).

 

Chances are, if you have anything to do with a supply chain—internal or external to your company—you have experienced how these legacy rules and systems create enormous friction and drive constant compromises and firefighting when planned orders fail to synchronize with actual demand.

 

The new Demand Driven methods can help achieve alignment across the entire organization—or, even, the entire supply chain. Resources, inventories and replenishment activities can be given clear signals and easy to understand priorities for action based on what is happening with actual demand. There can, at last, be a clear understanding of inventory investment strategies and tactics, and full cooperative harmony between sales, planning, scheduling and execution.

 

A Certified Demand Driven Planner (CDDP) can help you achieve these ends.

 

Benefits of Demand Driven planning and execution

 

Companies that have implemented Demand Driven methods have discovered significantly improved financial success—typically far outpacing their industry rivals. Supply chain risks are minimized; lead times are compressed; customer service is improved; and inventories are reduced.

 

Demand Driven methods have proven their worth.

 

What’s keeping your company from becoming truly Demand Driven?

Recently I read an article[1] entitled “3 Ways Manufacturing Companies Can Boost Efficiency with ERP.” The opening line really shocked me: “For those that operate in the manufacturing industry—efficiency is everything.”

 

Now, in an age and time when manufacturing capabilities fell well short of demand for manufactured goods, one might have made the argument successfully that, in manufacturing, “efficiency is everything.” However, that world has not existed for some time now.

 

Things are different now

 

Compare the circumstances in the table[2] below:

 

Circumstance

In 1965

Today

Supply chain complexity

LOW. Supply chains looked like chains—they tended to be linear, vertically integrated and domestic.

HIGH. Supply “chains” is, for the greater part, a misnomer. The supply chains of yesteryear have become the supply “webs” of today, and those webs extend around the world in many cases.

Product life cycles

LONG. Product life cycles were quite frequently measured in years.

SHORT. Product life cycles are now often measured in months, and some products don’t make it to market before they are obsoleted by new technologies or a competitor’s product.

Customer tolerance times

LONG. Customers for many products were willing to wait several weeks or months for delivery.

SHORT. Customers increasingly are asking for virtually immediate delivery.

Product complexity

Relatively low.

Many products today incorporate complex mechanical, electrical or electronic components.

Product customization

LOW. Only a few products offered options or custom features.

HIGH. The Web-empowered customers are now expecting—and frequently getting—the opportunity to customize each unit of the product delivered.

Product variety

SMALL. Most products offered only a single variety. For example, in 1965, Colgate and Crest each produced only one variety of toothpaste. The variety of cigarettes ranged to a couple of dozen popular brands.

LARGE. Product proliferation has dramatically increased. Even Coca-cola now comes in multiple flavors and a large variety of packaging options. Today, you can get a bottle of Coke with your own name printed on the label.

Long lead-time parts

FEW: Most parts were domestically sourced and, therefore, lead-times tended to be shorter.

MANY: The globally extended supply chain accompanied by highly diversified and specialized manufacturing partners, more and more components tend to have long lead times relative to customer tolerance times.

Forecast accuracy

LOW: Less product variety, longer product life cycles, and longer customer tolerance times tended to limit dramatically the impact of forecast errors. Frequently, there was time to make adjustments.

HIGH. Increasing product variety, mass customization, high levels of product complexity, shortened product life cycles, and supply chain complexities all combine to multiply the negative impacts of forecasts. Improvements in forecasting methods are offset continually by increases in market variability.

 

Flow is everything

 

In the midst of such conditions, more and more companies are proving again and again that it is not efficiency that is the creator of their success. Rather, FLOW is everything.

Efficiency may help companies make more and more of the products that customers are not buying a lower cost to the firm. This is particularly true when replenishment orders are based on forecasts, rather than being demand driven. Unfortunately, that simply increases the flow of irrelevant materials in response to irrelevant information.

 

Unfortunately, while resources are being “efficiently” employed in the production of goods that are not needed in the supply chain, their utilization is simultaneously preventing the production of goods that consumers really are demanding.

 

The net effect of all of this is what companies driven by traditional MRP and ERP systems are experiencing almost everywhere: too much inventory on the one hand, while experiencing dramatic shortfalls and out-of-stocks on the things that customers are actually demanding.

 

Unlearning what we think we know

 

It is, unfortunately, the deeply held truth that “efficiency is everything” that keeps companies mired in this experience in today’s world economy. When companies begin to understand that FLOW is everything, they have a real opportunity to increase profits and become leaders in their markets.

 

Dramatically improved profits come from discovering ways to become demand-driven, and not by simply eliminating paper (the fictional “paperless office”), improving communications (especially if you are merely improving the communication of irrelevant information), or making sales simpler.

 

Let us know your experiences with seeking real improvement in today’s demand-driven world economy.

 

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Notes

[1] Cebull, Kyle. "3 Ways Manufacturing Companies Can Boost Efficiency with ERP." Smart Data Collective. May 27, 2015. Accessed May 28, 2015. http://smartdatacollective.com/kyle-cebull/320566/3-ways-manufacturing-companies-can-boost-efficiency-erp.

[2] Table adapted from Smith, Debra, and Chad Smith. Demand Driven Performance: Using Smart Metrics. New York, NY: McGraw-Hill Education, 2014.

I have been a fan of the “Smarter Every Day” channel on YouTube.com for a while. When I first saw the channel’s post on “The Backwards Brain Bicycle,” I found it fascinating.

ThinkingOutOfTheBox.jpg
On the positive side

 

The United States Marine Corps teaches about “muscle memory.” They say that, if you do the same thing about 300 times, not only will you improve with each execution—“practice makes perfect”—but, by the end of that time, you will be able to do much of what you have been practicing blindfolded, in the dark, or while being actively engaged in some other thought process. We have all heard the old saying: “I’ve done that so often, I could do it in my sleep,” meaning without actively involving the cognitive functions of our brain during the execution.

 

This is what professional musicians do. The violinist or cellist who can play flawlessly a concerto that may run upwards of 30 minutes in length has not, necessarily, committed to memory each note on the many pages of music that constitute his or her part in the performance. Rather, through their repeated practice, muscle memory has taken hold and much of the performance is carried out without the performer’s cognitive thought processes about notes to be played and finger positions on the instrument.

 

Another explanation

 

Alfredo Angrisani, commenting on the backwards brain bicycle on LinkedIn.com says:

 

“[T]his makes me think of the findings of Daniel Kanemann [sic - Kahneman] and his book Thinking, Fast and Slow: Our actions are determined most of the time by what he calls System 1, a status or a part of our brains that governs in 'normality': it is always active, automatic, reactive, tightly connected with or body functions, completely outside of our control (like the bike experiment shows) and that doesn't get tired, To name just a few of its characteristics.

 

“System 1 is always on, it monitors constantly the environment and calls System 2 up on action as soon as it spots any potentially dangerous lack of coherence in the environment (maybe a car breaking at a short distance in front of us).

 

“One of the key factors that makes a pattern look safe to System 1 is repetition: the usual way things are done (wrong or right, doesn't matter much, we are still alive and kicking, aren't we?) including what everybody keeps repeating and implying over and over in their stories (like the good of about cost reduction, going lean, using more technology, adding complication, centralising control ... you name it).

 

“This confirms to System 1 that we are going down a safe path in an essentially coherent world, and he's happy.

 

“System 2 is our 'intelligent and elaborating self', it is smart and elaborating (Should I brake or should I go?, Should I move the handlebar to the right or to the left?), but it has the annoying feature of having a limited capacity and getting tired. So if you try to overburden, it disconnects or works only on priorities (Sorry, can you repeat, I was thinking about something else!).

 

“In conclusion, as going out of the beaten path is by definition a tiring matter, we better make it simple (visible), rewarding and reassuring (to us and to the others) in order to move our operating mode as quickly as possible into the System 1 domain, lest System 2 gets annoyed and gives up to the day-to-day priorities following the established (repeating and perceived as less risky) patterns.”

 

The danger of saying, “We know”

 

While I have not read the book, what Kahneman seems to be saying is this: Since employing our “system 2,” our “intelligent and elaborating self,” is hard work, we frequently take shortcuts in our reasoning by saying, in essence: “If system 1 can handle it, let it. Employing system 2 makes me too tired, or system 2 is too occupied with other seemingly more important matters.”

 

What we are declaring by taking this default position is: “I already know and understand this situation. I’ve been through it lots of times—may 300 or more times. There’s nothing new here. System 1 can take over and things will be fine.”

 

The problem is, Kahneman’s “system 1” has become “the box” for our thinking.

 

When we use the phrase, “thinking out of the box,” we are really saying: “Look. We think we’ve been here before. We think we know all there is to know about this situation, and we think we know what our response should be. However, doing what we’ve always done hasn’t gotten us what we need. We need to rethink this situation.”

 

Installing new “Thoughtware”

 

I wish I could take credit for it, but I can’t. The concept of “thoughtware” originated, apparently, at the Goldratt Institute in the 1980s and 90s. It first came to my attention while reading Demand Driven Performance Using Smart Metrics by Debra Smith and Chad Smith.

 

Most organizations have evolved using the traditional approach to problem solving. They try to break down the complexities they see all across their enterprise into functions and departments and attack each “problem” independent from a view of the entire “system”—read: the entire enterprise, or even supply chain.

 

In fact, Smith and Smith say, “In most cases, people inside companies are prohibited, discouraged, and / or incapable of thinking about problems and solutions from a systemic point of view…. Individuals and the organization can be made capable [however]. To drive meaningful and rapid improvement, problems must be defined and solutions must be developed from a systems and flow based perspective….” (Smith, Debra, and Chad Smith. Demand Driven Performance: Using Smart Metrics. p.21. New York, NY: McGraw-Hill Education, 2014.)

 

Smith and Smith’s premise is quite straightforward and could save companies hundreds of thousands of dollars—perhaps even millions: “[B]efore an organization should consider making additional huge investments in hardware and software to compensate for the New Normal [read: today’s highly volatile and rapidly changing business environment], it should first consider investing in Thoughtware. Thoughtware is people’s ability to think and communicate systematically [read: from a system-wide view]. By focusing our collective intuition, we will bring to light the problematic assumptions, outdated and conflicting rules, and knee-jerk reactions that not only make our bed but force us to lie on it. Without correct thoughtware installed, additional investments in hardware and software are often squandered either through their misapplication or due to the fact that they were not really required from a flow perspective in the first place.” (Ibid. p. 23)

 

Trying to ride a backwards brain bicycle

 

Trying to solve today’s supply chain and business problems using yesterday’s rules and logic—the things that work five, ten or 15 years ago—is like trying to ride a backwards brain bicycle using the things your brain learned riding a regular bicycle. You and your team desperately need new thoughtware!

 

Consider these questions:

  1. Are people in your organization formally trained to think from a system-wide view of the challenges you face in your company or across your supply chain?
  2. Do they have a common problem-solving language and framework to help them work toward sound solutions?
  3. Do people in your company understand the connections between departments, resources, and people, or is their intuition limited largely to their own silo of operations?
  4. Are people given enough visibility to see the relevant connections between departments, resources, and people? Do they know how to quickly convert data into relevant information that helps improve system-wide flow?
  5. Are people discouraged from thinking and offering solutions outside their functional silos?
  6. Can people readily identify how and where variability accumulates and amplifies to negatively affect the performance of the whole system or supply chain?

(Questions adapted from Smith, Debra, and Chad Smith. Demand Driven Performance: Using Smart Metrics. pp. 23f. New York, NY: McGraw-Hill Education, 2014.)

 

We believe we can help you learn to ride your own “backward brain bicycle”—your enterprise struggling with a dramatically changed and changing world of business.

Please tell us about your experiences and how you and your team “think outside the box.”

Recently I read an article entitled “How to Make Sure Your ERP Vendor Doesn’t Drag Your Good Name Through the Mud” by Adam Bluemner at FindAccountingSoftware.com.ToC KPI Linkages.jpg


It’s an interesting article, for sure. And it brings up some valid points.


Nevertheless, I’d go beyond what Bluemner proposes. Here’s my take on a few of the items:


Filter out providers who aren’t focused on your needs


Let’s add some reality to this statement. If the people the vendor sends your way before the sale are unable to make significant and cogent observations about your business, your processes, and your situation in your industry that go well beyond how the technology their peddling might improve things, then chances are the vendor is a “technology installer” and not a real “solution provider.”


My recommendation would be to look for some real love coming from the vendor, in terms of valuable, free advice about how your company can start making more money tomorrow, regardless of what technology or vendor might be selected. Tim Sanders, in his great book Love Is the Killer App, talks about how it pays to give people free, valuable advice—advice or recommendations that will really help them improve and grow—even if you don’t have a business relationship with them. If the vendor isn’t doing this from day-one, what makes you think they are really, sincerely focused on your company’s needs?


Insist on a detailed ROI justification


Here I would add this: do not settle for rule-of-thumb calculations. Get the vendor’s knowledgeable people to sit down together with a cross-functional team from your organization. Have them create estimates on three fronts based on the proposed new technologies and implementations:

  1. INCREASES in Throughput (T)—That is, revenues less only truly variable costs: This eliminates questionable allocations of expenses and the numbers will more closely reflect actual changes in cash-flow
  2. CHANGES in Investment (I)—How many dollars will the enterprise tie up in software, hardware and services to get the new technology delivering the planned increases in Throughput? Will inventories go up or down? What other investments might be required or, better yet, delayed or eliminated in the future?
  3. CHANGES in Operating Expenses (OE)—Including support and maintenance expenses on the new technology. However, do not include any pro-rated or rule-of-thumb “savings” on operating expense that are not backed by determinations to slash real payrolls or remove other hard expenses from the monthly financials.

Each of the proposed changes to T, I or OE should be backed up (on paper) with statements along these lines:

  • Because the new software will provide X, sales and marketing expect an increase of 15 percent in T from market segment A over the three years beginning MM/DD/YY. This will add $N in T in year 1, $P in year 2, and $Q in year three.
  • Because the new software will allow Y, we do not expect to have to open a new branch location in YYYY in <City>. This will reduce forecast Investment by $Z, and reduce forecast OE by $D over three years beginning MM/DD/YY.

 

All changes to T, I and OE should be directly linked to a benefit derived from the proposed implementation. The numbers need not be accurate to the Nth degree. It is more important to be approximately right, than it is to be precisely wrong—and the numbers, whatever they are, will be wrong to some degree or other.


Explore product recommendations with in-depth demos


The product demonstrations should be done ONLY AFTER the T, I and OE estimates have been worked out above. Then, request demonstrations that provide significant proof of concept around the functionality that is intended to deliver the calculated ROI.


This approach will keep the buyer and the seller focus on what is important in order to deliver the planned ROI—and not bells and whistles that demo well but will have little or no impact on ROI. Also, the number of proofs of concept are likely to be quite small—probably five or fewer really significant things that the vendor has said they can deliver.


Such proof of concept demos may take a week or longer in configuration and execution. Avoid those things that would require heavy customization—since those should be avoided anyway. Look for the simple solutions to complex problems. If your vendor doesn’t have the innovation too recommend such simplicity, then take your business elsewhere.


As Einstein said, “Any intelligent fool can make things bigger and more complex. It takes a touch of genius—and a lot of courage—to move in the opposite direction.”


Following these simple steps, I believe, would make every ERP or other technology choice and implementation far more effective at delivering real ROI to the customer.

I was speaking with a client recently, and their director of procurement asked me something along these lines: “Is there any way that our ERP system can set our inventory minimums, maximums and reorder points?”InventoryManagementFunctions.jpg

 

This question brought to the fore a problem we run across far too frequently in trying to help our clients improve their own inventory performance, and smooth out the challenges they face across their supply chains.

 

Missing the fundamentals

 

Any good inventory management system is intended to help you with four fundamental things:

  1. Planning
  2. Acquisition (purchasing)
  3. Stockkeeping (receiving, inventory control, accounting)
  4. Disposition (shipping, et al)

 

The question that our client raised related to the planning portion of inventory and supply chain management.

 

The key components found under planning are these:

  1. Inventory policy
  2. Inventory planning
  3. Forecasting

 

The most fundamental of these three components is inventory policy management. It is impossible to perform effectively any of the other functions in the inventory planning realm until inventory policies have been determined. Yet, here was a client that had been heavily involved and heavily invested in inventory for a number of years, still uncertain about what policies should surround their inventory.

 

By inventory policies (in this case), we mean those rules that will determine…

  • What products should be stocked? What products should not be stocked?
  • Where (which warehouse and bin) should the stocks of each product be held?
  • Who is responsible (from a planning and execution perspective) each given product or product line?
  • When should each product be ordered to satisfy demand?
  • At what level should each SKU-Location be stocked?
  • Why were these stocking levels chosen for each SKU-Location?

Lacking a theoretical framework for policy setting

 

Because we work mainly with small to mid-sized business enterprises—ranging from about $10 million to $500 million in annual revenues—most of our client businesses grew organically from entrepreneurial roots. The teams time, energy and money has been focused on being successful through a “git ’er done!” attitude.

 

In such organizations, more questions are asked about “how” than about “why.” We frequently discover that inventory levels are set in the warehouses based on who complained the loudest or last. If it was accounting and finance who complained last, there’s a good chance that inventory levels will be falling. If, on the other hand, it was sales and marketing, or the CEO, complaining about lost sales due to out-of-stocks, you can be pretty sure that inventory levels will be on the increase.

 

How much of a decrease or increase in inventory levels will occur in organizations like this depends on many factors—like how loud, how frequently, or who yelled last—but almost never upon any considered inventory management policy governing SKU-Locations (SKULs).

 

The reason there are few—or, no—inventory policies governing SKULs in organizations like this mostly due to the fact that no one in the organization is trained or equipped to set such policies, and they have no theoretical basis for determining what such policies should look like.

 

A quick short-cut to effective policy setting and management

 

Unless your small to mid-sized business happens to have an on-staff statistician and a full-time inventory manager (with no other duties), determining what inventory policies should be and how the various policies should be applied to various SKULs can be a daunting task. Sometimes, it even appears to be an insurmountable task.

 

SageInventoryAdvisor_Dtl_Policy_A.jpgHowever, Sage Inventory Advisor (Twitter: @SageInvAdv) can lift that burden off your shoulders in about 24 hours and make life much easier.

 

Sage Inventor Advisor (SIA) safely extracts your data (typically, during an overnight process) regarding historical demand, historical purchase, open demand, open purchases, vendors, and more. On its secure servers, it carefully analyzes every SKUL, and does curve-fitting to determine which of its 15 different forecasting algorithms best applies to each. From that analysis, it calculates a day-by-day forecast for one replenishment cycle into the future for each SKUL.

 

Based on all it knows from its analysis, SIA next sets a “policy” for each SKUL. The policy includes:

  • Lead Time: What is the SKUL’s lead-time, and how many units are needed to cover this lead time projected into the future?
  • Safety Stock: Based on a large number of factors, including both demand variability and supply-side risk, SIA calculates the number of days of demand coverage that should be held in safety stock.
  • Reorder Point = Safety Stock quantity + Lead Time quantity
  • Replenishment Cycle: In SIA, the replenishment cycle may be calculated or predetermined based on factors such as Minimum Order Quantity.
  • Order Up To = Reorder Point (quantity) + Replenishment Cycle (quantity) = Maximum Stock Level

 

Bingo!

 

Overnight, without a resident statistician and without a full-time inventory management secretary, every SKUL managed by SIA has an effective policy assignment based on proven industry best practices.

 

Even better

 

Even better, none of this is static!

 

Twenty-four hours later, with the next upload of data, SIA will have new information about demand, supply, vendor performance, and more. Appropriate changes will be made to keep each SKUL’s policy coordinated and moving toward the optimization at the target inventory level.

 

Imagine what it would cost to pay for help such as this to work in your back-office or warehouse.

 

Just an example

 

We are using Sage Inventory Advisor here by way of example. Are there other products that can help you in the same or similar ways? Certainly there are. We would invite you to investigate them.

In an earlier article, we talked about how supply chain managers should be “working their tails off” when it comes to reshaping the profile of the overall inventory in their supply chain.

DDMRP InventoryProfile_Change.jpg

By this (pun intended), we meant attacking the tails of the typical inventory profile (on the left in the figure above). The “tails” to which we referred are the all too common “too much stock” and the “out-of-stock” tail ends of the typical bi-modal inventory distribution found in most supply chains. Inventory and supply chain management teams can work effectively at these “tails,” and with each effective attack, an increasing number of SKUs can be pushed into the middle range—the “yellow zone”—of the stock profile.

 

Smarter metrics

 

In order to identify offending SKUs and locations, inventory and supply chain executives and managers need to have effective metrics for measuring “the tails.” We suggest considering two KPIs that others have found effective.

 

Days OTOG

 

The first metric provides information on the depth and breadth of overstock positions—the right-hand tail. Days OTOG (over top of green) is usually configured to report the number of days each SKU spent with quantities in their stock buffers that were over-the-top of green (or their designated maximum stock level). In most cases, in order to screen out the occasional offender, some minimum number of days is screened out in applying the metric. In the example shown in the accompanying diagram, a 15-day base is selected.

DDMRP Metrics OTOG Days.jpg

In such a case, then, OTOG Days becomes days spent OTOG in excess of 15 days out of the last 180 days. The KPI basis values may vary from enterprise to enterprise and should be managed by the inventory and supply chain management team.

 

By reporting on the, and charting them (as illustrated), it becomes a simple matter to track down the worst offenders (SKUs #304P and #101, for example) and to have a cross-functional team identify why these SKUs are ending up in this condition. Were bad buying decisions made? Were production and replenishment orders placed based on forecasts instead of actual demand? Has demand suddenly fallen off?

 

Whatever the cause, the matter should not be taken lightly nor simply shrugged off. Instead, concerted action should be undertaken to help assure that the causes affecting these SKUs are mitigated so as not to affect other SKUs, as well.

 

Days OOS and OSWD

 

The metrics used to attack the other tail end—the left-hand tail—deals with out-of-stocks (OOS) and, the worst case, OSWD (out-of-stocks with [actual] demand).

DDMRP Metrics OOS+OSWD.jpg

In a fashion similar to the OTOG metric above, OOS and OSWD count the number of days any item spends in an out-of-stock position (over the last 180 days), and also the number of OOS days that were accompanied by actual consumer-driven demand. Here again, the baseline parameters—like the number of days in the look-back period—should be decided upon by the inventory and supply chain management team.

 

Also, by charting the SKUs in a fashion similar to that shown, the worst offenders can be addressed in priority order. Root-causes should be determined and each SKU-Location (SKU-L) systematically attacked in order to prevent recurrences or from similar outcomes hitting other SKU-Ls.

 

Your business isn’t static—your supply chain settings shouldn’t be either

 

Most of the companies and supply chains that we meet for the first time have relatively static stocking levels. Stocking minimums, maximums, and safety stock levels are set relatively infrequently. In some cases, they have not been changes—literally and sadly—in years!

 

Just like the KPIs above collect data on stock positions daily, we generally encourage inventory and supply chain managers to automate the adjusting of stock positions based on the latest, most up-to-date, information available to the system on average daily usage (ADU) and lead times.

 

While it may seem overkill to update statistics on ADU and (actual) lead-times on a daily basis, we find that it is not. Some worry that daily updates are likely to increase system nervousness, but actually just the opposite is true. If you are updating weekly or monthly, the change in ADU or lead-time is likely to be larger with each update. However, if these are updated daily, the incremental change is likely to be very small.

 

Good reading

These ideas were adapted from Demand Driven Performance Using Smart Metrics by Debra Smith and Chad Smith. I would strongly encourage you to get a copy of this book. It will be well worth it—if you take the advice in the book.

 

We would like to hear your comments or field your questions, as well. Please leave your comments / questions below, or feel free to contact us directly, if you prefer.