Experiences with Dynamic or Extended Safety Stock

Executive Summary

  • Dynamic safety stock in ERP and external supply planning systems is commonly desired to be activated by companies.
  • Extensive testing and many observations illustrate that the standard dynamic safety stock calculation is incorrect.
  • In this article, we cover a wide number of topics around dynamic safety stock.

Introduction to Dynamic Safety Stock

This article covers the topic of dynamic or enhanced safety stock (SS) in SAP. This is one of the most common requested SS areas to be active in SAP SNP by SAP customers. However, the issues brought up in this article regarding the dynamic SS calculation also apply to the formula generally and as used in other supply planning applications. You will learn about the issues with dynamic safety stock have come from experiences on SAP projects.

The Concept of Dynamic Safety Stock

The standard dynamic safety stock formula was developed to provide a variable quantity of stock to account for the variability in demand and supply. Dynamic SS is often presented as something that companies want to move to as it is more sophisticated than other methods of setting SS.

Most companies people that use the following dynamic safety stock formula.

Safety Stock = Z*SQRT{(Avg. Lead Time * Standard Deviation of Demand^2) + (Avg. Demand * Standard Deviation of Lead Time)^2}

I have colorized the lead time-oriented values as orange, and the demand values as blue. “Z” is the service level. Roughly speaking the formula accounts for variability on both the supply side and the demand side, while increasing or decreasing the safety stock in conjunction with the service level.

The Dynamic Safety Stock Settings in SAP APO

Both SAP ECC and SAP APO (SNP) have the dynamic safety stock calculation. In fact, the dynamic safety stock calculation is the only area of functionality in ERP systems to account for variability and to account for service level. As we will discuss, and provide the specific reasons, the dynamic safety stock is rarely implemented in ERP systems. This means that the vast majority of companies that use ERP have no way of accounting for service levels in ERP.

Therefore, while companies almost universally declare their interest in high service levels, there is no real way to making the ERP system follow service levels in an automated fashion.

Safety Stock-2

This is a functionality which allows the SS to vary depending upon supply and demand variability. These values are entered into the Lot Size tab of the Product Location Master, as can be seen in the screenshot below.

  • Dynamic safety stock is set on the Lot Size tab of the Product Location Master in SAP APO.
  • This allows the safety stock to be set at the product location combination.

Extended/Dynamic Safety Stock

I have often wondered why no client that I have worked with has ever configured this functionality. I had often attributed it to the problem in maintaining this master data. It should be understood that this is the standard dynamic SS method that is taught in textbooks. That is, it is in no way SAP intellectual property. SAP is simply using what is the inventory management textbooks.

Interestingly, one person I discussed this topic with who had tested it, they stated that the SS it came up with was high (this is, of course, relative, as it calculates the  correct SS.) However, another comment was that it was not very adjustable, and that adjustability was a requirement for them. In fact, planners fall into a habit of adjusting the SS when it should be auto adjusted. This same feedback was repeated through various discussions at around six different clients throughout the years.

More specifically, I question if the requirement will lead to good planning outcomes.

Dynamic SS and the First Release and How Commonly Dynamic Safety Stock is Used

I was once with a client that was interested in implementing dynamic SS in their first release. My view is that the first release is best for dialing in the most basic functionality. Most implementations put too much functionality into their first release.

I have seen dynamic SS get yanked out of a number of implementations, or I have heard of it getting disabled after initial use. My view is that very few companies are presently using dynamic SS in APO.

Therefore, it is a “high risk” functionality, which is better left to later releases of implementation. Essentially, I see dynamic SS to be a high-risk luxury with a very low probability of successful implementation. In my view, there are many more important areas of functionality to work on, and simpler methods of SS such as days’ coverage are more durable and much higher probability of success.

Interesting Comment from LinkedIn on Why Dynamic SS Often Fails

I found this comment from David Ginsberg on a LinkedIn discussion which I found interesting.

“Most statistical models on inventory fail to work operationally because they focus exclusively on “deviation of demand”. There are two additional criteria that must be taken into account… replenishment lot size and supplier lead time. If I could have “any” quantity “tomorrow”; that would require a different safety stock model then “some” in “six months.””

While SAP’s dynamic SS functionality does have a location for deviation of demand, in fact, it is rarely used even with companies that have attempted dynamic SS. Therefore, David Ginsberg’s criticism would apply to how SAP dynamic SS is implemented, also if it does not use to the ability of the functionality.

“A third limitation of the safety stock model is that it carries the additional inventory throughout the inventory cycle. Why carry safety stock when your replenishment order has just arrived and your inventories are far above safety thresholds? Often it is better to bring in the next replenishment order a period or two early. This is referred to as “safety lead time” and offers superior operational and financial model to safety stock. Planning the number of stock out periods to manage and then reducing the lead time to cover them will buy you more operational and financial performance than tweaking the math of demand-based statistical models.”

I also found this final quote from David Ginsberg interesting.

“If there were good tools for this, they would be used in the stock market, not planning parts. Avoid the “we predict the future better than anyone” pitches.”

How Not to Calculate Safety Stock

One of the primary mistakes made when setting SS is setting it reactively and not controlling its setting. For instance, safety stock is often used as a form of forecast adjustments by supply planning. If the forecast is considered too high, SS might be reduced, and vice versa.

  • Different individuals can have input to SS, but ultimately SS should be controlled by a policy and centrally by a supply chain planning group. While this is often the case regarding having some central responsibility at some companies, there is still more often than not control is given to make the changes given to a small group.
  • Having groups such as sales or individuals in distributed locations adjust the SS — under the argument that they “know the products” means that there is an increasing likelihood that the safety stock will be changed by people that don’t understand how SS fits with other supply planning parameters.

As is explained further in this article calculation of the overall inventory available for SS and cycle stock. And then assigned to the inventory on a relative basis.

This is a weakness of many of the inventory parameters when they calculate SS individually by the system. All inventory parameters should be calculated based upon the relative consumption of whatever the resource limitations are.

George Plossl on Safety Stock

George Plossl has an interesting observation as to how safety stocks are often set that conforms with my experience at numerous clients.

“Guestimates: Guestimates are probably the most frequently used, being easiest to apply, and are based on planners’ frequent personal judgement. They usually increase immediately after a shortage occurs but are rarely decreased.

Rules of Thumb: These are equally irrational, and require additional work to apply. A popular one bases SS on A-B-C inventory classification; expensive A-items should have little, moderate B-items some more, and low-cost C-items plenty. This ignores the protection furnished by lot sizes in excess of immediate requirements; C-items usually have very large order quantities and short replenishment lead times; they may not even need safety stock. Conversely, A-items are exposed more frequently to stock outs because of frequent reordering.”

Evaluating the Dynamic Safety Stock Formula

The dynamic safety stock formula is often discussed, but it is rarely evaluated. When we look at just the formula, it is difficult to see why this should give the right answer on safety stock.

Also, I was not able to find the original paper where this formula was first published. This means that the many papers on dynamic safety stock are not pointing back to the original publication.

Let us look at the formula in segments.

The Dynamic Safety Stock Formula Segments

  • The Service Level Portion of the Formula
  • The Forecast Error and Lead Time Portion of the Formula
  • Error Measurements as an Absolute Value

1. The Service Level Portion of the Formula

The first part of the formula makes sense. That is when using the inverse of the normal distribution as applied to the service level.

This is for product location combinations with a reasonable volume. For low volume demand, a different probability distribution would be applied as the arrival of demand for low volume demand is not normally distributed. This provides the “ratcheting” effect consistent with increases or decreases in service level.

It is well known that a different probability distribution is to be used for lower volume items than higher volume items. Some vendors have proposed measuring the probability distribution of each item and applying the probability distribution that fits.

2. The Problems with Lead Time Portion of the Formula

The following questions naturally came to me when reviewing the formula:

  1. Why is the average lead time multiplied by the standard deviation of demand?
  2. Why would squaring the value this lead to the right output?

3. The Problem with Standard Deviation of Demand History

Why is only the standard deviation of the demand used instead of the forecast error?

If the variance of the demand history is accounted for by the forecast than the safety stock would calculate as lower. One can have a seasonal forecast that is high in variability but is accounted for by the forecast (that is it has a low error). However, using the standard dynamic safety stock formula, this would calculate a high safety stock.

The dynamic safety stock formula produces strange results. Normally the dynamic safety stock is not tested before it is activated in SAP. That is companies assume the dynamic safety stock formula will work properly.

Strange Behavior of the Dynamic Safety Stock Formula

The standard dynamic SS formula seems to have some strange assumptions, it also produces strange output. How many people know this? Not many. Many people propose using dynamic safety stock without testing the formula.

But if the formula is tested, it does not produce the expected safety stock that I would expect from changes in variability. This gets to the topic of the evidence for dynamic SS working in companies, either at SAP customers or other.

Dynamic safety stock calculations (there are several) are standard in inventory textbooks. The calculation is standard in supply planning applications. There is not any real evidence that the dynamic safety stock is useful when applied to industry. My hypothesis for this is that while the principle is correct, the standard dynamic safety stock formula itself is flawed.

The Lack of Evidence for the Effective Use of Dynamic Safety Stock Formula

It would be less necessary to intensively analyze the logic of the standard dynamic SS formula if there was a large amount of evidence that the standard dynamic SS formula was being widely used in companies.

But there isn’t evidence of this that I could find.

  • The mere fact that a formula is published does not prove it is in use and does not prove it is useful.
  • In fact, the evidence is quite to the contrary, that the dynamic SS formula is rarely used. And it is not for lack of trying. Every one of my previous clients that have tested enabling the dynamic safety stock calculation eventually turned it off.

Practical Versus Theoretical Questioning of the Formula

At first, I thought this might have been because of the high forecast inaccuracy causes companies not to want to carry the calculated safety stock. My detailed evaluation of the standard dynamic safety stock formula calls the standard formula into question. This is not just questioning practically (i.e. is it implemented successfully), but theoretically as well.

Creating a Customized and Constrained Safety Stock

A customized and safety stock calculator which takes into account variability, as well as constraints, can be developed per client. It must be customized for the limitations of the specific client.

The following are some extra areas to look out for when developing a safety stock calculation which incorporates forecast error.

  1.  Zero Periods of Demand
  2. The Proper Forecast Error Measurement in the Time Dimension

1. Zero Periods of Demand

The standard dynamic SS formula does not use a forecast error, but instead a standard deviation of demand. However, when you create a custom dynamic SS formula, one can use forecast error. This has advantages in that forecast error is far more often discussed within forecasting departments and companies generally regarding the forecast than the standard deviation of demand.


While many people attempted to list the standard SS formulas, I think what needs to be discussed is why the dynamic SS calculation is not used in companies. Rather than spending more time on reiterating complex SS formulas, the question needs to be asked:


Part of the answer lays with the high forecast error that most companies have. However, a second problem is the dynamic SS formula itself.

Contrary to what one might think, I found that there have not been studies that show that the dynamic SS formula works well for companies. In testing of the formula myself, I was not impressed with the output. This lead me to develop my SS formula, which is explained in this article.

Finally, while the standard dynamic SS formula will not meet a company’s inventory needs, the other end of the spectrum of guessing or not using math to determine safety stock is also not effective. In fact, even the most common approach of setting a safety days of supply combined with a lower value (to protect the SS when demand declines) leaves out many other dimensions that improve the SS.

The Problem: Maintaining Inventory Parameters

A major part of replenishment is inventory parameters. These parameters include values like safety stock or days of supply, rounding value, reorder point, lot size/economic order quantity and minimum order quantity. Whatever the planning procedure that is used, these parameters control what the supply planning system does.

Testing of the extracted parameters of ERP and external supply planning systems clearly shows that these values are poorly maintained. The result is far worse planning results than could be obtained otherwise.

Being Part of the Solution: Our Evolution of Thinking on Maintaining Reorder Points in ECC or APO

Maintaining reorder points in APO or ECC comes with a number of negatives that tend to not be discussed. One issue is that when using APO or ECC, the reorder points are typically managed on a “one by one” basis. This leads to individual planners entering values without any consideration for how inventory parameters are set across the supply network. We have developed a SaaS application that sets the inventory parameters for ECC or APO or both systems externally, and that allows for simulations to be created very quickly. These parameters can then be easily exported and it allows for far more control over the parameters in APO and ECC. Both APO and ECC are designed to receive parameters, they are not designed to develop the parameters.

In our testing, the approach, which is within the Brightwork Explorer is one of the most effective methods for managing planning in SAP applications.

Financial Disclosure

Financial Bias Disclosure

Neither this article nor any other article on the Brightwork website is paid for by a software vendor, including Oracle, SAP or their competitors. As part of our commitment to publishing independent, unbiased research; no paid media placements, commissions or incentives of any nature are allowed.

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Safety Stock and Service Level Book

Safety Stock

Safety Stock and Service Levels: A New Approach

Important Features About Safety Stock

Safety stock is one of the most commonly discussed topics in supply chain management. Every MRP application and every advanced planning application on the market has either a field for safety stock or can calculate safety stock. However, companies continue to struggle with the right level to set it. Service levels are strongly related to safety stock. However, companies also struggle with how to set service levels.

How Systems Set Safety Stock

The vast majority of systems allow the setting of safety stock by multiple means (static, dynamic, adjustable with the forecast in days’ supply, etc..). However, most systems do not allow the safety stock to be set in a way that is considerate of the inventory that is available to be applied.By reading this book you will:

  • Understand the concepts and formula used for safety stock and service level setting.
  • Common ways of setting safety stock.
  • Service levels and inventory optimization applications.
  • The best real ways of setting both service levels and safety stock.


Chapter 1: Introduction
Chapter 2: Safety Stock and Service Levels from a Conceptual Perspective
Chapter 3: The Common Ways of Setting Safety Stock
Chapter 4: The Common Issues with Safety Stock
Chapter 5: Common Issues with Service Level Setting
Chapter 6: Service Level Agreement
Chapter 7: Safety Stock and Service Levels in Inventory Optimization and Multi-Echelon Software
Chapter 8: A Simpler Approach to Comprehensively Setting Safety Stock and Service Levels