Smart Software VP of Research to Present at Business Analytics Conference, INFORMS 2022

Dr. Tom Willemain to lead INFORMS sessionDominating The Inventory Battlefield: Fighting Randomness With Randomness.”

Belmont, Mass., March 2022 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that Tom Willemain, Vice President for Research, will present at the INFORMS Business Analytics Conference, April 3-5, 2022, in Houston, TX.

Dr. Willemain will present a session on how next-generation analytics arms supply chain leaders in manufacturing, distribution, and MRO with tools to fight against randomness in demand and supply. During his session he will detail the following technologies:

(1) Regime change filtering to maintain data relevance against sudden shifts in the operating environment.

(2) Bootstrapping methods to generate large numbers of realistic demand and lead time scenarios to fuel models.

(3) Discrete event simulations to process the input scenarios and expose the links between management actions and key performance indicators.

(4) Stochastic optimization based on simulation experiments to tune each item for best results.

Without the analytics, inventory owners have two choices: sticking with rigid operating policies usually based on outdated and invalid rules of thumb or resorting to subjective, gut-feel guesswork that may not help and does not scale.

As the leading Business Analytics Conference, INFORMS provides the opportunity to interact with the world’s top forecasting researchers and practitioners. The attendance is large enough so that the best in the field are attracted, yet small enough that you can meet and discuss one-on-one. In addition, the conference features content from leading analytics professionals who share and showcase top analytics applications that save lives, save money, and solve problems.

 

About Dr. Thomas Willemain

Dr. Thomas Reed Willemain served as an Expert Statistical Consultant to the National Security Agency (NSA) at Ft. Meade, MD, and as a member of the Adjunct Research Staff at an affiliated think-tank, the Institute for Defense Analyses Center for Computing Sciences (IDA/CCS). He is Professor Emeritus of Industrial and Systems Engineering at Rensselaer Polytechnic Institute, having previously held faculty positions at Harvard’s Kennedy School of Government and Massachusetts Institute of Technology. He is also co-founder and Senior Vice President/Research at Smart Software, Inc. He is a member of the Association of Former Intelligence Officers, the Military Operations Research Society, the American Statistical Association, and several other professional organizations. Willemain received the BSE degree (summa cum laude, Phi Beta Kappa) from Princeton University and the MS and Ph.D. degrees from Massachusetts Institute of Technology. His other books include: Statistical Methods for Planners, Emergency Medical Systems Analysis (with R. C. Larson), and 80 articles in peer-reviewed journals on statistics, operations research, health care, and other topics. For more information, email: TomW@SmartCorp.com or visit www.TomWillemain.com.

 

About Smart Software, Inc.

Founded in 1981, Smart Software, Inc. is a leader in providing businesses with enterprise-wide demand forecasting, planning, and inventory optimization solutions.  Smart Software’s demand forecasting and inventory optimization solutions have helped thousands of users worldwide, including customers at mid-market enterprises and Fortune 500 companies, such as Disney, Otis Elevator, Hitachi, Siemens, Metro Transit, APS, and The American Red Cross.  Smart Inventory Planning & Optimization gives demand planners the tools to handle sales seasonality, promotions, new and aging products, multi-dimensional hierarchies, and intermittently demanded service parts and capital goods items.  It also provides inventory managers with accurate estimates of the optimal inventory and safety stock required to meet future orders and achieve desired service levels.  Smart Software is headquartered in Belmont, Massachusetts, and can be found on the World Wide Web at www.smartcorp.com.

 

SmartForecasts and Smart IP&O have registered trademarks of Smart Software, Inc.  All other trademarks are their respective owners’ property.

For more information, please contact Smart Software, Inc., Four Hill Road, Belmont, MA 02478.
Phone: 1-800-SMART-99 (800-762-7899); FAX: 1-617-489-2748; E-mail: info@smartcorp.com

 

 

 

A Primer on Probabilistic Forecasting

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

If you keep up with the news about supply chain analytics, you are more frequently encountering the phrase “probabilistic forecasting.” If this phrase is puzzling, read on.

You probably already know what “forecasting” means. And you probably also know that there seem to be lots of different ways to do it. And you’ve probably heard pungent little phrases like “every forecast is wrong.” So you know that some kind of mathemagic might calculate that “the forecast is you will sell 100 units next month”, and then you might sell 110 units, in which case you have a 10% forecast error.

You may not know that what I just described is a particular kind of forecast called a “point forecast.” A point forecast is so named because it consists of just a single number (i.e., one point on the number line, if you recall the number line from your youth).

Point forecasts have one virtue: They are simple. They also have a flaw: They give rise to snarky statements like “every forecast is wrong.” That is, in most realistic cases, it is unlikely that the actual value will exactly equal the forecast. (Which isn’t such a big deal if the forecast is close enough.)

This gets us to “probabilistic forecasting.” This approach is a step up, because instead of producing a single-number (point) forecast, it yields a probability distribution for the forecast. And unlike traditional extrapolative models that rely purely on the historical data, probabilistic forecasts have the ability to simulate future values that aren’t anchored to the past.

“Probability distribution” is a forbidding phrase, evoking some arcane math that you may have heard of but never studied. Luckily, most adults have enough life experience to have an intuitive grasp of the concept.  When broken down, it’s quite straightforward to understand.

Imagine the simple act of flipping two coins. You might call this harmless fun, but I call it a “probabilistic experiment.” The total number of heads that turn up on the two coins will be either zero, one or two. Flipping two coins is a “random experiment.” The resulting number of heads is a “random variable.” It has a “probability distribution”, which is nothing more than a table of how likely it is that the random variable will turn out to have any of its possible values. The probability of getting two heads when the coins are fair works out to be ¼, as is the probability of no heads. The chance of one head is ½.

The same approach can describe a more interesting random variable, like the daily demand for a spare part.  Figure 2 shows such a probability distribution. It was computed by compiling three years of daily demand data on a certain part used in a scientific instrument sold to hospitals.

 

Probabilistic demand forecast 1

Figure 1: The probability distribution of daily demand for a certain spare part

 

The distribution in Figure 1 can be thought of as a probabilistic forecast of demand in a single day. For this particular part, we see that the forecast is very likely to be zero (97% chance), but sometimes will be for a handful of units, and once in three years will be twenty units. Even though the most likely forecast is zero, you would want to keep a few on hand if this part were critical (“…for want of a nail…”)

Now let’s use this information to make a more complicated probabilistic forecast. Suppose you have three units on hand. How many days will it take for you to have none? There are many possible answers, ranging from a single day (if you immediately get a demand for three or more) up to a very large number (since 97% of days see no demand).  The analysis of this question is a bit complicated because of all the many ways this situation can play out, but the final answer that is most informative will be a probability distribution. It turns out that the number of days until there are no units left in stock has the distribution shown in Figure 2.

Probabilistic demand forecast 2

Figure 2: Distribution of the number of days until all three units are gone

 

The average number of days is 74, which would be a point forecast, but there is a lot of variation around the average. From the perspective of inventory management, it is notable that there is a 25% chance that all the units will be gone after 32 days. So if you decided to order more when you were down to only three on the shelf, it would be good to have the supplier get them to you before a month has passed. If they couldn’t, you’d have a 75% chance of stocking out – not good for a critical part.

The analysis behind Figure 2 involved making some assumptions that were convenient but not necessary if they were not true. The results came from a method called “Monte Carlo simulation”, in which we start with three units, pick a random demand from the distribution in Figure 1, subtract it from the current stock, and continue until the stock is gone, recording how many days went by before you ran out. Repeating this process 100,000 times produced Figure 2.

Applications of Monte Carlo simulation extend to problems of even larger scope than the “when do we run out” example above. Especially important are Monte Carlo forecasts of future demand. While the usual forecasting result is a set of point forecasts (e.g., expected unit demand over the next twelve months), we know that there are any number of ways that the actual demand could play out. Simulation could be used to produce, say, one thousand possible sets of 365 daily demand demands.

This set of demand scenarios would more fully expose the range of possible situations with which an inventory system would have to cope. This use of simulation is called “stress testing”, because it exposes a system to a range of varied but realistic scenarios, including some nasty ones. Those scenarios are then input to mathematical models of the system to see how well it will cope, as reflected in key performance indicators (KPI’s). For instance, in those thousand simulated years of operation, how many stockouts are there in the worst year? the average year? the best year? In fact, what is the full probability distribution of the number of stockouts in a year, and what is the distribution of their size?

Figures 3 and 4 illustrate probabilistic modeling of an inventory control system that converts stockouts to backorders. The system simulated uses a Min/Max control policy with Min = 10 units and Max = 20 units.

Figure 3 shows one simulated year of daily operations in four plots. The first plot shows a particular pattern of random daily demand in which average demand increases steadily from Monday to Friday but disappears on weekends. The second plot shows the number of units on hand each day. Note that there are a dozen times during this simulated year when inventory goes negative, indicating stockouts. The third plot shows the size and timing of replenishment orders. The fourth plot shows the size and timing of backorders.  The information in these plots can be translated into estimates of inventory investment, average units on hand, holding costs, ordering costs and shortage costs.

Probabilistic demand forecast 3

Figure 3: One simulated year of inventory system operation

 

Figure 3 shows one of one thousand simulated years. Each year will have different daily demands, resulting in different values of metrics like units on hand and the various components of operating cost. Figure 4 plots the distribution of 1,000 simulated values of four KPI’s. Simulating 1,000 years of imagined operation exposes the range of possible results so that planners can account not just for average results but also see best-case and worst-case values.

Probabilistic demand forecast 4

Figure 4: Distributions of four KPI’s based on 1,000 simulations

 

Monte Carlo simulation is a low-math/high-results approach to probabilistic forecasting: very practical and easy to explain. Advanced probabilistic forecasting methods employed by Smart Software expand upon standard Monte Carlo simulation, yielding extremely accurate estimates of required inventory levels.

 

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      SmartForecasts and Smart IP&O are registered trademarks of Smart Software, Inc.  All other trademarks are the property of their respective owners.
      For more information, please contact Smart Software,Inc., Four Hill Road, Belmont, MA 02478. Phone: 1-800-SMART-99 (800-762-7899); E-mail: info@smartcorp.com  
      Improve Forecast Accuracy by Managing Error

      The Smart Forecaster

       Pursuing best practices in demand planning,

      forecasting and inventory optimization

      Improve Forecast Accuracy, Eliminate Excess Inventory, & Maximize Service Levels

      In this video, Dr. Thomas Willemain, co-Founder and SVP Research, talks about improving Forecast Accuracy by Managing Error. This video is the first in our series on effective methods to Improve Forecast Accuracy.  We begin by looking at how forecast error causes pain and the consequential cost related to it. Then we will explain the three most common mistakes to avoid that can help us increase revenue and prevent excess inventory. Tom concludes by reviewing the methods to improve Forecast Accuracy, the importance of measuring forecast error, and the technological opportunities to improve it.

       

      Forecast error can be consequential

      Consider one item of many

      • Product X costs $100 to make and nets $50 profit per unit.
      • Sales of Product X will turn out to be 1,000/month over the next 12 months.
      • Consider one item of many

      What is the cost of forecast error?

      • If the forecast is 10% high, end the year with $120,000 of excess inventory.
      • 100 extra/month x 12 months x $100/unit
      • If the forecast is 10% low, miss out on $60,000 of profit.
      • 100 too few/month x 12 months x $50/unit

       

      Three mistakes to avoid

      1. Ignoring error.

      • Unprofessional, dereliction of duty.
      • Wishing will not make it so.
      • Treat accuracy assessment as data science, not a blame game.

      2. Tolerating more error than necessary.

      • Statistical forecasting methods can improve accuracy at scale.
      • Improving data inputs can help.
      • Collecting and analyzing forecast error metrics can identify weak spots.

      3. Wasting time and money going too far trying to eliminate error.

      • Some product/market combinations are inherently more difficult to forecast. After a point, let them be (but be alert for new specialized forecasting methods).
      • Sometimes steps meant to reduce error can backfire (e.g., adjustment).
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          Probabilistic vs. Deterministic Order Planning

          The Smart Forecaster

          Man with a computer in a warehouse best practices in demand planning, forecasting and inventory optimization

          Consider the problem of replenishing inventory. To be specific, suppose the inventory item in question is a spare part. Both you and your supplier will want some sense of how much you will be ordering and when. And your ERP system may be insisting that you let it in on the secret too.

          Deterministic Model of Replenishment

          The simplest way to get a decent answer to this question is to assume the world is, well, simple. In this case, simple means “not random” or, in geek speak, “deterministic.” In particular, you pretend that the random size and timing of demand is really a continuous drip-drip-drip of a fixed size coming at a fixed interval, e.g., 2, 2, 2, 2, 2, 2… If this seems unrealistic, it is. Real demand might look more like this: 0, 1, 10, 0, 1, 0, 0, 0 with lots of zeros, occasional but random spikes.

          But simplicity has its virtues. If you pretend that the average demand occurs every day like clockwork, it is easy to work out when you will need to place your next order, and how many units you will need.  For instance, suppose your inventory policy is of the (Q,R) type, where Q is a fixed order quantity and R is a fixed reorder point. When stock drops to or below the reorder point R, you order Q units more. To round out the fantasy, assume that the replenishment lead time is also fixed: after L days, those Q new units will be on the shelf ready to satisfy demand.

          All you need now to answer your questions is the average demand per day D for the item. The logic goes like this:

          1. You start each replenishment cycle with Q units on hand.
          2. You deplete that stock by D units per day.
          3. So, you hit the reorder point R after (Q-R)/D days.
          4. So, you order every (Q-R)/D days.
          5. Each replenishment cycle lasts (Q-R)/D + L days, so you make a total of 365D/(Q-R+LD) orders per year.
          6. As long as lead time L < R/D, you will never stock out and your inventory will be as small as possible.

          Figure 1 shows the plot of on-hand inventory vs time for the deterministic model. Around Smart Software, we refer to this plot as the “Deterministic Sawtooth.” The stock starts at the level of the last order quantity Q. After steadily decreasing over the drop time (Q-R)/D, the level hits the reorder point R and triggers an order for another Q units. Over the lead time L, the stock drops to exactly zero, then the reorder magically arrives and the next cycle begins.

          Figure 1 Deterministic model of on-hand inventory

          Figure 1: Deterministic model of on-hand inventory

           

          This model has two things going for it. It requires no more than high school algebra, and it combines (almost) all the relevant factors to answer the two related questions: When will we have to place the next order? How many orders will we place in a year?

          Probabilistic Model of Replenishment

          Not surprisingly, if we strip away some of the fantasy from the deterministic model, we get more useful information. The probabilistic model incorporates all the messy randomness in the real-world problem: the uncertainty in both the timing and size of demand, the variation in replenishment lead time, and the consequences of those two factors: the chance of stock on hand undershooting the reorder point, the chance that there will be a stockout, the variability in the time until the next order, and the variable number of orders executed in a year.

          The probabilistic model works by simulating the consequences of uncertain demand and variable lead time. By analyzing the item’s historical demand patterns (and excluding any observations that were recorded during a time when demand may have been fundamentally different), advanced statistical methods create an unlimited number of realistic demand scenarios. Similar analysis is applied to records of supplier lead times. Combining these supply and demand scenarios with the operational rules of any given inventory control policy produces scenarios of the number of parts on hand. From these scenarios, we can extract summaries of the varying intervals between orders.

          Figure 2 shows an example of a probabilistic scenario; demand is random, and the item is managed using reorder point R = 10 and order quantity Q=20. Gone is the Deterministic Sawtooth; in its place is something more complex and realistic (the Probabilistic Staircase). During the 90 simulated days of operation, there were 9 orders placed, and the time between orders clearly varied.

          Using the probabilistic model, the answers to the two questions (how long between orders and how many in a year) get expressed as probability distributions reflecting the relative likelihoods of various scenarios. Figure 3 shows the distribution of the number of days between orders after ten years of simulated operation. While the average is about 8 days, the actual number varies widely, from 2 to 17.

          Instead of telling your supplier that you will place X orders next year, you can now project X ± Y orders, and your supplier knows better their upside and downside risks. Better yet, you could provide the entire distribution as the richest possible answer.

          Figure 2 A probabilistic scenario of on-hand inventory

          Figure 2 A probabilistic scenario of on-hand inventory

           

          Figure 3 Distribution of days between orders

          Figure 3: Distribution of days between orders

           

          Climbing the Random Staircase to Greater Efficiency

          Moving beyond the deterministic model of  inventory opens up new possibilities for optimizing operations. First, the probabilistic model allows realistic assessment of stockout risk. The simple model in Figure 1 implies there is never a stockout, whereas probabilistic scenarios allow for the possibility (though in Figure 2 there was only one close call around day 70). Once the risk is known, software can optimize by searching  the “design space” (i.e., all possible values of R and Q) to find a design that meets a target level of stockout risk at minimal cost. The value of the deterministic model in this more realistic analysis is that it provides a good starting point for the search through design space.

          Summary

          Modern software provides answers to operational questions with various degrees of detail. Using the example of the time between replenishment orders, we’ve shown that the answer can be calculated approximately but quickly by a simple deterministic model. But it can also be provided in much richer detail with all the variability exposed by a probabilistic model. We think of these alternatives as complementary. The deterministic model bundles all the key variables into an easy-to-understand form. The probabilistic model provides additional realism that professionals expect and supports effective search for optimal choices of reorder point and order quantity.

           

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          Uncover data facts and improve inventory performance

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          Do your statistical forecasts suffer from the wiggle effect?

          Do your statistical forecasts suffer from the wiggle effect?

          What is the wiggle effect? It’s when your statistical forecast incorrectly predicts the ups and downs observed in your demand history when there really isn’t a pattern. It’s important to make sure your forecasts don’t wiggle unless there is a real pattern. Here is a transcript from a recent customer where this issue was discussed: