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Without the analytics, inventory owners have two choices: sticking with rigid operating policies usually based on outdated and invalid rules of thumb or resorting to subjective, gut-feel guesswork that may not help and does not scale.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

 

 

 

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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    Smart Software Celebrates 40 years

    40 years of Innovation for Demand Forecasting, Inventory Planning, and Supply Chain Analytics

     

    Belmont, MA, June 1, 2021 – Today marks the 40th anniversary for Smart Software, a leading innovator of demand planning, statistical forecasting, inventory management, and supply chain analytics software.

    Company CEO, Greg Hartunian remarked “Our success is built on continuous innovation. Our mission follows the path that our founders initiated 40 years ago; we provide cutting-edge analytical solutions that help our customers maximize sales and minimize waste.  We are enormously grateful to our customers who have given us their support, confidence, and trust.  Thank you to our partner community of resellers and consultants who have mobilized our growth and shared their expertise with us.  We are also indebted to our many employees, past and present, local and abroad, whose creativity and dedication have produced systems that are benefitting so many great companies worldwide.”

    Smart, Hartunian, and Willemain was incorporated in June 1981 by Charles Smart, Nelson Hartunian, and Thomas Willemain, our visionary founders. The firm later incorporated as Smart Software, Inc in 1984 reflecting their shift from boutique consultancy to software.  Over the years, their pioneering work produced the first-ever automatic statistical forecasting system for the personal computer, a patented APICS award-winning method for intermittent demand planning, and most recently a cloud-native probabilistic forecasting platform. All have produced major inventory cost reductions and service level improvements for our customers.  To learn more about Smart Software’s roots and journey, please click here:

     

      Smart Software Company History 

     

    Smart Software Logo 40 years

     

    “Smart gives us good information to work with.  The service level planning method has led to productive conversations between sales and supply chain and given us a common ground from which we base our discussions. People are feeling comfortable with numbers, and through our S&OP process we’ve been able to create buy-in across the company.”
    Rod Cardenas  – Purchasing Manager, Forum Energy

     

    “It was deployed as part of our implementation of a new centralized distribution model and highlighted significant blind spots in the original project plan. The accurate forecasts of stocking levels and SKU count provided fact-based data that allowed us to strategically phase the consolidation effort where warehouse space was at a premium.”
    Eric Nelson – CPA, CMA. Manager, Parts Supply and Logistics. BC Transit

     

    “Its easy for us to give suppliers information they never had before. Our suppliers can plan their production and work with their suppliers. That visibility has been invaluable. That’s where the real payoff will come. Not just reducing inventory or saving time on people managing the inventory but being more responsive to customers’ needs. To me, that’s the overarching benefit of this software.”
    Bud Schultz – Vice President of Finance  NKK Switches

     

     

     

     


     

    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

     

     

    Four Useful Ways to Measure Forecast 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 measuring forecast error. We begin by overviewing the various types of error metrics: scale-dependent error, percentage error, relative error, and scale-free error metrics. While some error is inevitable, there are ways to reduce it, and forecast metrics are necessary aids for monitoring and improving forecast accuracy. Then we will explain the special problem of intermittent demand and divide-by-zero problems. Tom concludes by explaining how to assess forecasts of multiple items and how it often makes sense to use weighted averages, weighting items differently by volume or revenue.

     

    Four general types of error metrics 

    1. Scale-dependent error
    2. Percentage error
    3. Relative error
    4 .Scale-free error

    Remark: Scale-dependent metrics are expressed in the units of the forecasted variable. The other three are expresses as percentages.

     

    1. Scale-dependent error metrics

    • Mean Absolute Error (MAE) aka Mean Absolute Deviation (MAD)
    • Median Absolute Error (MdAE)
    • Root Mean Square Error (RMSE)
    • These metrics express the error in the original units of the data.
      • Ex: units, cases, barrels, kilograms, dollars, liters, etc.
    • Since forecasts can be too high or too low, the signs of the errors will be either positive or negative, allowing for unwanted cancellations.
      • Ex: You don’t want errors of +50 and -50 to cancel and show “no error”.
    • To deal with the cancellation problem, these metrics take away negative signs by either squaring or using absolute value.

     

    2. Percentage error metric

    • Mean Absolute Percentage Error (MAPE)
    • This metric expresses the size of the error as a percentage of the actual value of the forecasted variable.
    • The advantage of this approach is that it immediately makes clear whether the error is a big deal or not.
    • Ex: Suppose the MAE is 100 units. Is a typical error of 100 units horrible? ok? great?
    • The answer depends on the size of the variable being forecasted. If the actual value is 100, then a MAE = 100 is as big as the thing being forecasted. But if the actual value is 10,000, then a MAE = 100 shows great accuracy, since the MAPE is only 1% of the actual.

     

    3. Relative error metric

    • Median Relative Absolute Error (MdRAE)
    • Relative to what? To a benchmark forecast.
    • What benchmark? Usually, the “naïve” forecast.
    • What is the naïve forecast? Next forecast value = last actual value.
    • Why use the naïve forecast? Because if you can’t beat that, you are in tough shape.

     

    4. Scale-Free error metric

    • Median Relative Scaled Error (MdRSE)
    • This metric expresses the absolute forecast error as a percentage of the natural level of randomness (volatility) in the data.
    • The volatility is measured by the average size of the change in the forecasted variable from one time period to the next.
      • (This is the same as the error made by the naïve forecast.)
    • How does this metric differ from the MdRAE above?
      • They do both use the naïve forecast, but this metric uses errors in forecasting the demand history, while the MdRAE uses errors in forecasting future values.
      • This matters because there are usually many more history values than there are forecasts.
      • In turn, that matters because this metric would “blow up” if all the data were zero, which is less likely when using the demand history.

     

    Intermittent Demand Planning and Parts Forecasting

     

    The special problem of intermittent demand

    • “Intermittent” demand has many zero demands mixed in with random non-zero demands.
    • MAPE gets ruined when errors are divided by zero.
    • MdRAE can also get ruined.
    • MdSAE is less likely to get ruined.

     

    Recap and remarks

    • Forecast metrics are necessary aids for monitoring and improving forecast accuracy.
    • There are two major classes of metrics: absolute and relative.
    • Absolute measures (MAE, MdAE, RMSE) are natural choices when assessing forecasts of one item.
    • Relative measures (MAPE, MdRAE, MdSAE) are useful when comparing accuracy across items or between alternative forecasts of the same item or assessing accuracy relative to the natural variability of an item.
    • Intermittent demand presents divide-by-zero problems which favor MdSAE over MAPE.
    • When assessing forecasts of multiple items, it often makes sense to use weighted averages, weighting items differently by volume or revenue.
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      Smart Software VP of Research to Present at Business Analytics Conference, INFORMS 2021

      Dr. Tom Willemain to lead INFORMS session on Generation of Probabilistic Time-Series Scenarios

      Belmont, Mass., March 2021 – 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 2021 Virtual INFORMS Business Analytics Conference from April 12 – 14.

      Dr. Willemain will present a session on Probabilistic Time-Series Scenarios, and how such scenarios are used, evaluated, and automatically generated using the statistical bootstrap. Frequently, OR models supporting business decisions are feed on massive numbers of probabilistic scenarios depicting future operating conditions. For example, with business operating at ever-lower levels of aggregation and ever-higher frequencies, demand planning and inventory optimization now use models fueled by scenarios representing the randomness of product demand at a daily scale. Dr. Willemain will discuss how even trivial decision tasks such as operator education benefit from large numbers of realistic training scenarios.

      As the leading Business Analytics Conference, INFORMS provides the opportunity to interact with the world’s leading 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. The conference features content from leading analytics professionals, who share and showcase top analytics applications that save lives, save money, and solve problems.

      Furthermore, to cutting-edge analytics content, the virtual analytics conference recognizes and prioritizes the need for quality “face-to-face” interactions, networking, and collaboration in a virtual setting.

       

      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