What makes a probabilistic forecast?

What’s all the hoopla around the term “probabilistic forecasting?” Is it just a more recent marketing term some software vendors and consultants have coined to feign innovation? Is there any real tangible difference compared to predecessor “best fit” techniques?  Aren’t all forecasts probabilistic anyway?

To answer this question, it is helpful to think about what the forecast really is telling you in terms of probabilities.  A “good” forecast should be unbiased and therefore yield a 50/50 probability being higher or lower than the actual.  A “bad” forecast will build in subjective buffers (or artificially depress the forecast) and result in demand that is either biased high or low.  Consider a salesperson that intentionally reduces their forecast by not reporting sales they expect to close to be “conservative.” Their forecasts will have negative forecast bias as actuals will nearly always be higher than what they predicted.   On the other hand, consider a customer that provides an inflated forecast to their manufacturer.  Worried about stockouts, they overestimate demand to ensure their supply.  Their forecast will have a positive bias as actuals will nearly always be lower than what they predicted. 

These types of one-number forecasts described above are problematic.  We refer to these predictions as “point forecasts” since they represent one point (or a series of points over time) on a plot of what might happen in the future.   They don’t provide a complete picture because to make effective business decisions such as determining how much inventory to stock or the number of employees to be available to support demand requires detailed information on how much lower or higher the actual will be!  In other words, you need the probabilities for each possible outcome that might occur.  So, by itself, the point forecast isn’t probabilistic one.   

To get a probabilistic forecast, you need to know the distribution of possible demands around that forecast.  Once you compute this, the forecast becomes “probabilistic.”  How forecasting systems and practitioners such as demand planners, inventory analysts, material managers, and CFOs determine these probabilities is the heart of the question: “what makes a forecast probabilistic?”     

Normal Distributions
Most forecasts and the systems/software that produce them start with a prediction of demand.  Then they figure out the range of possible demands around that forecast by making incorrect theoretical assumptions about the distribution.  If you’ve ever used a “confidence interval” in your forecasting software, this is based on a probability distribution around the forecast.  The way this range of demand is determined is to assume a particular type of distribution.  Most often this means assuming a bell shaped, otherwise known as a normal distribution.  When demand is intermittent, some inventory optimization and demand forecasting systems may assume the demand is Poisson shaped. 

After creating the forecast, the assumed distribution is slapped around the demand forecast and you then have your estimate of probabilities for every possible demand – i.e., a “probabilistic forecast.”  These estimates of demand and associated probabilities can then be used to determine extreme values or anything in between if desired.  The extreme values at the upper percentiles of the distribution (i.e., 92%, 95%, 99%, etc.) are most often used as inputs to inventory control models.  For example, reorder points for critical spare parts in an electrical utility might be planned based on a 99.5% service level or even higher.  While a non-critical service part might be planned at an 85% or 90% service level.

The problem with making assumptions about the distribution is that you’ll get these probabilities wrong.  For example, if the demand isn’t normally distributed but you are forcing a bell shaped/normal curve on the forecast then how can then the probabilities will be incorrect.  Specifically, you might want to know the level of inventory needed to achieve a 99% probability of not running out of stock and the normal distribution will tell you to stock 200 units.  But when compared to the actual demand, you come to find out that 200 units only filled demand entirely in 40/50 observations.  So, instead of getting a 99% service level you only achieved an 80% service level!  This is a gigantic miss resulting from trying to fit a square peg into a round hole.  The miss would have led you to take an incorrect inventory reduction.

Empirically Estimated Distributions are Smart
To produce a smart (read accurate) probabilistic forecast you need to first estimate the distribution of demand empirically without any naïve assumptions about the shape of the distribution.  Smart Software does this by running tens of thousands of simulated demand and lead time scenarios.  Our solution leverages patented techniques that incorporate Monte Carlo simulation, Statistical Bootstrapping, and other methods.  The scenarios are designed to simulate real life uncertainty and randomness of both demand and lead times.  Actual historical observations are utilized as the primary inputs, but the solution will give you the option of simulating from non-observed values as well.  For example, just because 100 units was the peak historical demand, that doesn’t mean you are guaranteed to peak out at 100 in the future.  After the scenarios are done you will know the exact probability for each outcome. The “point” forecast then becomes the center of that distribution.  Each future period over time is expressed in terms of the probability distribution associated with that period.

Leaders in Probabilistic Forecasting
Smart Software, Inc. was the first company to ever introduce statistical bootstrapping as part of a commercially available demand forecasting software system twenty years ago.  We were awarded a US patent at the time for it and named a finalist in the APICS Corporate Awards of Excellence for Technological Innovation.  Our NSF Sponsored research that led to this and other discoveries were instrumental in advancing forecasting and inventory optimization.    We are committed to ongoing innovation, and you can find further information about our most recent patent here.

 

 

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

 

 

Heroes of Disruptive Innovation

The Smart Forecaster

Pursuing best practices in demand planning,

forecasting and inventory optimization

Are you a hero?

The executive suites at most companies are populated by leaders who became corporate “heroes.” These exceptional performers led—and continue to lead—transformative initiatives that drive revenue growth, reduce costs and increase shareholder value.

Heroic accomplishments require a bold new approach, often fueled by a ground-breaking product or service. Harvard Business School professor Clayton M. Christensen speaks of “disruptive innovation,” the extreme case of a product or practice that creates a fundamentally new market or business approach. (The Harvard Business Review YouTube channel features an interview with Prof. Christensen on the subject here.) The trick is to recognize the possibility, and have the courage to do something about it.

This presents challenges on both sides of the fence. The “best in class” technology provider will have a hard time being heard—getting past entrenched vendors and established practices. The heroic practitioner has to want to hear what’s possible, be open to change and have the drive to execute. Building a community of believers and getting that shot to make a difference can be difficult, but that’s why this work is heroic.

You may be a budding hero, or an executive who can spot opportunities and “hero-making” opportunities in your team. I have encountered many of you over the years, and your successes have been our successes. My advice is simple: go for it. Life is short, possibilities are limitless and your courage will be rewarded.

Nelson Hartunian, PhD, co-founded Smart Software, formerly served as President, and currently oversees it as Chairman of the Board. He has, at various times, headed software development, sales and customer service.

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Daily Demand Scenarios

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In this Videoblog, we will explain how time series forecasting has emerged as a pivotal tool, particularly at the daily level, which Smart Software has been pioneering since its inception over forty years ago. The evolution of business practices from annual to more refined temporal increments like monthly and now daily data analysis illustrates a significant shift in operational strategies.

Constructive Play with Digital Twins

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Those of you who track hot topics will be familiar with the term “digital twin.” Those who have been too busy with work may want to read on and catch up. While there are several definitions of digital twin, here’s one that works well: A digital twin is a dynamic virtual copy of a physical asset, process, system, or environment that looks like and behaves identically to its real-world counterpart. A digital twin ingests data and replicates processes so you can predict possible performance outcomes and issues that the real-world product might undergo.

Direct to the Brain of the Boss – Inventory Analytics and Reporting

Direct to the Brain of the Boss – Inventory Analytics and Reporting

In this blog, the spotlight is cast on the software that creates reports for management, the silent hero that translates the beauty of furious calculations into actionable reports. Watch as the calculations, intricately guided by planners utilizing our software, seamlessly converge into Smart Operational Analytics (SOA) reports, dividing five key areas: inventory analysis, inventory performance, inventory trending, supplier performance, and demand anomalies.

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      Saving Billions? How Far the ‘Center for Innovation in Logistics Systems’ Might Take the US Army

      The Smart Forecaster

      Pursuing best practices in demand planning,

      forecasting and inventory optimization

      Contributed to The Smart Forecaster by Dr. Greg Parlier (Colonel, U.S. Army, retired). Details on Dr. Parlier’s background conclude the post.

      For over two decades, the General Accounting Office (GAO) has indicated that the Defense Department’s logistics management has been ineffective and wasteful, and that the Services lack strategic plans to improve overall inventory management and supply chain performance.

      For the US Army, this problem is directly related to a persistent inability to link inventory investment levels and policies with supply chain effectiveness to achieve combat equipment readiness objectives required for globally deployed forces. This shortcoming has been attributed to numerous complexities associated with managing geographically dispersed, independently operating organizations, further compounded by a lack of visibility, authority and accountability across this vast global enterprise.

      Unlike the corporate world, where powerful forces encourage innovation to drive competitiveness and efficiency, the Army is not a revenue generating organization focused on “quarterly earnings” and profitability. Certainly, the Army wants to be an efficient consumer of resources—but unlike the private sector’s focus on profit as a bottom line, the surrogate motivator for the Army is ‘force readiness’. This includes equipment availability and weapon system readiness for current operations in Afghanistan, as well as future capability requirements directed by the National Command Authority.

      To sustain that equipment availability, the Army must synchronize disparate organizational components using myriad processes with disconnected legacy management information systems across numerous supply support activities which frequently relocate to support deploying forces.

      Today, while still engaged in Afghanistan, the Army is also committed to a comprehensive and ongoing transformation. Central to this effort is recognition that dramatic improvements must be achieved in logistics operations and supply chain management. Owning one of the world’s largest and most complex supply chains, the Army is now investing in historically unprecedented efforts to fully capitalize on the promises offered by new information-based technologies. For example, the “Single Army Logistics Enterprise” is believed to be the most ambitious and expensive Enterprise Resource Planning (ERP) implementation project ever undertaken.

      These ERP implementation projects have met with very mixed results. While the evidence suggests that dramatic performance improvements for competitive advantage can be achieved in the commercial sector, this has occurred only where so called “IT solutions” are applied to an underlying foundation of mature, efficient and appropriate business processes.

      The reality of most cases in recent years, however, has not been this success. Rather, attempts have been made to “bolt on” a solution (like an ERP system, for example) to existing business processes, in misguided efforts to replicate legacy management practices. Such efforts to automate existing processes have, all too often, simply created chaos. In fact, these attempts have not only failed to achieve anticipated improvements, but have actually resulted in reduced performance.

      The general pattern has been: the greater the IT investment and organizational scope, the more likely “failure” occurs, in the form of cost overruns, missed schedules, and even project failure—where the effort has finally been abandoned.

      We believe the way to enable a coordinated, comprehensive approach for logistics transformation is by creating an “engine for innovation” to accelerate and sustain continuous performance improvement for Army logistics and supply chain management. We are developing a ‘Center for Innovation in Logistics Systems’ to systematically evaluate major organizational components, conduct root cause analyses, diagnose structural disorders and prescribe integrated solutions. We have now identified several ‘catalysts for innovation’ to reduce supply side variability and demand uncertainty—the proximate causes of the notorious ‘bull whip effect’. These include what we refer to as the ‘readiness equation’, ‘mission-based forecasting’, ‘readiness-based sparing’ and ‘readiness responsive retrograde’.

      Our goal is to develop a comprehensive modeling capacity to generate and test these innovation catalysts along with several other initiatives in order to estimate cost effective approaches before they are adopted as policy and implemented in practice. We are looking at performance analysis, organizational design, management information and decision support concepts, enterprise systems engineering and workforce considerations including human capital investment needs.

      Examining the ‘catalysts’ in isolation, we have seen significant potential for improvement which could yield hundreds of millions of dollars in savings. When combined into new, integrated management practices, however, the potential magnitude for improvement is truly dramatic—billions of dollars in further savings are likely. More importantly, it becomes possible to relate investment levels to current readiness and future capabilities.

      The center is capable of developing ‘management innovation as a strategic technology’ by integrating advanced analytics with transformational strategic planning. By harnessing, focusing and applying the power of analysis, we are promoting both qualitative and quantitative common sense—the compelling analytical arguments for necessary change to pursue a common vision. With this power, we are beginning to educate the Army’s leadership, motivate logistics managers to action and provide a source for innovation the culture can embrace. During our journey, we have certainly adapted and applied much from both academic domains and the corporate sector. They, in turn, might now benefit from what we have been able to learn and achieve as well.

      Prior to his retirement, Colonel Parlier was the Army’s senior, most experienced operations research analyst and served as Army Aviation and Missile Command’s (AMCOM) Deputy Commander for Transformation. He is the author of Transforming U.S. Army Supply Chains: Strategies for Management Innovation, describing the analytical framework of a multi-year Army Materiel Command (AMC) research and development project providing operations research insights for use by the Army and Department of Defense.

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          Smart Software Executive to Speak on Optimizing Military Spare Parts Inventories

          Tom Willemain to lead session and tutorial at 2012 INFORMS Conference to help military logistics personnel manage $70 Billion worth of parts & supplies

          Belmont, Mass., October 9, 2012 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced that Tom Willemain, vice president for research, will have two roles at this year’s INFORMS 2012 Annual Meeting, in Phoenix, Arizona, October 14-17. Dr. Willemain, who is also a professor of Industrial and Systems Engineering at Rensselaer Polytechnic Institute, will present a tutorial on managing spare parts on October 16, 8:00 – 9:30 am. He will also chair a session on “Methods Supporting Military Logistics and Testing” where he will also discuss “Accurate Forecasts of Spare Part Demand,” October 17, 8:00 – 9:30 am.

          Operations and support of military hardware, which includes maintaining, refurbishing and overhauling, can be 60 to 70 percent of the cost of owning a weapon system over its entire lifetime, which can be decades. Improving the management of parts involved in those operations poses a significant challenge for the U.S. military.

          According to a study by Deloitte Consulting LLP, the Department of Defense spends $70 billion a year on parts and supplies. Accurately forecasting spare parts is a major problem for any parts organizations because as much as 70% of spare parts have what’s known as “intermittent demand” which is very difficult to accurately forecast. This typically results in unbalanced inventories with many items overstocked and others under-stocked.

          In a book titled, Transforming U.S. Army Supply Chains: Strategies for Management Innovation, retired Army Col. Greg H. Parlier, who is now a defense logistics consultant, has proposed “mission based forecasting” software tools that will help the military to stop buying things they do not need. Dr. Willemain’s experience has helped numerous companies with similar inventory challenges do just that.

          Dr. Willemain has been at the forefront of research on better ways to forecast intermittent demand. With other colleagues at Smart Software, he holds a patent that provides accurate service level forecasts and estimates of safety stock and inventory stocking level requirements. Commercialized in Smart’s flagship product, SmartForecasts®, the patented technology has helped numerous manufacturing, distribution, and service/spare parts organizations optimize their inventories, save millions of dollars, improve cash flows, and meet corporate cost reduction objectives.

          INFORMS stands for The Institute for Operations Research and the Management Sciences. It is an international scientific society, with 10,000 members, dedicated to applying scientific methods to help improve decision-making, management, and operations. To learn more about INFORMS or its annual research conference, see www.informs.org.

          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 flagship product, SmartForecasts, has thousands of users worldwide, including customers at mid-market enterprises and Fortune 500 companies, such as Abbott Laboratories, Mitsubishi, Siemens, Disney, Nestle, GE and The Coca-Cola Company.  SmartForecasts 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.smartsoftware.wpengine.com.

          SmartForecasts is a registered trademark 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); FAX: 1-617-489-2748; E-mail: info@smartsoftware.wpengine.com