Belmont, MA, June 2023 – Smart Software, Inc., provider of industry-leading demand forecasting, planning, and inventory optimization solutions, today announced the award of US Patent 11,656,887, “SYSTEM AND METHOD TO SIMULATE DEMAND AND OPTIMIZE CONTROL PARAMETERS FOR A TECHNOLOGY PLATFORM.”

The patent directs “technical solutions for analyzing historical demand data of resources in a technology platform to facilitate management of an automated process in the platform.” One important application is optimization of parts inventories.

Aspects of the invention include: an advanced bootstrap process that converts a single observed time series of item demand into an unlimited number of realistic demand scenarios; a performance prediction process that executes Monte Carlo simulations of a proposed inventory control policy to assess its performance; and a performance improvement process that uses the performance prediction process to automatically explore the space of alternative system designs to identify optimal control parameter values, selecting ones that minimize operating cost while guaranteeing a target level of item availability.

The new analytic technology described in the patent will form the basis for the upcoming release of the next generation (“Gen2”) of Smart Demand Planner™ and Smart IP&O™. Current customers and resellers can preview Gen2 by contacting their Smart Software sales representative.

Research underlying the patent was self-funded by Smart, supplemented by competitive Small Business Innovation Research grants from the US National Science Foundation.

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 such as Disney, Arizona Public Service, Ameren, and The American Red Cross.  Smart’s Inventory Planning & Optimization Platform, Smart IP&O 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 our website is www.smartcorp.com.

Reveal how forecasts are used with these 4 questions.

Often companies will insist that they “don’t use forecasts” to plan inventory.  They often use reorder point methods and are struggling to improve on-time delivery, inventory turns, and other KPIs. While they don’t think of what they are doing as explicitly forecasting, they certainly use estimates of future demand to develop reorder points such as min/max.

Regardless of what it is called, everyone tries to estimate future demand in some way and uses this estimate to set stocking policies and drive orders. To improve inventory planning and make sure you aren’t over/under ordering and creating large stockouts and inventory bloat, it is important to understand exactly how your organization uses forecasts. Once this is understood, you can assess whether the quality of the forecasts can be improved.

Try getting answers to the following questions. It will reveal how forecasts are being used in your business – even if you don’t think you use forecasts.

1.  Is your forecast a period-by-period estimate over time that is used to predict what on-hand inventory will be in the future and triggers order suggestions in your ERP system?

2. Or is your forecast used to derive a reorder point but not explicitly used as a per-period driver to trigger orders? Here, I may predict we’ll sell 10 per week based on the history, but we are not loading 10, 10, 10, 10, etc., into the ERP. Instead, I derive a reorder point or Min that covers the two-period lead time + some amount of buffer to help protect against stock out. In this case, I’ll order more when on hand gets to 25.

3. Is your forecast used as a guide for the planner to help subjectively determine when they should order more?  Here, I predict 10 per week, and I assess the on-hand inventory periodically, review the expected lead time, and I decide, given the 40 units I have on hand today, that I have “enough.” So, I do nothing now but will check back again in a week.

4. Is it used to set up blanket orders with suppliers? Here, I predict 10 per week and agree to a blanket purchase order with the supplier of 520 per year. The orders are then placed in advance to arrive in quantities of 10 once per week until the blanket order is consumed.

Once you get the answers, you can then ask how the estimates of demand are created.  Is it an average? Is it deriving demand over lead time from a sales forecast?  Is there a statistical forecast generated somewhere?  What methods are considered? It will also be important to assess how safety stocks are used to protect against demand and supply variability.  More on all of this in a future article.

​If you had your head up lately, you may have noticed some additional madness off the basketball court: The failure of Silicon Valley Bank. Those of us in the supply chain world may have dismissed the bank failure as somebody else’s problem, but that sorry episode holds a big lesson for us, too: The importance of stress testing done right.

The Washington Post recently carried an opinion piece by Natasha Sarin called “Regulators missed Silicon Valley Bank’s problems for months. Here’s why.” Sarin outlined the flaws in the stress testing regime imposed on the bank by the Federal Reserve. One problem is that the stress tests are too static. The Fed’s stress factor for nominal GDP growth was a single scenario listing presumed values over the next 13 quarters (see Figure 1). Those 13 quarterly projections might be somebody’s consensus view of what a bad hair day would look like, but that’s not the only way things could play out.  As a society, we are being taught to appreciate a better way to display contingencies every time the National Weather Service shows us projected hurricane tracks (see Figure 2). Each scenario represented by a different colored line shows a possible storm path, with the concentrated lines representing the most likely.  By exposing the lower probability paths, risk planning is improved.

When stress testing the supply chain, we need realistic scenarios of possible future demands that might occur, even extreme demands.   Smart provides this in our software (with considerable improvements in our Gen2 methods).  The software generates a huge number of credible demand scenarios, enough to expose the full scope of risks (see Figure 3). Stress testing is all about generating massive numbers of planning scenarios, and Smart’s probabilistic methods are a radical departure from previous deterministic S&OP applications, being entirely scenario based.

The other flaw in the Fed’s stress tests was that they were designed months in advance but never updated for changing conditions.  Demand planners and inventory managers intuitively appreciate that key variables like item demand and supplier lead time are not only highly random even when things are stable but also subject to abrupt shifts that should require rapid rewriting of planning scenarios (see Figure 4, where the average demand jumps up dramatically between observations 19 and 20). Smart’s Gen2 products include new tech for detecting such “regime changes”  and automatically changing scenarios accordingly.

Banks are forced to undergo stress tests, however flawed they may be, to protect their depositors. Supply chain professionals now have a way to protect their supply chains by using modern software to stress test their demand plans and inventory management decisions.

Figure 1: Scenarios used the Fed to stress test banks.

Figure 2: Scenarios used by the National Weather Service to predict hurricane tracks

Figure 3: Demand scenarios of the type generated by Smart Demand Planner

Figure 4: Example of regime change in product demand after observation #19

“Regardless of how much effort is poured into training forecasters and developing elaborate forecast support systems, decision-makers will either modify or discard the predictions if they do not trust them.”  — Dilek Onkal, International Journal of Forecasting 38:3 (July-September 2022), p.802.

The words quoted above grabbed my attention and prompted this post. Those of a geekly persuasion, like your blogger, are inclined to think of forecasting as a statistical problem. While that is obviously true, those of a certain age, like your blogger, understand that forecasting is also a social activity and therefore has a large human component.

Who Do You Trust?

Trust is always a two-way street, but let’s stay on the demand forecaster’s side. What characteristics of and actions by forecasters and demand planners build trust in their work? The above quoted Professor Onkal reviewed academic research on this topic going back to 2006. She summarized results from practitioner surveys that identified key trust factors related to forecaster characteristics, forecasting process, and forecasting communication.

Forecaster characteristics

Key to building trust among the users of forecasts are perceptions of forecaster and demand planner competence and objectivity. Competence has a mathematical component, but many managers confuse computer skills with analytic skills, so users of forecasting software can usually clear this hurdle. However, since the two are not the same, it pays dividends to absorb your vendor’s training and learn not just the math but the lingo of your forecasting software. In my observation, trust can also be increased by showing knowledge of the company’s business.

Objectivity is also a key to trustworthiness. It may be uncomfortable for the forecaster to be put in the middle of occasional departmental squabbles, but those will come up and must be handled with tact. Squabbles? Well, silos exist and tilt in different directions. Sales departments favor higher demand forecasts that drive production increases, so that they never have to say “Sorry, we are fresh out of that.” Inventory managers are wary of high demand forecasts, because “excess enthusiasm” can leave them holding the bag, sitting on bloated inventory.

Sometimes the forecaster becomes a de facto referee, and in this role must display overt signs of objectivity. That can mean first recognizing that every management decision involves tradeoffs of good things against other good things, e.g., product availability versus lean operations, and then helping the parties strike a painful but tolerable balance by surfacing the links between operational decisions and the key performance metrics that matter to folks like Chief Financial Officers.

The Forecasting process

The forecasting process can be thought of as having three phases: data inputs, calculations, and outputs. Actions can be taken to increase trust in each phase.

Regarding inputs:

Trust can be increased if obviously relevant inputs are at least acknowledged if not directly used in calculations. Thus, factors like social media sentiment and regional sales managers’ gut instincts can be legitimate parts of a forecast consensus process. However, objectivity requires that these putative predictors of profit be tested objectively. For instance, a professional-grade forecasting process may well include subjective adjustment to statistical forecasts but must then also assess whether the adjustments actually end up improving accuracy, not just making some people feel listened to.

Regarding the second phase, calculations:

The forecaster will be trusted to the extent that they are able to deploy more than one way to calculate forecasts and then articulate a good reason why they chose the method eventually used. In addition, the forecaster should be able to explain in accessible language how even complicated techniques do their job. It is difficult to put trust in a “black box” method that is so opaque as to be inscrutable. The importance of explainability is amplified by the fact of life that the forecaster’s superior must themselves in turn be able to justify the choice of technique to their supervisor.

For instance, exponential smoothing uses this equation: S(t) = αX(t)+(1-α)S(t-1). Many forecasters are familiar with this equation, but many forecast users are not. There is a story that explains the equation in terms of averaging irrelevant “noise” in an item’s demand history and the need to strike a balance between smoothing out noise and being able to react to sudden shifts in the level of demand. The forecaster who can tell that story will be more credible. (My own version of that story uses phrases from sports, i.e., “head fakes” and “jukes”. Finding folksy analogs appropriate to your specific audience always pays dividends.)

A final point: best practice demands that any forecast be accompanied by an honest assessment of its uncertainty. A forecaster who tries to build trust by being overly specific (“Sales next quarter will be 12,184 units”) will always fail. A forecaster who says “Sales next quarter will have a 90% chance of falling between 12,000 and 12,300 units” will be both correct more often and  also more helpful to decision makers. After all, forecasting is essentially a job of risk management, so the decision maker is best served by knowing the risks.

Forecasting communication:

Finally, consider the third phase, communication of forecast results. Research suggests that continual communication with forecast users builds trust. It avoids those horrible, deflating moments when a nicely formatted report is shot down because of some fatal flaw that could have been foreseen: “This is no good because you didn’t take account of X, Y or Z” or “We really wanted you to present results rolled up to the top of the product hierarchies (or by sales region or by product line or…)”.

Even when everybody is aligned as to what is expected, trust is enhanced by presenting results using well-crafted graphics, with massive numerical tables provided for backup but not as the main way of communicating results. My experience has been that, just as a meeting-control device, a graph is usually much better than a large numerical table. With a graph, everybody’s attention is focused on the same thing and many aspects of the analysis are immediately (and literally) visible. With a table of results, the table of participants often splinters into side conversations in which each voice is focused on different pieces of the table.

Onkal summarizes the research this way: “Take-aways for those who make forecasts and those who use them converge around clarity of communication as well as perceptions of competence and integrity.”

What Do You Trust?

There is a related dimension of trust: not who do you trust but what do you trust? By this I mean both data and software….  Read the 2nd part of this Blog “What do you Trust” here  https://smartcorp.com/forecasting/the-role-of-trust-in-the-demand-forecasting-process-part-2-what/

5 Steps to Consensus Demand Planning

The whole point of demand forecasting is to establish the best possible view of future demand.  This requires that we draw upon the best data and inputs we can get, leverage statistics to capture underlying patterns, put our heads together to apply overrides based on business knowledge, and agree on a consensus demand plan that serves as cornerstone to the company’s overall demand plan.

Step 1: Develop an accurate demand signal.   What constitutes demand?  Consider how  your organization defines demand – say, confirmed sales orders net of cancellations or shipment data adjusted to remove the impact of historical stockouts  – and use this consistently.  This is your measure of what the market is requesting you to deliver.  Don’t confuse this with your ability to deliver – that should be reflected in the revenue plan.

Step 2: Generate a statistical forecast.  Plan for thousands of items, using a proven forecasting application that automatically pulls in your data and reliably produces accurate forecasts for all of your items.  Review the first pass of your forecast, then make adjustments.  A strike or train wreck may have interrupted shipping last month – don’t let that wag your forecast.  Adjust for these and reforecast.  Do the best you can, then invite others to weigh in.

Step 3: Bring on the experts.  Product line managers, sales leaders, key distribution partners know their markets.  Share your forecast with them.  Smart uses the concept of a “Snapshot” to share a facsimile of your forecast – at any level, for any product line – with people who may know better.  There could be an enormous order that hasn’t hit the pipeline, or a channel partner is about to run their annual promotion.  Give them an easy way to take their portion of the forecast and change it.  Drag this month up, that one down …

Step 4:  Measure Accuracy and Forecast Value Add.  Some of your contributors may be right on the money, other tend to be biased high or low.  Use forecast vs. actuals reporting and measure forecast value add analysis to measure forecast errors and whether changes to the forecast are hurting or helping.  By informing the process with this information, your company will improve it’s ability to forecast more accurately.

Step 5: Agree on the Consensus Forecast.  You can do this one product line or geography at a time, or business by  business.  Convene the team, graphically stack up their inputs, review past accuracy performance, discuss their reasons for increasing or reducing the forecast, and agree on whose inputs to use.  This becomes your consensus plan.  Finalize the plan and send it off – upload forecasts to MRP, send to finance and manufacturing.  You have just kicked off your Sales, Inventory and Operational Planning process.

You can do this.  And we can help.  If you have any questions about collaborative demand planning please reply to this blog, we will follow up.