Weathering a Demand Forecast

For some of our customers, weather has a significant influence on demand. Extreme short-term weather events like fires, droughts, hot spells, and so forth can have a significant near-term influence on demand.

There are two ways to factor weather into a demand forecast: indirectly and directly. The indirect route is easier using the scenario-based approach of Smart Demand Planner. The direct approach requires a tailored special project requiring additional data and hand-crafted modeling.

Indirect Accounting for Weather

The standard model built into Smart Demand Planner (SDP) accommodates weather effects in four ways:

  1. If the world is steadily getting warmer/colder/drier/wetter in ways that impact your sales, SDP detects these trends automatically and incorporates them into the demand scenarios it generates.
  2. If your business has a regular rhythm in which certain days of the week or certain months of the year have consistently higher or lower-than-average demand, SDP also automatically detects this seasonality and incorporates it into its demand scenarios.
  3. Often it is the cussed randomness of weather that interferes with forecast accuracy. We often refer to this effect as “noise”. Noise is a catch-all term that incorporates all kinds of random trouble. Besides weather, a geopolitical flareup, the surprise failure of a regional bank, or a ship getting stuck in the Suez Canal can and have added surprises to product demand. SDP assesses the volatility of demand and reproduces it in its demand scenarios.
  4. Management overrides. Most of the time, customers let SDP churn away to automatically generate tens of thousands of demand scenarios. But if users feel the need to touch up specific forecasts using their insider knowledge, SDP can make that happen through management overrides.

Direct Accounting for Weather

Sometimes a user will be able to articulate subject matter expertise linking factors outside their company (such as interest rates or raw materials costs or technology trends) to their own aggregate sales. In these situations, Smart Software can arrange for one-off special projects that provide alternative (“causal”) models to supplement our standard statistical forecasting models. Contact your Smart Software representative to discuss a possible causal modeling project.

Meanwhile, don’t forget your umbrella.

 

 

 

A Rough Map of Forecasting-Related Terms

People new to the jobs of “demand planner” or “supply planner” are likely to have questions about the various forecasting terms and methods used in their jobs. This note may help by explaining these terms and showing how they relate.

 

Demand Planning

Demand planning is about how much of what you have to sell will go out the door in the future, e.g., how many what-nots you will sell next quarter. Here are six methodologies often used in demand planning.

  • Statistical Forecasting
    • These methods use demand history to forecast future values. The two most common methods are curve fitting and data smoothing.
    • Curve fitting matches a simple mathematical function, like the equation for a straight line (y= a +b∙t) or an interest-rate type curve (y=a∙bt), to the demand history. Then it extends that line or curve forward in time as the forecast.
    • In contrast, data smoothing does not result in an equation. Instead it sweeps through the demand history, averaging values along the way, to create a smoother version of the history. These methods are called exponential smoothing and moving average. In the simplest case (i.e., in the absence of trend or seasonality, for which variants exist), the goal is to estimate the current average level of demand and use that as the forecast.
    • These methods produce “point forecasts”, which are single-number estimates for each future time period (e.g., “Sales in March will be 218 units”). Sometimes they come with estimates of potential forecast error bolted on using separate models of demand variability (“Sales in March will be 218 ± 120 units”).
  • Probabilistic Forecasting
    • This approach keys on the randomness of demand and works hard to estimate forecast uncertainty. It regards forecasting less as an exercise in cranking out specific numbers and more as an exercise in risk management.
    • It explicitly models the variability in demand and uses that to present results in the form of large numbers of scenarios constructed to show the full range of possible demand sequences. These are especially useful in tactical supply planning tasks, such as setting reorder points and order quantities.
  • Causal Forecasting
    • Statistical forecasting models use as inputs only the past demand history of the item in question. They regard the up-and-down wiggles in the demand plot as the end result of myriad unnamed factors (interest rates, the price of tea in China, phases of the moon, whatever). Causal forecasting explicitly identifies one or more influences (interest rates, advertising spend, competitors’ prices, …) that could plausibly influence sales. Then it builds an equation relating the numerical values of these “drivers” or “causal factors” to item sales. The equation’s coefficients are estimated by “regression analysis”.
  • Judgemental Forecasting
    • Golden Gut. Despite the general availability of gobs of data, some companies pay little attention to the numbers and give greater weight to the subjective judgements of an executive deemed to have a “Golden Gut”, which allows him or her to use “gut feel” to predict what future demand will be. If that person has great experience, has spent a career actually looking at the numbers, and is not prone to wishful thinking or other forms of cognitive bias, the Golden Gut can be a cheap, fast way to plan. But there is good evidence from studies of companies run this way that relying on the Golden Gut is risky.
    • Group Consensus. More common is a process that uses a periodic meeting to create a group consensus forecast. The group will have access to shared objective data and forecasts, but members will also have knowledge of factors that may not be measured well or at all, such as consumer sentiment or the stories relayed by sales reps. It is helpful to have a shared, objective starting point for these discussions consisting of some sort of objective statistical analysis. Then the group can consider adjusting the statistical forecast. This process anchors the forecast in objective reality but exploits all the other information available outside the forecasting database.
    • Scenario Generation. Sometimes several people will meet and discuss “strategic what-if” questions. “What if we lose our Australian customers?” “What if our new product roll-out is delayed by six months?” “What if our sales manager for the mid-west jumps to a competitor?” These bigger-picture questions can have implications for item-specific forecasts and might be added to any group-consensus forecasting meeting.
  • New product forecasting
    • New products, by definition, have no sales history to support statistical, probability, or causal forecasting. Subjective forecasting methods can always be used here, but these often rely on a dangerous ratio of hopes to facts. Fortunately, there is at least partial support for objective forecasting in the form of curve fitting.
    • A graph of the cumulative sales of an item often describes some sort of “S-curve”, i.e., a graph that starts at zero, builds up, then levels off to a final lifetime total sales. The curve gets its name because it looks like a letter S somehow smeared and stretched to the right. Now there are an infinite number of S-curves, so forecasters typically pick an equation and subjectively specify some key parameter values, like when sales will hit 25%, 50% and 75% of total lifetime sales and what that final level will be. This is also overtly subjective, but it produces detailed period-by-period forecasts that can be updated as experience builds up. Finally, S-curves are sometimes shaped to match the known history of a similar, predecessor product (“Sales for our last gizmo looked like this, so let’s use that as a template.”).

 

Supply Planning

Demand planning feeds into supply planning by predicting future sales (e.g., for finished goods) or usage (e.g., for spare parts). Then it is up to supply planning to make sure the items in question will be available to sell or to use.

  • Dependent demand
    • Dependent demand is demand that can be determined by its relationship to demand for another item. For instance, a bill of materials may show that a little red wagon consists of a body, a pull bar, four wheels, two axles, and various fasteners to keep the wheels on the axles and connect the pull bar to the body. So if you hope to sell 10 little red wagons, you’d better make 10, which means you need 10×2 = 20 axles, 10×4 = 40 wheels, etc. Dependent demand governs raw materials purchasing, component and subsystems purchasing, even personnel hiring (10 wagons need one high-school kid to put them together over a 1 hour shift).
    • If you have multiple products with partially overlapping bills of materials, you have a choice of two forecasting approaches. Suppose you sell not only little red wagons but little blue baby carriages and that both use the same axles. To predict the number of axles you need you could (1) predict the dependent demand for axles from each product and add the forecasts or (2) observe the total demand history for axles as its own time series and forecast that separately. Which works better is an empirical question that can be tested.
  • Inventory management
    • Inventory management entails many different tasks. These include setting inventory control parameters such as reorder points and order quantities, reacting to contingencies such as stockouts and order expediting, setting staffing levels, and selecting suppliers.
  • Forecasting plays a role in the first three. The number of replenishment orders that will be made in a year for each product determines how many people are needed to cut PO’s. The number and severity of stockouts in a year determines the number of contingencies that must be handled. The number of PO’s and stockouts in a year will be random but be governed by the choices of inventory control parameters. The implications of any such choices can be modeled by inventory simulations. These simulations will be driven by detailed demand scenarios generated by probabilistic forecasts.

 

 

 

Six Demand Planning Best Practices You Should Think Twice About

Every field, including forecasting, accumulates folk wisdom that eventually starts masquerading as “best practices.”  These best practices are often wise, at least in part, but they often lack context and may not be appropriate for certain customers, industries, or business situations.  There is often a catch, a “Yes, but”. This note is about six usually true forecasting precepts that nevertheless do have their caveats.

 

  1. Organize your company around a one-number forecast. This sounds sensible: it’s good to have a shared vision. But each part of the company will have its own idea about which number is the number. Finance may want quarterly revenue, Marketing may want web site visits, Sales may want churn, Maintenance may want mean time to failure. For that matter, each unit probably has a handful of key metrics. You don’t need a slogan – you need to get your job done.

 

  1. Incorporate business knowledge into a collaborative forecasting process. This is a good general rule, but if your collaborative process is flawed, messing with a statistical forecast via management overrides can decrease accuracy. You don’t need a slogan – you need to measure and compare the accuracy of any and all methods and go with the winners.

 

  1. Forecast using causal modeling. Extrapolative forecasting methods take no account of the underlying forces driving your sales, they just work with the results. Causal modeling takes you deeper into the fundamental drivers and can improve both accuracy and insight. However, causal models (implemented through regression analysis) can be less accurate, especially when they require forecasts of the drivers (“predictions of the predictors”) rather than simply plugging in recorded values of lagged predictor variables. You don’t need a slogan: You need a head-to-head comparison.

 

  1. Forecast demand instead of shipments. Demand is what you really want, but “composing a demand signal” can be tricky: what do you do with internal transfers? One-off’s?  Lost sales? Furthermore, demand data can be manipulated.  For example, if customers intentionally don’t place orders or try to game their orders by ordering too far in advance, then order history won’t be better than shipment history.  At least with shipment history, it’s accurate:  You know what you shipped. Forecasts of shipments are not forecasts of  “demand”, but they are a solid starting point.

 

  1. Use Machine Learning methods. First, “Machine learning” is an elastic concept that includes an ever-growing set of alternatives. Under the hood of many ML advertised models is just an auto-pick an extrapolative forecast method (i.e., best fit) which while great at forecasting normal demand, has been around since the 1980’s (Smart Software was the first company to release an auto-pick method for the PC).   ML models are data hogs that require larger data sets than you may have available. Properly choosing then training an ML model requires a level of statistical expertise that is uncommon in many manufacturing and distribution businesses. You might want to find somebody to hold your hand before you start playing this game.

 

  1. Removing outliers creates better forecasts. While it is true that very unusual spikes or drops in demand will mask underlying demand patterns such as trend or seasonality, it isn’t always true that you should remove the spikes. Often these demand surges reflect the uncertainty that can randomly interfere with your business and thus need to be accounted for.  Removing this type of data from your demand forecast model might make the data more predictable on paper but will leave you surprised when it happens again. So, be careful about removing outliers, especially en masse.

 

 

 

 

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.

 

 

Correlation vs Causation: Is This Relevant to Your Job?

Outside of work, you may have heard the famous dictum “Correlation is not causation.” It may sound like a piece of theoretical fluff that, though involved in a recent Noble Prize in economics, isn’t relevant to your work as a demand planner. Is so, you may be only partially correct.

Extrapolative vs Causal Models

Most demand forecasting uses extrapolative models. Also called time-series models, these forecast demand using only the past values of an item’s demand. Plots of past values reveal trend and seasonality and volatility, so there is a lot they are good for. But there is another type of model – causal models —that can potentially improve forecast accuracy beyond what you can get from extrapolative models.

Causal models bring more input data to the forecasting task: information on presumed forecast “drivers” external to the demand history of an item. Examples of potentially useful causal factors include macroeconomic variables like the inflation rate, the rate of GDP growth, and raw material prices. Examples not tied to the national economy include industry-specific growth rates and your own and competitors’ ad spending.  These variables are usually used as inputs to regression models, which are equations with demand as an output and causal variables as inputs.

Forecasting using Causal Models

Many firms have an S&OP process that involves a monthly review of statistical (extrapolative) forecasts in which management adjusts forecasts based on their judgement. Often this is an indirect and subjective way to work causal models into the process without doing the regression modeling.

To actually make a causal regression model, first you have to nominate a list of potentially-useful causal predictor variables. These may come from your subject matter expertise. For example, suppose you manufacture window glass. Much of your glass may end up in new homes and new office buildings. So, the number of new homes and offices being built are plausible predictor variables in a regression equation.

There is a complication here: if you are using the equation to predict something, you must first predict the predictors. For example, sales of glass next quarter may be strongly related to numbers of new homes and new office buildings next quarter. But how many new homes will there be next quarter? That’s its own forecasting problem. So, you have a potentially powerful forecasting model, but you have extra work to do to make it usable.

There is one way to simplify things: if the predictor variables are “lagged” versions of themselves. For example, the number of new building permits issued six months ago may be a good predictor of glass sales next month. You don’t have to predict the building permit data – you just have to look it up.

Is it a causal relationship or just a spurious correlation?

Causal models are the real deal: there is an actual mechanism that relates the predictor variable to the predicted variable. The example of predicting glass sales from building permits is an example.

A correlation relationship is more iffy. There is a statistical association that may or may not provide a solid basis for forecasting. For example, suppose you sell a product that happens to appeal most strongly to Dutch people but you don’t realize this. The Dutch are, on average, the tallest people in Europe. If your sales are increasing and the average height of Europeans is increasing, you might use that relationship to good effect. However, if the proportion of Dutch in the Euro zone is decreasing while the average height is increasing because the mix of men versus women is shifting toward men, what can go wrong? You will expect sales to increase because average height is increasing. But your sales are really mostly to the Dutch, and their relative share of the population is shrinking, so your sales are really going to decrease instead. In this case the association between sales and customer height is a spurious correlation.

How can you tell the difference between true and spurious relationships? The gold standard is to do a rigorous scientific experiment. But you are not likely to be in position to do that. Instead, you have to rely on your personal “mental model” of how your market works. If your hunches are right, then your potential causal models will correlate with demand and causal modeling will pay off for you, either to supplement extrapolative models or to replace them.