Probabilistic Forecasting

Overview

Probabilistic Forecasting is a feature in Arkieva's Demand Planning module that helps you move beyond a single-point forecast and understand the range of possible demand outcomes — so you can make more informed, confident planning decisions.

Instead of showing only one forecasted number per period, probabilistic forecasting estimates a probability distribution (or percentile bounds where the distribution is unknown). This lets you see not just what demand is expected to be, but how certain or uncertain that expectation is.

Key Concepts

Before using this feature, it helps to understand a few foundational ideas.

Single-Point vs. Probabilistic Forecasting

Standard forecasting generates a single value per period — typically close to the average expected demand. This is useful but gives no indication of how confident the system is in that number.

Probabilistic forecasting runs multiple forecast methods, each producing a different outcome. The variability across those outcomes is transformed into a probability distribution, which estimates the variability in the forecast.

Probability Distributions

Once a probability distribution is available, you can answer questions like:

• What is the demand value that only 20% of outcomes fall below?
• What value do 90% of outcomes fall below?
• Where is the median (50th percentile) demand likely to land?

Arkieva supports both normal distributions and quantile-based (non-parametric) distributions, so you are not restricted to assuming a specific shape.

Percentile Bounds

The three core outputs of a probabilistic run are percentile lines, calculated at each individual period — not across the whole forecast horizon.

Accessing Probabilistic Forecasting

Navigate to the feature using the following path within the SaaS application:

To configure your probabilistic model, click "Adjust Probabilistic Strategy." This opens a configuration drawer with all the settings described below.

Edit Probabilistic Strategy 

1. Toggle from Standard to Probabilistic to view the Probabilistic Strategy. 
   

Configuring the Probabilistic Strategy

The Adjust Probabilistic Strategy drawer contains several settings. Work through them in order.

1. Time Buckets

Select whether you want to evaluate the model in weekly or monthly buckets.

2. Use Published / Adjusted Values

Check this option if you have performed outlier corrections in your standard forecast and want those adjusted values to be reflected in the probabilistic model. If you use this setting in standard forecasting, enable it here as well to ensure a valid comparison.

3. Percentile Definitions

Define the three percentile bounds you want to display. The default values are 0.20, 0.50, and 0.90, but you can change these to any valid probability between 0 and 1.

Example configurations:

• 0.20 / 0.50 / 0.90 — Wide spread; useful for seeing the full range of demand uncertainty.
• 0.40 / 0.50 / 0.60 — Tight spread; useful for comparing the probabilistic bands closely against your standard forecast.
• 0.01 / 0.50 / 0.99 — Maximum range; shows nearly the full extent of the distribution.

4. Calculation Type

Select how Arkieva estimates the probability distribution:

Normal Distribution

When demand is assumed to follow a bell curve, confidence intervals can be read directly from percentile markers. Use when you have stable, symmetric demand patterns. This is the default.

The chart below shows how the 50th, 80th, 90th, 95th, and 99th percentiles map onto a standard normal curve.

Quantile Distribution

Makes no assumption about the distribution shape. Use when demand is irregular, skewed, or difficult to characterize.

Traditional vs. Probabilistic Forecast: Side-by-Side

5. Global Filter (Optional)

Use this field to limit the probabilistic run to a specific product family or dataset. Leave blank to run across all available data.

6. Partition (Analysis Level)

Select the level at which you want to evaluate the probabilistic model against your standard model. Options include item, item + location, item + customer, or other attribute combinations.

The underlying forecast is always generated and saved at the tuple level (item, location, customer). The partition setting controls the aggregation level used for comparison and analysis.

Running the Model

Once all settings are configured, click Apply. Arkieva will generate a probabilistic forecast and display results in the chart above the settings panel.

Reading the Results

The results chart shows multiple lines overlaid for each item at the selected partition level.

Chart Elements

Interpreting What You See

Use the relationship between the standard forecast line and the percentile bands to assess forecast quality and take action.

Forecast Within the Bands

If the standard forecast line runs close to or through the percentile bands, the standard model is performing well. The probabilistic bounds confirm that the forecast is aligned with what the demand distribution suggests.

Forecast Outside the Bands

There is always a statistical chance that a single forecast value falls outside a given probability range — even a very wide range (e.g., 1st to 99th percentile). When this occurs, the Generated Forecast can be considered a statistical outlier. Consider the following responses:

• Override the standard forecast manually for that period.
• Adopt one of the percentile lines (e.g., the 50th percentile) as your working forecast.
• Investigate whether an outlier, market event, or data quality issue is affecting the standard model.

Making Decisions with Probabilistic Forecasts

Probabilistic forecasting does not automatically replace your standard forecast. It provides a lens for evaluating it and gives you structured options when the standard model needs review. It also enables direct input into S&OP and supply planning by producing multiple named demand scenarios, replacing ad-hoc percentage adjustments with data-driven bounds.

Common Decision Scenarios

Selecting a Percentile as Your Forecast

You can choose to use any of the percentile lines as your planning forecast value. The percentile you select reflects the level of confidence you are planning to meet:

• Lower percentile (e.g., 20th) — Lean inventory strategy; acceptable risk of stockout in some cases.
• Median (50th) — Balanced approach aligned with expected demand.
• Upper percentile (e.g., 90th) — Service-level protection; ensures coverage for most demand scenarios.

Frequently Asked Questions

Q: What is the difference between a Probabilistic Forecast and a Generated Forecast?

The Generated Forecast is based on historical data (typically the previous 3 years of sales history) and produces a single point estimate. The Probabilistic Forecast produces a range of values — a lower bound, a midpoint, and an upper bound — representing the statistical likelihood of where actual demand will fall.

Q: What do the lower, middle, and upper percentile values represent?

Each percentile setting defines the probability that actual demand will fall below that forecasted value: the lower percentile (e.g., 0.2) means there is a 20% chance actual demand will fall below this value; the middle percentile (0.5) represents the median; and the upper percentile (e.g., 0.9) means there is only a 10% chance demand will exceed this value.

Q: Can users customize the probability range?

Yes. Users are not limited to preset percentiles. Any lower and upper probability can be selected — for example, 1% and 99% — to create a wider or narrower range depending on planning needs.

Q: What happens if I increase the lower percentile setting?

Raising the lower percentile value pushes the lower bound forecast higher. This makes it more likely that the Standard Forecast will fall below the lower percentile, as you are effectively tightening the lower boundary upward.

Q: Why does the Generated Forecast sometimes fall outside the Probabilistic Forecast range?

There is always a statistical chance that any single forecast value falls outside a given probability range. Even with a very wide range (e.g., 1st to 99th percentile), outlier outcomes are possible. When this occurs, the Generated Forecast value can be considered a statistical outlier relative to the probabilistic distribution.

Q: What statistical assumption does Probabilistic Forecasting make about the data?

By default, it assumes a normal distribution. For skewed data, a normal distribution may not be the best fit — in such cases, quantile-based estimates are more appropriate.

Q: Why use a Probabilistic Forecast instead of the Generated Forecast?

Probabilistic forecasting produces multiple demand scenarios — standard, lower percentile, middle, and upper percentile — that can feed directly into S&OP and supply planning processes. Without it, planners typically create scenarios by manually applying an arbitrary percentage adjustment (e.g., ±X%) to the current forecast, which is less rigorous and less data-informed.

Q: What is the Standard Forecast in relation to the percentiles?

The Standard Forecast is loosely a weighted average across the percentile values, combined with other factors derived from sales history. It is not simply one of the percentile outputs but incorporates broader historical patterns.

Q: Does probabilistic forecasting replace the standard forecast?

No. The standard (deterministic) forecast remains your primary baseline. Probabilistic forecasting is a complementary analysis tool that helps you evaluate and, where appropriate, override or supplement the standard forecast.

Q: What forecast method does probabilistic use?

Probabilistic forecasting does not select a single method. Instead, it runs multiple forecast methods simultaneously, uses the variability across their outputs to build a probability distribution, and expresses the result as percentile bounds. The concept of a "best method" no longer applies — the distribution itself is the model.

Q: Can I run probabilistic at a different aggregation level than my standard forecast?

Yes. The partition setting controls the aggregation level for analysis and comparison. The underlying forecast is always saved at the tuple level (item, location, customer), so you can analyze at any level without changing the stored forecast data.

Quick Reference: Configuration Checklist

Before clicking Apply, confirm the following:

• Time bucket matches your Standard Forecasting configuration (monthly or weekly).
• Use Published Values setting is consistent with your standard forecast settings.
• Three percentile values are entered (e.g., 0.20, 0.50, 0.90).
• Calculation type is selected (Normal Distribution or Quantile Distribution).
• Partition level is set to your desired analysis level.
• (Optional) Global filter applied if running on a subset of data.

• Forecasting (original)
• Forecasting: Overview & Pyramid Strategy
• Forecasting: Adjusting the Pyramid Strategy
  
• Segmented Forecast Strategies: Up to 10 Segments