In this article we are going to define forecast accuracy, why it is important, how to use the right metrics for each case, what factors can influence it and how it can be monitored.

Forecast accuracy, which is also often referred to as forecast accuracy, is a process within the supply chain whose main objective is to facilitate decision-making and the way in which they impact inventories, purchases, production, transportation, financial margins and customer service.

Its value does not lie in determining a number itself, but in how that number can help make important decisions. If a highly accurate forecast doesn't help reduce stock failures or reduce excess inventory, it's useless.

What is forecasting accuracy?

Forecast accuracy measures how accurate the forecasts generated are in relation to actual sales. This calculation allows us to evaluate how well the planning process is working and allows us to improve future supply and production decisions.

Its purpose is not only representative, but it must be useful for the operation. For example, 90% accuracy may sound like a good thing, but if the error occurs on a high-selling product (a major SKU), it can cause stock failures, customer discomfort and a drop in sales. In short, what is important is not the absolute value of precision, but its real impact on the operation.

On a daily basis, accuracy is measured as a balance between forecast, reality and the costs associated with the error. A forecast with a low level of error that does not capture market trends or changes in promotions may be less useful than one with greater absolute error but greater accuracy.

Why is forecast accuracy important?

A well-executed forecast accuracy can be beneficial at different levels of production:

1. More efficient inventory management allows:
   
   • That stock failures are reduced and availability guaranteed.
   • A reduction in excess inventory, the avoidance of fixed capital and the reduction of storage costs.
   • That there be less waste of perishable and short-lived products.
2. Tighter operations favor:
   
   • That the planning of purchase volumes is more accurate.
   • That production can be adapted to meet real demand.
   • Optimize routes and logistics planning to reduce variability
3. Reliable supply planning helps:
   
   • Improve coordination with suppliers
   • Plan production capacity.
   • Reduce safety mattresses.
4. Planned financial management allows:
   
   • Develop realistic budgets.
   • Allocate resources based on projected demand.
   • Limit systematic overestimates or underestimates.
5. Customer Service Improvements:
   
   • Guarantees product availability.
   • It increases consumer satisfaction and confidence.
   • It strengthens the loyalty of customers and business partners.

Having an accurate forecast generates internal efficiency, providing competitiveness and sustainability. Fewer planning errors result in fewer resources being wasted, greater responsiveness, and improved market position.

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What does a good accuracy rate look like?

There is no single formula that can be used to define if a percentage of accuracy is “good”. The appropriate precision will depend on the context:

• Level of aggregation. Aggregate forecasts (by chain, region or category) are often more accurate and individual errors compensate for each other. When the variability is greater and the accuracy is usually lower.
• Time horizon. Forecasts can be short-term or long-term. The former, which consider days or weeks, are usually more accurate than the latter, which take months and years. This happens because uncertainty increases over time.
• Sales volume. The higher the sales volume, the lower the variability. When products are low in turnover, a small error can represent large percentages.
• Market stability. More stable businesses allow for more accurate forecasts; in markets that are naturally dynamic, accuracy is more difficult to sustain.
• Use of the forecast. Not all decisions require the same level of accuracy, so the use of the forecast may vary. For daily in-store replenishment, granularity is needed; for weekly capacity planning, an added forecast is sufficient.

Let's look at a practical example:

• A beverage category can show an MAPE of 5% at the aggregated level.
• The same calculation, SKU by SKU, can give 30%.

Both calculations are correct, but the first is for raw material purchases and the second for making replacement decisions in the store.

What factors influence the accuracy of your demand forecast?

The factors that cannot be lost sight of when calculating the forecast can be of two types, depending on how much influence the company may have on them:

Internal factors

These are the factors that are under the company's control and can be most easily monitored.

1. Data quality.
   
   • The forecast can be disrupted by incomplete data, errors in data capture or the lack of adequate stock records.
   • Clear, consistent and up-to-date data favors the process.
2. Collaboration between areas.
   
   • Demand planning must consider all parties involved in a comprehensive manner.
   • It must be integrated into sales, marketing, operations, finance, etc. to obtain accurate results.
3. Forecasting methods.
   
   • Time series: to consider issues of seasonality and trends.
   • Causal models: to incorporate external variables.
   • Collaborative forecasting: to consider the vision of the different areas.
   • Artificial Intelligence and Machine Learning: to identify complex patterns and promote improvements.

External factors

The accuracy of the demand forecast is also conditioned by elements that are beyond the company's control. Among the most relevant:

• Seasonality and market trends:
  The ups and downs associated with different seasons (summer, Christmas, etc.) or to long-term consumption trends can generate deviations in accuracy if they are not taken into account.
• Economic conditions:
  Local macroeconomic situations, such as inflationary processes, changes in interest rates or in the purchasing power of buyers, have a direct impact on the accuracy of the forecast.
• Unexpected events:
  Some situations such as health crises, weather phenomena or new regulations can affect demand abruptly, altering results.
• Specific exogenous variables:
  Competitive actions, product changes or marketing campaigns can affect demand behavior and should be considered in the forecast.

All of these factors create uncertainty that cannot be eliminated, but that can be managed with constant monitoring and timely adjustment.

How to calculate the accuracy of your forecast?

Forecast accuracy is generally expressed as 1 — error (%), but to make the measurement accurately, different types of specific metrics are used. Each metric offers a different way of viewing the forecast and must be chosen considering the objective of the analysis. Let's look at the main formulas, how they are calculated and how their results can be interpreted.

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MAPE (Mean Absolute Percentage Error)

• Formula:

This formula measures the average percentage error in relation to the actual sale and is useful for comparing accuracy between products with different sales scales. However, this calculation can artificially increase the error in products with low turnover, and it doesn't work if there are no sales.

A representative example may be:

• Forecast = 120 units, Actual sales = 100 units
• Percentage error = |120 — 100|/100 = 20%
• This calculation must be repeated over several periods and averaged, that result is the MAPE.

WMAPE (Weighted Average Absolute Percentage Error)

• Formula:
  

This formula measures the error as a percentage of the total sales volume, but it takes into account the importance of each SKU according to the level of sales, so it is weighted, since it weighs the level of sales of each product. This calculation reflects the economic impact since it gives more relevance to the products with the highest sales volume.

• Being based on the level of sales, this formula can mask large errors in low-volume products.

For example:

• SKU A: Forecast = 120, Actual = 100 → Error = 20
• SKU B: Forecast = 55, Actual = 50 → Error = 5
• Total error = 25, Total sales = 150 → WMAPE = 25/150 = 16.6%

BIAS (Prognostic Bias)

• Formula:
  

This formula indicates if the forecast tends to overestimate or underestimate the demand.
It is a useful calculation for identifying systematic biases in planning, but it can hide major errors when compensating (over and underestimates).

An example might be:

• Forecast total = 1,050 units, Actual sales = 1,000 units
• BIAS = 1050/1000 × 100 = 105%
• In this example, the company is consistently overestimating demand by 5%.

MAE (Mean Absolute Error)

• Formula:
  

This formula measures the average error based on absolute units, no matter if it was overstated or underestimated, it is ideal to apply to specific SKUs because it is expressed in the same units that are sold, but it does not differentiate between large and small errors, they all have the same weight.

An example of this case:

• Period 1: Forecast = 120, Real = 100 → Error = 20
• Period 2: Forecast = 180, Real = 200 → Error = 20
• Period 3: Forecast = 150, Real = 140 → Error = 10
• MAE = (20 + 20 + 10)/3 = 16.6 units

RMSE (Root of Mean Square Error)

• Formula:

This case measures the magnitude of the error, giving greater relevance to large errors, and is useful when errors generate very high costs. It is very useful when it comes to perishable products or products with expensive storage. However, it can be difficult to interpret because it is not in percentage but in units.

Example:

• Errors: 20, 20, 10
• RMSE = √ [(20² + 20² + 10²) /3] = √ [(400 + 400 + 100) /3] = √ (900/3) = √300 ≈ 17.3 units.
• In this example, errors tend to be ~17 units, but large deviations are more penalized.

Key note on the calculation level

It is very important to be able to define At what level are metrics calculated:

• Un MAPE added by category It can give 3%, but if you average the Individual MAPEs by SKU, the result can be 30%.
• Both calculations are correct, but they must be applied depending on the objective:
  
  • Un MAPE added by category works to make purchasing and production decisions.
  • Los Individual MAPEs by SKU they work to make replenishment decisions and detailed planning.

It is crucial to choose in an appropriate way the formula to be applied according to the business process that you want to optimize.

What's the best way to measure the accuracy of your forecast?

The choice of one metric over another will depend on the type of product, the objective of the forecast and the level of decision where you want to apply it. Below we will see what the main metrics are, how to use them correctly and some examples to understand in which situations each one should be used.

1. To give more importance to what sells the most = WMAPE

• El WMAPE It is the most appropriate when seeking to evaluate the real economic impact of errors, since in this formula an error in a high-volume product weighs more than in a low-turnover one.
• When to apply it:
  
  • To make an overall assessment of a particular category.
  • To track the performance of the planning process.
  • To identify which are the most relevant items due to their economic impact.
• Example: If the forecast fails on a SKU with 1,000 sales, that error should matter more than if it happens on one with 5 sales.

2. To find out the average error in the same units you sell = MAE

• El MAE express the error in absolute units, showing how many units on average are left out of the calculation in each period.
• When to apply it:
  
  • To do a review of critical SKUs with operational impact.
  • To compare between forecast models in the same product.
  • When you need to talk about units and not about percentages.
• Example: If the MAE is 25, it means that, on average, each week the forecast deviates by 25 units for a given SKU.

3. To compare the error between products with very different sales = MAPE

• El MAPE standardises the error as a percentage, making it possible to compare products with different sales scales.
• When to apply it:
  
  • When you want to compare different categories.
  • When you want to communicate numbers to other sectors that are not familiar with formulas, since it's easy to understand.
  • In cases where the products have regular sales.
• However, it can be reliable in products with few sales or when there are periods without sales.
• For Example: A 15% MAPE on a SKU means that, on average, the forecast differs by 15% from the actual sale.

4. To heavily penalize large and unexpected errors = RMSE

• El RMSE Square large errors, making an atypical deviation weigh more heavily on the result.
• When to apply it:
  
  • It is ideal for use with perishable products, where excess leads to waste and high costs.
  • In logistics with expensive storage.
  • In cases where a single significant error can seriously damage the operation.
• Example: If you normally fail in 10 units, but once you fail in 100, the RMSE will reflect that extreme deviation with more weight than the MAE or MAPE.

5. To find out if your forecast is consistently optimistic or pessimistic = BIAS

• El BIAS shows if the forecast is systematically overestimating or underestimating demand.
• When to apply it:
  
  • When you want to monitor the trend in planning teams.
  • To identify chronic biases.
  • To evaluate and make manual adjustments to the forecast.
• Example: A BIAS of 110% means that, on average, the forecast is always 10% above real sales.

Practical rules for choosing the right metric

• Choose WMAPE to measure financial impact.
• Usa MAE when you want to speak in “units” and facilitate understanding.
• Utilize MAPE to compare between products.
• Usa RMSE when big mistakes are more costly than small ones.
• Calculate the BIAS to uncover over- or underestimation biases.

The ideal is Don't rely on a single metric, but rather combine them according to your objectives. Some possible combinations include:

• For a strategic forecast: WMAPE + BIAS.
• For an operational forecast: MADE + RMSE.
• To benchmark between categories: MAPS.

Common errors in forecast accuracy

Often the low accuracy is due to How is forecast measured, how is it interpreted and how is it connected to processes. These are the most common errors:

1. Measuring “beautiful numbers” disconnected from the real process

Often aggregated metrics are calculated that show a “very accurate” forecast, but that do not reflect daily reality. As a result, there is confidence in an unrealistic precision, which favors the persistence of errors.

For example: A 5% MAPE at the chain level may sound great, but if the errors per SKU in the store exceed 30%, there will continue to be stock failures.

2. Get obsessed with the model instead of reviewing operating parameters

Sometimes, sophisticated algorithms are sought when the problem is in how the system is configured.

Por example: A forecast with AI can predict 1,020 units, but if the minimum purchase lots are 500 and the presentation stock is 200, the accuracy of the model is obsolete.

3. Compare precisions between companies or products without normalizing

When direct comparisons of MAPE or WMAPE are made without considering the horizon, level of aggregation or formula used, misleading benchmarks are obtained that can lead to poor strategic decisions.

For example: To say that “company A has a forecast accuracy of 90% and B of 70%” doesn't mean anything if A measures on a monthly level and B on a daily basis by SKU.

4. Do not separate the components of the forecast

Often the forecast is analyzed as a whole, without distinguishing what part comes from the base model, what corresponds to promotions, what corresponds to events or manual adjustments, etc. This concludes that the symptoms are corrected and not the causes.

For example: a promotion that increases demand by 200% can make it appear that the model failed, when in reality the problem was that it was not reported or it was not considered that there was a promotion.

5. Not considering proper granularity in the measurement

Sometimes a level of calculation is chosen that does not correspond to the decision that needs to be made, so the metrics cease to be guides for the operation and become reports.

For example: a weekly forecast can show good accuracy for raw material purchases, and not serve to replenish stores day by day.

6. Ignore the effects of external events

If accuracy is evaluated without contextualizing external changes, the model is overadjusted for atypical events and performance under normal conditions is affected.

For example: the model cannot be blamed for a 50% error in the sale of masks during a pandemic, when the deviation is due to an unplanned event.

7. Don't regularly update or review results

When metrics are calculated once a year or sporadically, errors accumulate and the forecast ceases to be a reliable tool.

For example: A forecast accuracy that seemed stable may deteriorate slowly due to changes in the product mix, but it is not detected in time because there is no monthly monitoring.

What to do when the forecast accuracy is low?

Changing the model is not enough if the forecast does not reach the expected level of accuracy. The ideal is identify what is going wrong: the data? , the methods? , integration with the business? , the way to measure?.

Correcting the problems that these faults may be causing is possible. The most effective steps to do this are:

1. Evaluate data quality

It is important to review the integrity, consistency, and availability of the analyzed data. It can be corrected:

• Including records of out of stock and lost sales.
• Applying cleaning rules for extreme values.
• Ensure that the capture of promotions and discounts is aligned with sales.

2. Diagnose internal and external factors

It is necessary to identify if the errors come from internal variables (promotions, prices, changes in assortment) or external variables (climate, inflation, health crises). For example, a forecast that underestimated demand in summer did not have a problem with the model, but temperature was not incorporated as a variable. This can be corrected by taking some forecasts:

• Mapping internal and external factors in a business calendar.

3. Evaluate forecasting methods and models

Recurrent evaluation is necessary to review whether current models are suitable for each product's pattern. It may happen that a simple model can work for mature and stable products, and fail on items with intermittent demand.

This can be corrected:

• Using models that capture seasonality, trends and short-term patterns.
• Combining statistical methods with technology such as AI and machine learning in complex scenarios.
• Adjusting the models according to the product.

4. Incorporate expert judgment and qualitative data

It is important to allow the sales and operations team to complement the forecast with insights not reflected in the data.

This is why it is important to have case procedures in which manual interventions are applied to:

• Avoid individual biases.

• Measure its influence on accuracy.

5. Improve internal collaboration and communication

It is necessary to synchronize the areas of sales, marketing, finance and operations, etc. so that they all work on the same forecast.

This will prevent that if the marketing team launches a promotion and does not inform the planning team, the forecast fails.

Establishing regular consensus meetings, aligning calendars and having shared platforms in real time help correct these errors.

6. Monitor and adjust the forecast

It is necessary to maintain a continuous measurement cycle and to act by exception rather than reviewing everything every time.

How is it corrected?

• Defining metrics.
• Automating regular calculations.
• Segmenting items by importance and applying different tolerances.

When accuracy is low, it's not just about “improving the model”, but about Strengthen the entire system, including data, methods, collaboration and measurement. Thus, the forecast ceases to be an isolated number and becomes a reliable planning tool.

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How to monitor your forecast accuracy in your demand planning?

The key is in the continuous monitoring and in translating metrics into real decisions. A functional control system does not search for numbers, but rather to detect relevant deviations in time to be able to correct them.

Some of the most effective practical practices are:

Define your metrics and goals

There is no need to measure everything, you have to establish clear indicators according to the business objective.

For example:

• If your goal is to reduce stock bankruptcies, a WMAPE < 20% is more relevant than a low MAPE.
• If you are looking to eliminate biases, control BIAS within a range of 95— 105%.
• If large errors generate critical (e.g. perishable) costs, monitor RMSE.

2. Establish an automatic routine

Having an automated routine is essential to obtain calculations on a recurring and automatic basis to free up and reduce the number of operational tasks. Ideal values can be defined and alert systems that are activated when metrics fall outside those values.

To implement it you can:

• Set up automatic reports in your ERP or planning tool.
• Use scripts or dashboards to refresh metrics without manual intervention.
• Design alerts by exception: the planner only attends to what is out of control.

3. Segment your analysis

Not all products require the same control, so the effort must be concentrated where the error has the greatest impact.

How to implement it? Build a matrix that combines value and stability, and assign different metrics to each group.

4. View on a dashboard

Translating metrics into clear graphics will allow you to identify trends and prioritize actions. A graph will allow you to quickly see:

• Detours.
• Evolution of the error over time with curves,
• Error ranking.
• BIAS map to see about or systematically underestimate.
  To implement it, there are native tools or dashboards in planning software.

Adequate monitoring of the forecast will promote learning and will facilitate decision-making. This will cause models to be recalibrated, biases detected early and the team spends less time calculating and more time calculating analyze and correct.

Conclusion

Forecast accuracy is decisive in demand planning, but not an end in itself. Its value lies in being able to use it when making inventory, production, logistics decisions, etc.

Demand Forecasting Accuracy FAQs

What method makes demand forecasts more accurate?
The best results come from combining time series, causal models, AI techniques, and expert judgment of the areas involved.

How to improve the accuracy of forecasts?
With quality data, appropriate methods, inclusion of external variables, collaboration between areas and continuous monitoring.

What are the best KPIs for forecast accuracy?
The most used are WMAPE, MAE, RMSE and BIAS, applied at the appropriate aggregation level.

How to calculate forecast accuracy in Excel?
Applying MAPE, WMAPE, MAE, RMSE and BIAS formulas by period and SKU, aligned with the current forecast at the time of the decision.

How does forecast accuracy impact inventory management?
Higher accuracy reduces stock failures and waste. However, if some parameters are ignored, the improvement may have a limited effect.

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