It is essential to continuously revisit and modernize your demand forecasting processes so they adapt to current market realities. This article outlines the basic yet often overlooked steps that need to be followed to ensure that your forecasting process reflects the latest operational setup, provides value with minimal waste and, most importantly, enhances your business’s bottom line.
1. Forecast Keys: What is The Right level To Forecast At?
Determination of the forecast keys ultimately needs to be tied to the organizational processes that demand forecasting serves. Historically, demand forecasting has been established along the product, location and period dimensions. It is important to evaluate multiple options that will achieve greater forecast accuracy:
- You might find that you can produce more accurate results when generating forecasts at the lowest level by having your model capture true consumer demand signals. In this case you can roll such forecasts up to the needed level.
- On the other side, due to sparsity of data, you might learn that forecasting at the higher level is a better option. In this case, to serve your downstream purposes, you can spread the data to the required level.
It is essential to revisit these decisions on a periodic basis. With marketing incentives being tied to CRM outputs, customer segment might be an important dimension for demand forecasting. In an omnichannel setting, you might find that extending your location dimension to geographic area might add more value. Therefore, continue to assess demand shaping activities and adjust your forecasting process accordingly.
2. Demand Drivers: What Inputs To Plan For?
Demand forecasting is becoming more sophisticated with Machine Learning algorithms. Machine Learning engines establish forecasting for total demand with a single approach, allowing modeling for direct impacts and interactions between the features. In addition to typical seasonality, holiday traffic and various promo incentives modeling, demand forecasting can also be enhanced by incorporating additional data feeds like demographics, weather, product images, etc.
Leveraging the latest algorithms is the path forward, however such conversion requires organizational effort despite the convenience of mining data externally. Forecasting output is only as good as the input data. It should be a coordinated effort across multiple teams within the organization including the product, placement, pricing, promotions and planning teams. Their input allows you to continuously feed good quality data into the forecast engine.
3. Forecasting Frequency And Period: How Often And Long To Forecast For?
The length of a forecast and its frequency are interrelated. It is known that forecasts further into the future are less reliable. Hence, forecasts need to be updated on a periodic basis. It is worthwhile to assess how often your demand driving inputs change and by how much:
- You can find that for some of your products, changes are so frequent and justifiable that you can outweigh the cost of forecasting on a daily or even hourly basis.
- On the other side, you can have another set of intersections in your mix with far more stable behavior where such frequent efforts provide absolutely no value.
Organizations can also exploit forecasting for a range of values rather than a single point to allow processes like ordering to function on the probabilities.
4. Forecast Snapshots: How Far In Advance To Freeze Forecasts?
As forecasts update continuously when moving closer to the forecast period, it is important to establish a cadence around taking forecast snapshots to be used for accuracy evaluation. All demand forecasting outputs used across multiple organizational processes should have their own snapshot. When the forecast frozen horizon is determined, it is too late to change your predicted quantity decision.
5. Forecast Accuracy: What Forecast Error Measurements To Use?
There are multiple forecast error measurements to use but all of them are essentially based on how far the forecast was from the actual demand. It is known that business impact from underforecasting can result in lost sales leading to customer dissatisfaction, while overforecasting leads to excess inventory costs, spoilage, etc. Yet organizations often blend under and overforecasting when aggregating errors, computing absolutes etc. You should “punish” your underforecast (negative error of forecast – actual) vs. overforecast (positive error of forecast – actual) separately:
Overforecast Cost*Positive Error – Underforecast Cost* Negative Error)/Actual Sales Units
The above is a basic conceptual expression of a formula that could be used for overall error results driven by the individual product & location aspects. With this approach you can better target the bottom line by tweaking your forecasting boundaries to be more tolerant to overforecasting in areas where lost sales are more damaging and favor underforecasting output when excess inventory cost and spoilage are not that affordable.
6. Forecast Accuracy Roll-ups: How To Action On Overall Results?
Organizations tend to measure forecast accuracy at the aggregate levels, while more actionable results are identified in the lower level mix. It is not helpful to know that out of the entire volume of products forecasted, these were matching to the actual sales units, but rather focus on making sure that the right products were available in the right locations at the right time.
When looking into the aggregated and properly weighted results, one can then do investigation by drilling down to the top worst offenders of high errors. This can lead to action items for reducing errors in areas having the highest financial impact.
7. Forecast Benchmarking: What Is The Right Yardstick To Compare Forecast Accuracy To?
For being one of the most common questions, unfortunately there is no one simple answer, as it really depends on the nature of the intersections needed to be forecasted. It is known that forecast results tend to be more accurate for higher sales volume and more stable demand, rather than sparse and very volatile intersections.
You should be setting different targets depending on forecastability, or ease of predicting accurate results depending on the historical behavior.
8. Forecast Streamlining: How To Focus Resources On The Value Add Areas?
It is recommended to classify forecasted intersections into importance/error-variation groups. Depending on organization size and assortment, you can establish as few as 4 quadrants, relying on integrated ABC-XYZ analysis, where ABC can represent contribution from margin, volume or other important characteristics and XYZ would be classified by historical accuracy, demand variability and related forecastability characteristics.
You can allow your forecast engine to produce forecasts automatically across all the intersections and have your resources to focus on more value adding activities.
9. Forecast Impact: Why Do End To End Simulations Matter?
In perfecting forecasting outputs, it is sometimes easy to get trapped into wasteful analysis. By analyzing your data, you might learn that for most of the very slow moving products, forecast values have very little impact and real effect is driven by other triggers. Hence, it always worthwhile to perform end to end simulation to understand the boundaries and associated thresholds beyond which intensification of forecasting efforts is wasteful.
10. Forecast Exceptions: When Is Automation Not Enough?
With all the power of forecasting algorithms feeding from multiple data sources, it is still important to establish a process for exceptions. Basic rules and sanity checks of what do not make sense based on historical data, typical user judgement and common observations would form a base for such exceptions. Center your exceptions around important activities, define your tolerance based on financial impact and actionability of a given exception. Having an exceptions mechanism in place serves as a system of checks and balances to make your forecasting a well-rounded process that can function automatically, yet identify the focus areas and alert the needed personnel.