6 key components of an integrated sales forecasting solution

This post covers the 6 main pillars of any sales forecasting solution. Yes, it relates them to our solution, Prophecy. But they apply equally to any solution, not just ours, and therefore represent a blueprint for a sales forecasting solution.

Here’s a summary graphic:

Components of a sales forecast software solution
6 components of a comprehensive sales forecasting software solution

1. Multi-level forecasting

Multi-level sales forecasting means forecasting through time, over measures, across multi-level hierarchies of customers and products.

In other words, it lets you forecast at the level of hierarchical detail that exactly meets your need at the time. Bottom-up, top-down or middle and both ways.

It’s true that there is probably no substitute for forecasting at the sku level for the best accuracy. However, a top-down view and forecast override can help correct generally over-optimistic or over-pessimistic forecasts at the sku level.

And top-down saves potentially many hours of manual work when flexing Budget.

Prophecy supports bottom-up, top-down and middle and both ways multi-level forecasting, subject to each user’s security setting.

2. Multi-user forecasting

Forecasting is seldom a single-user activity. Frequently it is the major account sales people who are the active forecasters – after all, they know most about what is going on in their customers.

Concurrent multi-user access to a forecasting solution is a technical challenge. Spreadsheet based solutions face this major hurdle.

Without a single-source, shared forecasting solution you get:

  • Cumbersome manual consolidations
  • Integrity issues with multiple individual documents
  • Maintenance issues – e.g. adding new products, actuals etc..
  • Delays in the forecast / review / re-forecast cycle

Prophecy has supported multiple user, concurrent read-write access to a single, shared database from day 1.

3. A statistical forecasting engine

Business forecasting in the real world doesn’t always lend itself to statistical forecasting. Peaks and troughs caused by promotions, competitor activity, distribution and listing changes and other non-recurrent factors don’t reliably happen at the same time each year. Statistical algorithms don’t ‘know’ about them.

In addition, adding one new period of actuals to the history used by the statistical algorithm surely adds a lot less value to the new forecast than the up-to-date specialist market and customer knowledge of the business forecaster. Please see our white-paper on the pros and cons of statistical forecasting here.

On the other hand, statistical forecasting may be useful as a starting point for an an initial forecast. For example, when you’re initially forecasting a new year. It may also be useful in applying the Pareto principle – where 80% of skus typically account for just 20% of sales. These smaller-selling items can be forecasted with statistical forecasting and reviewed on an exceptions basis via periodic accuracy analysis.

Prophecy has long had a core statistical forecasting engine. It generates forecasts in batch, on demand or as a way of calculating the value added factor of human forecasting.

However, with effect from February 2019, Prophecy statistical forecasting received a major ‘power-up’. It now enables Prophecy users to run of all the forecasting algorithms in the open source R framework for statistical computing, either in ‘Automatic’ mode (forecaster requires minimal statistical / zero R knowledge) or ‘R-Expert’ mode (which provides ‘to the metal’ access to all elements of the full R framework).

As a result, Prophecy enables the generation of statistical forecasts on a par with the very best statistical forecasting systems.

4. Multi-level revenue and profit forecasting

If your forecasting solution cannot integrate volume forecasting for production and inventory control with financial forecasting, how do you know whether what’s planned to sell will deliver the financial forecast?

At a minimum, the volume plan must feed through selling prices and standard costs into revenue and margin forecasts. Ideally, volume, revenue and margin forecasting will be handled in the same solution, removing latency and giving an instant financial forward view.

It probably won’t be relevant (or practical) to analyse revenues and margins at sku and customer level – way too much detail. So using a multi-level view of the financial measures through time is the way to go. Start, for example, at a product group and customer sector level. Then drill through the hierarchies to identify issues or opportunities.

In Prophecy, depending on the measures you configure in your database, you can view revenue, margin or any other measure at any combination of product and customer level through time. For every volume forecast there is a revenue and margin forecast. Prices can be imported or manually forecasted and standard costs are imported automatically. Prophecy handles the hierarchical solves, in real time, providing an always-on view of the financial data broken down over the full hierarchies.

5. Analysis and control

Sales forecasting generates A LOT of numbers! Especially when you’re extending to revenues and margins over large hierarchies of products and customers. You need a solution which lets you tame this data overload and helps you compare the evolving forecasts with previous years, Budgets and Plans.

Prophecy uses a multi-dimensional reporting and graphical environment which lets you flexibly create your own reports and forecast update screens. These screens support drilling though the hierarchies as well as ‘data-driven’ product and customer selections. (An example of a ‘data-driven’ selection is “retrieve the data for products which are currently forecasted at 10% or more below Budget”.)

Prophecy supports a ‘rolling’ forecast for the standard forecasting cycle – i.e. a new forecast version is published each month, with a backward view of (say) the previous 11 versions. In addition, any number of ‘ad-hoc’ versions can be created – for Budgets, Targets or What-If explorations.

Prophecy’s powerful analysis and reporting environment supports full comparatives between all these versions, such as current forecast version versus a Budget.

Prophecy also provides an optional forecaster / reviewer management work-flow cycle.

6. Plan Trade Promotions

Itemised Trade Promotions are very important to many businesses. For these businesses, it’s important to be able to handle the forecasting of these promotions from within their overall forecasting solution.

Prophecy has an optional Trade Promotions Planning module that lets your forecasters plan and manage trade promotions, and to analyse each promotion’s planned and actual incrementality within Prophecy. It aims to provide an appropriate level of business benefit from Trade Promotions Planning, without the very considerable implementation and maintenance complexities and expenses associated with the more full-blown Trade Promotions systems available.

Conclusions

This post has identified the 6 major elements of a viable sales forecasting software solution. One that services all the business processes and helps everyone plan and control the future from within a single, consistent, robust solution. If you’re looking to cover these bases in your new sales forecasting software solution, Prophecy is a great place to start your search!

5 methods for measuring sales forecast accuracy

Why Measure?

Failing to learn from your forecasting mistakes makes you a lot less likely to become a better sales forecaster.  Measuring accuracy should be a positive thing (and definitely not a stick for beating sales forecasters with).

However, calculating accuracy data over hundreds, possibly thousands of items can be a real challenge – especially finding a balanced ‘single number’ measure for accuracy.  Here, we explain the different alternatives of calculating accuracy available in Prophecy, Data Perceptions sales forecasting software solution.  They are mostly well-known methods for getting to a single, summary number that describes the overall accuracy of a group of many separate forecasts.

Exceptions Analysis

Before we get to exceptions analysis, let’s remember that summary measurement is useful for tracking accuracy over time.  Exceptions analysis – identifying and explaining the reasons for the biggest / most expensive forecast errors – arguably has a bigger payoff.  It provides a valuable opportunity to learn from mistakes and apply the lessons of experience to current and future forecasts.

Therefore, step 1 in accuracy analysis should be a process for rapidly identifying the exceptions – the big deviations that caused the most problems.  Prophecy’s Accuracy Analysis control dialog lets you specify an exception percent and only items exceeding this limit are listed in the resulting detail report:

Step 1 is to review these exceptions and try to explain them.  Could the reasons have been anticipated?  Forecasting is constrained to skilfully playing the hand the forecaster was dealt at the time the forecast was made.  What’s to learn from reviewing the exceptions?  Clearly, if better information or interpretation was available at the time the forecast was made there is a lesson to apply to future forecasts.

Moving on to obtaining a single number to describe forecast accuracy, there are several common measures and a couple of deviated ones.  Prophecy’s Accuracy module calculates them ‘for free’ – Excel forecasters will need a little more work but they are mathematically simple.

The important thing to bear in mind when looking at all these measures is that they measure what they measure – i.e. simple maths.  They DO NOT measure the real impact on your company’s supply chain or sales, revenue and profit.

Weighted Average % Error

This statistic calculates a percentage deviation by dividing the sum of the absolute, unit deviations by the sum of the actuals, as illustrated in the following example:

The ‘simple’ % error, 15%, shown in the ‘TOTAL’ line does not allow for a mix of over and under-forecasts.  They cancel each other out and are therefore misleading.

The weighted average method effectively weights the absolute variance by the size of the actual.  This gives a more appropriate indication of overall forecast accuracy relative to volume, but assumes a product selling twice as much has twice as much impact.  This is not necessarily the case.  For example, where one item has a very high price and the other a very low price.  In that case, consider measuring accuracy using revenue instead of quantity.

However, if you’re trying to measure supply chain impact, both quantity and revenue can be misleading.  Sku-1 could have a long lead time, Sku-2 a short lead time.  If they sell the same and are the same price, how can accuracy measures measure supply chain impact?

Alternate Weighted Average % Error

This calculation is similar to the Weighted Average % Error.  However, instead of dividing by the sum of the actuals as shown in the example above, it divides by the sum of the forecast or actual for each line, whichever is greater.

This means that actual=0/forecast=100 has the same weight as actual=100/forecast=0.  For two items with these forecasts and actuals, the weighted percent is 200% (misleadingly high?) whereas the Alternate Weighted percent is 100% (perhaps closer to ‘correct’ because both items were effectively 100% out?).

The Alternate Weighted Average % approach was suggested by a Prophecy customer and is thought to be non-standard.  Nevertheless, it has the stated advantages over the more usual calculation.

Mean Absolute Percent Error (MAPE)

This is a commonly used measure in sales forecasting.  It is the mathematical average of the individual percent errors, ignoring their signs, as shown in the cell outlined in red here:

It is a moot point whether to divide by 3 or 4 in the example shown, where Sku-4 has no actual.  If you divide by 4 you are rewarded with a lower MAPE than dividing by 3, but if you divide by 3 you are effectively ignoring Sku-4!

In addition, this method gives equal weight to a percent deviation, irrespective of the relative size of the forecast or actual.  An item that sells a million has the same weight as one that sells 10.

Mean Average Deviation (MAD)

This measure definitely has the most appropriate acronym!  It is calculated from the mathematical average of the individual unit deviations, regardless of their sign:

The advantage of MAD over MAPE is that instances like Sku-4, where there is no actual, are included in the measure.  The same percentage error on a high volume item has a much bigger effect on the result.  This may or may not be desirable where, for example, the high seller is low value and the low seller is high value.

In addition, unlike a percent measure, it can be hard to know if the MAD is good or bad – it is just a number.

Conclusions

As with so many areas of sales forecasting, there is no right answer or single ‘best’ measure that can be used to describe sales forecasting accuracy.

The first and most beneficial purpose of accuracy analysis is to learn from your mistakes.  In that sense, exceptions analysis has the highest return.  The summary measures are helpful for tracking cumulative improvement over time and are control barometers.

Author: Peter Boulton, Data Perceptions.