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Virtually every manufacturing or service company needs to generate forecasts of their short to medium term sales. Being able to forecast demand more accurately has major commercial advantages, whether the forecast is used:* to plan purchasing, production and inventory,* as the basis of marketing or sales planning,* or for financial planning and reporting or budgeting.

Yet within real world markets, many factors conspire to make accurate forecasting difficult to achieve.

In the first place, sales forecasts are frequently used for all the purposes suggested above. This leads to conflicts between optimism and pessimism and potentially introduces 'political' influences into the process. Examples are the different role of the profit forecast (probably conservative) and the sales plan (probably optimistic), or where marketing expenditure is closely associated with the turnover of brands (and therefore leads to defensive forecasting to protect planned marketing spends). There are also conflicts in terms of which units should be forecasted - orders-based for production forecasting and invoice-based for financial forecasting.

Similarly, forecasts by week by sku (stock keeping unit) for the next 12 weeks may be required by production planning. But, this time horizon is far too short and this level of detail is potentially much too great for marketing and sales planning purposes.

The important point is to have a clear vision of who the primary Customer or Customers of the forecasts are. Select the appropriate level of detail and time horizon accordingly and accept that secondary customers will probably have to accept sub-optimal forecasts. In many situations it is helpful for both Marketing and Sales to generate sales forecasts. Sales are often more likely to possess the detailed short term knowledge whilst Marketing need to 'own' the forecasts as a result of their role as brand profit 'custodians', and possibly have a clearer knowledge of longer term influences. It is vital here that each area is clear about the role and purpose of the forecasts they produce, and that issuance schedules optimise the currency of the data used as inputs, and given as outputs, by each forecaster.

The second major difficulty of forecasting in real world markets is the very nature of these markets. They frequently exhibit some or all of the following characteristics:* Frequent promotional activity* High level and variety of competitor activity* Promotions are seldom at the same time each year* The size of the distribution 'pipeline' tends to vary* Growing concentration in sales to biggest customers* Fluctuating positioning at point of sale - between 'value' (i.e. low prices) and 'added value' (i.e. quality)

In essence, the dominant characteristic of real world markets is probably "NEVER THE SAME THING TWICE".

This makes it hard for traditional forecasting approaches such as statistical methods to provide acceptable results over a short to medium time horizon.

CHOOSING THE RIGHT FORECASTING METHODOLOGY

All statistical methods either even out the peaks and troughs in sales history to produce trend-based forecasts, or else they look for repeated patterns in the historical peaks and troughs to make future forecasts.

However, if the peaks and troughs in the sales of real-world products are caused by what are often 'random' events, such as promotions or competitor activity, how can statistical methods help you forecast? On the one hand, a smoothed forecast has little value if the primary purpose for forecasting is to predict the short term sales peaks and troughs. On the other hand, how valid is the second approach given the random nature of historical peaks and troughs?

If you cannot use statistics, what can you use? In the majority of situations, informed judgment (or 'finger to the wind' as cynics might describe it) is actually more likely to produce better results within real-world markets.

The essence of judgmental forecasting is the application of the business manager's knowledge and interpretation of past events and activities, and their effects on sales, to planned future events and activities. The result is a 'judgmental' forecast for the future sales periods.

The key factors to consider are fairly well known:* Trade promotions* Launch / relaunch activity* Promotions / special packs* Historical out of stocks* Distribution changes* Seasonality (if relevant)* Competitor activity* Advertising effect* Market trends

Although there is never the same thing twice, developing and using an understanding of how sales respond to different types and combinations of events is the most effective way of generating a forecast. It has spin-off benefits too, because it forces marketing and sales people to think long and hard, and hopefully objectively, about which factors really drive their sales.

The method most likely to succeed is forecasting from the 'bottom up', and reviewing from the 'top down'. This means generating the forecasts at the lowest (relevant) level of detail using the process described above : the 'bottom up' method. One then compares how the resulting forecasted year on year growth rates and Moving Annual Totals compare to expectation, historical or current growth rates and Moving Annual Totals. If the 'bottom up' results are out of line with the 'top down', then the 'bottom up' forecasts need to be revisited to identify the sources of the difference.

This process must continue until the 'top down' and 'bottom up' forecasts are consistent.

CHOOSING THE RIGHT SYSTEM

The forecasting methodology recommended in this article places a lot of emphasis on the knowledge and judgment of the forecaster. This is unavoidable given the nature of the market, but it follows that developing a good forecast is a labour-intensive process.

Computer systems can help here, by providing the forecasters with a productive and flexible environment in which to analyse and manipulate numbers. A lot of companies use spreadsheet based systems. Some use systems that have been developed to run via terminal emulation on their corporate midrange or mainframe machines. Finally, some use the an option from their ERP (Enterprise Resource Planning) system.

None of these approaches are ideal.

Spreadsheet based systems are generally difficult to maintain, in terms of adding new products or customers, updating actuals or rolling forward years. They also tend to show the data in fixed views due to the fixed rows and columns structure of spreadsheet programs. Some analytical capability can be introduced by building clever spreadsheet macros, or by users reformatting data in different ways within their spreadsheets, but this approach tends to be clumsy and labour intensive. In addition, aggregation of data across products and customers tends to require considerable manual processing.

In addition, spreadsheets are essentially single-user productivity aids, whereas forecasting is normally a multi-user activity. Delays and inaccuracies get introduced through the need for consolidation of spreadsheets. One change can require the whole, cumbersome process to be repeated.

Terminal / browser-based based midrange or mainframe systems and ERP options overcome the maintenance problems but tend to be inflexible, and do not provide the variety of instant graphical views that a PC based system makes possible. In addition, such systems can sometimes have performance problems - where transaction processing systems and decision support systems operate on the same host, transaction processing systems necessarily get preference in receiving processor time. In addition, it is hard to give these systems the degree of user-friendliness which sales and marketing users generally prefer.

Therefore, whilst these traditional approaches offer elements of the ideal approach, one really needs a system which combines the ease of maintenance and robustness of the mainframe / ERP approach with the speed, flexibility, graphics and user friendliness of the PC.

Nowadays, PC based systems which meet this need are available. Here is a checklist of features to look out for:

* Can you customise the system for your market and needs - in terms of facts, periods, product and customer levels etc.?

* Does the system give you the ability to input forecasts at different levels of product or customer detail and have the changes recalculated 'up' and 'down' the product and customer hierarchies?

* Does the system allow you to capture qualitative information too?

* What caused the historical peaks and troughs? What was the forecaster's rationale for this forecast?

* Does the system allow you to store and analyse different sets of forecasts through the year?

* Does the system give you forecasting accuracy analysis?

* How flexible is the reporting and analytical engine offered by the system? Can you store and replay favoured views of the data?

* How flexible and helpful are the graphs included?

* Can the system run in a true networked environment, or support remote forecasters?

* How user friendly is the system? How much on line help is available?

* Does the system have any options which analyse forecasts and warn forecasters of risks identified versus previous sales history?

* Check data interchange with your corporate systems – how easy is it to keep the system up to date with latest sales actuals and send the forecasts back to the corporate systems?

* Can the system take you beyond volume based forecasting to overall customer account planning, profitability, budgeting? Maybe you can combine and integrate the processes of forecasting, budgeting and medium term planning within a single process / business application?

If you do not already have an information analysis tool for users to ‘slice and dice’ through sales to pick up trends etc., expect some of this functionality from your forecasting and planning system!

-Would you like your forecasting system to work over the web? Is ‘forecasting over the web’ an option with your software? What about securely exposing segments of your forecasts to your suppliers over the internet? If not now, maybe you will need this in the future.

Conclusion

Forecasting in the real-world is a difficult process which does not lend itself to automated statistical approaches. The so called 'finger in the wind' / ‘judgemental forecasting’ method, if carefully implemented and with appropriate systems support, can yield quality improvements in forecasting results.

You need a good system, forecasters who really understand their markets, and above all, the strongly held determination to put it into practice.

About The Author

Peter Boulton is the Managing Director of Data Perceptions, the developer of Prophecy, an easy to use collaborative sales forecasting software solution for real world business users.

Visit http://www.DataPerceptions.co.uk to learn more about sales forecasting in business and how Prophecy addresses the complex issues involved.

This article was posted on March 01, 2005some content courtesy ArticleCity.com

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