Is your business taking account of the weather?

What if you knew today, that which would happen tomorrow?
By Topline Data Limited
www.toplinedata.com

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This year, 2002, has seen many UK manufacturers and retailers announce poor trading performance and profits due to the effects of the weather.  This tells us that many companies are making no plans to account for the weather and are probably unlikely to do so unless they know what can be achieved and how?

Weather is the single biggest external factor that affects consumer demand and therefore sales.

We know this statement to be true.  When companies forecast sales for the coming season or year, how do they approach the creation of this forecast?  Many will look at last year’s sales history and add an increase based on desire/expectation of the manufacturer together with the goals and aspirations of the retailer allowing for planned promotional activity.  This however, is not a skilled approach and takes no account of the weather and its effect.

Collaborative Planning Forecasting and Replenishment (CPFR)

Much is spoken about the above, but what is agreed and actioned between the two parties?

Planalytics of the USA has teamed up with Topline Data in the UK to add substantial value to historic Epos data that will enable manufacturers and retailers to effectively see into the future as far forward as 12 months and know:

• When a product season will start

• When a product season will peak

• And when a product season will end

The above enables manufacturers to determine the timing of production and the levels required, the introduction of additional or casual labour, the implementation and placement of field force.  Determine product pricing geared to optimum selling times, when to promote and the type of mechanic to use, depth of promotion, media engagement, financial planning and finally, the review process and the preparation of forecasts for the next season.

Planalytics unlike any other, has developed a process which has been widely used in the USA by both manufacturers and retailers such as SC Johnson and Wal-Mart. This information has been used to reduce wastage, improve efficiencies and deliver increased profit for the whole organisation.  Retailers Debenhams, ASDA and the Arcadia group have already embraced this process in the UK.  The same benefits are now being offered to manufacturers via Topline Data.

The process correlates predictable events in the jet stream with surface weather patterns. Taking Epos data for a minimum 2 year’s at store level, weather based demand indexes are created that most accurately forecast the sales pattern of the product or brand in question.  These weather based demand indexes are very accurate, do not need to be updated so provide a very stable basis for planning, and also identify the start of the season and naturally, identify the peak and the end.  This process is materially different from that of short-range weather forecasts, which only forecast out a few days, are not stable, and whose purpose is to the public at large.  These types of forecasts tell you what the immediate conditions will be like and therefore, what to wear, when not to travel, what social activities they may carry out this weekend.

Using this process, Planalytics accurately forecast that unseasonably warm weather would break in 2002 at the end of February/beginning of March and that this would create opportunities in a variety of markets.  The example below considers the gardening sector and well illustrates the benefits of employing this process.

The client

The client had reviewed 2001 sales and based on this created a forecast for 2002.  In doing so they had assumed that the season would start at exactly the same time and they desired an increase in sales of x%.  This was their basis for planning for this year.

It is a fact that the weather only repeats itself year on year, 25% of the time.  Taking this on board, the client embraced the Planalytics consumer demand forecast, which meant acknowledging that the season would start early and volumes would spike significantly.  As a result, the client had to order more volume, get stock into store earlier, make space available in the store and at the same time, remove other items where demand would not be so high.

The client also knew that they had limited promotional slots which were allocated to the summer and media spend had been reduced in that year.

The chart below illustrates the initial results.