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Sales forecasting is like any forecasting it depends on using the historical time-based data and the right formula. As for me I prefer to use the time series analysis, because it is reliable and it can reveal seasonal fluctuation patterns that can serve as the basis for future forecasts.
Do a trend anali of previous sales
We had to take previous history for sale of the product for as many years possible and as many regions possible , also we need to find out the market share for other competitors in the market with this we can know the total sale made for product and in that sale what was our market share. By this we can find out why our market share was less and what are the ways to increase the market share, after studying we can define the sale for the next season
Based on the last year sale.
Analysis of Sales History coupled with present year growth and customer spend trend analysis.
When it comes to Retail Supply Chain Management, people are beginning to understand that Demand Planning should start at store level. What we must now agree upon is that Demand Planning should also end at store level
Due to the financial situation all over the world its difficult to make such a move but i would make it at the same level of the current year expecting no increase and be ready for any changes would happen
Today the market is unpredictable, we must have updated knowledge about market. in purchasing we take past record to assess the future demand considering the seasonal and climatic change.
Accurate and strong database should be there to make proper analysis, incase of new product a proper demand of customer from sales department should be follow also have to make proper surveys.
with the advancement of IT we have huge opportunities are available especially social media which give immense reach for all kinds.
It’s all about forecasting. Factors causing seasonality could be holiday, vacation or weather e.t.c. For seasonal products, you need to calculate demand for the coming season by taking an average of the previous demand or usage history, say 6 months. A seasonal trend factor (whether a week day, quarter of the year or month) which states the predicted increase or decrease that occurred in the past year will then be applied
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