Forecasting customer demand is one of the critical processes within the business world, both for manufacturing and distribution companies. This process is a critical step to achieve a good level of service at reasonable costs based on an appropriate level of inventory.
Forecasting customer demand guide decisions about which items to buy including when and how much, every business needs reasonable accuracy to define an efficient plan to meet customer requirements prognosis.
The reality is that no prediction can achieve 100% accuracy. Predicting directly or indirectly future demand involves trying to predict uncontrolled variables that somehow influence the behavior of sales, such as economic variables, climatic, legal regulations, competitor activities, preferences and consumer trends, new products and activities promotional. No one can expect someone to be able to predict the behavior of these variables all the time.
At the same time, the lack of accuracy in forecasting sales is costly, causes excess inventories, exhausted, low productivity, poor level of service, and usually friction between the sales-marketing-manufacturing and distribution in any business.
Then raise the level of accuracy is a mission impossible?
In a highly competitive world today, most companies of all sizes use an integrated information system (ERP) These systems allow planning and monitoring daily operations, also provide basic functionality to forecast customer demand, without But in most cases, these tools do not meet all the requirements for a proper prognosis the business.
Understanding the need to increase forecast accuracy, companies tend to pay a high cost to use the knowledge of senior executives in sales and marketing in data collection for sale and then place it in a Lotus or Excel and develop the forecast, a complex process that usually takes several days.
In many cases 95% of meeting time is used in collecting data and calculating odds, the remaining 5% is inviertenen in evaluating the results. As a result of this situation the same proportion seen in the results, 95% of the time with a poor outcome and 5% with one lucky. Many companies accept with resignation this situation and do little to improve the quality and accuracy of sales forecasting, unnecessarily wasting precious resources.
But Why companies are resigned to accept these results where possible streamline the forecasting process to increase the quality and accuracy of the forecast?
Obviously not an easy road and like everything has a cost benefit. So how to justify the investment? Symptoms such as high inventory levels, exhausted, a poor level of service and the word urgent may be the best justification. But if still not convinced, do the following exercise to systematically calculate where it could reduce inventory levels increase security if accuracy in prediction.
By raising between 10 and 15% accuracy in forecasting will be clear to you that the effort will be worthwhile.
In the next two installments will look like two Australian companies managed to implement and streamline their forecasting process, Cardinal Health and Freedom Furniture.
Forecasting customer demand guide decisions about which items to buy including when and how much, every business needs reasonable accuracy to define an efficient plan to meet customer requirements prognosis.
The reality is that no prediction can achieve 100% accuracy. Predicting directly or indirectly future demand involves trying to predict uncontrolled variables that somehow influence the behavior of sales, such as economic variables, climatic, legal regulations, competitor activities, preferences and consumer trends, new products and activities promotional. No one can expect someone to be able to predict the behavior of these variables all the time.
At the same time, the lack of accuracy in forecasting sales is costly, causes excess inventories, exhausted, low productivity, poor level of service, and usually friction between the sales-marketing-manufacturing and distribution in any business.
Then raise the level of accuracy is a mission impossible?
In a highly competitive world today, most companies of all sizes use an integrated information system (ERP) These systems allow planning and monitoring daily operations, also provide basic functionality to forecast customer demand, without But in most cases, these tools do not meet all the requirements for a proper prognosis the business.
Understanding the need to increase forecast accuracy, companies tend to pay a high cost to use the knowledge of senior executives in sales and marketing in data collection for sale and then place it in a Lotus or Excel and develop the forecast, a complex process that usually takes several days.
In many cases 95% of meeting time is used in collecting data and calculating odds, the remaining 5% is inviertenen in evaluating the results. As a result of this situation the same proportion seen in the results, 95% of the time with a poor outcome and 5% with one lucky. Many companies accept with resignation this situation and do little to improve the quality and accuracy of sales forecasting, unnecessarily wasting precious resources.
But Why companies are resigned to accept these results where possible streamline the forecasting process to increase the quality and accuracy of the forecast?
Obviously not an easy road and like everything has a cost benefit. So how to justify the investment? Symptoms such as high inventory levels, exhausted, a poor level of service and the word urgent may be the best justification. But if still not convinced, do the following exercise to systematically calculate where it could reduce inventory levels increase security if accuracy in prediction.
By raising between 10 and 15% accuracy in forecasting will be clear to you that the effort will be worthwhile.
In the next two installments will look like two Australian companies managed to implement and streamline their forecasting process, Cardinal Health and Freedom Furniture.
