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Demand planning provides you with several tools to generate forecasts for various scenarios or for specific parts of your business. Using historical data and scientifically based statistical algorithms, it allows you to improve the accuracy of revenue forecasts, align inventory levels with predictable changes in demand, and enhance profitability for a given channel or product.
We are all known about the statistical forecasting process in a standard manner, which means loading the sales data and sales history-cleansing process, finally, execute the statistical forecast methods identify the better forecast error method adopt to business data. This process may vary but an overall goal to achieve a consensus plan, during this process multiple business stakeholders involve like sales, marketing, region and global teams. This process deal with mid-long term planning (12,18,24 months)
Now SAP introduced a demand sensing algorithm to do a better forecast for short-term planning (4-8 Weeks).
Demand sensing is a forecasting method that leverages new mathematical techniques and near real-time information to create an accurate forecast of demand, based on the current realities of the supply chain. Gartner, Inc. insight on-demand sensing can be found in its report, "Supply Chain Strategy for Manufacturing Leaders: The Handbook for Becoming Demand Driven.
Traditionally, forecasting accuracy was based on time series techniques that create a forecast based on prior sales history and draws on several years of data to provide insights into predictable seasonal patterns. However, past sales are frequently a poor predictor of future sales. Demand sensing is fundamentally different in that it uses a much broader range of demand signals (including current data from the supply chain) and different mathematics to create a more accurate forecast that responds to real-world events such as market shifts, weather changes, natural disasters, consumer buying behavior etc
Demand sensing will support the tactical plan of business needs.
Demand sensing is a part of the SAP IBP demand module and it is a very effective impact on business to generate short term forecasts considering multiple inputs.
Basically, every company that wants to achieve a more accurate short-term demand plan can benefit from Demand Sensing. The use cases are widespread.
Below are some examples, e.g. companies that…
SAP Integrated Business Planning Demand Sensing is one of the best application in the market to cover short term forecast planning based on pattern recognition to improves the forecast accuracy and lower the inventory levels.
https://help.sap.com/viewer/feae3cea3cc549aaa9d9de7d363a83e6/2002/en-US/c4143c55a5ef9a2de10000000a17...
I would very much appreciate your comments and suggestions.
Best Regards,
lingaiahvanam
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We are all known about the statistical forecasting process in a standard manner, which means loading the sales data and sales history-cleansing process, finally, execute the statistical forecast methods identify the better forecast error method adopt to business data. This process may vary but an overall goal to achieve a consensus plan, during this process multiple business stakeholders involve like sales, marketing, region and global teams. This process deal with mid-long term planning (12,18,24 months)
Now SAP introduced a demand sensing algorithm to do a better forecast for short-term planning (4-8 Weeks).
What is Demand Sensing?
Demand sensing is a forecasting method that leverages new mathematical techniques and near real-time information to create an accurate forecast of demand, based on the current realities of the supply chain. Gartner, Inc. insight on-demand sensing can be found in its report, "Supply Chain Strategy for Manufacturing Leaders: The Handbook for Becoming Demand Driven.
Traditionally, forecasting accuracy was based on time series techniques that create a forecast based on prior sales history and draws on several years of data to provide insights into predictable seasonal patterns. However, past sales are frequently a poor predictor of future sales. Demand sensing is fundamentally different in that it uses a much broader range of demand signals (including current data from the supply chain) and different mathematics to create a more accurate forecast that responds to real-world events such as market shifts, weather changes, natural disasters, consumer buying behavior etc
Demand sensing will support the tactical plan of business needs.
Where Demand Sensing fit in SAP IBP?
Demand sensing is a part of the SAP IBP demand module and it is a very effective impact on business to generate short term forecasts considering multiple inputs.
Who Benefits from Demand Sensing?
Basically, every company that wants to achieve a more accurate short-term demand plan can benefit from Demand Sensing. The use cases are widespread.
Below are some examples, e.g. companies that…
- Struggle with volatile markets and demand shifts
- Already applied demand-driven business strategies but want to further improve
- Capable of adjusting their production plan within a short term horizon and want to further improve on
- Capable of adjusting their transport plan within a short term horizon and want to further improve on
- Want to close the gap between a monthly demand planning cycle and daily or weekly demand requirements for the short term horizon
What planning processes does the Demand Sensing impact?
Difference between Demand sensing and normal Statistical Forecasting?
Demand Sensing Proof of Concept Results–Two Examples
Customer who did not have a demand sensing process before
- Reduction in forecast error across the demand sensing horizon: 18% -49 %
- Weekly consensus demand forecast error across the demand sensing horizon: 50% -68%
Customer who had a demand sensing process before
- Reduction in forecast error across the demand sensing horizon: 4% -6%
- Weekly consensus demand forecast error across the demand sensing horizon: 40% -43%
How to run demand sensing the first time?
Conclusion
SAP Integrated Business Planning Demand Sensing is one of the best application in the market to cover short term forecast planning based on pattern recognition to improves the forecast accuracy and lower the inventory levels.
Additional Documentation
https://help.sap.com/viewer/feae3cea3cc549aaa9d9de7d363a83e6/2002/en-US/c4143c55a5ef9a2de10000000a17...
I would very much appreciate your comments and suggestions.
Best Regards,
lingaiahvanam
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