Weighted Forecast Accuracy: In addition to the Standard Forecast Accuracy, | this measure takes into account the volumes delivered at the lowest level of granularity (Material x Ship-to x Distribution Channel (if any) ), then aggregates them a second method for calculating the Forecast Accuracy is available.This measure weights the Forecast Error based on the Gross History of the selected month, according to the selected level | with the weight on one (rolling) month of Actual Sales.of aggregation. Weighted Forecast Accuracy = Sum[Max(0; 1- Abs((Final Forecast - | Actual Sales | Actual SalesGross History))1MonthWeight] / [Sum | Actual Sales Gross History 1MonthWeight] x 100 | We currently have 12 levels
Levels of aggregation are available with dynamic calculation for each level: | 1. | Destination | -to / Distribution Channel |
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| 2. | 3.Product | Zone | Activity4.Product Hierarchy
5. | 6.Ship Destination Country
7.Ship Destination Zone / Sales Rep
8.or
Product Hierarchy / Sold-To | Sales Rep / Product / Ship-to |
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| - KA or Product / Sales Rep |
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| Timeframe | Operational purpose | All purposes | All purposes | Operational purpose | | Purpose | Measurement to review schedule/forecast accuracy correlation in relation to the schedule adherence at plant level Detailed reviews and Deep dive to understand the gaps | Measurement to review Forecast Accuracy at the Global S&OP meetings (Quarterly) Understanding the needs of a product in a given region Used in the E2E VC dashboard and at the GBU level | Measurement to review planning accuracy in Supply Reviews (actual correlation forecast/planning)
Proper procurement of forecasts. Impact on raw material planning
Review performance of work centers Note: Product Hierarchy/Sold-to is used for SpP and Peroxides also for their SIP reviews | Measurement for SIP targets: sales representative performance reviews | | Roles & Responsibilities | Responsible: none, is a KPI used for the details Accountable: Demand Planner/S&OP Mgr | Responsible: Demand Planner/S&OP Mgr Accountable: Sales Mgr | Responsible: Supply Planners/SC Site Mgr Accountable: Demand Planner/S&OP Mgr | Responsible: GBU SCE/Demand Planner Accountable: Sales Mgr |
Purpose: what can we really measure with each dimension/aggregation level in order to understand who is the Key Responsible to track this KPIs. Roles & Responsibilities: Responsible is considered the person who will look after this KPI in a monthly basis and Accountable is the person whose decisions can leverage a better insight and/or opportunities to improve the KPI Timeframes (Lag): they depend on how the business is structured and their standard leadtimes, eg. if we talk about a business mostly MTO driven with total replenishment lead times that last around 3 months then the operational purpose is at M+4, if instead it is a business mostly MTS driven M+2 will give an insight on operational purposes. Overall we can say that: M-n: Operational purpose, M-n+2: Procurement purpose and M-n+5: Workload/Contract purpose
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Forecast Bias ratio (%):Average deviation It measures the tendency for a forecast to be consistently higher or lower than the actual value.Forecast Bias is distinct from the forecast error, in that a forecast can have any level of error, but still be completely unbiased. It is calculated as the average deviation (over or below) of forecasts from actuals. Forecast Bias = Sum(Final Forecast)-Sum( Actual SalesGross History)/Sum( Actual SalesGross History) x 100 For Forecast Bias, we don’t take absolute value in the calculation, because the objective is to identify the positive or negative deviation
If the Forecast is greater than Actualthe Gross History, then the Forecast Bias is positive (indicates over forecast)At the opposite, if the Forecast is smaller lower than Actualthe Gross History, then the Forecast Bias is negative (indicates under forecast)A Forecast Bias = 0, indicates a total absence of Biasgap (bias)Forecast Bias is a “tracking signal” (positive or negative) and percentage can be above 100%
In many cases, it is useful to know if demand is systematically over- or under-estimated. For example, even if a slight forecast bias would not have a notable effect on store replenishment, it can lead to over- or under-supply at the central warehouse or distribution centers if this kind of systematic error concerns many stores. |
Exemple : For June, the FA M is the picture taken the 6th of July for the month of June (to capture the changes made on June). M-1 will the the picture taken the 6th of June for June (to capture the changes made on May)