Supply Chain Excellence

Mathieu Pourqué



D&PS Expert

Donia Rachdi

Regional animators

EMEA: Emma Glasson
LAM & NAM: Karina Tsuji
APAC: Lilian Cheong

Forecast Accuracy Champions

Aroma Performance: TBD
Novecare: Azize Aberre
Peroxides: Nicolas Delplanque
Silica: Christine Cipollone
Special Chem: Joerg Braunschweig
Soda Ash & Derivatives: Irina Piticas
Technology Solutions: Adele Fleming

General Description

The KPI  Forecast Accuracy (FA)  evaluates the ability   to get a visibility on customers' demand in terms of quantity.
As an input of S&OP process, reliable forecasts constitute the major way to improve the customer satisfaction via an optimized planning of operations. The Forecast Accuracy in this dashboard is calculated based on the Final Forecast and the Gross History. 
For a particular month, the Forecast Accuracy is calculated for the last 5 months forecast (M-1 ... M-5); allowing the possibility to evaluate the accuracy of the forecast with reference to several different forecast periods.
The refresh of the KPI is made every month on the 7th, including the full history.
It is available for all GBUs currently using Dynasys or APO as Demand Planning systems: Aroma Performance, Composite Materials, Novecare, Peroxides, Silica, Soda Ash&Derivatives, Special Chem and Technology Solutions.
 

Source Data in the GSCD


Source

Gross History

Final Forecast

Aroma

Dynasys

Demand History ETA

Last validated Forecast ETA

Silica

Dynasys

Demand History ETA

Last validated Forecast ETA

TS

Dynasys

Demand History ETA

(Sales Orders volumes in ETA)

Sales Team Forecast ETA 

Novecare

Dynasys

Demand History ETA

(Last Requested Delivery Date)

Last validated Forecast ETA

SA&D

Dynasys

Actual Shipped Qty in ETD

Last Validated Forecast ETD (Unconstrained Demand validated after demand review meeting)

Peroxides

Dynasys

NA: Shipped History ETD

Others: Demand History ETA

NA: Last validated Forecast ETD

Others: Last validated Forecast ETA

Spec Chem

Dynasys

Demand History ETA

Last validated Forecast ETA

Key figures

Weighted Forecast Accuracy: In addition to the Standard Forecast Accuracy, 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 of aggregation.

 

Weighted Forecast Accuracy = Sum[Max(0; 1- Abs((Final Forecast - Gross History)/ Gross History))1MonthWeight] / [Sum Gross History 1MonthWeight] x 100


Levels of aggregation are available with dynamic calculation for each level:

Material / Ship-to / Distribution ChannelProduct / Ship Destination zone / Group of activityProduct / Plant or
Product Hierarchy / Sold-To

Sales Rep / Product / Ship-to KA

or Product / Sales Rep

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


Standard Forecast Accuracy: This figure measures the gap between the Gross History and the forecasted quantities. To have a better representation of the forecast error, an absolute value of the forecast error is calculated.
This is also called the Mean Absolute Percentage Error (MAPE).

 

Standard Forecast Accuracy = Max(0; 1- Abs((Final Forecast - Gross History)/Gross History)) x 100

Forecast Accuracy = 100%, it means there is no gap between the Forecast and the Gross History
Forecast Accuracy = 0%, it means inaccurate forecasts
Forecast Accuracy M onth-2 is the time between produced forecast and the Gross History (FA June M-2: means forecasts done in April)

 

Forecast Bias ratio (%): 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(Gross History)/Sum(Gross History) x 100


If the Forecast is greater than the Gross History, then the Forecast Bias is positive (indicates over forecast)
At the opposite, if the Forecast is lower than the Gross History, then the Forecast Bias is negative (indicates under forecast)
A Forecast Bias = 0, indicates a total absence of gap (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.

Target Users

  • Global Supply Chain Managers
  • Regional Supply Chain
  • Local Supply Chain
  • Supply Chain Analyst
  • S&OP Manager
  • Purchasing Manager / Buyers
  • Supply Chain Excellence
  • Account Managers

Authorization & Rights

All the accesses must be validated by:
GBUs and Supply Chain Excellence

Request access to global SC dashboard by Service One ticket (Corporate Dashboard)

Glossary

Figures

Final Forecast = Total on quantity forecast to be shipped. Final Forecast is the forecast validated after the Demand Review (=Unconstrained Forecast)
Gross History = Customer Demand = Last Customer Request

Dimensions

Distribution Channel = Sales Distribution Channel
GBU Prod. Family = by default the Product Family, but also the Product Hierarchy, the GBU Material Group, or the Forecasts Family
GBU Zone = Mini-Zone = Group of Countries, Specific by GBU
Packaging Type = used on a material
Product Group (PGMI) = Group of products from Dynasys
Product Line 00 = Attribute of a material. There are 6 differents product line levels, corresponding to differents aggregations of products
Sales Rep = Sales Representative from Dynasys Sales Group = DFU owner
Ship-to KA = Ship-to Key Account, final account
Tactical Material = Concatenation of Product Hierarchy and Packaging

Full Dimensions Glossary here

Training
Link to the training material (G Slides)
Request training

How does it work?

1. Select Filters
On top of the dashboard, several different filters allow you to reduce the scope of data for the analysis

as well as Specific Filters to select in the dedicated box


2. Select the Forecast Accuracy calculation method
As explained above, the Forecast Accuracy is calculated according to:
    • the standard calculation at the detailed level Material x Distribution Channel x Ship-to
    • the weighted calculation at selected level within the 12 available ones in the drop-down menu (dynamic calculation at displayed level)
3. Select the reference period
On every 6th of each month, a snapshot of the Forecast is taken, keeping the reference of the forecast provided up to 5 months before. This will allow to calculate the FA for periods M-1, M-2, M-3, M-4, and M-5.
Click here to retrieve the naming conventions by system
4. Check the Forecast Accuracy table
After applying the filters, selecting an aggregation level and a reference period, there is still a way to customize a detail table with the desired Indicators and Dimensions
Please note that all dimensions don't match with all aggregation levels.
As a consequence, only the dimensions that are available for the selected aggregation level are displayed.
M ismatching dimensions are disabled to avoid calculation errors.
In the custom table, dimensions in the cyclic depend on the selected aggregation level. If a column is displayed twice because of the cyclic, just deselect the corresponding dimension in the table.
In the pivot table format (Click on the Fast change if you don't see it), the total is available for the 1st column. Move your columns to get the total for the dimension you wish.


Technical Documentation
Source: DYNASYS / APO (WP1) / APO (PF1) - QV_BW_QRY_MVDYN11_0005
Update: Monthly full reload (history included) on the night of the 6th and the 7th (minor corrections in Gross History may still occur from one month to another)
Technical BW Documentation: BW - DP - Forecast Accuracy (Core Query)
Naming convention by Source System (Dynasys vs APO)
Global SC Dashboard
Dynasys
APO (WP1)
APO (PF1)
Gross History
GH / Gross History
Demand History
History
Final Forecast
FF / Final Forecast
PreSOIP Plan
Consensus Forecast
Dimensions: Global Overview of Dimensions



Forecast Accuracy
Global Filters
Period
Month Year
Calendar Year/Month [0CALMONTH]
Organization
GBU
BFC GBU [CPFCTR1_2]
Group of Activity
PF1: Group of activity = [C_DYN_010] BU\Attributs\[CPFCTR2_2] BFC Group of activities
WP1: Manual Mapping based on [G_CWWE1] IECRA to get Group of Activity (MAPPING DYNASIS.xls)
Sub-Activity
PF1: Activity = [0G_CWWE01__C_MAGNITU] BFC Activity 1
WP1: Manual Mapping based on [G_CWWE1] IECRA to get the Sub-activity (MAPPING DYNASIS.xls)
Company
[C_DYN_018__C_COMPCDE] Company code
Plant
Zone
Main Shipping Plant Geo Zone [C_DYN_018__C_MPPLANT__C_GEOZONE]
Country
Main Shipping Plant Country [C_DYN_018__C_MPPLANT__C_0COUNTRY]
Plant
Main Shipping Plant [C_DYN_018__C_MPPLANT]
Ship Destination
Zone
Ship-to BFC Geo Zone [C_SHIPTID__C_ZONE]
Country
Ship-to Country [C_SHIPTID__C_0COUNTRY]
Corporate Group
Ship-to Corporate group [C_SHIPTID__C_CORPGR]
Ship Destination
Ship-to [C_SHIPTID]
Product
Product Hierarchy
Prod.hierarchy [C_MATNR2__0PROD_HIER]
Product
Com Prod / Mat Grp [C_MATNR2__C_PROD]
Bulk/Packed
N/A
Transport Mode
N/A
Specific Filters

Distribution Channel
Distribution Channel [0DISTR_CHAN]
GBU Product Family
Default: Product Family Code [C_MATNR2] Material\Attributes\[C_FMPRD]
Exceptions:
Soda Ash, Fibras:
WP1: C_MATNR2_C_LIP03
PF1: [C_MATNR2__0PROD_HIER] Prod. Hierarchy
Aroma, Silica = GBU Material Group
Special Chem = GBU Material Group (both WP1 & PF1)
Peroxides:
WP1:LIP2
PF1: Product_Hierarchy_ Cheops PIF (Missing- not possible)
Novecare, Technology Solutions:
WP1: Default
PF1: [C_MATNR2__0PROD_HIER] Prod. Hierarchy
Polytechnyl, Performance Polyamides, Alsachimie:
[C_MATNR2] Material\Attributes\[C_ACRI015] Forecasts Family


GBU Zone
[CGBUZONE__C_ZONEH2] Ship-to GBU zone 2
Exception for TS: [CGBUZONE__C_ZONEH1] GBU zone (hier .1) for TS Mining
No hierarchy used for Phosphorus Specialities
Material
Material [C_MATNR2]
Material Group
Material Group [C_MATNR2__C_MAT_GRP]
Packaging Type
Packaging Type [C_MATNR2__C_MAT_GRP]
Product Group (PGMI)
Product Group (PGMI) [C_DYN_005__C_GRPPGMI]
Product Line 00
Product line 00 [C_MATNR2] Material\Attributes\[C_LPROD]
Sales Rep
Sales Employee [C_DYN_021__C_DYN_065] 
Ship-to KA
Ship-to Key Account [C_GBR15_C_SHIPKA]
Tactical Material
Tactical Material [C DYN_005_C_TACTIC2]
Corporate Sold-To
[C_CORPGR] CRM Customer Corp. Group (PRS) 

Not able to find the solution? Contact SBS Support.

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