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Supply Chain Excellence

Carolina Camacho Serrano 

Forecast Accuracy Champions

Aroma Performance: Marie Pereira
Novecare: Jose-Pablo Leyva
Peroxides: Nicolas Delplanque/Benoit Hornyak
Silica: Jessica Israel-Hiles / ChunLe Dai
Special Chem: Joerg Braunschweig / Francois Belet
Soda Ash & Derivatives:  Geert Schodts
Technology Solutions: Aleksis Parfens

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

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. Check definitions by GBU here

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.

Aggregation levels: what is the purpose of each level?

Material/ Ship-to / Distribution Channel

Product / ShipDestination zone/ Group of activity

Product / Plant or

Product Hierarchy / Sold-To

Sales Rep / Product / Ship-to KA

or Product / Sales rep

Timeframe: 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



Timeframe: all purposes

Purpose: 

  • 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 



Timeframe: all purposes

Purpose: 

  • 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

Timeframe: operational purpose

Purpose: 

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

Roles & responsibilities: 

  • Responsible: Demand Planner/S&OP Mgr
  • Accountable: Sales Mgr

Roles & responsibilities: 

  • Responsible: Supply Planners/SC Site Mgr
  • Accountable: Demand Planner/S&OP Mgr

Roles & responsibilities: 

  • 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


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

Check the above definitions by GBU here


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)

WMAPE: Improvement of Forecast Accuracy Weighted formula

The Forecast Accuracy (MAPE) measures the accuracy of the forecasting figures. In Solvay the figures are based on the Final Forecast validated in the Demand monthly Reviews vs. the Gross History.  Forecast accuracy at the SKU level is critical for proper allocation of resources.

Weighted Forecast Accuracy (wMAPE) is a variant of MAPE in which errors are weighted. In the past we used to weight the values of actuals, which did not penalize the over forecasting cases. To overcome this situation, now we weight using the Total of Actuals and Forecasted volumes by the Aggregation Level (Material, SREP or product).


NEW Weighted Forecast Accuracy Formula 

WMAPE = MAPE x Weight

Forecast accuracy (MAPE) = (1- Abs((Final Forecast - Gross History)/Gross History))) for each line by aggregation level

Weight =  (Final Forecast + Gross History)/(Sum(Final Forecast + Gross History)) for each SREP, material or product



Forecast Error: Bias & Tracking Signal (NFM)

Forecast error measurement should serve a purpose. From a demand forecasting perspective, the purpose is to understand the planning process capability and identify products that are systematically the biggest error contributors and set actions to offset the effects of them. 

The objective is to introduce a framework for using Solvay’s forecast error metrics according to a structured process:

  1. Measure forecast deviation (PE (Bias)) 
  2. Diagnose the forecast error root causes (WMAPE, MAPE and Tracking Signal (NFM))
  3. Report the results (WMAPE, MAPE, Mean Bias)

Measure Forecast Deviation

Forecast Bias % (PE) = (sum(Final Forecast)- sum(Gross History))/Sum(Gross History) x 100

When to use: The conversion of forecast error into percentages, allows for comparison across products with different magnitudes of demand volumes. It is also used to indicate underforecast or overforecast. When the computed error is greater than 100% in either direction, the measurement result should be shown as it appears.

Diagnose the forecast error and root causes:

Normalized Forecast Metric = (Final Forecast - Gross History)/ (Gross History + Final Forecast))

When to use: as a diagnostic measure for finding forecast line items with the most significant forecast bias in a diverse group of products. It can be used regardless of their scales of volume or units of measure.

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.
Mismatching 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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