Improving Forecast Accuracy by
Optimizing Statistical Forecasting Results
Usha Dasari, PhD - Ascend Performance Materials
Gijoy Mathew - Cognizant Technology Solutions
Learning Points
Review best practices in Statistical Forecast modeling
Discuss forecast accuracy improvement by using Multilevel Forecast runs
Review continuous improvement of Forecasting
Accuracy
Agenda
Overview
Ascend
Supply Chain Management Process Overview
Why Statistical Forecasting?
Implementation Approach
Continuous Improvement Plan
Q&A
ASCEND - Markets
ASCEND – Supply Chain
5 Manufacturing Plants
46 Warehouses
13 Sub Contractors
100 + Shipping Points
Products
Plastics
Chemicals
Polymers & Fibers
Supply Chain Planning – Process Overview
SAP Systems
SAP ECC
SAP SCM (DP/PPDS)
SAP CRM
Why Statistical Forecasting?
Statistical forecasting can be very helpful in managing Ascend’s maketo-stock planning scenario
Forecast accuracy at SKU level is essential to maintain Optimal
Inventory Levels and improve On-Time-Delivery
Ability to analyze historical data at various planning levels & tracking
Signal Analysis with the help of Excel tools
Combining Bottom-Up & Top-Down methods with proper disaggregation mechanisms to help improve forecast accuracy
Forecast accuracy measurement reports considering various error
factors and lead times
Implementation Approach
Data analysis of the product group and product
Forecast modeling approach at various levels of data
Forecast model selection using APO-DP and Excel for analysis
Select the best-fit model for each data element using “Tracking
Signal” methodology
Apply the best-fit model, first at the product level, followed by at
product group level for appropriate ratio of disaggregation
Publish the Statistical Forecast in APO-DP
Measurement of Forecast Accuracy
Improvement Path
3
2
Continuous Improvement
using Forecast accuracy
analysis and action plan
development
Best model selection
using Excel Tools
Historical Data
1 Analysis/Data Patterns
Data Analysis
Identification of Historical Data Pattern
Analyze for patterns like Trend, Seasonality,
Constant, Random etc.
Availability of history of sales
Pareto analysis
Pattern analysis - Trend,
seasonality etc.
Pattern same at Product
group and Product level
No
Yes
Bottom Up & Top Down Forecasting
Initial forecasting at Product level to
capture variation followed by forecast
run at product group level
Top Down Forecasting
Forecasting at Product group
level and disaggregate based
on last 6 months of history
Identify the History Pattern at Various Levels of the Data
Data Analysis
Historical Data Pattern
3500000
600000
3000000
500000
2500000
400000
2000000
300000
1500000
100000
0
0
200911
201001
201003
201005
201007
201009
201011
201101
201103
201105
201107
201109
201111
201201
201203
201205
201207
201209
500000
Product Group Level
200911
201001
201003
201005
201007
201009
201011
201101
201103
201105
201107
201109
201111
201201
201203
201205
201207
201209
200000
1000000
Product Level
Pattern at product group level is NOT similar to product level
• Step 1 :Forecasting happens at product level and data is aggregated to
product group level.
• Step 2 : Forecasting is carried out at product group level and data is
disaggregated based on statistical forecast values of step 1
Forecast Modeling Approach
Statistical Forecast is run in APO-DP with
Auto Model strategy (system provides
the best model fit and provides logs for
various factors)
Horizontal
Large data sample is available,
Values have equal weight & is not
exponentially decreasing
No
Exponential
Smoothing
No
Yes
Both trend test and
seasonal test negative
Yes
Horizontal or
Random
Random
No
Both trend test and
seasonal test positive
Yes
Moving Average
Crostons Model
Trend and
Seasonal
Holts-Winters
Method
Trend
Holts Model
No
Only trend test is
positive
Yes
No
Seasonal Pattern
Winters Method
Selection of Statistical Forecasting Model
Forecast runs using different
models and extract output
for analysis in spreadsheet
Check for seasonality, Trend
etc. at product level
Analyze the log of the
forecast run for different
parameters values
Iterative approach to find the
lowest MAPE value
Forecast Model Selection
Several iterations to arrive
at the lowest MAPE by
adjusting the forecast
parameters (α, β, Ɣ)
Test for model validity using
“Tracking signal” test.
Finalize the Forecast Model Parameters
Multi-level Forecasting Process
Forecasting model selection with relevant parameters for product
and product group
Forecast run first at product level to capture the SKU level
disaggregation based on history, followed by Product group level
such that the SKU level disaggregation ratio is maintained
Publish Statistical Forecast
Forecast Accuracy Analysis
Forecast Accuracy reporting using appropriate lag
Multi-level forecast Accuracy analysis
Improve forecast accuracy and provide more visibility to various
intricate challenges with demand forecasting
Observing the market demand indicators, economy, sales etc. in
statistical forecast correction
Comparison of Statistical forecast with the existing Sales forecast
showed an improvement of 19% (both compared to sales history)
Continuous Improvement Plan
User trainings/User involvements in Forecast model/parameter
selection
Optimize the history load and realignment processes
After achieving credibility, use statistical forecast as the base for
sales forecast
Alerts, tolerance levels for sales forecast in comparison with
statistical forecast
Q&A
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SESSION CODE: 4113
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