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Forecasting BASFs Custom Material Demand Using ABC/XYZ Analysis - - PowerPoint PPT Presentation
Forecasting BASFs Custom Material Demand Using ABC/XYZ Analysis - - PowerPoint PPT Presentation
Forecasting BASFs Custom Material Demand Using ABC/XYZ Analysis Team 7 Travis Greene, Patrizia Mach, Triguna Ashin Wijaya , Li-Cheng Pan BASF - chemical company Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im
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Business Problem
- Intermittent Demand
- Forecasting, demand planning and inventory management is key
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Forecasting Goal
Monthly 2-month ahead forecasts Quantity of units shipped per month
December January February March
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Project Workflow Overview
Remove negative demand Aggregate into “Monthly” demand Remove series without orders in test period 1 2 3 Data Cleaning Separate in ABC-XYZ Get test/RMSE for every group
Choose group forecast method based on best test RMSE
Refit full series and forecast based on desired horizon 4 5 6 7 Grouping Series Forecasting
Choose group forecast method based on best test RMSE
Refit full series and forecast based on desired horizon 6 7
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Data Description
Training: 10/2012 - 08/2017 (59 months) Testing 09/2017 - 08/2018 (12 months) Original data 108,324 rows, 826 materials, 73 months of data After cleaning 58,575 rows, 825 materials, 71 months of data
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Methods
Pareto Principle
ABC-XYZ Analysis
Importance Forecastability XYZ Analysis ABC Analysis
Results
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The best performing models tend to underpredict in January/April and overpredict in August
Evaluation
ETS test set forecast errors (A) August ‘18 Jan ‘18 April ‘18
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Deployment and Maintenance
Deployment through Business Analytics Department on R Shiny server. Employees to log in and check forecasts regularly.
Business Analytics Team R Shiny Server Factory Managers Production Decisions Maintain Log in Review Forecasts Annual Regrouping and re-evaluating forecasts of existing materials And grouping of new material products
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Recommendations
- Confirm whether 2018 order volumes
correspond to behavioral pattern change
- ETS model underpredicts for
January/April and overpredicts August
- Ensemble in current model limited to
seasonal naive
- Capacity estimate 90% of historical
high instead of actual facility threshold
Limitations
- Combine technical forecast with
production facility experience
- Weight “importance” by unit cost
- Tie in external material information
depending on nature of product e.g. steel price indices
- Experiment with additional forecasting
techniques in development e.g. variants of Croston’s methods
- Explore 2-month test set period