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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

  1. Forecasting BASF’s Custom Material Demand Using ABC/XYZ Analysis Team 7 Travis Greene, Patrizia Mach, Triguna Ashin Wijaya , Li-Cheng Pan

  2. BASF - chemical company ↑ Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im Folienmaster verwenden

  3. Business Problem ● Intermittent Demand ● Forecasting, demand planning and inventory management is key ↑ Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im Folienmaster verwenden

  4. Forecasting Goal Quantity of units shipped per month December January February March Monthly 2-month ahead forecasts ↑ Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im Folienmaster verwenden

  5. Project Workflow Overview Data Grouping Forecasting Cleaning Series 1 Choose group forecast 6 4 Remove negative Separate in method based on best test demand ABC-XYZ RMSE Refit full series and 2 7 Aggregate into 5 Get test/RMSE for forecast based on “Monthly” demand every group desired horizon Choose group forecast 6 method based on best Remove series 3 test RMSE without orders in test period Refit full series and 7 forecast based on desired horizon ↑ Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im Folienmaster verwenden

  6. Data Description Original data 108,324 rows, 826 materials, 73 months of data After cleaning 58,575 rows, 825 materials, 71 months of data Testing Training: 10/2012 - 08/2017 (59 months) 09/2017 - 08/2018 (12 months) ↑ Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im Folienmaster verwenden

  7. Results Methods XYZ Analysis ABC-XYZ Analysis Forecastability ABC Analysis Importance Pareto Principle ↑ Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im Folienmaster verwenden

  8. Evaluation ETS test set forecast errors (A) Jan ‘18 April ‘18 August ‘18 The best performing models tend to underpredict in January/April and overpredict in August ↑ Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im Folienmaster verwenden

  9. Deployment and Maintenance Deployment through Business Analytics Department on R Shiny server. Employees to log in and check forecasts regularly. Review Maintain Log in Forecasts Business R Shiny Factory Production Analytics Server Managers Decisions Team Annual Regrouping and re-evaluating forecasts of existing materials And grouping of new material products ↑ Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im Folienmaster verwenden

  10. Limitations Recommendations ● Combine technical forecast with ● Confirm whether 2018 order volumes production facility experience correspond to behavioral pattern ● Weight “importance” by unit cost change ● Tie in external material information ● ETS model underpredicts for depending on nature of product e.g. January/April and overpredicts August steel price indices ● Ensemble in current model limited to ● Experiment with additional forecasting seasonal naive techniques in development e.g. ● Capacity estimate 90% of historical variants of Croston’s methods high instead of actual facility threshold ● Explore 2-month test set period ↑ Zur Angabe der Klassifizierung (VERTRAULICH etc.) bitte dieses Textfeld im Folienmaster verwenden

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