forecasting basf’s custom material demand using abc/xyz

Forecasting BASF’s Custom Material Demand Using ABC/XYZ Analysis

Team 7Travis Greene, Patrizia Mach, Triguna Ashin Wijaya , Li-Cheng Pan

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BASF - chemical company



Business Problem

● Intermittent Demand● Forecasting, demand planning and inventory management is key

https://www.lucidchart.com/documents/edit/9491905c-7bbf-4591-a3b5-bac7c87c92e3/0?callback=close&name=slides&callback_type=back&v=701&s=605.7401642085999



Forecasting Goal

Monthly 2-month ahead forecasts

Quantity of units shipped per month

December January February March



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



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



Methods

Pareto Principle

ABC-XYZ Analysis

Importance

Fore

cast

abili

tyXYZ Analysis

ABC Analysis

Results



The best performing models tend to underpredict in January/April and overpredict in August

EvaluationETS test set forecast errors (A)

August ‘18

Jan ‘18April ‘18



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 inReview Forecasts

AnnualRegrouping and re-evaluating forecasts of existing materialsAnd grouping of new material products

https://docs.google.com/file/d/12wI7xtueTr5-t-nod6l6jwCJFAheoLxT/preview



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

forecasting basf’s custom material demand using abc/xyz

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