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![Forecasting Model Competitions
Competitions
Competitions
Long history
Long history
M3-Competition
M3-Competition
[Makridakis, Hibon 2000, IJOF]
[Makridakis, Hibon 2000, IJOF]
Total of 3,000 different test data sets
Total of 3,000 different test data sets
24 methods from many areas
24 methods from many areas
Explicit trend
Explicit trend
ARIMA
ARIMA
Expert systems
Expert systems
Neural networks
Neural networks
Results in aanutshell
Results in nutshell
Combinations of models do well !!
Combinations of models do well
Some M3-Competition results:
Parameters matter a lot !!
Parameters matter a lot Average symmetric MAPE for all data
Source: [Makridakis and Hibon 2000]](https://image.slidesharecdn.com/iio-111023051032-phpapp01/85/INVENTORY-OPTIMIZATION-8-320.jpg)
![Benefits I
Example: Electrical Tools Manufacturer, Spare Parts Inventory
High service levels despite reduced stock levels.
High service levels despite reduced stock levels.
[%] 100
Availability
Parts
99
98 €1,900,000.- Inventory Reduction (18%)
€1,900,000.- Inventory Reduction (18%)
[Mio €] 11 within 44months after go-live
within months after go-live
Inventory
10
9
10. 17. 28. 06. 13. 20. 27. 04. 12. 19. 26.
April May June](https://image.slidesharecdn.com/iio-111023051032-phpapp01/85/INVENTORY-OPTIMIZATION-28-320.jpg)
Uploaded byRajeev Sharan
INVENTORY OPTIMIZATION
1) Inventory optimization aims to maximize availability while minimizing costs through better demand forecasting and planning. 2) Accurate demand forecasting is key, and requires selecting the best forecasting models and optimizing parameters for each item. 3) State-of-the-art approaches use automatic, adaptive techniques to update models and parameters over time as new demand data becomes available.
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INVENTORY OPTIMIZATION
- 1. Inventory Optimization Inventory Optimization: BetterInventory Management through Forecasting and Planning
- 2. Company INFORM INFORMInstitute for Operations Research and Management INFORM Institute for Operations Research and Management Aachen, Germany Aachen, Germany Advanced Decision Support Optimization based on Operations Research, Fuzzy Logic Administrative IT Systems e.g. SAP and other ERP-Systems, Order Management, etc. IT Infrastructure e.g. DB/2, Oracle, e.g. TCP/IP, Databases / Connectivity Informix, Sybase UNIX, Windows, etc. First company ever to receive Enterprise Award First company ever to receive Enterprise Award by German Operations Research Society (GOR) by German Operations Research Society (GOR)
- 3. Company INFORM 250 Company Headquaters in Aachen, Germany Since 1986, more than 24% Since 1986, more than 24% 200 annual growth each year annual growth each year 150 1h 100 1.5h 2h 50 0 1969 4 1 2 3 2005 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Seattle Tokyo Monterrey Johannesburg Brisbane
- 4. Inventory Optimisation –Objectives Item = applicable to Finished Goods Maximising Item Availability Production Parts & Materials = Maximising Customer Service Aftermarket Spare Parts Plant Maitenance Spare Parts 100 % -20 % Return On Minimising Inventories Investment 0% Stock reduction financing IT investment by 100% ...500% Significant ROI without affecting any labour issues Storage & Capital Costs Discount Pricing Minimising Logistics Costs Transport & Receiving Planning & Administration
- 5. Inventory Optimisation: EssentialFunctions Inventory Inventory Demand Demand Controlling Controlling Planning Planning Demand Demand Forecasting Forecasting Capacity & Capacity & Line Item Line Item Network Network Inventory Inventory Optimization Planning Planning Planning Planning Optimization Supplier & Supplier & Shipment Shipment Consolidation Consolidation
- 6. Demand Forecasting Matters! Close to 100 % service level Stock the required stock value Value tends to 'explode‘ High quality forecasting enables High quality forecasting enables to meet any stock availability target asting to meet any stock availability target poo r forc •• with less inventories, with less inventories, sting •• and lower logistics costs! and lower logistics costs! good foreca Target Service Level
- 7. Factor 1: ForecastingModels Models & Methods Models & Methods Simple Moving Averages Simple Moving Averages Exponential Smoothing, e.g. Exponential Smoothing, e.g. No Additive Multiplicative Stationary series (SES) Stationary series (SES) Season Season Season Linear trend (DES) Linear trend (DES) No Trend Seasonal trend (Holt-Winters) Seasonal trend (Holt-Winters) Dampened trend Dampened trend Additive Trend Irregular demand (Croston) Irregular demand (Croston) Multiplicative Regression Regression Trend ARIMA / /Box-Jenkins ARIMA Box-Jenkins Strong theoeretical Strong theoeretical Patterns based on Pegels’ classification background but empirical for some exponential smoothing methods background but empirical results not as impressive results not as impressive Parameter selection not easy Parameter selection not easy Combinations Combinations
- 8. Forecasting Model Competitions Competitions Competitions Long history Long history M3-Competition M3-Competition [Makridakis, Hibon 2000, IJOF] [Makridakis, Hibon 2000, IJOF] Total of 3,000 different test data sets Total of 3,000 different test data sets 24 methods from many areas 24 methods from many areas Explicit trend Explicit trend ARIMA ARIMA Expert systems Expert systems Neural networks Neural networks Results in aanutshell Results in nutshell Combinations of models do well !! Combinations of models do well Some M3-Competition results: Parameters matter a lot !! Parameters matter a lot Average symmetric MAPE for all data Source: [Makridakis and Hibon 2000]
- 9. Factor 2: ForecastingParameters Parameter Adaptation Parameter Adaptation All methods require attention to All methods require attention to 'good' parameters 'good' parameters Hence, forecast quality strongly Hence, forecast quality strongly depends on parameter selection depends on parameter selection Some Examples: Some Examples: Original data series with noise Simple moving average forecast with different parameters DES forecast with better parameters
- 10. Auto Adaptive, DynamicForecasting State-of-the-Art Forecasting Models State-of-the-Art Forecasting Models •• coping with seasonal factors, trends, coping with seasonal factors, trends, stochastic outliers, and structural breaks stochastic outliers, and structural breaks Auto Adaptive Forecasting Auto Adaptive Forecasting •• Automatic adaptation of model & Automatic adaptation of model & parameters daily / /weekly parameters daily weekly (depending on data volume) (depending on data volume) Dynamic Forecasting Dynamic Forecasting •• Updating the demand profile every time Updating the demand profile every time new data becomes known new data becomes known Manual Intervention Manual Intervention Future Demand Profile Future Demand Profile •• Manual modification of forecast where Manual modification of forecast where applicable (e.g. sales promotions) applicable (e.g. sales promotions) •• Item substitution rules Item substitution rules
- 11. Inventory Optimisation: EssentialFunctions Inventory Inventory Demand Demand Controlling Controlling Planning Planning Demand Demand Forecasting Forecasting Capacity & Capacity & Line Item Line Item Network Network Inventory Inventory Optimization Planning Planning Planning Planning Optimization Supplier & Supplier & Shipment Shipment Consolidation Consolidation
- 12. Demand Planning I key-account structure r e warehouse ctu s tru .. n al regio .. .. distribution .. center a customer are storage ion reg .. variant product y .. ntr cou product group rld total product range wo product structure
- 13. Demand Planning II Multiple Hierarchical Dimensions Multiple Hierarchical Dimensions Permanent Planning Consistency Permanent Planning Consistency: : All changes will be automatically aggregated and All changes will be automatically aggregated and disaggregated within the entire planning hierarchy disaggregated within the entire planning hierarchy Alternative use of quantities or values Alternative use of quantities or values Efficient editing of distribution keys Efficient editing of distribution keys
- 14.
- 15. Inventory Optimisation: EssentialFunctions Inventory Inventory Demand Demand Controlling Controlling Planning Planning Demand Demand Forecasting Forecasting Capacity & Capacity & Line Item Line Item Network Network Inventory Inventory Optimization Planning Planning Planning Planning Optimization Supplier & Supplier & Shipment Shipment Consolidation Consolidation
- 16. Line Item PlanningI order quantity X Annual Costs maximum stock cumulative costs reorder point storage costs lead time total cost minimum ordering + handling costs Avg. Order Quantity Fixed Rythm / /Order Point Procedures Fixed Rythm Order Point Procedures Economic Order Quantity Procedures Economic Order Quantity Procedures •• Traditional method Traditional method •• Various heuristical algorithms Various heuristical algorithms •• Simple control mechanism (Kanban) Simple control mechanism (Kanban) •• Optimisation: Wagner Within Algorithm Optimisation: Wagner Within Algorithm •• Extended to cover discount schemes Extended to cover discount schemes
- 17. Line Item PlanningII Ordering / /Setup Costs Ordering Setup Costs Storage Costs Storage Costs Discounts / /Price Changes Discounts Price Changes Supply Calenders Supply Calenders Planned Service Level Planned Service Level Dynamic Safety Stock Dynamic Safety Stock Supplier Contract Specifics Supplier Contract Specifics Minimum Ordering Quantities Minimum Ordering Quantities Packaging / /Shipment Units Packaging Shipment Units Planning Focus Matrix etc. etc.
- 18. Inventory Optimisation: EssentialFunctions Inventory Inventory Demand Demand Controlling Controlling Planning Planning Demand Demand Forecasting Forecasting Capacity & Capacity & Line Item Line Item Network Network Inventory Inventory Optimization Planning Planning Planning Planning Optimization Supplier & Supplier & Shipment Shipment Consolidation Consolidation
- 19. Supplier & ShipmentConsolidation Order A Cost optimised ordering schedules Planning Planning by Line Item by Supplier Planning Order B by Shipment
- 20. Inventory Optimisation: EssentialFunctions Inventory Inventory Demand Demand Controlling Controlling Planning Planning Demand Demand Forecasting Forecasting Capacity& Capacity& Line Item Line Item Network Network Inventory Inventory Optimization Planning Planning Planning Planning Optimization Supplier & Supplier & Shipment Shipment Consolidation Consolidation
- 21. Capacity Planning Capacity compliantscheduling of production orders No setup time constraints, no order splitting, no sub-task planning Priority based capacity scheduling Identification of bottlenecks, and long-term capacity usage What-if scenario simulation capabilities
- 22. Multiple Tier NetworkPlanning •• The planning and supply relationship between different plants / /warehouses is modelled as The planning and supply relationship between different plants warehouses is modelled as aa'network' for inventory planning purposes. 'network' for inventory planning purposes. DC DC DC Plant Global DC Regio DC Regio DC Plant DC DC DC DC DC
- 23. Inventory Optimisation: EssentialFunctions Inventory Inventory Demand Demand Controlling Controlling Planning Planning Demand Demand Forecasting Forecasting Capacity & Capacity & Line Item Line Item Network Network Inventory Inventory Optimization Planning Planning Planning Planning Optimization Supplier & Supplier & Shipment Shipment Consolidation Consolidation
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- 26. IT Support forInventory Optimisation SCM SCM ERP // WMS ERP WMS Supply Chain Supply Chain Management Management Focus on data management Numerical Complexity suitable and information infrastructure only for key items very limited math. decision support (100 .. 1,000 <> 10,000 .. 500,000) (e.g. no auto-adapive parameters) Decision Making is often de-centralised (even within one company) Analytical Analytical APS APS Advanced Advanced Systems Systems Planning Systems Planning Systems Statistics, Data Warehouse, Math. Models right decision level Controlling, BI, etc. Coping with large data volumes Focus on 'insight' Day-to-day Operations Support no direct operations support Synchronised with ERP / WMS
- 27. Synchronised APS Systemadd*ONE Download add*ONE add*ONE Automatically released Items Server Night Processing Manually released items ERP System add*ONE Clients Capacity/ Capacity/ Demand Line Item Item Group Item Group Inventory Demand Line Item Network Inventory Forecasting Planning Planning Network Controlling Forecasting Planning Planning Planning Controlling Planning ... add-on complementing rather than replacing existing IT functions ... add-on complementing rather than replacing existing IT functions
- 28. Benefits I Example: ElectricalTools Manufacturer, Spare Parts Inventory High service levels despite reduced stock levels. High service levels despite reduced stock levels. [%] 100 Availability Parts 99 98 €1,900,000.- Inventory Reduction (18%) €1,900,000.- Inventory Reduction (18%) [Mio €] 11 within 44months after go-live within months after go-live Inventory 10 9 10. 17. 28. 06. 13. 20. 27. 04. 12. 19. 26. April May June
- 29. Benefits II Example: EnergyProducer, Plant Maintenance Inventory Return-on-Investment: Return-on-Investment: €€20,000,000.- just 77months after add*ONE go-live 20,000,000.- just months after add*ONE go-live 110,0 105,4 100,0 - 20 Mio € 90,0 85,5 80,0 70,0 Start add*One Start Zentrale Disposition 60,0 00 1 2 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 Q Q el itt M
- 30. Benefits III Return-on-Investment, Example: Small manufacturing company, Return-on-Investment, Example: Small manufacturing company, 30,000 line items spare parts inventory of ca. 77Mio. value 30,000 line items spare parts inventory of ca. Mio. value 3,25 Aufwand (Personen) 3 2,1 2,1 Planning Workload Staff Hours Reduced by 35% 88 85 85 Servicegrad (%) 0 1 Jahr 2 3 Service Level 80 (Materials & Parts Availability) Improved by 10% 6,91 0 1 2 3 Jahr Bestand (Mio. DM) 5,39 Inventory Level 4,91 4,82 Reduced by 1.52 Mio. = (22%) in first year; yielding pay-back of invested software within 3 months! 0 1 2 3 Jahr
- 31. add*ONE Selected ReferenceClients Linde Gas More than 90 companies More than 90 companies in Europe, Asia, South America in Europe, Asia, South America