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INVENTORY OPTIMIZATION
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INVENTORY OPTIMIZATION

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    INVENTORY OPTIMIZATION INVENTORY OPTIMIZATION Presentation Transcript

    • Inventory OptimizationInventory Optimization:Better Inventory Management through Forecasting and Planning
    • Company INFORM INFORM Institute for Operations Research and ManagementINFORM Institute for Operations Research and Management Aachen, GermanyAachen, 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)
    • Company INFORM 250Company 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
    • Inventory Optimisation – Objectives Item = applicable to Finished Goods Maximising Item Availability Production Parts & Materials = Maximising Customer Service Aftermarket Spare Parts Plant Maitenance Spare Parts100 % -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
    • Inventory Optimisation: Essential Functions Inventory Inventory Demand DemandControllingControlling 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
    • Demand Forecasting Matters! Close to 100 % service levelStock the required stock valueValue 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
    • Factor 1: Forecasting ModelsModels & MethodsModels & 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
    • Forecasting Model CompetitionsCompetitionsCompetitions 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]
    • Factor 2: Forecasting ParametersParameter AdaptationParameter 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 noiseSimple moving average forecast with different parameters DES forecast with better parameters
    • Auto Adaptive, Dynamic Forecasting 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
    • Inventory Optimisation: Essential Functions Inventory Inventory Demand DemandControllingControlling 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
    • Demand Planning I key-account structure r e warehouse ctu s tru .. n al regio .. .. distribution .. center a customerare storage ionreg .. variant product y .. ntrcou product group rld total product range wo product structure
    • Demand Planning II Multiple Hierarchical Dimensions Multiple Hierarchical Dimensions Permanent Planning Consistency Permanent Planning Consistency: : All changes will be automatically aggregated andAll changes will be automatically aggregated and disaggregated within the entire planning hierarchydisaggregated 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
    • Demand Planning III
    • Inventory Optimisation: Essential Functions Inventory Inventory Demand DemandControllingControlling 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
    • Line Item Planning I order quantity X Annual Costsmaximum stock cumulative costsreorder point storage costs lead time total cost minimum ordering + handling costs Avg. Order QuantityFixed 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
    • Line Item Planning IIOrdering / /Setup Costs Ordering Setup Costs Storage CostsStorage CostsDiscounts / /Price Changes Discounts Price Changes Supply CalendersSupply Calenders Planned Service LevelPlanned Service Level Dynamic Safety StockDynamic Safety Stock Supplier Contract SpecificsSupplier Contract Specifics Minimum Ordering QuantitiesMinimum Ordering QuantitiesPackaging / /Shipment Units Packaging Shipment Units Planning Focus Matrix etc.etc.
    • Inventory Optimisation: Essential Functions Inventory Inventory Demand DemandControllingControlling 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
    • Supplier & Shipment Consolidation Order A Cost optimised ordering schedules Planning Planningby Line Item by Supplier Planning Order B by Shipment
    • Inventory Optimisation: Essential Functions Inventory Inventory Demand DemandControllingControlling 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
    • Capacity PlanningCapacity compliant scheduling of production ordersNo setup time constraints, no order splitting, no sub-task planningPriority based capacity schedulingIdentification of bottlenecks, and long-term capacity usageWhat-if scenario simulation capabilities
    • Multiple Tier Network Planning•• The planning and supply relationship between different plants / /warehouses is modelled as The planning and supply relationship between different plants warehouses is modelled as aanetwork 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
    • Inventory Optimisation: Essential Functions Inventory Inventory Demand DemandControllingControlling 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
    • Inventory Controlling
    • Inventory Controlling
    • IT Support for Inventory Optimisation SCM SCM ERP // WMS ERP WMS Supply Chain Supply Chain Management ManagementFocus on data management Numerical Complexity suitableand information infrastructure only for key itemsvery 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 SystemsStatistics, Data Warehouse, Math. Models right decision levelControlling, BI, etc. Coping with large data volumesFocus on insight Day-to-day Operations Supportno direct operations support Synchronised with ERP / WMS
    • Synchronised APS System add*ONE Download add*ONE add*ONE Automatically released Items Server Night Processing Manually released itemsERPSystem 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
    • Benefits IExample: 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
    • Benefits IIExample: Energy Producer, 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-live110,0 105,4100,0 - 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
    • 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 3,25Aufwand (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
    • add*ONE Selected Reference ClientsLinde Gas More than 90 companies More than 90 companies in Europe, Asia, South America in Europe, Asia, South America