Forecasting & Replenishment with IBM DIOS. Dynamic Inventory Optimization Solution

Transcription

1 Forecasting & Replenishment with IBM DIOS Dynamic Inventory Optimization Solution

2 Retail Business is getting Bigger, Faster, Better... My Business is Growing: More Products More Stores Higher Sales My Business is Speeding Up: Shorter Product Life Cycles My Competitors are Getting Better: Lower Margins Higher Quality Standards 2

3 Planning should improve at the same speed The Retail Planning Dream: Forecasting is done daily for all products in all stores parameters are continuously adjusted to be optimal automatically handles trends and seasonalities properly accounts for all past and future promotions Replenishment Planning is done daily for all products in all stores parameters are continuously adjusted to be optimal yields a very high availability at lowest total costs properly accounts for bundles, franco limits, MOQs,... 3

4 That Dream can come True! Example Max Bahr (German Retailer Baumarkt ) 88 Stores Products per Store on average Product Availability increased from 93,5% to 99,1% More than 90% Automatic Replenishment Order Generation (Planners can focus on exceptions) Daily Forecast, Inventory Optimization and Replenishment Order Generation for all stores + central warehouse Less than 4 Hours Total Runtime including POS data download and Planning Results Upload 4

5 Featuring: IBM Dynamic Inventory Optimization Solution Joint development IBM Research & IBM Global Business Services since 1998 More than 40 client engagements Retail, distribution, raw materials, spare parts, CPG, Automotive, electronics, chemical, pharmaceutical, Integrations into SAP, Baan and other ERP / legacy systems Uses Best-of-Breed High-Speed Algorithms for Forecasting Order Quantities Safety Stocks 5

6 DIOS Demand Forecasting Mathematical Forecasting Model Aggregation Level & Integration Hierarchy Historical Demand Data External influences (promotions, new products, ) 6

7 DIOS Demand Forecasting How DIOS calculates demand forecasts: Uses the outbound transactional data Supports all common forecasting methods On the fly data cleansing (e.g. removal of promotion effects) Individual forecast time buckets for each SKU Provides Pick Best Forecast including determination of optimal forecasting parameters Forecast generation based on rolling forecast buckets provides an updated forecast each time a forecast is calculated independent of the size of the time bucket fast reaction time even for large time buckets Variable Forecast Horizons 7

8 DIOS Demand Forecasting 8

9 Benefits of Rolling Forecasting Buckets: Fast Reaction Time Qty 50.0 # Holt's Smoothing w ith trend (alpha = 0.2, beta = 0.6), forecast accuracy: Day 1 demand: 0 Qty 60.0 # 50.0 Holt's Smoothing w ith trend (alpha = 0.2, beta = 0.5), forecast accuracy: Day 2 demand: Jun Mar Dec Jun Mar Dec 2004 Qty 60.0 # 50.0 Holt's Smoothing w ith trend (alpha = 0.2, beta = 0.6), forecast accuracy: Day Day 3 Qty 60.0 # 50.0 Holt's Smoothing w ith trend (alpha = 0.2, beta = 0.7), forecast accuracy: Day Day demand: demand: Jun Mar Dec Jun Mar Dec 2004 Day Gradual built-up of a local negative trend Day 9

10 DIOS Modeling of the Replenishment Process Article Bundle Lot Sizes Promotion Planning Franco Limit Logic Pretermination of Articles Article Distribution Supermarkets Minimum stocking factors per inventory class Demand Generation new locations/ Demand Aggregation Central Whse Reports Order Proposals Exception Lists Statistics Log-Files Aggregated Replenishment Planning Logic Central Warehouses Replenishment Planning Logic Supermarkets Data Interfaces Data Base Access. Example: DB2 Access via JDBC 10

11 Modeling of the Replenishment Process - Example 11

12 Continuous Inventory Management Inventory should be managed continuously Smoother replenishment Use of rolling forecast buckets Forecast errors have less impact Forecasts can be renewed and adjusted every day Faster reaction to changes in demand Continuous inventory Management not only means the use of a reorder point but also continuously updating all replenishment parameters for each SKU 12

13 DIOS 3-Phase Realization Approach Phase 1: Assessment 6-8 Weeks 1-2 Stores / Warehouses Setting Optimal Replenishment Parameters Evaluation of Savings Potential Typical Finding: 20% 25% Cost Savings Phase 2: Pilot 2-4 Months 2-3 Stores / Warehouses Confirm Savings Potentials Phase 3: Roll-Out 4-8 Months Depends on number of locations Integration into existing ERP System Education & Training of Planners / Buyers Parameters Settings for all Locations Guidance, Support, Change Management 13