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SUCCESSFUL INVENTORY PLANNING REQUIRES A NEW APPROACH Presentation at GSK October 15, 2002.

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Presentation on theme: "SUCCESSFUL INVENTORY PLANNING REQUIRES A NEW APPROACH Presentation at GSK October 15, 2002."— Presentation transcript:

1 SUCCESSFUL INVENTORY PLANNING REQUIRES A NEW APPROACH Presentation at GSK October 15, 2002

2 ©2002 SmartOps Corporation 2 Fundamental, persistent forces behind supply chain inefficiency:  Inability to accommodate and actively manage inevitable uncertainty and increasing complexity across multistage supply chains  Local vs. global (“total cost”) optimization and incentives – uncoordinated inventory and logistics decisions within enterprises, across supply chains  Underutilization of current systems and available best practices, e.g., “planner variability”, "too much transactional data" DESPITE ERP AND APS, THERE IS SIGNIFICANT INVENTORY INEFFICIENCY IN OUR ECONOMY $1.0 trillion50+%$500+ billion U.S. inventories Estimated inefficiency Economic opportunity What is missing? What is Missing? Tactical and Strategic Inventory Planning to accommodate and manage these forces

3 ©2002 SmartOps Corporation 3 ACADEMIC BUILDING BLOCKS: 40+ YEARS OF EVOLUTION, BREAKTHROUGHS, AND APPLICATION Late 1950s – 1960s  Fundamental issues identified setting the stage for decades of research  Early inventory and stochastic* optimization models created  Breaking of problems into manageable pieces  Practitioners use rules of thumb and put pieces together heuristically 1970s-1980s 1990s  Searching for simpler ways of computing optimal inventory policies for basic problems  Improved computational approaches developed to address larger problems in “isolation”  Stochastic optimization models developed to explicitly accommodate supply and demand variability, multiple time periods, capacitated, multi-echelon supply chains  Successful “one-off” application to industrial-size problems  Clark and Scarf  Arrow, Karlin  Federgruen;Zipkin;  Lee; Cohen; Roundy  Muckstadt;Thomas;Zheng  Glasserman; Tayur Key progress Key contributors ©2002 SmartOps Corporation * Stochastic: Involving or containing random or “uncertain” variables (e.g., uncertain demand, lead time, capacity, yield, etc.)

4 ©2002 SmartOps Corporation 4 DRIVERS OF SUPPLY CHAIN INEFFICIENCY Limited understanding and visibility into inventory drivers Complex multi- stage supply chain Uncoordinated inventory targets across stages Numerous, disparate planning systems Capacity vs. inventory trade-off not modeled Inconsistent approach to determining shipment frequencies and quantities Plants Chicago, IL Gary, IN Cross-dock facilities/ distribution pools Windsor, ONT RTP, NC Richmond, VA Oshkosh, WI Norcross, GA Knoxville, TN 2,500 Dealers/retailers

5 ©2002 SmartOps Corporation 5 UNDERSTANDING MODELING APPROACHES Annually/quarterlyWeekly/daily Quarterly/monthly Low detail/granularityHigh detail/granularity N/A Planner Planner & O.R. engineer O.R. engineer Business Unit Planning and Operations Corporate/ Business Unit Strategy Organization Data management/update process Relation to existing processes Stand-aloneDynamic One-off studiesDriving execution Structural changes Continuous improvement “Dynamic value chain” ©2002 SmartOps Corporation ERP/APS detailed, dynamic data inputs Manual, “meta- level” inputs, click and drag design SWEET SPOT The goal is to pick an approach that ensures confidence in the answer, quick hit improvements, and sustained execution Timed, regular data loading Data-loader with manual start Data wizard and interface Timing/dynamic frequency

6 ©2002 SmartOps Corporation 6 UNDERSTANDING MODELING APPROACHES Annually/quarterlyWeekly/daily Quarterly/monthly Low detail/granularityHigh detail/granularity Planner Planner & O.R. engineer O.R. engineer Business Unit Planning and Operations Corporate/ Business Unit Strategy Organization Data management/update process Relation to existing processes Stand-aloneDynamic One-off studiesDriving execution Structural changes Continuous improvement “Dynamic value chain” ERP/APS detailed, dynamic data inputs Manual, “meta- level” inputs, click and drag design N/A Timed, regular data loading Data-loader with manual start Data wizard and interface Timing/dynamic frequency The goal is to pick an approach that ensures confidence in the answer, quick hit improvements, and sustained execution. SmartOps: GSK SmartOps: John Deere SmartOps: Giant Eagle

7 ©2002 SmartOps Corporation 7 A SUPPLY CHAIN MODELING PROCESS Map the current value chain Select relevant variables, constraints, and objective function Initial collection, cleaning, and QA of data Selection of planning granularity Select optimization algorithms Commence data integration process  Full, partial, or no automation of inputs and outputs  Entire network or subset  All nodes or simplification of nodes  Simplifying assumptions to include or exclude variables, constraints, or nodes considering quality of answer vs. speed of answer  Understand underlying data assumptions  Ensure data makes sense in business and supply chain terms  Days, weeks, months  Product hierarchy – sales model vs. MA  # of nodes and time periods  Stationary or non- stationary model (e.g. # of forecast periods)  Single or multi-echelon or hybrid  Capacitated, un- capacitated Load data and pre-process meta-data Calculation/ optimization Scenarios/ what-if QA outputsPost-process and summarize Review outputs - send to operational system/ process Change structure of value chain  Run test cases vs. actual data  Understand processing speed  Design, build, and run logical scenarios  Test boundary conditions  Compare results with expectations based on theory and domain expertise   Aggregation/dis- aggregation  Units/$s/Weeks  Rounding  Manual, exception-based, or automatic export of targets to planning systems  Changes to “nodes” and “arcs” vs. changes to echelons and BOMs  Compute meta- data: lead-times, lead time variabilites, forecast disagg. etc. Refresh inputs ©2002 SmartOps Corporation

8 8

9 9 WHAT IS THE OPTIMAL INVENTORY DEPLOYMENT FOR YOUR BUSINESS? Inventory Forms Inventory Purposes

10 ©2002 SmartOps Corporation 10 NOT ALL INVENTORY IS CREATED EQUAL Buffers against supply and demand uncertainty Results from economies of production, transport, procurement In-transit and in-process inventory Buffers against upstream capacity un-reliability Covers expected demand, driven by capacity constraints Display/demo stock Safety Stock Cycle Stock Pipeline Stock Shortfall Stock Pre-build Stock Merchandising

11 ©2002 SmartOps Corporation 11

12 ©2002 SmartOps Corporation 12 A COMPREHENSIVE APPROACH TO SUPPLY CHAIN PLANNING AND OPTIMIZATION Measuring all inventory drivers Output for each SKU at each inventory stocking location over time  Target inventory positions – Cycle stock – Safety stock – Shortfall stock – Pipeline stock – Merchandising stock – Pre-build stock  Minimum total inventory needed to deliver current service levels  Optimal service levels and inventory required, given product margins  Scenario analysis for comparing different sets of inputs and outputs  Lead times and lead time variability  Frequency of shipments, both factory to warehouse and warehouse to dealer  Demand, demand variability, intermittent  Forecasts and forecast errors  Seasonality, Non-stationary demand  Service levels and amount of lost sales  Customer wait times (patience levels)  Promotions, Vendor Deals, Forward Buys  Capacities at upstream production, transportation, warehouse, retail outlets  Transportation alternatives, expediting costs  Budget constraints on total inventory dollars  Showroom inventory levels  Aggregation and disaggregation

13 ©2002 SmartOps Corporation 13 STOCHASTIC OPTIMIZATION IS NECESSARY  Total Cost Optimization – Cycle stock – Pre-build stock – Pipeline stock  APS challenges – Scheduling a factory – Packing a truck – Routing a truck  Managing uncertainty  Safety stock  Shortfall stock Certain or near-certain “Deterministic” Uncertain “Stochastic” Linear and Integer Non-linear Linear, deterministic models are not appropriate for most critical inventory decisions in multistage, multi-product, capacitated, stochastic environments

14 ©2002 SmartOps Corporation 14 SMARTOPS AT GLAXOSMITHKLINE OverviewOperational Features  $40 billion Global Pharmaceutical manufacturer  SAP and Manugistics customer  Product supply chains: 21  Echelons per supply chain: 7-9  Nodes per supply chain: 100s  Planning periods: 36 months  Number of product-location-periods: 25,000+  Users: Corporate supply chain and business planners/super users as well as business unit planners  Objective: Maximize product availability to meet high customer service targets at minimum total chain cost, inventory, and risk  Time-varying multi-period forecast with error  Availability (advance order information) /customer service levels  LT variability  Capacity constraints  Varying batch sizes  Different pbs/review periods  Policies other than base-stock  Yield variability  BOM/common components  Multiple sourcing options used simultaneously  What –if Analysis  Forecast error  Lead times  Lead time variability  Batch sizes Reporting  Breakdown of inventory components  By $’s  By days-on-hand  By units ©2002 SmartOps Corporation

15 15 Imigran Detailed Supply Chain

16 ©2002 SmartOps Corporation 16 “TOTAL CHAIN” INVENTORY OPTIMIZATION DRIVES KEY BUSINESS DECISIONS Where should we be now? (Finished Goods and Raw Materials/Components)  Based on target manufacturing lead times, target availabilities, and capacities where should we strategically place inventory? At what target inventory levels?  What are optimal inventory levels by product, component, and location over time? – Markets as well as factory, warehouse, and pool inventories  What are the key factors that shift the optimal inventory curve?  What happens to the optimal inventory curve in different demand scenarios? How low can we go? In what stages? By when?  How low can we reduce inventory before we begin to impact sales?  What is the sensitivity of inventory to certain key factors? – Lead time, forecast error, target availability, customer wait time, merchandising requirements, etc. – What is the cost/benefit of inflexible/flexible capacity?  What is the benefit or business case for attacking certain supply chain parameters – e.g. lead times, availabilities, flexible capacity?  For a given corporate inventory budget constraint, where should I deploy inventory and what does this mean for customer service and product availability? ©2002 SmartOps Corporation

17 17 SUPPLY CHAIN PLANNING WORKFLOW Supply plan Operational planning synchronizing demand to material and capacity constraints  Visibility into inventory drivers  What-if analysis Derived supply data  Pricing  Lead times  Logistics  Capacity constraints  Variability, uncertainty Supply Execution systems 115± Time-phased key operating targets  Multistage inventory positions  Lot sizes  Service levels Demand 100 125 115 Uncertainty & variability Product management Sales channels Historical demand Deterministic consensus demand forecast Derived demand data Strategic & Tactical  Budgeting  Design  S&OP Strategic & Tactical  Budgeting  Design  S&OP

18 ©2002 SmartOps Corporation 18 Strategic (Yearly/quarterly) Tactical (Quarterly/monthly) Operational (Weekly/daily)  Inventory Budgeting  Designing rapid response supply networks  Sourcing  Postponement Strategy  Sales and operations planning  Inventory planning  Scenario analysis  Inventory management  Order execution and fulfillment  Aggregate inventory budgets  Inventory policies and placement  Expediting policies  Optimal planning parameters – Form of inventory: raw, WIP, postponement, finished – Purpose of inventory: safety, cycle, shortfall, pipeline, pre- build, merchandising  Alerts and exceptions  Updated supply and demand statistics ActivityPlanning processKey outputs INVENTORY PLANNING AND OPTIMIZATION SUPPORTS CRITICAL BUSINESS PROCESSES

19 ©2002 SmartOps Corporation 19 Product Architecture

20 ©2002 SmartOps Corporation 20 DEFINING AND AN ORDER FULFILLMENT STRATEGY Availability management Key policy choices  Promising and meeting order fulfillment lead times  Set to maintain or gain market share Capacity management  Stabilizing production rate to maximize efficiency or flexing capacity to meet demand Demand management  Managing sales/order rate variation  Limiting number of allowed “standard” configurations in build-to-stock environment Inventory management  Optimal deployment of inventory to maximize availability at minimum cost  Also used to insulate manufacturing from demand variability Lead time management  Consistent with Lean principles - working to reduce supply and in-process lead-times  Monitoring and managing lead-time variability  Fixed or flexible  Segmentation by product or customer (e.g. sales vs. rentals)  Fixed or flexible capacity  Willingness to subject plant to incresed demand variability  Static or dynamic inventory targets  Rules of thumb vs. product/location/time specific targets  Based on total chain or local viewpoint To achieve maximum availability at minimum cost:  A comprehensive order fulfillment strategy must appropriately define a coordinated set of policies for these interrelated variables  No one variable can be managed in isolation and changing or fixing one variable has implications for the others  Active management of demand variability (e.g. promotions/incentives)  Monitoring and managing forecast error  Active management of lead-times and lead-time variability  Incentives and penalties for performance

21 ©2002 SmartOps Corporation 21 REALIZING THE POTENTIAL VALUE OF ENTERPRISE-WIDE INVENTORY OPTIMIZATION Cost (inventory, variable, period cost) “Cost of availability” Availability and customer service x Quick hits (12-18 months)  Focused, prioritized lead time and lead time variability reductions and ongoing management  Reduction in self-imposed demand variability  Managing to optimal total chain inventory targets Longer term (24-28 months)  Structurally alter supply chain  Rationalize number of standard configurations and decrease forecast error  Flex capacity  Manage to OF strategy key metrics Through better strategic, tactical, and operational planning we are trying to improve, stabilize, and sustain availability at lower total chain cost Availability focused Cost focused ©2002 SmartOps Corporation

22 22 CLOSING REMARKS  Despite ERP and APS investments significant inventory inefficiencies persist  Fundamental causes of supply chain inefficiency must be addressed: – Inherent uncertainty and complexity in multistage supply chains Stochastic optimization approach is the appropriate solution – Uncoordinated planning decisions Total cost optimization by providing visibility and coordination between functional and external groups – Inconsistent and/or insufficient planning practices Software can provide a standardized “best planning” solution  All the drivers of inventory must be measured to determine: – Optimal inventory targets for all inventory purposes safety, cycle, shortfall, pipeline, pre-build, and merchandising stock – Total cost solution to deliver service levels – Optimal service levels given budget objectives, product margins, and portfolio of products


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