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© 2000 AspenTech All Rights Reserved.
Integrated Supply Chain Management for a Polymers Business INFORMS Houston June 29, 2001 Ann Bixby, Aspen Technology
© 2000 AspenTech All Rights Reserved. Page 3 Agenda Introduction Data Model and Integration Overview Demand Management Overview Production Scheduling Overview Production Planning
© 2000 AspenTech All Rights Reserved. Demand Manager Sales History Open Orders Customer & Warehouse Shipments Inventories Master Data – Customer, Plant, Material, Recipe History adjustments Empirical forecasts What-if scenarios Optimization requests Demand Plan Inventory Planner Supply Chain Planner Production Requirements Plant Scheduler SAP Batch Completions (Classification) Consumption Confirmation Business Transactions Business Financials Mfg. Process Information Production Orders SUPPLY CHAIN OPTIMIZATION Batch status Work-in-Process Inventories Process Definition Updates Production Schedule Business Plans (Scenarios) Deployment Plan Production Schedules Consequences of decisions Applications and Data Flows MES Volume Plan
© 2000 AspenTech All Rights Reserved. Page 5 Sales & Operations Planning Process (S&OP) Historical sales data gathered and cleansed Unconstrained forecast generated and posted Collaboration on forecast from sales, marketing etc. “Demand Plan” sent to Production/Distribution/Inventory Planning for capacity balancing and economic optimization Constrained “Volume Plan” passed back to DM for performance tracking Volume Plan published to ERP Daily Review of Period-to-Date Actual vs Sales Plan Period-end Performance Reporting Inventory targets, Production preferences and Distribution plan passed to scheduling Plant sequenced Process and Transport orders posted to ERP Supply – Demand Balancing
© 2000 AspenTech All Rights Reserved. Page 6 AspenTech Polymers Customers Elenac Enichem Equistar ExxonMobil F. Hoffman Laroche Fardem Far East Textile Fina Flour-Daniel Formosa Plastics General Electric Geon Goodyear Gow-Jiun Grand Polymer Co. Grupo Cydsa Hercules Hoechst Celanese Huntsman Intevep IPT ISP John Brown JSR Kolon Fiber Kuraray Lee Chang LG Chemicals Lyondell Mitsubishi Chemicals Mitsui Nan Ya Plastics Nova Chemical NSP OC Systems Onward Tech. Petromont Phillips Polifin PPG Reliance Repsol Rhodia Rohm & Haas RWE-DAE Saehan Salzgitter Sam Yang Shell Sirtis Solvay SRIC Sunkyong Targor Uhde Union Carbide Union Chemical Lab Unistar S.A. Wellman Westlake Yang Zhi Yizheng Yukong Chemicals Zimmer 3M Air Products Albermarle Allied Signal ARCO Bayer Beijing Yanshan Borealis BPAmoco Braedase Polystyreen Cabot Cheil Synthetics Chevron ChiaShing Chinese Petroleum Copesul Creanova Daicel Dow DSM DuPont Eastman
© 2000 AspenTech All Rights Reserved. A.E.StaleyAGIPA. K. SteelALCOA Aginomoto Gen. FoodsAmocoAnheuser-Busch PackagingAsahi Chemical Asahi GlassAsahi MedicalAshland PetroleumBASF Ben & Jerry’sBOC GasesBP OilBridgestone/Firestone Bryan Foods (Sara Lee)Bush Boake AllenCable Systems InternationalCalgon Carbon Champion InternationalChichibu Onoda CementCLH PipelineComCom Cosmo OilCypress SemiconductorDenki Kagaku Chem.DeSter Daelim Industrial Corp.DuPontEngelhardExcel Beef (Cargill) Exxon ChemicalsExxon Company Int’l.Exxon ResearchFEMSA Fort James Corp.Fuji Heavy IndustriesFuji FilmGARMCO GenrefGoodyear ChemicalsGrand Polymer Co.Gulf States Steel Hoechst A. G.Hoechst Celanese Corp.Hoechst Hostalen Hokai Can Honam OilHormelMEMC Japan(Confidential - Semi Mfg.) (Confidential - Disk Drives)IdemitsuIggesundInternational Home Foods Irving OilISPIVECO (Fiat)Japan Elastomer Josiah Wedgwood & Co.J. R.SimplotKnoll PharmaceuticalsKonica Kraft General FoodsKyushu OilLSI LogicLTV Steel MacronixMarathon OilMead Fine PaperMeiji Foods MEMCMethanexMiller Brewing Co.Mitsubishi Oil Mitsubishi Silicon AmericaMitsui ChemicalsMolson BrewingMonsanto MotorolaNational Starch/Chem.NihonNippon Synthetic Chem. AspenTech Supply Chain Clients
© 2000 AspenTech All Rights Reserved. P & G (Japan)Quaker Oats Repsol QuimicaRhone Poulenc Rockwell SemiconductorRohm & Haas SARAS Schering-Plough Seiko Epson Corp. Sematech Shaw Industries Shell ChemicalShell Lubricants Shell Oil Shering-Plough Showa Shell Solutia StatOil Stoldt Parcel Tanker Sumitomo ChemicalsSun Refining & Mktg. Symbios Logic Taiyo Oil Taisil Texaco Thappline Tohoku Oil TonenTonen Chemical Toyo Comms. Equip.Trevira Ube Cement Union Camp UnocalU. S. Robotics Valero Refining Wellcome Foundation WeyerhaeuserWitco Yoshitomi Pharm Yukong NOVA ChemicalsOcensaPetro-Canada PétromontPetronas Pharmacia & Upjohn Philip Morris Phillips ChemicalPhillips Petroleum Plasmon Heinz Praxair Supply Chain Clients, continued
© 2000 AspenTech All Rights Reserved. Data Model and Integration Gary Provance, Aspen Technology James Steiner, Aspen Technology
© 2000 AspenTech All Rights Reserved. Page 10 Fully automated batch interfaces Full range of supply chain data Standard rather than custom interfaces ERP is repository for all data Daily, weekly, and monthly versions Must support multiple business units Must be low maintenance Must have error logging and audit trail Requirements of ERP Integration
© 2000 AspenTech All Rights Reserved. Page 11 Interface control table in ERP system Entire batch system is parameter driven Nightly schedule with week/month options Interface scalable to add more businesses Synchronized uploads and downloads System available to users 16 hours a day Status reports to users and support staff Features of Interface Design
© 2000 AspenTech All Rights Reserved. Page 12 Master Data Material – Customer – Plant Bill of Materials - Routings Transactional Data Open OrdersPlanned Orders Shipment HistoryProcess Orders Invoice HistoryPurchase Orders Current InventoryTransport Orders Partial List of Download Interfaces
© 2000 AspenTech All Rights Reserved. Page 13 Planned Orders Process Orders Purchase Requisitions Transport Requisitions Sales Forecast List of Upload Interfaces
© 2000 AspenTech All Rights Reserved. Page 14 Supply Chain System Architecture ERP Data Model Interface Model Data Model DM PS PP DM PS PP
© 2000 AspenTech All Rights Reserved. Page 15 Download Receive data from ERP system Identify and label data by business unit Prepare data for supply chain use Report results in logs to support staff Upload Gather data from multiple businesses Merge data for upload to ERP Report results in logs to support staff Purpose of ERP Interface Model
© 2000 AspenTech All Rights Reserved. Page 16 Download Read in specific data from interface model Prepare data for functional model use Report results in logs to users Upload Read in data from functional models Prepare data to upload to ERP Report results in logs to users Purpose of Data Models
© 2000 AspenTech All Rights Reserved. Demand Management Nives Stanfelj, Aspen Technology Kevin Zyskowski, PriceWaterhouseCoopers Robert Ellis, Aspen Technology
© 2000 AspenTech All Rights Reserved. Page 18 What is Demand Management? React How quickly do I recognize demand changes? How can I communicate market changes quickly to production planning? Predict What do I expect to sell? When do I expect to sell it? Measure Are actual orders meeting expectations? How well am I predicting the market? Deploy How do I position inventory? How do I allocate my production to meet existing demand?
© 2000 AspenTech All Rights Reserved. Page 19 Demand Management Model Perform customer segmentation (ABC analysis) Calculate safety stock inventory targets Model Scope = Business Unit Manage/cleanse/realign sales history Maintain multiple views/levels/aggregations Generate statistical forecast – multiple algorithms including seasonality and causal Manage unique events – pricing changes, competition changes View and adjust forecast at multiple aggregation levels Collaboration - obtain and track overrides by business personnel at appropriate levels Track forecast consumption Metrics - compare forecast accuracy and actual sales with plan/budget
© 2000 AspenTech All Rights Reserved. Page 20 DM Data “Dimensions” Demand has independent dimensions Material Product, package, group, family, profit center, application … Customer Sold-to Customer hierarchy, group, region, sales rep, sales manager … Sourcing Location Region Time periods Sales/shipment history (2 – 3 years) Forecast horizon (12 – 18 months) Demand “buckets” (months; sometimes weeks; or combination)
© 2000 AspenTech All Rights Reserved. Page 21 Algorithms Model determines Optimal fit or user specified method Linear Regression Moving Average Single and Double Exponential Smoothing Additive and Multiplicative Winters Seasonal ARIMA
© 2000 AspenTech All Rights Reserved. Page 22 Monthly Forecasting Business Process Monthly roll-forward (batch) Cleanse history – remove outliers Realign data; obsolete product replacements, new products and customers, customer mergers/changes, plant/warehouse changes Generate new statistical forecast and review (at forecast “view”) Apply overrides and events as necessary (at selected view) Distribute forecast for collaboration (at selected view) Collect and review collaborative input; view differences; accept changes are desired Send unconstrained demand plan to planning model for constraining Calculate safety stock requirements and post to planning/scheduling models Read constrained forecast from planning model Net forecast and send to scheduling model Ad Hoc during month Perform customer segmentation Review previous forecast accuracy
© 2000 AspenTech All Rights Reserved. Page 23 Forecast Table Flow Statistical Forecast Working Forecast Events & Overrides Collaborative Forecast Working Forecast Accept Changes? Demand Plan (Unconstrained) Planning Model Volume Plan (Constrained) Historical Data Collaboration Sales Marketing Customers Sales Budget Revenue Forecast Input Output
© 2000 AspenTech All Rights Reserved. Page 24 Interaction with other Models Write to other models Unconstrained forecast to production-distribution planning model Customer rankings and safety stock targets to production- distribution and scheduling models Net forecast to scheduling model Read from other models Constrained forecast from production-distribution
© 2000 AspenTech All Rights Reserved. Page 25 MIMI DM Advantages Very flexible views / groupings Forecast algorithms optimal selection or user fixed Separation between sales history and master data If customer or material hierarchies change, model automatically updates Speed Solution time (forecasting) On-the-fly slicing and dicing Initial model configuration to create forecast very quick and easy Easily adaptable to customer’s unique work process Accelerated starting point with template
© 2000 AspenTech All Rights Reserved. Production Scheduling Laura Pacher, Aspen Technology Sukran Kadipasaoglu, Aspen Technology Robert Ellis, Aspen Technology
© 2000 AspenTech All Rights Reserved. Page 27 What is Production Scheduling? Are my orders being met on time? Are my distribution requirements met? Do I have sufficient raw materials available? What am I making in the near term? Am I using my resources effectively? How much time is spent on transitions? What is the impact of downtime on my orders? Do I have spare capacity to schedule new incoming orders What are my inventory projections? Can I meet incoming orders from inventory Demand Production Inventory
© 2000 AspenTech All Rights Reserved. Page 28 Scope: Site specific / Multi-plant Time horizon: Weeks - 6 months Functionality: Creating production activities on facilities Sequencing production activities Defining purchasing needs Ensuring capacity and material feasibility Dynamic rescheduling for single and multiple activities Rapid what-if analysis Selection of alternate recipes and bill of materials Primary User Production Scheduler, Materials Manager Production Scheduling Model
© 2000 AspenTech All Rights Reserved. Page 29 Aspen’s Supply Chain Solution Methods The scheduling logic found in the Polymer Scheduler looks at minimizing the total cost of a schedule given certain objectives and restrictions The scheduling logic found in the Polymer Scheduler looks at minimizing the total cost of a schedule given certain objectives and restrictions Cost information can be actual or relative and includes: Cost information can be actual or relative and includes: Late and very late order costs Late and very late order costs Inventory carrying costs Inventory carrying costs Production costs Production costs Transition/setup costs Transition/setup costs
© 2000 AspenTech All Rights Reserved. Page 30 What types of sequences can we address with the Aspen Polymer Scheduler? FixedAB C D E SubwheelsA B C E F H I K L FlexibleA B C D E
© 2000 AspenTech All Rights Reserved. Page 31 Manual planning/scheduling Spreadsheets Isolated “islands of automation” Little or no integration Current Practices
© 2000 AspenTech All Rights Reserved. Page 32 Hidden Costs Manual error Labor intensive & dependant on a few experienced personnel “Hedging” behavior Inventories Capacities Poor response to disruptions Inaccurate trade-offs No eBusiness infrastructure
© 2000 AspenTech All Rights Reserved. Page 33 Polymers Scheduler Features By-products modeling Co-products modeling Transfer of intermediate products between plants Campaign optimization, product wheels Blending Changeovers by product attributes
© 2000 AspenTech All Rights Reserved. Page 34 Demo Scheduling Scenarios
© 2000 AspenTech All Rights Reserved. Page 35 The Manufacturing-centric Scenario
© 2000 AspenTech All Rights Reserved. Page 36 The Customer-centric Scenario
© 2000 AspenTech All Rights Reserved. Page 37 The Balanced Scenario
© 2000 AspenTech All Rights Reserved. Page 38 The Final Schedule
© 2000 AspenTech All Rights Reserved. Production Planning Mark Rockey, Profit Point Ann Bixby, Aspen Technology Danielle Cohen Robert Ellis, Aspen Technology
© 2000 AspenTech All Rights Reserved. Page 40 What is production planning? Decisions: What to make What to buy Where to make it How to make it Where to ship it Planning time horizon Generally months, monthly time buckets (depending on application) LP/IP model used to optimize production and distribution to maximize profit or minimize cost
© 2000 AspenTech All Rights Reserved. Page 41 Business Objectives Optimize current planning work process Balance supply and demand (adjust unconstrained forecast to meet capacity constraints) Determine optimal shipping locations Satisfy safety stock inventory levels as determined by DM Minimize production, inventory and shipping costs Facilitate frequent plan updates and what-if analysis capability Integrate with demand management and scheduling models Provide metrics such as cost impact of a change in capacity, demand, etc.
© 2000 AspenTech All Rights Reserved. Page 42 Interaction with Scheduling The planning model receives the following information from the scheduling model: Minimum lot sizes Recipe preferences Average transition times Starting inventory The planning model passes the following information to the scheduling model: Inventory targets Distribution plan
© 2000 AspenTech All Rights Reserved. Page 43 Interaction with Demand Management The planning model receives the following information from the demand management model: Forecast Safety Stock Customer Ranking The planning model passes the following information to the demand management model: Constrained forecast
© 2000 AspenTech All Rights Reserved. Page 44 Model Dimensions Plants Production units Inventory locations Alternate source locations Distribution centers Customers Raw materials Finished products Production runs Time periods
© 2000 AspenTech All Rights Reserved. Page 45 Decision Variables Raw material purchase Production run indicators Production Alternate source purchase indicators Alternate source purchase amounts Material substitution Customer sales & demand shortage Inventory Capacity usage Transportation Constraint Violations
© 2000 AspenTech All Rights Reserved. Page 46 Objective Function Costs: Alternate source purchase cost Variable production cost Inventory holding cost Capacity usage cost Material substitution cost Penalty for demand shortage Penalties for other constraint violations (safety stock violation, etc.) Revenue: Sales revenue
© 2000 AspenTech All Rights Reserved. Page 47 Model Constraints Minimum & maximum alternate source purchases Material balance Demand fulfillment Capacity Minimum & maximum run lengths Safety stock Inventory storage
© 2000 AspenTech All Rights Reserved. Page 48 Model Solution User is alerted to possible data problems before optimization, such as: Demands for which there are no active transportation links Transportation links with no cost data User has the option to solve as mixed integer program or as LP relaxation User can turn infeasibility variables on or off For example, the user can specify that all demand be satisfied. User can specify active model time horizon User can choose profit maximization or cost minimization
© 2000 AspenTech All Rights Reserved. Page 49 What-if Capability Many of the data tables in the model can be modified by the user for what-if analysis— Increased demand Different safety stock levels Changes in production capacity, etc. Different scenarios can be saved off and reloaded for comparison
© 2000 AspenTech All Rights Reserved. Demonstration
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