Integrated Supply Chain Management for a Polymers Business

2. Integrated Supply Chain Managementfor a Polymers Business Ann Bixby, Aspen Technology INFORMS Houston June 29, 2001

3. Agenda • Introduction • Data Model and Integration Overview • Demand Management Overview • Production Scheduling Overview • Production Planning

4. 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 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 AspenTech Polymers Customers

5. A.E. Staley AGIP A. K. Steel ALCOA Aginomoto Gen. Foods Amoco Anheuser-Busch Packaging Asahi Chemical Asahi Glass Asahi Medical Ashland Petroleum BASF Ben & Jerry’s BOC Gases BP Oil Bridgestone/Firestone Bryan Foods (Sara Lee) Bush Boake Allen Cable Systems International Calgon Carbon Champion International Chichibu Onoda Cement CLH Pipeline ComCom Cosmo Oil Cypress Semiconductor Denki Kagaku Chem. De Ster Daelim Industrial Corp. DuPont Engelhard Excel Beef (Cargill) Exxon Chemicals Exxon Company Int’l . Exxon Research FEMSA Fort James Corp. Fuji Heavy Industries Fuji Film GARMCO Genref Goodyear Chemicals Grand Polymer Co. Gulf States Steel Hoechst A. G. Hoechst Celanese Corp. Hoechst Hostalen Hokai Can Honam Oil Hormel MEMC Japan (Confidential - Semi Mfg.) (Confidential - Disk Drives) Idemitsu Iggesund International Home Foods Irving Oil ISP IVECO (Fiat) Japan Elastomer Josiah Wedgwood & Co. J. R. Simplot Knoll Pharmaceuticals Konica Kraft General Foods Kyushu Oil LSI Logic LTV Steel Macronix Marathon Oil Mead Fine Paper Meiji Foods MEMC Methanex Miller Brewing Co. Mitsubishi Oil Mitsubishi Silicon America Mitsui Chemicals Molson Brewing Monsanto Motorola National Starch/Chem. Nihon Nippon Synthetic Chem. AspenTech Supply Chain Clients

6. NOVA Chemicals Ocensa Pharmacia & Upjohn Petro-Canada Plasmon Heinz Philip Morris Pétromont Petronas Repsol Praxair Phillips Chemical Phillips Petroleum Quimica Rhone P & G (Japan) Quaker Oats SARAS Poulenc Rockwell Semiconductor Rohm & Haas Sematech Schering-Plough Seiko Epson Corp. Shell Oil Shaw Industries Shell Chemical Shell Lubricants StatOil Shering-Plough Showa Shell Solutia Symbios Logic Stoldt Parcel Tanker Sumitomo Chemicals Sun Refining & Mktg. Thappline Taiyo Oil Taisil Texaco Toyo Tohoku Oil Tonen Tonen Chemical Comms. Equip. Trevira Ube Cement Union Camp Unocal U. S. Robotics Valero Refining Wellcome Foundation Weyerhaeuser Witco Yoshitomi Pharm Yukong Supply Chain Clients, continued

7. Data Model and Integration Gary Provance, Aspen Technology James Steiner, Aspen Technology

8. Requirements of ERP Integration • 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

9. Features of Interface Design • 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

10. Partial List of Download Interfaces • Master Data • Material – Customer – Plant • Bill of Materials - Routings • Transactional Data • Open Orders Planned Orders • Shipment History Process Orders • Invoice History Purchase Orders • Current Inventory Transport Orders

11. List of Upload Interfaces • Planned Orders • Process Orders • Purchase Requisitions • Transport Requisitions • Sales Forecast

12. Supply Chain System Architecture ERP Interface Model Data Model Data Model PP PP DM DM PS PS

13. Purpose of ERP Interface Model • 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

14. Purpose of Data Models • 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

15. Demand Management Nives Stanfelj, Aspen Technology Kevin Zyskowski, PriceWaterhouseCoopers Robert Ellis, Aspen Technology

16. What do I expect to sell? • When do I expect to sell it? Predict • Are actual orders meeting expectations? • How well am I predicting the market? Measure • How do I position inventory? • How do I allocate my production to meet existing demand? Deploy • How quickly do I recognize demand changes? • How can I communicate market changes quickly to production planning? React What is Demand Management?

17. Demand Management Model Model Scope = Business Unit • Manage/cleanse/realign sales history • Maintain multiple views/levels/aggregations • Perform customer segmentation (ABC analysis) • Calculate safety stock inventory targets • 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

18. 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)

19. Algorithms • Model determines Optimal fit or user specified method • Linear Regression • Moving Average • Single and Double Exponential Smoothing • Additive and Multiplicative Winters • Seasonal • ARIMA

20. Collaboration • Sales • Marketing • Customers Historical Data Forecast Table Flow Events & Overrides Statistical Forecast Collaborative Forecast Working Forecast Accept Changes? Input Output Sales Budget Revenue Forecast Working Forecast Volume Plan (Constrained) Demand Plan (Unconstrained) Planning Model

21. 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

22. 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

23. Production Scheduling Laura Pacher, Aspen Technology Sukran Kadipasaoglu, Aspen Technology Robert Ellis, Aspen Technology

24. 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

25. Production Scheduling Model • 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

26. 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 • Cost information can be actual or relative and includes: • Late and very late order costs • Inventory carrying costs • Production costs • Transition/setup costs

27. A Fixed E B D Flexible C A B E D C Subwheels L B K C A E I F H What types of sequences can we address with the Aspen Polymer Scheduler?

28. Current Practices • Manual planning/scheduling • Spreadsheets • Isolated “islands of automation” • Little or no integration

29. Manual error Labor intensive & dependant on a few experienced personnel “Hedging” behavior Inventories Capacities Poor response to disruptions Inaccurate trade-offs No eBusiness infrastructure Hidden Costs

30. Polymers Scheduler Features • By-products modeling • Co-products modeling • Transfer of intermediate products between plants • Campaign optimization, product wheels • Blending • Changeovers by product attributes

31. Demo Scheduling Scenarios

32. The Manufacturing-centric Scenario

33. The Customer-centric Scenario

34. The Balanced Scenario

35. The Final Schedule

36. Production Planning Mark Rockey, Profit Point Ann Bixby, Aspen Technology Danielle Cohen Robert Ellis, Aspen Technology

37. 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 12-18 months, monthly time buckets (depending on application) • LP/IP model used to optimize production and distribution to maximize profit or minimize cost

38. 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.

39. 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

40. 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

41. Model Dimensions • Plants • Production units • Inventory locations • Alternate source locations • Distribution centers • Customers • Raw materials • Finished products • Production runs • Time periods

42. 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

43. 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

44. Model Constraints • Minimum & maximum alternate source purchases • Material balance • Demand fulfillment • Capacity • Minimum & maximum run lengths • Safety stock • Inventory storage

45. 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

46. 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

47. Demonstration