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SAP APS

SAP APS. Optimization with SAP APS. Content. Introduction Modeling Supply Chain Management Problems Optimization in APO (Overview). Introduction. Modeling Supply Chain Management Problems Optimization in APO. Optimizing the Supply Chain. Benefits lower inventories

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SAP APS

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  1. SAP APS Optimization with SAP APS

  2. Content Introduction • Modeling Supply Chain Management Problems • Optimization in APO (Overview)

  3. Introduction • Modeling Supply Chain Management Problems • Optimization in APO

  4. Optimizing the Supply Chain • Benefits • lower inventories • reduced production and transportation costs • higher service level • increased manufacturing throughput • better return on assets

  5. Optimizing the Supply Chain • Challenges • model accuracy • very detailed modeling => large optimization problems -> several GigaByte main memory allow an adequate modeling of the supply chain planning problem • generic optimizer • standard software approach should be configurable to solve supply chain scenarios for various business areas -> advanced optimization libraries can be used as basic building blocks • scalability • tradeoff between solution quality and used runtime

  6. APO: Optimizing the Supply Chain • model accuracy => dual modeling approach • aggregate planning (SNP) • detailed planning (PP/DS) • generic optimizer • generic framework, e.g., several scheduling algorithms • planner may select most appropriate method to solve his problem • integration of further optimization algorithms possible • scalability • system architecture, e.g., 3-tier client server architecture • combination of basic optimizers and decomposition techniques

  7. Life Cycle of an OR Project

  8. Problem specification • business analysis and data collection • definition of problem, identification of business goals • problem must be quantified to derive a mathematical model • in APO: variety of problems encountered by manufacturing industries

  9. Model formulation • model = abstract representation of the real-world problem • important topics: • which factors affect the outcome? • what are the achieved goals? • what are the restrictions? • quantification of decision variables, objectives and constraints => mathematical model • in APO: generic models which can be applied to different planning scenarios

  10. Algorithm • choice of appropriate algorithm for different problem classes • some algorithms are more efficient than others for a given problem • in APO: advanced state-of-the-art algorithms for solving various OR problems

  11. Solution assessment • tradeoff between the quality of the solution and the tractability of the model • evaluation of result, usually: enrichement and refinement of the model • in APO: • models offered by APO cover diverse needs for different industrial applications • algorithms are thoroughly tested to provide the best-of-breed solution

  12. Optimization in APO • Solution Techniques in APO

  13. Optimization in APO • Use of ILOG Libraries in APO ILOG Concert Solver CPLEX Scheduler Dispatcher LP, MILP CP TS (ND, SNP) (PP/DS) (TP/VS, Campaign Optimizer in PP/DS)

  14. APO Implementation: flexibility & expense APO ABAPApplications C++ Modules:Optimizers ABAP C++

  15. APX programming& configuring APO Implementation: flexibility & expense flexibility expense flexibility expense low low customizing APO customizing ABAPApplications C++ Modules:Optimizers Optimization “Cartridges” (Libraries, ODF, etc.) programming ABAP C++ C++ programming high high • SAP AG 2000 Optimization Extension Workbench (APX) 15

  16. Optimization Extension Workbench (APX) • Introduction: The power of standard optimization • The challenge: Extended requirements of the market • An example: “Trim Optimization” • The solution: APO Optimization Extension Workbench

  17. Introduction: The power of standard optimization Demand Planning Supply Network Planning APO DP SNP Production Planning / Detailed Scheduling Deployment Depl. PP/DS execution R/3

  18. Demand Planning Supply Network Planning When and where will which demand be satisfied best? APO DP SNP Production Planning / Detailed Scheduling When and where will which order or operation processed best? Deployment How are good receipts matched with the demands? Solver Depl. PP/DS execution R/3 The power of standard optimization Demand Planning Supply Network Planning When and where will which demand be satisfied best? APO DP SNP Production Planning / Detailed Scheduling Deployment Solver Depl. PP/DS execution R/3

  19. The power of standard optimization Demand Planning Supply Network Planning When and where will which demand be satisfied best? APO DP SNP Production Planning / Detailed Scheduling When and where will which order or operation processed best? Deployment Solver Depl. PP/DS execution R/3

  20. The power of standard optimization Demand Planning Supply Network Planning When and where will which demand be satisfied best? APO DP SNP Production Planning / Detailed Scheduling When and where will which order or operation processed best? Deployment How are good receipts matched with the demands? Solver Depl. PP/DS execution R/3

  21. Extended requirements • Dedicated users have special requirements concerning • additional or different constraintsmaterial requirements (geometry, volume, weight, ...)constraints concerning the pegging, PPM or location selectiontechnical process requirements • individual target valuesminimize inventory, minimize garbage or off-cuts,maximize balancing, maximize machine usage, minimize machine wear  optimize performance measures which are important for a specific user  it must be possible to extend all planning components with individual optimizers

  22. Customers with already existing optimization tools • customers with operations research departments or • other legacy systems • Work to be done: adapt the component to the Workbench • customers with operations research departments or • customers who would like to have their special optimizationtool built on a project basis by a third party (software house) Customers willing to invest in a special solution Integration of customizable optimizers within the APO getting one planning system with databases and generic applications as common backbone The market

  23. An Example: “Trim Optimization” Customer orders: Cutted coils (length + width) Off-cuts Cutting Coils from rolling-mill • SAP AG 2000 Optimization Extension Workbench (APX) 23

  24. An Example: “Trim Optimization” Customer orders: Cutted coils (length + width) Off-cuts 2 4 1 3 5 Huge amount of off-cuts Cutting1 Cutting2 Additional constraints: Ex.: length(B)  length(4) width(B)  width(4)+width(5) most probable hurt by std. tools A B Coils from rolling-mill • SAP AG 2000 Optimization Extension Workbench (APX) 24

  25. 4 3 4 3 An Example: “Trim Optimization” Customer orders: Cutted coils (length + width) Off-cuts 2 1 5 Target 2: minimize special measures Cutting1 Cutting2 A B Target 1: respect additional constraints Coils from rolling-mill • SAP AG 2000 Optimization Extension Workbench (APX) 25

  26. External Optimizer External Optimizer GUI The external (customized) optimizer becomes an integral component of APO! APO Optimization Extension Workbench (APX) APO GUI

  27. Customized menu of external optimizers } APO Optimization Extension Workbench (APX) “Frame”: OCX container Erlig hjqeroiut ghaq Embedded External optimizer provided as OCX • Its up to the ocx programmer • to build the optimizer’s GUI • to supply it with necessary data • to control the optimizer’s run

  28. Data integration:read master data transactional data write transactional data Preview:extension of DB andcorresponding components External Optimizer External Optimizer GUI The external (customized) optimizer becomes an integral component of APO! APO Optimization Extension Workbench (APX) APO GUI The database should remain in the APO system!

  29. Usage of external optimizers A.) exclusive, i.e. without any other optimization tool B.) in combination with other optimizers  standard optimizerto be used in sequence: initial optimization  following “repair” optimizer  other external optimizersex.: casting: “coffin shape” optimization rolling mill: “coffin shape” optimization cutting: “trim” optimization

  30. Summary & Preview Summary Any external optimizer becomes an integral component of APOeven thoughthere is a clear interface between APO and the external optimizer. Data are to be exchanged via BAPIs. The set of BAPIs is to be extended step by step. Preview Give the user the ability to extend the database and its corresponding components concerning his own requirements. Integration of the Optimization Extension Workbench with the Heuristics Framework of PP/DS (batch optimization).

  31. APO App. Server SAP GUI Application Optimizer APO DB Server liveCache Multi-Tier-Architecture with APO • Dedicated hardware/ software system • Same data, different representation • Reason is performance

  32. Integration of the Optimizers in APO Frontend GUI, Opt.-OCX APO Appl.-Server APO Opt.-Server APO DB-Server APO LiveCache

  33. Optimizer Architecture • Optimization Server • Powerful C++ algorithms • Fast access data models • “All in one box” possible • One or more external servers:CPU power tailored to computingneeds • Parallel processing / multi-agent optimization

  34. Parallelization Multi user 3-tier Client Server Separation LiveCache and optimization server Multiple optimization server Multi Processor Loadbalancing Challenge: Scalability (Hardware)

  35. Optimization Server • few optimization (e.g. optimization run over night) => optimization integrated in application server • optimization online => separate optimization server gateway to application server • a lot of optimization online, a lot of users, failsafe => several optimization as well as application server APP OPT APP OPT GW APP OPT GW APP OPT GW

  36. Optimization – Improvements in APO 3.0 • 3.0 SNP Optimizer available for 2.0 Installations • Improved Performance • Reduced Memory Consumption • 3.0 PP/DS Optimizer available for 2.0 Installations with APO 3.0 GA Release • Common Optimization Framework (Installation, Customizing)

  37. liveCache Components of an APO 3.0 System SAPNet alias “dbosplatforms” Presentation Server: NT Intel 4.0, Windows 98, Windows 2000, SAP GUI 4.6D (recommended) Database Server (DB): NT 4.0, AIX 4.3.x (x>=2), HP-UX 11.0, Solaris 7 (8 recommended), Tru64 UNIX 4.0F Supported Databases: Oracle 8.1.6, Informix 7.31, SAP DB 7.2, MS SQL Server 7.0, DB2/UDB EEE Application Server (AP): NT 4.0, AIX 4.3.x (x>=2), HP-UX 11.0, Solaris 7 (8 recommended), Tru64 UNIX 4.0F liveCache: NT/Intel 4.0 (also Intel PSE-36), Windows 2000 Optimisation Server: NT/Intel 4.0, Windows 2000

  38. Platforms APO Release 3.0 • 4.6C Basis System • Business Information Warehouse 2.0B • Unix platforms for liveCache • Sun Solaris 64 Bit (4Q / 2000) • IBM AIX 64 Bit (4Q / 2000) • HP-UX 64 Bit (4Q / 2000) • Compaq Tru-64 (4Q / 2000) • Siemens Reliant 64 Bit (1Q / 2001)

  39. System Landscape during Project Life Cycle Test APO Consolidation Training Production R/3 Test Consolidation Training Production t Evaluation Test APO only Test with OLTP Pre go live (Post) go live Mainte-nance

  40. System Sizing - Going Live • Self-Service Quicksizer for APO available SAPNet alias sizing • Process of sizing similar to R/3 • Questionnaire for data volume • Memory / CPU category proposal by Quicksizer • System offer by hardware partner System Landscape • Development System • Test / Q&A System • Production System • SAP Benchmark for APO available for GA shipment Prod. Q&A Dev.

  41. GoingLive and EarlyWatch TCC Service and Monitor for APO • Technical analysis and tuning • Top resource consumers • Optimization of indexes, database, reorganizations etc. • Optimization by coding changes • Business process analysis and tuning • Optimization by redesigning reporting, background jobs, and business processes • Examples: Planning book structure, SNP-Optimizer (profiles), parallel processing • Available for 2.0 and 3.0

  42. Start of Production GoingLive and EarlyWatch 4 5 FinalPreparation Go Live &Support • Analysis • Sizing Plausibility Check • Configuration • Load distribution • R/3 Basis • Optimization • Check central business process • Transactions withhigh resource consumption • Sizing adjustments • Verification • Configuration • Sizing Verification • System usage and bottleneck analysis RegularEarlyWatchAnalysis 2 Months 1 Month +1 Month

  43. Useful Transactions • sm59 -> RFC Destinations • /sapapo/copt01 -> Optimization Server Master Data • /sapapo/copt02 -> Parallel Optimization • /sapapo/opt00 -> PP/DS Optimizer: Customizing/LC Connection Test (+ F8) • /sapapo/opt01 -> PP/DS Optimizer: User Display • /sapapo/opt03 -> Users on Optimization Servers • /sapapo/opt09 -> Version Overview Optimization Server • /sapapo/opt10 -> Optimizer Internal Settings • /sapapo/opt11 -> History of Optimization Runs • /sapapo/opt12 -> Process Overview of Optimization Server • /sapapo/om17 -> Consistency Check Live Cache - Database

  44. Summary APO-Solvers LP MILP Heuristics, Decomposition Techniques GA, CP,TS, Decomposition Techniques ND SNP PP/DS Touren-planung Generating first solution TP/VS (e.g. Heuristic) Improving a solution (e.g. TS) + Optimization Extension Workbench (APX)

  45. Summary • Network Design • strategic decision tool • geographical heuristics and cost-based optimization • Supply Network Planning • mid-term production and distribution planning • heuristics and cost-based optimization -> continuous optimization: LP -> discrete optimization: MILP • discretization should only be used if absolutely necessary • decomposition techniques • limitation of run time / number of improvements

  46. Summary • PP/DS • short-term production planning / detailed scheduling • ´Advanced Heuristics´ • GA and CP • decomposition techniques • test: • solution method • run time • weights in objective function • specific solution method for campaign optimization • Multi Agent Optimization for reducing run time

  47. Summary • TP/VS • short-term distribution planning • ´Advanced Heuristic´ • TS • two-step optimization • vehicle scheduling • carrier selection • limit optimizer load by several selection criteria (locations, orders,...) • test: • run time • weights in objective function

  48. Summary • Optimization Extension Workbench • for specific optimization problems • individual objectives • additional constraints • external optimizer becomes an integral component of APO

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