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M. Grunow Technical University of Denmark , Copenhagen

H.-O. Günther Technical University of Berlin, Germany. M. Grunow Technical University of Denmark , Copenhagen. Supply Network Planning and. Advanced Planning Systems. National Tsing Hua University, Hsinchu, Taiwan December 13, 2007. Outline. Introduction. Advanced planning systems.

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M. Grunow Technical University of Denmark , Copenhagen

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  1. H.-O. GüntherTechnical University of Berlin, Germany M. GrunowTechnical University ofDenmark, Copenhagen Supply Network Planning and Advanced Planning Systems National Tsing Hua University, Hsinchu, Taiwan December 13, 2007

  2. Outline Introduction Advanced planning systems Case 1: Global network design Case 2: Supply network planning Case 3: Production planning and scheduling Case 4: Value chain management Outlook: Teaching SNP & APS

  3. SupplyChain Management • www.wikipedia.org: “The primary objective of supply chain management is to fulfill customer demands through the most efficient use of resources.“

  4. SupplyChain Management • www.wikipedia.org: “A supply chain, logistics network, or supply network is a coordinated system ... …of entities, activities, information and resources involved in moving a product or service from supplier to customer.“

  5. Outline Introduction Advanced planning systems Case 1: Global network design Case 2: Supply network planning Case 3: Production planning and scheduling Case 4: Value chain management Outlook: Teaching SNP & APS

  6. Generations of PPC software • Predecessors of PPC systems • Focus on inventory control • Basic order processing 1960s • Material requirements planning (MRP) • Bill of material files • Calculation of net requirements 1970s • Manufacturing resources planning (MRP II) • Enhanced planning functions • Integration of financial accounting and management functions 1980s • Integrated systems • CIM: Integration of manufacturing • ERP systems covering the whole enterprise 1990s 2000s • Advanced Planning and Scheduling systems • Integration into supply chain management concept • Use of true optimization techniques

  7. Strategic Network Design DemandPlanning Supply Network Planning Production Planning / DetailedScheduling Transportation Planning / Vehicle Scheduling External Procurement Order Fulfilment ATP / CTP Advanced Planning Systems (APS) • APS modules

  8. Strategic Network Design long-term Supply Network Planning mid-term Production Planning / DetailedScheduling short-term Case 4: Value chain management Industrial case studies Case 1: Global network design for the production of electrical components Case 3: Characteristics dependent planning in the consumer goods industry Case 2: Multi-site production-distribution planning in the chemical industry

  9. Outline Introduction Advanced planning systems Case 1: Global network design Case 2: Supply network planning Case 3: Production planning and scheduling Case 4: Value chain management Outlook: Teaching SNP & APS

  10. Strategic Network Design DemandPlanning long-term Supply Network Planning mid-term Production Planning / DetailedScheduling Transportation Planning / Vehicle Scheduling External Procurement short-term Order FulfilmentATP / CTP Case 1: Global network design Grunow, M., Günther, H.O., Burdenik, H., Alting, L.:Evolving production network structures.CIRP Annals (to appear) Case 1: Global network design for the production of electrical components

  11. Strategic Network Design Strategic network design • Decisions Number of plants and DCs Locations and capacities Assignment of products to plants Assignment of locations to each other e.g. customers to DCs Determination of transportation links • Mathematical methods Heuristics, MILP, Clustering techniques

  12. Case 1: Insights APS based on generic model formulations provide limited support for strategic network design. Redesign of global network also affects the organizational structure and business processes. Potential cost savings achieved through quantitative modeling underestimated by decision makers (engineers).

  13. Products 3 Levels • Vacuum interrupters • Kits (for local content and taxation reasons) • Switches (usually containing 3 vacuum interrupters)

  14. Supply network Current centralized network • Production sites at • Head production centers (produce vacuum interrupters): GER (with R&D) and CHINA 1 • USA, INDIA, CHINA 2 • MEX, BRA (from kits only)

  15. Supply network Future network • Reduced production and logistics costs • Reduced production complexity at head production centers • Increased flexibility through networked supply and distribution

  16. Supplynetwork Product relocation depends on • Product maturity • new products at sites with R&D department • Personnel • required for product relocation (to qualify the new production facility for the production of the new products). • Production capacities • test runs and ramp-up of new products Complexity and flexibility • Diversity of products at facility increases scheduling complexity • Products at multiple facilities increase network complexity • Production close to customer markets • Exchange rate risks and taxation

  17. Supply network Product relocation depends on • Product maturity • new products at sites with R&D department Error ! Not covered ! • Personnel • required for product relocation (to qualify the new production facility for the production of the new products). • Production capacities • test runs and ramp-up of new products Standard commercial APS Complexity and flexibility • Diversity of products at facility increases scheduling complexity • Products at multiple facilities increases network complexity • Production close to customer markets • Exchange rate risks and taxation

  18. MILP optimization model Minimization of the relevant production and logistics costs • variable manufacturing costs (including material and personnel) • costs for shipments to customers (+ customs duty) • costs for shipments of intermediates and kits from head production centers to other facilities in the network • investment costs required for the expansion of the production capacities for final products • costs associated with the complexity in a facility • net present value calculation

  19. MILP optimization model • Demand • Production (final products) (kits) (intermediates) • Shipment of intermediates (kits) (intermediates)

  20. MILP optimization model • Capacities (final product capacity incl. expansions, here: testing equipment) (no. of expansions per facility) (no. of expansions in network per perioddue to personnel requirements, budget constraints) (intermediates capacity) (kit capacity)

  21. MILP optimization model • Relocation M (continuous-binary production variables) (relocation-production) (number of total relocations per period, due to personnel requirements) (number of relocations per facility and period, due to capacity requirements for test runs and ramp-ups) (ban on relocation of products in R&D phase)

  22. MILP optimization model • Complexity (production of group) (network complexity) (facility complexity) • Local production (flexibility, limitation of exchange risks, demonstration of goodwill, marketing)

  23. MILP optimization model Manager: „Sometimes we make strategic SND decisions very intuitively.“ Advantages of optimization models • Consideration of interdependencies between entities in the SC. • Quick and easy evaluation of scenarios • Provide understandable quantitative results (e.g. cost savings) as basis for final management decisions.

  24. Exemplary results Scenarios • Current business plan • Scenario 1: Actual configuration with optimized production/distribution • Scenario 2: Reassignment of products with fixed capacity • Scenario 3: Reassignment of products with capacity adjustments Results • Savings in production costsovercompensate additional logistics costs. • Shift of production volumes and products from Germany to China • Increased capacity in Mexico and China

  25. Outline Introduction Advanced planning systems Case 1: Global network design Case 2: Supply network planning Case 3: Production planning and scheduling Case 4: Value chain management Outlook: Teaching SNP & APS

  26. Strategic Network Design DemandPlanning long-term Supply Network Planning mid-term Production Planning / DetailedScheduling Transportation Planning / Vehicle Scheduling External Procurement short-term Order FulfilmentATP / CTP Case 2: Supply network planning Case 2: Multi-site production-distribution planning in the chemical industry

  27. Supply Network Planning Supply network planning • Mathematical methods LP and MILP, heuristics • Decisions Allocation of production quantities between plants Supply from the plants to DCs and from the DCs to customers Smoothing out seasonal cycles in demand Consideration of production, transportation, and handling capacities as hard constraints Consideration of demand, due dates, and safety stocks as soft constraints

  28. Case 2: Insights Generic model formulations embedded in SNP modules of APS do not consider application-specific features. Considerable cost savings achieved through application of optimization model. High degree of acceptance by management.

  29. Company profile • DEGUSSA AG, Düsseldorf, Germany • World largest producer of special chemicals • Subsidiaries in all continents • 46.000 employees / 300 plants worldwide • Turnover in 2005: 11,800 billion €

  30. Trans-formation Energy Gas Feed A Process Mainproduct Feed B Silo Silo Silo Feed C Production process Carbon Black • Production volume: several 100.000 t per annum • 100 product specifications • Continuous production process

  31. Implementation of an APS • Company negotiates annual volumes with key customers. Forecasting the period distribution of annual demand Allocation of customer demands to production sites • Customers request deliveries upon short notice.

  32. 1997 1998 1999 20000 t 18000 16000 14000 12000 10000 Observed demand 8000 Forecast incl. seasonality 6000 4000 2000 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Month Demand planning • Adaptation of Winters forecasting technique • Considerably increased forecast accuracy • Forecast represents network-wide commitment

  33. Decisions in supply network planning Forecasting the period distribution of annual demand Allocation of customer demands to production sites • Production volume in the production sites and at each production train • Transportation volumes between production sites and customers • Generation of energy from side-products

  34. Objectives in Supply Network Planning • Production costs(site, train) Minimization of • Transportation costs(Distance, carrier) • (negative) energy refund • Inventory costs

  35. Selected constraints: Distribution • Customer Demand • Limited substitutability of products • Delivery only from sites, which have a customer approval for the product • A customer demand may only be covered by deliveries from alimited number of sites

  36. Selected constraints: Energy • Energy • Transformation capacity of the sites Transformation

  37. Data base OPL Studio SQL MILP Solver Data models (CPLEX) SQL (OPL) Option COM- User and scenario administration Graphical user interface Optimization software architecture

  38. Application of the optimization model • Currently numerous managers are using the tool for operative planning. • Rolled out in Europe, US and Asia. • Estimated financial benefitper year from supply network planning exceeded project costs by far. • Further benefits arise from improved supply network design. • Scenario mode is used extensively, e.g., for capacity decisions, evaluation of approvals and of the profitability of energy transformation.

  39. Performance improvement in optimization • Average algorithmic improvement 1988  2001 • Estimated machine improvement 1988  2003: 800 x • Net improvement for LP problems 1988  today: ~2,000,000 x Bixby, B., Solving real-world linear programs: a decade and more of progress. Operations Research, 50 (2002), pp. 3-15

  40. Outline Introduction Advanced planning systems Case 1: Global network design Case 2: Supply network planning Case 3: Production planning and scheduling Case 4: Value chain management Outlook: Teaching SNP & APS

  41. Strategic Network Design DemandPlanning long-term Supply Network Planning mid-term Production Planning / DetailedScheduling Transportation Planning / Vehicle Scheduling External Procurement short-term Order FulfilmentATP / CTP Case 3: Production planning / detailed scheduling Günther H.O., Grunow, M., Neuhaus, U.:Realizing block planning concepts in make-and-pack production using MILP modelling and SAP APO. IJPR (2006), 3711-3726 Lütke Entrup, M., Günther, H.O., van Beek, P., Grunow, M., Seiler, T., MILP approaches to shelf life integrated planning and scheduling in yogurt production. IJPR (2005), 5071-5100 Case 3: Characteristics dependent planning in the consumer goods industry

  42. Production Planning / DetailedScheduling Production planning / detailed scheduling • Decisions • Mathematical methods Generation of production orders Genetic algorithms Constraint programming Allocation of resources according to a finite scheduling policy Rules and heuristics Application specific algorithms Lot-sizing, sequencing, and procurement proposals Consideration of the availability of resources as hard constraints Consideration of due dates, time windows etc. as soft constraints

  43. Case 3: Insights Efficient support by characteristic based planning tool of APS module. Development of a mathematical modelling approach based on human expertise. High performance of MILP model enables reactive planning mode.

  44. Production cycle Block 1 Block 2 Block 3 Block 4 Block planningconcept • Given a natural sequence of set-ups (e.g. light to dark) • Decisions on cyclic production patterns (i.e. lot sizes) • Minimizing inventory and set-up costs

  45. 1 2 3 4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Production cycle 4 weeks Production order Major setup Numericalexperiments • Production of hair dyes time Setup families

  46. 1 2 3 4 4 weeks 8 weeks 12 weeks ... Setup families 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 ... Production cycle Period 1 Period 2 Period 3 4 weeks Production order Major setup Numerical experiments • Production of hair dyes time Solution • MILP model: ILOG OPL Studio and CPLEX 7.0 • Characteristic dependent planning of SAP APO 3.1

  47. Combination of characteristics dependent planning and MILP modelling seems appealing Numerical experiments MILP block planning model APO implementation • Recuced data volume • Small CPU times

  48. Outline Introduction Advanced planning systems Case 1: Global network design Case 2: Supply network planning Case 3: Production planning and scheduling Case 4: Value chain management Outlook: Teaching SNP & APS

  49. Strategic Network Design DemandPlanning long-term Supply Network Planning mid-term Production Planning / DetailedScheduling Transportation Planning / Vehicle Scheduling External Procurement short-term Order FulfilmentATP / CTP Case 4: Value chain management Case 4: Value chainmanagement SCM is usually understood as a “coordinated system … involved in moving a product or service from supplier to customer.“

  50. Case 4: Insights Dynamic spot pricing for sales and procurement makes a business unit highly profitable. Coordinated decision making along the entire supply chain appears to be very effective. Change of paradigm from “supply chain management” to “value chain management”?

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