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Dr. Lazaros Papageorgiou “Supply Chain 2.0 for the Process Industries”

Presentation from Elemica's inSIGHT 2013 Conference

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Dr. Lazaros Papageorgiou “Supply Chain 2.0 for the Process Industries”

  1. 1. Supply Chain Optimisation 2.0 for the Process Industries Prof. Lazaros Papageorgiou Centre for Process Systems Engineering Dept. of Chemical Engineering UCL (University College London) inSIGHT 2013 EU, Wiesbaden, Germany,10 October 2013
  2. 2. About UCL Founded in 1826 The first to admit students regardless of class, race or religion The first to admit women students on equal terms with men The first to offer the systematic teaching of Medicine, Law and Engineering in England UCL is ranked 4th in the world in the 2013 QS World University Rankings
  3. 3. First Chemical Engineering Department in UK 1895-1900 Sir William Ramsay UCL Chemistry discovers the noble gases 1904 Wins Nobel Prize for Chemistry 1916 Memorial fund for Chemical Engineering at UCL established 1923 First UK Chair and Department of Chemical Engineering
  4. 4. Overview • Process industry supply chains • Healthcare - Capacity planning for pharmaceuticals • Agrochemicals – Global supply chain planning • Chemicals – Incorporation of sustainability • Energy - Bioethanol production • Concluding remarks
  5. 5. The process industries Source: www.cefic.org, 2012 • The process sector, excluding pharmaceuticals, globally represents almost 2750bn Euro in annual revenues (www.cefic.org) – approximately 20% from the European Union • Chemical and pharmaceutical manufacturing is at the heart of the UK economy (www.cia.org.uk) – Annual sales of £60bn – 600,000 people depend on the chemical and pharmaceutical industry – 12% of total manufacturing, more than twice that of aerospace
  6. 6. Process Supply Chains § Global, growing industry § Inter-regional trade flows § New investments Ø Increased need/scope for supply chain optimisation - design/infrastructure - planning/scheduling Source: www.cefic.org, 2012
  7. 7. Supply Chain Network Suppliers Manufacturing Plants Warehouses Distribution Centres Customer Demand Zones Material Flow Information Flow Supply chain: “The network of facilities and distribution options that performs the functions of procurement of materials, transformation of these materials into intermediate and finished products, and distribution of these products to customers” (Ganeshan and Harrison, 1995)
  8. 8. Key issues in Supply Chain management 1. Supply chain design/infrastructure • Where to locate new facilities (production, storage, logistics, etc.) • Significant changes to existing facilities, e.g. expansion, contraction or closure • Sourcing decisions – what suppliers and supply base to use for each facility • Allocation decisions – what products should be produced at each production facility; – which markets should be served by which warehouses, etc.
  9. 9. Key issues in Supply Chain management 1. Supply chain design/infrastructure 2. Supply chain planning and scheduling • Production planning – What should be made where & how • Distribution – How best to distribute material • Daily production scheduling at each site – minimise cost/waste/changeovers – meet targets set by higher level planning • Daily vehicle routing – minimise distance – maximise capacity utilisation
  10. 10. Key issues in Supply Chain management 1. Supply chain design/infrastructure 2. Supply chain planning and scheduling 3. Supply chain control: real-time management • How do we get the right product in the right place at the right time? – Replenishment strategy – Rescheduling – E-commerce and data sharing/visibility – …
  11. 11. Methodologies for Supply Chain management • Usually mixed-integer optimisation • Models vary in: – Network representation (how many “echelons”) – Steady-state or multiperiod – Deterministic or stochastic – Single/multiple-objective – Degree of “financial” modelling (taxes, duties etc) – Detail of process representation • Plant level or major equipment level • Mathematical Programming – High-level decisions • strategic/tactical – Fixed/unknown configuration – Aggregate view of dynamics • Simulation-based – Fixed configuration – Detailed dynamic operation – Evaluation of performance measures
  12. 12. Supply chain optimisation: trade-offs • Differences in regional production costs • Distribution costs of raw materials, intermediates and products • Differences in regional taxation and duty structures, exchange rate variations • Manufacturing complexity and efficiency – related to the number of different products being produced at any one site • Network complexity – related to the number of different possible pathways from raw materials to ultimate consumers
  13. 13. 1. Healthcare – Pharmaceutical planning 2. Agrochemicals – Global supply chain planning 3. Chemicals – Sustainability 4. Energy – Biomass supply chains Four focus areas
  14. 14. Pharmaceutical supply chains • Discovery generates candidate molecules • Variety of trials evaluates efficacy and safety • Complex regulation process • Manufacturing – Primary manufacture of active ingredient – Secondary manufacture – production of actual doses – Often geographically separate for taxation, political etc reasons • Distribution – Complex logistics; often global Research & development 15% Primary manufacturing 5-10% Secondary mfg/packaging 15-20% Marketing/distribution 30-35% General administration 5% Profit 20% Source: Shah, FOCAPO (2003)
  15. 15. Global network planning for pharmaceuticals • Each primary site may supply any of the secondary sites • Final product market is divided in 5 geographical areas. Each geographical area has its own product portfolio, demand profile, secondary sites and markets • Secondary products flow between geographical areas is not allowed • Key decisions: -Product (re)allocation -Production/inventory profiles -Logistics plans S. Amer. N. Amer. Africa M. East Asia Europe Primary Primary sites Secondary sites and markets Sousa et al., Chem. Eng. Res. Des., 89, 2396-2409 (2011) – awarded with the 2012 IChemE Hutcsinson medal
  16. 16. Large models ® Decomposition algorithms • Spatial – Step 1: Aggregate market; Relax secondary binary variables; Solve reduced MILP to determine primary binary variables – Step 2: Fix primary decisions; Disaggregate markets; Solve each of the secondary geographical areas separately to determine secondary binary variables – Step 3: Fix secondary binary variables; Solve reduced MILP to reallocate primary products and adjust the production rates/flows • Temporal – Step1: Forward rolling horizon to determine primary and secondary binary variables – Step2: Fix binaries; Solve reduced problem to determine production rates/flows (LP) Sousa et al., Chem. Eng. Res. Des., 89, 2396-2409 (2011)
  17. 17. Illustrative case • 10 active ingredients • 10 primary sites • 100 final products • 5 secondary geographical areas • 70 secondary sites • 10 market areas • 12 time periods (4 months each) • 136,200 continuous and 84,100 binary variables Zopt Gap (%) CPU (s) Relaxed Linear Programming 1,008,827 - - Full space 944,593 6.4 50,049 Spatial decomposition 969,660 3.9 2,513 Temporal decomposition 960,577 4.8 48
  18. 18. Flows of primary products Primary Product Allocation Typical solution
  19. 19. 1. Healthcare – Pharmaceutical planning 2. Agrochemicals – Global supply chain planning 3. Chemicals – Sustainability 4. Energy – Biomass supply chains Four focus areas
  20. 20. Agrochemicals supply chain optimisation • Global supply chain network – AI production – Formulation plants – Markets • Production and distribution planning • Capacity planning • Objectives: – Cost – Responsiveness – Customer service level • Multi-objective MILP model Liu and Papageorgiou, Omega, 41, 369 – 382 (2013)
  21. 21. Case Study • Supply chain network – 32 products, 8 plants, 10 markets • Planning horizon – 1 year / 52 weeks • Discrete lost sales levels: – 1%, 3%, 5% • ε-constraint method – Pareto-optimal solutions
  22. 22. Capacities for different scenarios • Minimum cost – PCE: F2 – CCE: F4 • Minimum flow time – PCE: F2 – CCE: F6 • F2 under PCE: higher capacity at flow-time minimisation 22 Minimum cost Minimum flow-time
  23. 23. Material Flows • Fewer long-distance flows for minimum flow-time scenario PCE CCE Minimum cost Minimum flow-time Minimum flow-time Minimum cost
  24. 24. 1. Healthcare – Pharmaceutical planning 2. Agrochemicals – supply chain optimisation 3. Chemicals – Sustainability 4. Energy – Biomass supply chains Four focus areas
  25. 25. Incorporation of sustainability indicators into supply chain optimisation • Supply Chain Characteristics: – 11 major raw materials – 17 raw material suppliers – 7 production plants – 270 products – 268 customer regions • Given: Network structure, customer demands, reactor, plant, and transportation capacities • Objective: Optimise tactical planning for one year • Data Sources: Dow plant production, waste, emissions, & energy use; CEFIC transportation emissions Zhang et al., LCA XIII conference (2013)
  26. 26. Product allocations for the different objectives What if no capacity constraints?
  27. 27. Cost & GHG emission: Pareto Curve
  28. 28. Cost & GHG Emission & Lead Time § Clear trade-offs between economic, environmental, and responsiveness performance § Considerable decrease in GHG emission or lead time can be achieved by small increase in cost
  29. 29. 1. Healthcare – Pharmaceutical planning 2. Agrochemicals – Global supply chain planning 3. Chemicals – Sustainability 4. Energy – Biomass supply chains Four focus areas
  30. 30. Bioenergy supply chains § Main challenges the globe is facing today: ü Global climate change ü Security of energy supply ü Rising oil prices and depleting natural resources § Transportation sector is one of the main contributors to global CO2 emissions § Bioenergy: most promising option to tackle these challenges § Bioethanol production – rapidly increasing Source: IEA, 2008, Worldwide Trends in Energy Use and Efficiency Source: Timilsina and Shrestha, Energy (2011)
  31. 31. • Biofuel supply chain • Multi-objective MILP 1 2 3 4 4N 8N 1 3 5 7 28 6 4 Optimisation of biofuel supply chains Minimise TDC Production constraints Demand constraints Sustainability constraints Transportation constraints TDCmin TEI TEImax TEImin TDCmax max TEITEI l£ ( )10 ££ l • Spatially-explicit representation • “Neigbourhood flow” approach Akgul et al., Ind. Eng. Chem. Res, 50, 4927-4938 (2011) Akgul et al., Comput. Chem. Eng., 42, 101-114 (2012) TDC
  32. 32. Case study: UK bioethanol supply chain 31 26 27 28 29 30 24232221 32 33 34 1918 2017 13 14 15 9 10 11 6 7 8 3 4 5 1 2 25 16 12 525 1,674 1,708 1,505 687 1,800 Demand centre Total demand: 7,899 t ethanol/d § Bioethanol production in the UK from wheat as first generation feedstock and wheat straw, miscanthus and SRC as second generation feedstocks § 2020 demand scenario with an EU biofuel target of 10% (by energy) has been studied § Set-aside land in the UK (570.2 kha) is used for the cultivation of dedicated energy crops (miscanthus and SRC) Akgul et al., Comput. Chem. Eng., 42, 101-114 (2012)
  33. 33. Case study – 2020 demand scenario Biofuel production rate (t/d) Resource demand (t/d) Resource flow (t/d) Rail 100 100 Demand centre Biofuel plant Wheat cultivation rate (t/d) Ship Wheat straw collection rate (t/d) Road 100 100 100 2020A: Minimum cost 2020B: Minimum Environmental Impact 31 26 27 28 29 30 24232221 32 33 34 1918 2017 13 14 15 9 10 11 6 7 8 3 4 5 1 2 25 16 12 525 1,674 1,708 1,505 687 1,800 712 686 712 521 438 575 219 712 712 712 219 1,835 464 2,578 1,749 4,024 1,388 3,189 731 1,260 819 978 421 274 560 238 139 188 219472 213 584 521 128 238 564 219 219 219 219 7 4 91 59 189 24 15 1,402 911 1,891 1,823 19 13 103 67 548 570 347 349 525 525 575 208 30 563 219 219 1 219 219 31 26 27 28 29 30 24232221 32 33 34 1918 2017 13 14 15 9 10 11 6 7 8 3 4 5 1 2 25 16 12 525 1,674 1,708 1,505 687 1,800 525 712 712 575 712 712 662 712 575 712 575 Wheat import: 2,389 t/d 712 135 88 260 169 333 217 86 56 238 155 76 49 333 217 908 590 978 26 17 421 274 560 450 293 1,835 897 293 464 54 35 2,576 139 90 64 41 1,891 2,678 689 448 1,388 2,678 765 497 731 712 386 326 575 94 618 224 488 112 600 575 Miscanthus cultivation rate (t/d)100
  34. 34. Case study - Optimal UK bioethanol supply chain 5.6 5.7 5.8 5.9 6.0 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7.0 5.8 5.9 6.0 6.1 6.2 6.3 6.4 6.5 6.6 TDC (m£/d) TEI(ktCO2-eq/d) 62% GHG savings 69% GHG savings TDC (m£/d) TEI(ktCO2-eq/d) - 100% domestic wheat use - 70% set-aside land use - 58% domestic wheat use - 100% set-aside land use Biofuel production (% of the overall) Domestic wheat Imported wheat Wheat straw Miscanthus Minimum cost configuration 24% 10% 13% 53% Minimum environmental impact configuration 14% 0% 8% 78%
  35. 35. Concluding remarks • Optimisation-based methodologies are increasingly used for deterministic supply chain planning and design • Challenges – Detail of process representation – Integration across length and time scales – Dealing with complex uncertainties/robustness – Multiple performance measures – Efficient solution algorithms – Designing supply chains of the future • Low carbon energy supply chain systems • Water • Customised and affordable healthcare • Biomass driven: Bioenergy/biomaterials and biorefineries • Waste-to-value and reverse production systems (closed loop supply chains) • …
  36. 36. Acknowledgements • UK Engineering and Physical Sciences Research Council • Centre for Process Systems Engineering • Prof Nilay Shah • Dr Songsong Liu, Ms Ozlem Akgul, Dr Rui Sousa

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