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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
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
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
Overview
• Process industry supply chains
• Healthcare - Capacity planning for pharmaceuticals
• Agrochemicals – Global supply chain planning
• Chemicals – Incorporation of sustainability • Energy - Bioethanol production
• Concluding remarks
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
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
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)
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.
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
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 – …
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
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
1. Healthcare – Pharmaceutical planning
2. Agrochemicals – Global supply chain planning
3. Chemicals – Sustainability
4. Energy – Biomass supply chains
Four focus areas
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)
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
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)
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
Flows of primary products
Primary Product Allocation
Typical solution
1. Healthcare – Pharmaceutical planning
2. Agrochemicals – Global supply chain planning
3. Chemicals – Sustainability
4. Energy – Biomass supply chains
Four focus areas
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)
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
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
Material Flows
• Fewer long-distance flows for minimum flow-time scenario
PCE CCE
Minimum cost
Minimum flow-time Minimum flow-time
Minimum cost
1. Healthcare – Pharmaceutical planning
2. Agrochemicals – supply chain optimisation
3. Chemicals – Sustainability
4. Energy – Biomass supply chains
Four focus areas
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)
Product allocations for the different objectives
What if no capacity constraints?
Cost & GHG emission: Pareto Curve
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
1. Healthcare – Pharmaceutical planning
2. Agrochemicals – Global supply chain planning
3. Chemicals – Sustainability
4. Energy – Biomass supply chains
Four focus areas
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)
• Biofuel supply chain
• Multi-objective MILP
1
2
3
4
4N 8N
1
3
5
7
2 8
6 4
Optimisation of biofuel supply chains
Minimise TDC
Production constraints Demand constraints Sustainability constraints Transportation constraints
TDCmin
TEI TEImax
TEImin
TDCmax
maxTEITEI 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
Case study: UK bioethanol supply chain
31
26 27 28 29 30
24 23 22 21
32 33 34
19 18 20 17
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)
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
24 23 22 21
32 33 34
19 18 20 17
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
219 472
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
24 23 22 21
32 33 34
19 18 20 17
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
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 (
kt C
O2-
eq/d
)
62% GHG savings
69% GHG savings
TDC (m£/d)
TEI (
kt C
O2-
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%
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) • …
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