Presentation from Elemica's inSIGHT 2013 Conference

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 1. Healthcare – Pharmaceutical planning
2. Agrochemicals – Global supply chain planning
3. Chemicals – Sustainability
4. Energy – Biomass supply chains
Four focus areas

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. 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. 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. 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. Flows of primary products
Primary Product Allocation
Typical solution

19. 1. Healthcare – Pharmaceutical planning
2. Agrochemicals – Global supply chain planning
3. Chemicals – Sustainability
4. Energy – Biomass supply chains
Four focus areas

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. 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. 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. Material Flows
• Fewer long-distance flows for minimum flow-time scenario
PCE CCE
Minimum cost
Minimum flow-time Minimum flow-time
Minimum cost

24. 1. Healthcare – Pharmaceutical planning
2. Agrochemicals – supply chain optimisation
3. Chemicals – Sustainability
4. Energy – Biomass supply chains
Four focus areas

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. Product allocations for the different objectives
What if no capacity constraints?

27. Cost & GHG emission: Pareto Curve

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. 1. Healthcare – Pharmaceutical planning
2. Agrochemicals – Global supply chain planning
3. Chemicals – Sustainability
4. Energy – Biomass supply chains
Four focus areas

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. • 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. 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. 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. 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. 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. 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