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