Didier DayenSenior Director Global Advanced Planning

Berlin - November 2017

Supply Chain Management Strategies Summit

WHICH DIGITAL TRANSFORMATIONS WILL CHANGE THE WAY OF WORKING AT MERCK HEALTHCARE

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Prescriptive models

Predictive models

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Who is Merck ?

Conclusion

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Who is Merck ?

Conclusion

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WE ARE MERCK –THE ORIGINAL

Who we are - History

Nearly 350 years of experience for patients and customers

Friedrich Jacob Merck purchases the Angel Pharmacy (Engel-Apotheke) in Darmstadt

Emanuel Merck begins production on an industrial scale

In 1887, Merck opened its own office in New York, which gave rise to the subsidiary Merck & Co. three years later.

Our U.S. subsidi-ary Merck & Co.is expropriatedas a consequence of World War I

1668 1827 1917

Merck –the originalholds the globalrights to the Merck name and brand. Exceptions are Canada and the United States,where we operate as EMD Serono,MilliporeSigma and EMD Performance Materials.

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What we do

Life SciencePerformance

MaterialsHealthcare

• A wide range of high-tech chemicals, such as:

Liquid crystals and OLEDmaterials for displays & lighting

Effect pigments for coatings and cosmetic products

Specialty chemicals for the semiconductor industry

Functional materials for solar panels

• Innovative tools and laboratory supplies for the life science industry that make research and biotech production better, faster and safer

• Broad and in-depth portfolio of 300,000 products

• Industry leading e-commerce platform, SigmaAldrich.com

• Award-winning innovation

• Prescription drugs and solutions to treat cancer, multiple sclerosis, infertility, cardiovascular and metabolic diseases

• Over-the-counter products for a healthy lifestyle

• Allergy products

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Healthcare

Our key therapeutic areas

Erbitux®

Colorectal cancer, head and neck cancer

Rebif®

Relapsing multiple sclerosis

Gonal-f®

Infertility

Luveris®/Ovidrel®

Female infertility

Saizen®

Growth hormone disorders

Serostim®

HIV-associated wasting

Glucophage®

Type 2 diabetes

Concor®

Cardiovascular diseases

Euthyrox®

Thyroid disorders

Oncology Neurodegenerative diseases

Fertility Endocrinology CardioMetabolicdisorders

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Our business sectors in numbers 2016

Salesin € million

EBITDA pre exceptionals*

in € millionResearch and developmentin € million

Healthcare Life Science Performance Materials

2,511

(17%)

6,855

(45%)

5,658

(38%)

1,106

(23%)

2,128

(43%)

1,652

(34%)

260

(13%)

213

(11%)

1,496

(76%)

15 bio 4.9 bio 2 bio

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We are global

Sales by region 2016in € million

Employees by region 2016 (as of December 31)

We operate around the world and have a strong presence in growth markets.

129 nationalities are represented at Merck and our employees work at sites in 66 countries around the world.

1,136

(8%)

4,735

(31%)

4,736

(31%)

Europe North America Asia-Pacific (APAC)

3,858

(26%)

4,140

(8.2%)

24,438

(48.5%)

10,754

(21.3%) 10,037

(20.0%)

Latin America

559

(4%)

1,045

(2.0%)

Middle Eastand Africa (MEA)

50'00015 bio

MDA Semoy Spittal Mollet Mexico Other

APISubcontractors

Global SNO

From Local Steering To Global Steering

Affiliate 1 Affiliate 2 Affiliate 3 Affiliate 4 Affiliate 5 Affiliate 6

Affiliate 1 Affiliate 2 Affiliate 3 Affiliate 4 Affiliate 5 Affiliate 6

MDA Semoy Spittal Mollet Mexico Other

APISubcontractors

C1

C2 C3Cn C1

C2 C3Cn C1

C2 C3Cn C1

C2 C3Cn C1

C2 C3Cn C1

C2 C3Cn

C1

C2 C3Cn C1

C2 C3Cn C1

C2 C3Cn C1

C2 C3Cn C1

C2 C3Cn C1

C2 C3Cn

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Global Supply Chain organization

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…Countries

Business Units

ConsolidatedDemand

Supply ChainMaster Plan

Master Production

Plan Packing

Master ProductionPlan SFP

Active Ingredients

requirements

Production execution

Production execution

Key Materials requirementsCapacity planning

SAP IBPDemand

ERP

DistributionDC/

Market

Distribution Plan

(DRP)

• Materials requirements• Detailed Scheduling• Procurement• Plan execution

SupplyPlanning

InventoryDecoupling point

Shipmentexecution

DemandPlanning

Mfg Site

JDAMaster Planning

JDAFulfilment

Inte

gra

ted B

usin

ess P

lan p

rocess (

IBP)

Planning processes: linking Demand, Supply and Production

~14’000 DFU’s

~10’000 active SKU’s

>50 affiliates

120 countries

10 global sites

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Our ambitionChange the way people work with data

Real-time data visualization

Data-driven decisionmaking

Automated dataacquisition & integration

Empower reporting in leveraging technology

to bring data-driven decision making12

Digital journeyKey Strategic Objectives

E2E Visibility and Real

Time

Advanced Analytics

Internet of Things

Digital Teams / people

Automation / Robotics

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Service / Sales

SC Cost

Demand

IBP

SC control tower

Inventory

Advanced BlendedMeasures + Alerts

Supply

Transport VisibilitySystem

SC Control Tower

• Fulfilment risk

• Safety variations Tracker

• NPI Tracker

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Example: Regional cockpit

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Countries

Example: Inventory

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Example: Predictive Logistics costs

Analysis on Costs, Volumes, Main Deviations by Country17

Example: Predictive Logistics costs

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Integrated Business Planning (IBP)

Demand Planner

“we forecast 200” Commercial:

“due to our sales

initiative we will sell 400”

Finance:

“we have

budget of 300”

Integrated

Plans

Manufacturing:

“we have

capacity for 350”

“The Integrated Business Planning is the evolution of S&OP into a fully integrated management and supply chain collaboration process” – Oliver Wight, White Paper series

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1) Bottom-up sales forecasting

2) Monthly Financial alignment

3) Breathing space for upsides

Supply

Planning

Financial

Planning

4) Single source of truth

• Commercial

• Controlling

• GMS

• Each item

• Each month

• Quantity

• Value Uncons-trainedSFOR

Con-sensusSFOR

Breath.Space

4 things to remember about IBP @ Merck BioPharma

IBP Dashboardavailable to all

One set ofnumbers

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Algorithms

Forecasting Tools key points

DEMAND MACHINE LEARNING

Forecasting tool

Outlier Treatment

Double MAD

Threshold 3.5

Replace outliers by mean

Machine Learning and Classical AlgorithmsBest Fit Approach

Full History

After selecting the Best AlgorithmsRe-Run including the test period

IMS

Use IMS data as an exogenous signal

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Forecast Performance

FORECAST% of SKU’s

<100% Error% of SKU’s <20% Error

FusionOps 87% 52%

MarketIntelligence

76% 37%

ClassicalStatistic

74% 34%

2’000

1’500

1’000

500

# S

KU

Mean Absolute Percentage Error

Machine Learning

MarketIntelligence

ClassicStatistics

Note: demo data for ilustration purposes only

DEMAND MACHINE LEARNING

June 2017

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ADVANCED ANALYTICSEXAMPLE OF DEMAND STATISTICAL FORECASTING

LEGACY

HISTORICAL SELL IN

NOW

FORECASTED SELL OUT

HISTORICAL SELL OUT

MARKET TRENDS

3rd Party Market Research

MARKETING AUTHORIZATIONS

HISTORICAL SELL IN

ADVANCED ANALYTICSEXAMPLE OF DEMAND STATISTICAL FORECASTING - CENTRO

OBJECTIVE• To highlight anomalies in market and

importation data that show potential opportunities to increase sales and size market share.

DATA• Competitor Sales (IMS).• Competitor importations (customs).

EXAMPLE • Sales have slightly decreased, but

importations of this product been stopped.

• This product is experiencing strong generic competition and the company is pulling it back from certain markets.

• Opportunity to size the share of the market the competitor product is vacating.

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Stock-in-Trade Inventory - ChinaStock level and projections transparency at all Tiers

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Distribution 360°

Deep visibility into Distribution and Terminal geo-locations, relationships, flows and stocks

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IOT EXPERIMENTATIONA precise detection and real-time signaling to the operator will improve operations

Punch

tablet

tablet

tablet

tablet

Production Inspection Visualization Analysis

120 pills per second are produced Two high-speed cameras capturing images from both sides

Deep Learning techniques are used to detect even smallest deviations and shown in real-time

Results and detections will be analyzed for correlations

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No human intervention

• Statistical forecasting (IBP, JDA..)

• Predictive Advanced Forecasting(Advanced Analytics) through DemandArchitects

• Customers/Patients data based

DEMAND automatically predicted

• Control Tower driving real-time synchronization with demand

• Modelling/What-if scenarios to anticipate Quality/Supply failures

• Predictive Maintenance across the E2E- supply network

SUPPLYproactively prescribed

Forward looking supply management

Innovation and digitalization of the future Supply Chain

• Proactive Collaboration across all chain partners to leverage scale (4PL, Smart packaging…)

• Segmented logistics to deliver personalised medicines

LOGISTICS Affordableflexibility

Highest Quality @ Lowest Cost

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Design and operate a «self driving» supply chain powered by AI

DEMAND Signal

SUPPLY Plan

PRODUCTION Schedule

Warehousing & Distribution

FINAL CUSTOMER

Human Intervention (Today)

- Upcoming tenders not in calendar- Competitors stock-out info- Speculative buying tactics- New distribution channels- Life Cycle Mgt & New launches

- Regulatory constraints (new)- Unplanned Maintenance- Inventory target change

- Line downtime/uptime- Quality incidents/release- Operations staffing

- Inventory Record Accuracy- Quality Release

- Shipping deviations

Artificial Intelligence (Tomorrow)

Hystorical BackwardLooking Forecasting

Predictive ForwardLooking Forecasting

360 DegreesKnowledgeAnalyzer

Regulatory

Quality

SupplyNetworkInventory

Levels

Segmentation Criteria

Cost Dual Sourcing

MarketAccess

Real Time Data Gathering fromShopFloor (IoT)

Real Time Data Gathering from

Distribution

PRED

ICTIV

ECapabilitie

s

PRESC

RIP

TIV

ECapabilitie

sD

ESCRIP

TIV

ECapabilitie

s

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trib

uti

on

Pla

nn

ing

Maste

r P

ro

du

cti

on

Pla

nn

ing

Deta

iled

Sch

ed

uli

ng

&P

ro

du

cti

on

Pla

nn

ing

Pro

du

tcio

n&

Dis

trib

uti

on

execu

toin

Dep

loym

en

t

Dem

an

d

Pla

nn

ing

Qu

ali

ty d

evia

tio

ns

Co

sts

an

d m

arg

in

Reg

ula

tory

ER

P …

End-to-end Planning and Execution Optimizer

Optimization layer

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Prescribe

What shouldhappen?

Predict

What couldhappen?

Describe

What ishappening?

DEMANDautomatically

predicted

SUPPLYproactivelyprescribed

Machine learningReal-time synchro

Duigitalization journey

Our vision: self-driving operations

Machine learning and Advanced Analytics

End to end SC Visibility and alerts

Our Advanced analytics journey

Forecasting tool

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Q&A

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Didier DayenSenior Director Global Advanced Planning

November 2017

Supply Chain Management Strategies SummitBerlin

WHICH DIGITAL TRANSFORMATIONS WILL CHANGE THE WAY OF WORKING AT MERCK HEALTHCARE