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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
2
Descriptive models
Content
Prescriptive models
Predictive models
2
1
345
Who is Merck ?
Conclusion
2
3
Descriptive models
Content
Prescriptive models
Predictive models
2
1
345
Who is Merck ?
Conclusion
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4
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.
5
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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Descriptive models
Content
Prescriptive models
Predictive models
2
1
345
Who is Merck ?
Conclusion
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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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19
Descriptive models
Content
Prescriptive models
Predictive models
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Who is Merck ?
Conclusion
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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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Descriptive models
Content
Prescriptive models
Predictive models
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Who is Merck ?
Conclusion
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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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Dis
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
33
Descriptive models
Content
Prescriptive models
Predictive models
2
1
345
Who is Merck ?
Conclusion
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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
35
Q&A
35
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