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Phy•gi•tal
A Global E2E Planning Transformation
@ Janssen Pharma
[convergence of <physical> & <digital> worlds]
Good Morning!
Johan Maes Koen Jaspers
Sr. Director Global Planning Partner
1th life sciences company in the
Gartner Supply Chain Top 25
#8
170 Brands
5 ERP Systems
Pharma = Complex Business Environment
18.219 Recipes13.692 SKUs
393 Planners 800 Resources33.000 Routes
40.7 B$ Sales
True Global Supply Chain
#NoSynchronization
#GlobalOrchestration
↑ Maturity Level
Site
Planning
End-To-End
Planning
Frictionless
Planning
Improve Customer
Experience
Cash Conversion
Cycle
Efficiency
Effectiveness
Our Planning Transformation Journey
2012 2013-2015 2016-2020 >2021
TOBE
PROCESS
INTERIM
SOLUTIONS
SUSTAINABLE
SOLUTIONS
NEXT
WAVES
What does
End-To-End
really mean?
TOBE
PROCESS
in-market → first internal production (API)
DOWNSTREAM
UPSTREAM
1
SUPPLIER API BULK FILL/PACK DELIVER SELL PATIENT
DOWNSTREAM
UPSTREAM
SUPPLIER API BULK FILL/PACK DELIVER SELL PATIENT
2 patient → internal → supplier
<network●of●networks>
1 Global Planning Concept Design
DP-20 Generate base demand – Process decomposition
DEMAND PLANNING
D
W
M
Q
A
Daily
Weekly
Monthly
Quarterly
Annually
Level4SummaryLevel5Level3
Supply Network PlanningDemand Planning Production Planning Inventory Management E2E Planning S&OP Planning
DP-10
Define demand planning strategy
DP-20
Generate base demand
DP-30
Generate event demand
DP-40
Validate demand plan
DP-20-70
Release base demand
DP-20-60
Translate base demand
into value
DP-20-50
Validate base demand
DP-20-40
Translate to weekly
forecast granularity for
first 3 months
DP-20-10
Compile source data
M M M M M
▪ Generated base demand
(at lowest demand
forecasting unit) feeds
into event demand
planning
▪ Valorize the base
demand
▪ Generate and review
exception alerts
▪ Check results of
analytical models
▪ Review any exceptions
that are flagged based
on the exception
parameters
▪ Translate monthly
base demand
forecast to weekly
granularity for first
3 months
DP-20-30
Generate forecast
M
▪ Generate forecast based
on best analytical model
at optimal planning
hierarchy
▪ Recalibration of
statistical model based
on DP-20-50
DP-20-20
Update source data based
on event classification
M
▪ Exclude data points /
periods that
misrepresent base
demand trends
→ Automatic outlier
detection with OMP
▪ Classify historical
demand into “event
history” and “base
history”
▪ Map demand history for
product swaps or
rationalization
▪ Collect historical sales
data
▪ Compile other objective
inputs where applicable
(e.g., customer/patient
data)
▪ Where applicable, capture
trade partners demand
▪ Check availability of
master data (e.g., price
for value translation)
Scattered
IT landscape &
master data
TECHNOLOGY
SOLUTIONS
Scattered IT & Data Landscape
EXECUTION
• Orders
• Inventory
PLAN ORCHESTRATION
• End-to-end planning
• Detailed scheduling
VIRTUAL TWIN
• Actual network flows
• Supply chain parameters
‘Augmented’ Technology Framework
@ Maturity Stage 5
Global
mindset
& new ways of working
PEOPLE
LongerMPS horizons
↑ agility with ↓ frozen
period
Switch off XLS
From site to E2E brand
focus
Follow 1global planning
cadence
↑ E2E collaboration
1
2
3
4
5
6
Value Creation?
Fast Tracks
• Virtual twin & planning
sensitive parameters
• 30% of full target realized upfront
• E2E visibilityused across other
business functions
Value Creation?
Full Solution
• Inventory: $ 200+ MM
• MAPEimprovement: $ 7 MM
• Automation: $ 3 MM
• SLOB: $ 12 MM
EXECUTION
• Orders
• Inventory
PLAN ORCHESTRATION
• End-to-end planning
• Detailed scheduling
VIRTUAL TWIN
• Actual network flows
• Supply chain parameters
What’s next?
Node-to-node
driven
Frictionless
Planning
Network of
networks
Augmented
Analytics
Robotics
Want to know more?
✓ Let’s talk at boot 200
✓ Presentation shared via
bluecrux LinkedIn
(follow us!)
greetings, Johan & Koen
Global E2E Planning Transformation: how bluecrux and Johnson & Johnson got 'Phygital'

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Global E2E Planning Transformation: how bluecrux and Johnson & Johnson got 'Phygital'

  • 1. Phy•gi•tal A Global E2E Planning Transformation @ Janssen Pharma [convergence of <physical> & <digital> worlds]
  • 2. Good Morning! Johan Maes Koen Jaspers Sr. Director Global Planning Partner
  • 3. 1th life sciences company in the Gartner Supply Chain Top 25 #8
  • 4. 170 Brands 5 ERP Systems Pharma = Complex Business Environment 18.219 Recipes13.692 SKUs 393 Planners 800 Resources33.000 Routes 40.7 B$ Sales
  • 10. Our Planning Transformation Journey 2012 2013-2015 2016-2020 >2021 TOBE PROCESS INTERIM SOLUTIONS SUSTAINABLE SOLUTIONS NEXT WAVES
  • 12. in-market → first internal production (API) DOWNSTREAM UPSTREAM 1 SUPPLIER API BULK FILL/PACK DELIVER SELL PATIENT
  • 13. DOWNSTREAM UPSTREAM SUPPLIER API BULK FILL/PACK DELIVER SELL PATIENT 2 patient → internal → supplier <network●of●networks>
  • 14. 1 Global Planning Concept Design DP-20 Generate base demand – Process decomposition DEMAND PLANNING D W M Q A Daily Weekly Monthly Quarterly Annually Level4SummaryLevel5Level3 Supply Network PlanningDemand Planning Production Planning Inventory Management E2E Planning S&OP Planning DP-10 Define demand planning strategy DP-20 Generate base demand DP-30 Generate event demand DP-40 Validate demand plan DP-20-70 Release base demand DP-20-60 Translate base demand into value DP-20-50 Validate base demand DP-20-40 Translate to weekly forecast granularity for first 3 months DP-20-10 Compile source data M M M M M ▪ Generated base demand (at lowest demand forecasting unit) feeds into event demand planning ▪ Valorize the base demand ▪ Generate and review exception alerts ▪ Check results of analytical models ▪ Review any exceptions that are flagged based on the exception parameters ▪ Translate monthly base demand forecast to weekly granularity for first 3 months DP-20-30 Generate forecast M ▪ Generate forecast based on best analytical model at optimal planning hierarchy ▪ Recalibration of statistical model based on DP-20-50 DP-20-20 Update source data based on event classification M ▪ Exclude data points / periods that misrepresent base demand trends → Automatic outlier detection with OMP ▪ Classify historical demand into “event history” and “base history” ▪ Map demand history for product swaps or rationalization ▪ Collect historical sales data ▪ Compile other objective inputs where applicable (e.g., customer/patient data) ▪ Where applicable, capture trade partners demand ▪ Check availability of master data (e.g., price for value translation)
  • 15. Scattered IT landscape & master data TECHNOLOGY SOLUTIONS
  • 16. Scattered IT & Data Landscape
  • 17. EXECUTION • Orders • Inventory PLAN ORCHESTRATION • End-to-end planning • Detailed scheduling VIRTUAL TWIN • Actual network flows • Supply chain parameters ‘Augmented’ Technology Framework @ Maturity Stage 5
  • 18. Global mindset & new ways of working PEOPLE
  • 19. LongerMPS horizons ↑ agility with ↓ frozen period Switch off XLS From site to E2E brand focus Follow 1global planning cadence ↑ E2E collaboration 1 2 3 4 5 6
  • 20. Value Creation? Fast Tracks • Virtual twin & planning sensitive parameters • 30% of full target realized upfront • E2E visibilityused across other business functions
  • 21. Value Creation? Full Solution • Inventory: $ 200+ MM • MAPEimprovement: $ 7 MM • Automation: $ 3 MM • SLOB: $ 12 MM EXECUTION • Orders • Inventory PLAN ORCHESTRATION • End-to-end planning • Detailed scheduling VIRTUAL TWIN • Actual network flows • Supply chain parameters
  • 23. Want to know more? ✓ Let’s talk at boot 200 ✓ Presentation shared via bluecrux LinkedIn (follow us!) greetings, Johan & Koen