Optimization of the mid-term
master production schedule
using SAP-APO
Dr. Ulf Neuhaus, Dr. Markus Storz
Bad Dürkheim, 25.04.2008
GOR-Arbeitsgruppentreffen
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 1
Agenda
01
01
Introduction
Introduction
02
02
Planning
Planning problem
problem
03
03
Model
Model overview
overview
04
04
Numerical
Numerical results
results
05
05
Conclusion
Conclusion
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 2
Agenda
01
01
Introduction
Introduction
02
02
Planning
Planning problem
problem
03
03
Model
Model overview
overview
04
04
Numerical
Numerical results
results
05
05
Conclusion
Conclusion
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 3
Bayer Business Services in the Bayer Group
Bayer AG
Business Areas
Service Areas
Bayer Business
Services
Bayer Technology
Services
Bayer
HealthCare
Bayer
CropScience
Bayer
MaterialScience Currenta
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 4
Our Service – Your Advantage
Bayer Business Services is the
Bayer Group’s international
competence center for
IT-based services.
~~ EUR
EUR 11 billion
billion in
in sales
sales**
~~ 5,000
5,000 employees
employees**
* 2007 globally
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 5
BBS GmbH Struktur
BBS
IT
Services
Integrated
Services
Specialty
Services
HR
HR
COM
M&S
ITO
IBS
S&T
FAS
IES
P&T
BC
L&P
MS
BPA
ITO
IBS
S&T
= IT Operations
= IT Business Solutions
= Science & Technology
FAS
IES
P&T
= Finance & Accounting Services
= Integrated Employee Services
= Procurement & Transport
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 6
BC
L&P
MS
= Business Consulting
= Law & Patents
= Media Services
Central Functions
BPA
HR
COM
= Business Planning
& Administration
= Human Resources
= Communications
Agenda
01
01
Introduction
Introduction
02
02
Planning
Planning problem
problem
03
03
Model
Model overview
overview
04
04
Numerical
Numerical results
results
05
05
Conclusion
Conclusion
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 7
Introduction
„
„
Environment:
„
Pharmaceutical industry
„
Capacitated lot sizing
Horizon:
„
„
Scope:
„
„
up to 36 months
API-production ¼ Formulation ¼ Packaging
Tool:
„
SAP SCM 5.0 ¼ SNP-Optimizer
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 8
Supply Chain Planning Process
Afilliate
DC
Production
Sub-contractor
Step 1: (SNP heuristics)
Step 2: (SNP optimization)
Step 3: (PPDS)
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Step 4:of the(SNP
Optimization
mid-term deployment)
master production schedule using SAP-APO • Seite 9
Structure – Production Plant
Formulation
Packaging
FormSol
API
PackSol
Bulk
FG
Solida
FG
Materia
Prima
FG
Liquida
MatPrima
FormLiq
PackLiq
API
Bulk
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 10
SNP optimization - Overview
Function
Demand
(customer VMI orders,
Distr. demands)
Actual Plan
(PP/DS planned orders)
•
•
•
•
•
•
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 11
Erase not fixed PP/DS orders
Perform SNP-Optimization
SNP
planned orders
Output
•
Monthly buckets
Multi-level simultaneous
(formulation, packaging)
Dynamic (real demands)
Finite capacity planning
Only bottleneck resources and
major items considered
Usage of alternative resources
No rounding values and minimal lot sizes
Process
Features
Input
Bucket based rough cut planning
• Determine optimized campaign sizes
(considering setup-, inventory holding costs)
•
Hierarchical planning approach
month M month M+1 month M+2
Horizons
…
Time
SNP (36 m)
MRP (12 m)
Det. Scheduling (6 m)
SNP Pl.-ord.
finite
SNP
PP/DS Pl.-ord.
infinite
prescheduled
PP/DS
PP/DS Pl.-ord.
finite scheduled Unit
Information
content of the
planning levels
J Campaign sizes
J Capacity utilization
J Dep. demands for
all components
J Technical lot sizes
J Sequence dep.
setup times
J Campaign sizes
J Capacity utilization
J Dep. demands for
all components
J Technical lot sizes
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 12
J Campaign sizes
J Capacity utilization
J Dep. demands for
major components
Former methodology
„
Annually determination of the „optimal” lot-size based on Andler
→ Used as fixed lot size in MRP
„
Assumptions:
Andler
SNP-Opt.
Demands
Constant rate
Real demands
Æ i.e. seasonal fluctuations
Set-up costs
Average value (product specific)
Average value (product specific)
Storage costs
Capital lockup (product specific)
Capital lockup (product specific)
Production process
Single-level
Single- or Multi-level
Products
One
N
Resources
Infinite
Finite
Receipts
Date: Depending on demands
Quantity: Fixed
Date: Depending on demand/capacity
Quantity: Dynamic
Determination
Iterative
Simultaneous
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 13
Agenda
01
01
Introduction
Introduction
02
02
Planning
Planning problem
problem
03
03
Model
Model overview
overview
04
04
Numerical
Numerical results
results
05
05
Conclusion
Conclusion
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 14
Overview: SNP optimization
Actual Plan
Demands
Fixed PP/DS planed orders
Technical Settings
Optimization profile
Constraints
Production capacity
Production calendar
Due dates
Safety stock
Cost model
Storage
Set-up
Penalties
Priorities
Updated Plan
SNP-Planed orders
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 15
SNP optimization - Constraints
Constraints to be taken into account:
Soft
Hard
„ Production
capacity
„ Due
dates (demand)
„ Calendar
„ Safety
Pseudo Hard
„ Shelf
„ Material
availability
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 16
stock
life
SNP cost profile
The following cost model is used
1) inventory holding cost
2) fixed production cost
(based on standard price R3)
(based on cost calculation for
setup / cleaning / qc)
The following additional penalties are considered
3) penalty for safety stock violations
4) penalty for non delivery
5) penalty for shelf life (maximum range of coverage)
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 17
Storage cost
Inventory holding costs based on standard price R3 for plant location product
12345
R/3
Sample
APO
100
12345
Sample
APO
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 18
15
Setup cost
„
Calculate set-up costs, using cost estimation for each material, based on the standard
production version
„
Identify relevant cost elements by a set of activity types (per plant)
„
Product calculation (R/3):
Machine set-up
Mach. clean-out
Pers. set-up
Pers. clean-out
Sample
„
Changeover-costs (APO):
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 19
QA
Maximum range of coverage
Soft shelf life (with continued using of expired product)
Storage costs
per BMU
and bucket
Penalty
Max. Range of coverage
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 20
Range of
coverage
Supply of relevant costs (1)
Customizing Table
Additional
Parameter
CIF
R/3
Master-Data
Transaction
APO
Master data
maintenance
Planning
Transactional-Data
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 21
Supply of relevant costs (1)
Source
Cost element
Supply
R/3
Cust.
table
Set-up
X
X
Storage
X
Automatic Manually
(CIF)
(Mass)
Detail
X
Location product
X
Location product
Non Del.
X
X
Shelf Life
X
X
Safety stock
X
X
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 22
X
Plant
Location product
Product group
X
Plant
Agenda
01
01
Introduction
Introduction
02
02
Planning
Planning problem
problem
03
03
Model
Model overview
overview
04
04
Numerical
Numerical results
results
05
05
Conclusion
Conclusion
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 23
Decomposition sub-plants
Parallel processing of disjunctive sub-models:
Formulation
Packaging
FormSol
PackSol
Solida
MatPrim
Materia prima
PackLiq
Liquida
Sub-model 1
FormLiq
Sub-model 2
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 24
Product-Decomposition
„
„
Total model
Solution step 1
Packaging
„
Determination of the global solution
¼
Pre-allocation
Formulation
Solution step 2
„
Definition of sub-models for connected components
„
Sequential solution of the sub-models
¼
Local optimization
Sub-model 1 Sub-model 2
Packaging
Formulation
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 25
Numerical results Solida
Model data
Sub-plants
Solution indicator
SolPack
+SolForm
+MatPr
CPU
Intel Xeon (Netburst)
4 X 3 Ghz
CPU-time (h)
Products
2.148
Planned orders
PDSe
3.978
Service level
Demands
14.081
Resources
63
Horizon (Months)
24
Binary variables
Opt. Gap
~ 2:50
1.688
~ 98 %
~ 0,005 %
Opt. Gap*
~ 3,3 %
*(Based on production-/storage costs)
64.371
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 26
Agenda
01
01
Introduction
Introduction
02
02
Planning
Planning problem
problem
03
03
Model
Model overview
overview
04
04
Numerical
Numerical results
results
05
05
Conclusion
Conclusion
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 27
Benefits
„
Capacitated, dynamic lot sizes
„
Based on real demands
„
Consideration of production capacities
„
Multi level optimization (packaging <> formulation)
¼
Generation of a feasible and optimized medium term plan
¼
Reduction of manual planning activities
¼
Alert based planning
„
Decision support
„
Capacity planning
„
Material requirements planning
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 28
Challenges
„
„
High expectations
„
Quality of input data
„
Short term planning horizon
„
„
Optimization based on real
costs
Master data ¼ Reviewing
process
„
Transactional data
User acceptance
„
Problem complexity
„
Transparence of solution
„
CPU-time vs. level of detail
„
Global vs. local view
¼
Appropriate modeling
approach
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 29
Vielen Dank für Ihre Aufmerksamkeit
Dr. Franz-Josef Toelle
Manager of the Department “Supply Chain Management”
Telefon: +49 214 30 71346
E-Mail: Franz-Josef.Toelle@bayerbbs.com
Dr. Markus Storz
Team Lead “Demand & Network Planning”
Telefon: +49 214 30 56855
E-Mail: Markus.Storz@bayerbbs.com
Dr. Ulf Neuhaus
Supply Chain Consultant
Telefon: +49 214 30 72736
E-Mail: Ulf.Neuhaus@bayerbbs.com
BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008
Optimization of the mid-term master production schedule using SAP-APO • Seite 30
Bayer Business Services GmbH
Customer & Sales Service Center
51368 Leverkusen
E-Mail: Service@BayerBBS.com
Internet: www.BayerBBS.com