Planning and Scheduling
Issues in Supply Chains
Stephan Kreipl – SAP Germany AG
Michael Pinedo – New York University
Agenda
1
Planning and Scheduling in a
Supply Chain
2 Implementing APO at Carlsberg
A/S Denmark
3 Discussion

SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
1
Planning and Scheduling in a
Supply Chain
2 Implementing APO at Carlsberg
A/S Denmark
3 Discussion

SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Example of a global Supply Chain
Supplier

Plants
SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
DCs
Customers
Products
Resources
Medium vs. Short Term Planning (1)
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Medium vs. Short Term Planning (2)
Medium Term Planning
Global Optimization
Local Optimization
Maximum Profit
Disaggregate global plan
Product Hierarchies
Time continuous (seconds)
Time Buckets (days, weeks, …)
Decide
Decide
Where to produce
How much to produce
How much to deliver
How much capacities

Short Term Planning
SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
When to produce
On which resources to produce
Optimize production sequence
Integration between Medium and Short Term Planning
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Scheduling in Medium and Short Term Planning
Medium Term Planning
z
Respects Short Term planning
orders as fixed
Capacity reduction from Short Term
Planning Orders
Short Term Planning
z
Respects medium term planned
demands as due dates
No capacity reduction from
medium term planning orders
Material flow
Material flow
Short Term Horizon
Medium Term Horizon
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
1
Planning and Scheduling in a
Supply Chain
2 Implementing APO at Carlsberg
A/S Denmark
3 Discussion

SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Carlsberg A/S Denmark
5 th. largest beer brewer in the world
Founded in 1881
2001: Carlsberg is producing and distributing Coca Cola products in
Denmark
~ 300 beer products
~ 150 Coca Cola products

SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Business Objectives
Decrease inventory costs
Optimize sourcing decisions
Increase customer service level
Change business into a more demand driven process
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Carlsberg Supply Chain in Denmark
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Solution Approach (1)
Use advanced safety stock considering
Service level
Replenishment lead time
Lot size
Forecast error
Future Forecast
Midterm optimization considering
Capacity constraints of the filling lines
Transportation lead times between locations
Set-up times modelled as fixed resource consumption
Production cost of the different filling lines (bottleneck)
Storage costs in the different locations
Transportation costs between locations
Violation of safety stock
Late and non-delivery costs
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Solution Approach (2)
Short-term optimization considering
Detailed production durations
Sequence dependent set-up times
Due dates based on the results from the midterm optimization
Deliver- / Outbound planning
Transportation lead times between locations
Storage costs in the different locations
Transportation costs between locations
Violation of safety stock
Late and non-delivery costs
Transport planning
Cross product (!)
Objective: full trucks
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Medium Term Optimization
MIP Program (3 MIPs)
309 number of products – 3184 number of location products (1.run)
Planning period 12 weeks – the first 5 weeks in daily buckets
432631 number of variables (1.run)
132649 number of constraints (1.run)
66083 number of discrete variables (1.run)
Using product decomposition (5% partition)
Pre-Phase to calculate starting solution ([email protected])
Pre-Phase to calculate product priority for the product decomposition
Result quality
Runtime 10h
LP solves problem in less than 15 minutes optimal
Solution quality: 1-3% > compared to relaxation solution
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Medium Term Optimizer Architecture
LiveCache
GUI
Time
Decomposition
Model Generator
Product
Decomposition
Reporting
Checking
Priority
Decomposition
Core-Model
Control
Meta-Heuristics
LP
MIP
Basic Optimizer
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Medium Term User Interface
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Short term Optimization
Genetic Algorithm
Optimized Period: 7days
Only one operation per product
Sequence dependent set-up times
Due dates are the planned transports from the midterm optimization
Up to three alternatives resources
Number of operations: 20-30
Runtime: 1min
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Scheduling Optimizer Architecture
LiveCache
GUI
Model Generator
Time
Decomposition
Reporting
Core Model
Checking
Control
Meta-Heuristics
Constraint
Programming
Genetic
Algorithm
Basic Optimizer

Bottleneck
SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Campaign
Optimizer
Short Term Planning User Interface
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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
1
Planning and Scheduling in a
Supply Chain
2 Implementing APO at Carlsberg
A/S Denmark
3 Discussion

SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl
Copyright

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SAP AG 2002, Planning and Scheduling in Supply Chains, Stephan Kreipl