mysap supply chain management -...

SAP 20.10.2000 / 1 SAP AG 2001,Optimizing the SN, Dr. Dirk Meier-Barthold page 1

Optimizing the Supply Network in

mySAP Supply Chain Management

Dr. Dirk Meier-Barthold GBU SCM


Agenda

111

444

222 Modeling the Supply Network

Integrated Supply Network Planning and Optimization with APO

Selected planning scenario

333 Optimizing the Supply Network


Supply Network Planning

Decisions to be made: Global sourcing decisions Global load-balancing decisionsGlobal lot-sizing decisions

Decision support for supply network planner:

Supply Chain Planning Horizon Level of Detail


Network Design

Supply Network Planning DemandPlanning

DistributionPlanning

Availableto Promise

ProductionPlanning

ProcurementPlanning

VehicleScheduling

DetailedScheduling

PurchasingWorkbench

Integrated Supply Network Planning and Optimization

Supply Network Planning

Sourcing Balancing Lot-Sizing

Result: Network wide supply decisionsproducts, locations, periods and quantities

HeuristicsOptimizer

Propagation CTMLP MILP DRP/MRP


Agenda

111

444






Modeling the Supply Network

P

Production

OSI

IR R

Transportation

OTI

R R

GRGI

R

ExternalProcurement

OT

R

GR


P

Production

OSI

IR R

ExternalProcurement

OT

R

GR

Transportation

OTI

R R

GRGI

R

Decision Variables

Transportation Quantity

Additional Capacity

External Procurement

Production Quantity


P

Production

OSI

IR R

ExternalProcurement

OT

R

GR

Transportation

OTI

R R

GRGI

R

Decision Constraints

Customer Constraints- Back order - Lost sales- Safety stockResource Constraints

(Production, Transport, Handling, Storage)- Capacity (normal, additional, calendar)- Consumption (set up, variable)

Product Constraints- Consumption (fix, variable)- Minimal lot size- Fixed lot size- Shelf life


P

Production

OSI

IR R

ExternalProcurement

OT

R

GR

Transportation

OTI

R R

GRGI

R

Cost of Decisions

Cost of Customer Constraints(Demand classes)- Cost of back order - Cost of lost sales- Cost of using safety stock

Cost of Resource Constraints(Production, Transport, Handling, Storage)- Cost of additional capacity - Cost of Inventory consumption

Cost of Product Constraints- Cost of violating Shelf Life

Cost of Procurement(piecewise linear cost function)- Production quantity- Transportation quantity- External Procurement


Problem Complexity

3 classes of problem complexity:

-> linear program (LP)-> all decision variables are proportional

-> mixed integer linear program (MILP)a) yes/ no decisions

-> set up-> minimal lot size-> piecewise linear cost function

b) integer decisions-> fixed lot size-> full truck loads


Reduction of Problem Complexity (1)

Are mixed integer really necessary?

- Set up-> not reasonable, if a lot of products are on resource per bucket

- Minimal lot size -> not reasonable, if minimal lot size is small to average lot size

- Piecewise linear cost function-> only reasonable, if few pieces are modeled

- Discrete lot size/ rounding -> not reasonable, if lot size is very high (e.g. 97.5)-> not necessary, if production over buckets is allowed


Reduction of Problem Complexity (2)

How can we create a reasonable SNP model?

- Use aggregated time-buckets

- Focus on Supply Chain relationships

- Use key products and bottleneck resources, only

- Design easy PPM’s


Agenda

111

444






Optimizing the Supply Network

Objectives for the SNP-Optimizer:

- good performance of planning result- good performance of planning runtime

Planning ResultPlanning ResultPlanning RuntimePlanning Runtime


Incremental Optimization

Can be necessary due to: - Problem size- User experiences

-> Important: Focusing on strongest constraints

- with Selection-> horizontal aggregation-> vertical aggregation-> DRP/ MRP-like planning

- within Selection-> product decomposition-> time decomposition-> priority decomposition-> activate constraints-> restrict runtime


Horizontal Aggregation

Aggregation of demands by classesAggregation of shortage costs

Advantage: Customer information are taken into account


Vertical Aggregation

Production

1.

2.

3.

1. Aggregation by product-location hierarchy -> supply, demand, stock, costs

2. Optimization on aggregated level3. Disaggregation by deployment algorithm push fair share A

-> product-location hierarchy, ppm hierarchy

-> Vertical aggregation for special production structure, only !


DRP/ MRP-like Planning

Step by Step Planning over the Supply Chain

To get feasible solution: Set secondary and distribution demand as soft constraint (demand class)

1.2.

3.4.


Optimization with Decomposition

Decomposition via Time

Time

Decomposition via Product

Product 1

Product n

:

Decomposition via Priority

Demand class 1

Demand class n

:


Activate Constraints

Resource Constraints- production capacity- transportation capacity- handling capacity- inventory capacity

Variable Constraints(end date, bucket oriented)- set up

-> production- minimal lot-size

-> production- piecewise linear cost function

-> production, transport, external procurement

- fixed lot-size/ rounding -> production, transport

P

Production

OSI

IR R

ExternalProcurement

OT

R

GR

Transportation

OTI

R R

GRGI

R


Agenda

111

444






Combination of Vertical and Horizontal Aggregation

1. Selection of bottleneck part of supply chain2. Optimization with

2a. Horizontal Aggregation2b. Vertical Aggregation

3. Optimization of non bottleneck part

Production

2b.

1.

2a.3.


SNP Optimizer - Customer Problems (1)

● Discrete industry■ Model: 13 Buckets, 15012 Locations-Products, 4887 Arc-Materials,

7581 PPMs■ Solution: optimal after 10 minutes

● Consumer industry■ Model: 30 Buckets, 19.000 Locations-Products, 23.000 Arc-Materials,

8.500 PPMs■ LP: 2.600.000 Variables, 600.000 Constraints■ Solution: optimal after 30 minutes

● Chemical industry■ Model: 3 Buckets, 2131 Locations-Products, 1461 Arc-Materials, 356

PPMs■ MIP: 20.300 Variables (1.050 discrete, 1.050 binare), 10.500 Constraints■ Solution: < 1% optimality-gap after 1 minute


SNP Optimizer - Customer Problems (2)

● Consumer industry ■ Model: 22 Buckets, 916 Location-Products, 333 Arc-Materials, 741

PPMs■ MIP: 104.000 Variables (14.000 discrete), 46.000 Constraints■ Solution:

◆ < 5% optimality-gap after 5 minutes◆ < 3% optimality-gap after 80 minutes

● Financial sector ■ Model: 23 Buckets, 1 Product, 22 Locations, 30 Lanes■ MIP: 3000 Variables (300 binare), 1600 Constraints■ Solution: < 1% optimality-gap after 1 minutes

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