mysap supply chain management -...

SAP AG 2001, SCM Optimization Infodays, Dr. Heinrich Braun 1

Optimization Architecture in

mySAP Supply Chain Management

PD. Dr. Heinrich BraunDevelopment Manager SCM-Optimization


Agenda

Challenge of Supply Chain Planning

Challenge of Generic Optimizer

Optimizer Architecture of mySAP SCM


Example of a global Supply Chain

-> Objective: Monetary-based Optimization of Supply Chain-> Prerequisite: Integrated Planning of Supply Chain

Plants DCs Customers Products ResourcesSupplier


Supply Chain Management – mySAP SCM

Network

PrivateTradingExchange

Network

Supplier

Partner Partner

Customer

DirectProcurement

Source Deliver

OrderFulfillment

Make

Manufacturing

Supply Chain Design

Strategize

Demand and Supply Planning

Plan

Supply Chain Performance ManagementMeasure

Supply Chain Event ManagementTrack

Supply Chain C

ollaborationC

ollaborateSu

pply

Cha

in C

olla

bora

tion

Col

labo

rate

PrivateTrading

Exchange


Operational

Tactical

Strategic

mySAP SCM: Planning Levels

DirectProcurement

Source Deliver

OrderFulfillment

Make

Manufacturing

Supply Chain Design

Strategize


Plan



Example: Demand and Supply Planning Procedures

Sourcing Balancing Lot-Sizing

Plan

LP MILP

Optimizer

CTMPropagation DRP/MRP

Heuristics



mySAP SCM Planning Philosophy

Modeling

Navigation, Controlling, Management by Exceptions

Objec

tives,

Con

strain

ts

Collaboration

Scores

liveCacheliveCache

Orders Timeseries

realtime

Optimization/ Heuristics

Model Model

Version Version Version Version

...

......


Online-Scheduling versus Optimization

Online-Scheduling

" Online#Insert order #Check material availability

" Greedy Heuristics" Response: in seconds

Gantt ChartGantt ChartOptimizationOptimization

Optimization

" Objective Function#Weighting several criterias#Goal Programming (phases)

" Evaluating many schedules" Response: minutes - hours


Hierarchical Planning

Aggregate Planning" Global optimization" Maximize Profit" Decide

# Where to produce# How much to produce# How much to deliver# How much capacities

Detailed Planning" Local optimization" Disaggregate global plan

# Time: When to produce# Resource:

On which alternative resource

" Optimize production sequence


Aggregation

Aggregate Planning" Mid term" time in buckets (weeks)" linear optimization

Detailed Planning" short term" time in seconds" scheduling algorithms


Decision Variables

Detailed Scheduling" starting time" resource selection

" given# Set of orders# Quantities of orders# Location of production

Supply Network PlanningFor each location:" Production quantity" Transportation quantity" Additional capacities" External supplies


Objective function

Detailed Scheduling" Delay costs

# Order priorities" Setup

# Time# costs

" Makespan# For rolling planning schema# Compressing in planning periode

" Production Costs # Prioritizing prefered modes

" Inventory Costs (Earliness)

Supply Network Planning" Delay costs

# Order priorities" Nondelivery Costs (Maxim. Profit) " Production costs" Transportation costs" Inventory costs

" Costs for additional capacities# Transportation (Outsourcing)# Production (over time)# Product (Outsourcing)


Not recommended: Global optimization with Scheduling

Modeling Supply Planning objectives in Detailed Scheduling

" Transportation #Use production resources#Model transportation time as setup time /costs

" Nondelivery Costs (Maxim. Profit)#Use order priorities#Non deliverable orders are delayed after planning window

" Production costs#Use penalties for mode priorities

" Costs for additional capacities#Model with dummy resources (available during overtime)#Penalize use of these using mode priorities


Constraints

Detailed Scheduling" alternative resources" delivery time" storage capacities

# discrete material flow# continuous material flow

" resource capacities # production

" calendar# capacities# breaks / shifts# productivity# block planning

" time constraints# minimal (routing)# maximal (shelf life)# buffer time

" Setup# times# secondary resources

Supply Network Planning" alternative routings (PPM)" delivery time" storage capacities

# safety stocks# shelf life

" resource capacities# Production# transport# handling

" calendar# capacities# breaks (weekends)

" discretization# integer lot sizes / campaigns# minimal lot sizes# additional shifts# Setup time# piecewise linear cost functions


Optimization Performance

Detailed Scheduling" up to 100 000 activities

(no hard limitation)" First solution

as fast as online heuristics" More run time

improves solution quality

Supply Network Planning" Pure LP

# Without discrete constraints# Up to several million decision

variables and about a million constraints

# Global optimum guaranteed" For discrete constraints

# No global optimum guaranteed# Quality depends on run time and

approximation by pure LP" First solution

needs solution for pure LP

No „optimize mySCM“ button" Decompose problem using hierarchical planning" Global optimization using aggregation" Feasible plans by local optimization" Rolling planning schema


Challenge: Generic Optimizer

Generic and Best of Breed " planning level" vertical Industries" run time requirement" model complexity (size, constraints, objectives)

Generic Model (-> planning level)" aggregated planning (LP / MILP) " detailed planning (scheduling)

Customization (-> vertical industries)" specialization the generic model to customer problem" scripting the strategies (decomposition, goal programming)

Scalability (-> run time)" greedy versus complex optimizations strategies" parallelization

Open Architecture" internal: adding new special optimizer (software evolution)" external: integration of optimizer packages


Expectation for Optimization

$$$$ Best-of-Breed Solution !# Depends on Problem Complexity (Model, Size)# Computation time

Solution: Scalability ?!

☺☺☺☺ Optimal Soluton ?

☺☺☺☺ Better than 5% below optimum ?

&&&&

&&&&


Challenge: Hardware Scalability

Parallelization

" Multi user

" 3-tier Client Server# Separation LiveCache and Optimizer server# Several Optimizer server

" Multi Processor# parallel optimization runs# multi optimizer agents in one optimization run


Challenge: Algorithmic Scalability

Tradeoff: generalization versus computation time

" Two Optimization Models# Linear Optimization versus Scheduling# Aggregate Planning versus Detailed Planning

" Several optimization algorithms# e.g. 4 different scheduling optimizer# e.g. 4 different LP optimizer

Tradeoff: algorithmic complexity versus computation time" Cubic computation time acceptable for small problems" Linear computation time required for large problems' Solution: Metaheuristics / Decomposition' Control: Scripts


Scheduling Optimizer Architecture

Core Model

LiveCache

Model Generator

CampaignOptimizer

ConstraintProgramming

Genetic Algorithm

Basic Optimizer

SequenceOptimizer

Time Decomposition

Bottleneck

Meta-Heuristics

Multi Agent

Reporting

GUI

Control

Checking


SNP Optimizer Architecture

Core-Model

LiveCache

Model Generator

VehicleAllocationSNP Deployment

Basic Optimizer

NetworkDesign

Time Decomposition

Product Decomposition

Meta-Heuristics

Priority Decomposition

Reporting

GUI

Control

Checking


Metaheuristics

ObjectiveBest quality of solution for given (computation )time frame(Scalability for problem size

Decomposition

Local Improvement Strategy" Focus on a Subproblem (planning window)" Optimize planning window(script mechanism


Time Decomposition - Local Improvement

Resources

TimeCurrent window

Gliding window script1. Optimize only in current window2. Move window by a time delta3. Go to first step


Time Decomposition - First feasible Solution

‘Look ahead strategy’ script" Evaluate several branches with e.g. 50 activities" Select the best scored branch" Fix the beginning of this branch

Planning level

Look ahead

Fixation


Metaheuristics - Bottleneck

Bottleneck Script1. Determine bottleneck2. Schedule bottleneck resources only3. Fix sequence on bottleneck resource4. Schedule all resources

Time

Resources

Bottleneck


Resource and Time Decomposition


Multi Agent Optimization (Genetic Algorithm)

0

50

100

150

200

250

Solut.1

Solut.2

Solut.3

Solut.4

DelaySetupQuality= D+S

Objective" Multi Criteria Optimization" user selects out of solutions with

# similar overall quality# different components

" Use power of Pallelization (GA)

Multi Agent Strategy" Different AGENTS focusing on Setup or Delay

or Makespan " New solutions by local improvement" Integrated in Optimizer Architecture

(independent of basic optimizer)

Performance" Speedup ≈≈≈≈ available processors


Several Solutions

Delay0

Setu

p


Mastering the Challenge with mySAP SCM

Scalability / Flexibility" generic modeling on each planning level

# Strategic/tactical: LP/MILP # Operational/ Execution: Scheduling

" Specialization to customer problem# activate constraints# activate objectives

" Scripting the strategies (metaheuristics)# Decomposition techniques# Multiple Phases (goal programming)# Parallelization by Agents

Open Optimization Architecture" best of breed libraries

# Linear Programming (ILOG CPLEX)# Constraint Programming (ILOG SCHEDULER)# Genetic Algorithms (SAP)

" extendible toolbox of # business oriented basic optimizer # Metaheuristics

" Open to partner solutions: Optimizer extension workbench

mysap supply chain management -...

Documents