sap apo overview

MindTree Consulting Proprietary & Confidential

SAP APO Overview

MindTree Consulting Confidential

1997 SAP AG APO 2

SAP APO ArchitectureA Solution to Provide An Integrated & Synchronized SC Process

BusinessWarehouse(Reporting)

APOSolversliveliveCacheCache

SC Cockpit

Application Link EnablingModel Generator, Mapping, Connectivity

APO

OLTP

R/3R/3 R/3R/3 LegacyOLTP

Non-R/3OLTP

Non-R/3OLTP

SAP Advanced Planner & Optimizer (APO)

PP/DSSNPDP TP/VS ATP


R/3

SAP APO Application Architecture

Supply Chain Cockpit

ATPATP

DeploymentDeployment

BIW

HistoricalData

KeyPerform.

Indicators

External data

(e.g.POS)

Advanced Forecasting and Demand PlanningAdvanced Forecasting and Demand Planning

Customerorder

ManufacturingExecution

InventoryManagement

ProductionPlanning andScheduling SupplySupply

NetworkNetworkPlanningPlanning


Multiple System Environment

R/34.x

Internet &

EDI

R/33.x

BW

ALE

• infocubes• liveCache

APO

LegacySystemsPlanning

Systems

WebGUIs

SAPGUIs


liveCache

liveCache

Memory-resident data

object processing

Application

Production ProcessModel

Optimized for real-time scheduling and pegging (supply demand, constraints)

Supply ChainNetwork

Representation ofthe extended supply

chain

APO Solvers

Model Generator,Metaheuristics,

Optimizing AlgorithmsHeuristics

Time Series

Optimized forfast

response ATP

-5+20+10


liveCache

• Application– Manages large amounts of data in main memory– Uses both relational and object-oriented functionality

(ADABAS)

• Integration with R/3– Uses SQL Interface

• Standard Transaction Handling– Locks, Rollbacks, Commits

• Advantages (Performance)– Avoids disk I/O– References object via pointers– Stores complex data structures in object oriented data

structures– Recovers quickly in case of system crash


liveCache

Memory-resident data

object processing

Application

liveCache

• A tool – A tool for processing large volumes of data in main memory.

• Main objective = higher performance• Avoid disk I/O• Stores optimized data structures• Scalability

– number of processors within one liveCache– number of liveCaches


ApplicationApplication

Data StorageData Storage

> 1 ms

Application

Data Storage

< 10 µs

liveliveCacheCache

liveCache

• Main objective = higher performance: – Performance critical routines (in C++) are running in address space

of liveliveCache Management System Cache Management System => no heavy data transfer between application and data storage


SD MM PP

DPDP

Sales Order

Planned Order

Production OrderConfirmation

Purchase ReqmtsTransport ReqmtsTransport Orders

On HandIn transitsPurchase ReqmtsPurchase Order

Forecast

Sales Order

liveCacheSupply Plans

liveCache Integration with R/3


Optimizer

Optimizers are used for:– Supply Network Planning (SNP)– Production Planning / Detailed Scheduling (DPS)– Capable to Match (CTM)

Optimizer routines are developed in C++, which increases the speed of the program

Only available on NT and Windows 2000 Communication between the Optimizer routines and APO

through SAP Gateway


SAP APO Integration

R/34.x

R/34.xR/3

4.x

R/34.x

R/34.xR/3

3.x

R/34.xR/3

4.xNonR/3

Production Planning / Detail Scheduling

Supply Network Planning

Demand Planning

ATP

APO

Live Cache®

Supply

Chain

Cockpit

For SAP R/3 instances, SAP provides the Core Interface Facility (CIF) which dramatically simplifies integration to/from APO. Integration with non-R/3 systems is achieved through ALE and SAP provided BAPI’s.:


APO - R/3 Integration

Core Interface Facility (CIF)• CIF is an online transaction that defines active data

channel(s) in R/3 for data transfer between R/3 Systems and APO. It has the following features :– Real Time Interface– Determines Source and Target Systems within Complex

System Environments– Supplies APO with Relevant Master and Transaction Data– Forwards Data Changes (Transaction Data)– Returns Planning Results to SAP R/3


CIF : Core Interface

APO Core Interface (CIF) is the communication layer to be applied to R/3 to enable an exchange of data between R/3 and APO.

APO-CIF is delivered as a plug-in . This is a general product name given by SAP for the R/3 interfaces to the new dimension applications.


APO -> ERPPlanning Results

ATP Results Manufacturing Orders Procurement Orders VMI Sales Orders

APOAPO

APOAPO

APOAPOBWBWERPERP

ERPERP ERPERP

ERPERP

ERP -> APOMaster Data

Locations Products PPMs (BOM+Routing) Characteristics Capacities

Transaction Data

Planned/Production Orders

Sales Orders Purchase Orders Stocks ATP Requests

CIF Functions Architecture


Technical Considerations

• Modules being Implemented• Multiple Parallel Rollouts• Distributed vs. Central Architecture• UNIX vs. NT• Single Client Strategy• Number / Location of Users• Amount of ALE Traffic• Volume of Data


SAP APODemand Planning


Supply Network Planning (SNP)

Demand Planning (DP)

Production Planning (PP)

Detailed Production Scheduling (DPS)

Deployment

Planning horizon

Transportation Plan / Vehicle Sched.

Supply Chain Planning Cycle


Page 19

Planning Area Functionality

• Defined like an InfoCube• Contains Characteristics and Key Figures• Maps where key figures for the planning area are

stored (InfoCube, Orders in liveCache, Time Series in liveCache)

• Planning Areas can be relevant for DP and SNP at the same time

• Forecast settings are done for planning area• Creation of time series objects


Page 20

Planning Area Flow

Planning objectsPlanning objects Key FiguresKey Figures

Sales Production Stock

Assign Key Figuresby Aggregate

Brand

Customer

Brand

Product

Customer

Sales

Production

Sales

Production

Stock

Aggregate

Details

APO Planning Version

Planning UOM

Time Bucket Profile

…


Page 21

Consistent Planning

DisaggregationProportional values

generated

Pro Rataor

Proportional Factors

Aggre

gatio

n

Lowest Level

PlanningPlanning Level


Page 22

Statistical Toolbox

• Univariate Forecasting– Moving Average– Simple Linear Regression– Exponential Smoothing – Holt-Winters – Croston’s Model (for sporadic demand)

• Causal Analysis– Multiple Linear Regression

• Composite Forecasting– Weighted Averaging of Multiple Models

(Ex. Constant, Trend, Seasonal, MLR)


Page 23

Master Forecast profile

Univariate profile

MLR profile

Composite profile

Forecast Profiles

Profiles:• Assign a Planning Area• Define which key figure you

want to be forecasted• Define past and future

periods• Specify models to be used

for:

– Univariate forecast– Multiple linear regression– Composite forecast


Page 24

SAP APO Overview:Supply Network Planning


Page 25

Increase Customer Responsiveness at Least Cost

Supply Chain OptimizationSupply Chain Optimization

ManufacturingManufacturing DistributionDistributionSupplierSupplier

INFORMATION FLOW

Retail OutletRetail Outlet ConsumerConsumer

CASH FLOW

Transfer Transfer Transfer Transfer

The Supply Chain: Original Supply to Final Consumption


Page 26

• A planning approach to create Tactical Plans and Sourcing Decisions that takes the complete supply network into consideration

DeploymentDeployment TransportTransportLoad BuilderLoad Builder

Supply Network Planning

• Meet Forecast and Actual Demand by:– Optimal use of Manufacturing, Distribution and

Transportation Resources– Consider all constraints in the supply chain

What is Supply Network Planning ?


Page 27

SNP Planning Functionality

• Supply Network Planning Strategies– Heuristics– Optimization– Capable to Match– Propagation

• Deployment– Fair share, push rules and deploy to order– Optimization

• Transport Load Builder– Leveling in transport loading

SNP

Deploy-ment

TLB

PP/DS


Page 28

APO Planning Functionality Sequence




Detailed Production Scheduling (DPS)

Deployment

Planning HorizonPlanning Horizon

Transport Load Builder (TLB)

Transportation Planning and VehicleScheduling (TPVS)


Page 29

Planning Area Administration

Planning Method Determination & Profile Settings

Supply NetworkPlanning RunTransport Load Building

Deployment Run

Conversion intoPP/DS orders

PP/DS Planning

Release of SNPPlan to DP

Interactive Planning

APO Master Data Setup

Model/Version Creation

Supply Chain Model Setup

Release of Demand Plan to SNP

Simulation

Management by Exception

SNP Process Flow


Page 30

Examples : SNP Business uses

• Beyond day-to-day planning• High level capacity planning and macro global scheming

to aid with capital equipment decision making based on sales runs.

• Support system for modeling “what if” scenarios that would impact the bottom line.

• Visibility for planning with confidence. System provides cost- trend analysis for control on manufacturing expansion, Out-sourcing, 3rd party supply, etc.


Page 31

Beyond Traditional “DRP”

Supplier WH

ManufacurerDC

Customer DC

Production Process Model

Manufacturer Plant

Supplier Plant


Page 32

CustomerDemands

DistributionCenters

Plants

Transport Order Planned Order / Procurement

Suppliers

SNP Distribution Network


Page 33

Objects in the Supply Chain Network

Objects include:

Locations

Products

Resources

Production Process Models

Transportation Lanes provide links between objects in a supply chain model


Page 34

Vendor Managed Inventory

MANUFACTURER RETAILER

Plant Warehouse DistributionCenter

SNP

Inventory(integrated)

Forecast(integrated)

Sales order

Inventory(EDI 852)

Forecast(EDI 830)

Order (EDI 855)

VMI

APO

R/3,Legacy


Page 35

Planning Area Administration

Planning Method Determination & Profile Settings

Supply NetworkPlanning Run

Transport Load Building

Deployment Run

Conversion intoPP/DS orders

PP/DS Planning

Release of SNPPlan to DP

Interactive Planning

APO Master Data Setup

Model/Version Creation

Supply Chain Model Setup

Release of Demand Plan to SNP

Simulation

Management by Exception

SNP Process Flow


Page 36

SNP Planning Area Administration - Process Flow

Create PlanningObject structure

Create Planning Area

Initialise Planning area

CreatePlanning book

CreatePlanning views

Assign users toPlanning book

Characteristics

Key figures

Attributes

Storage bucket profile

Planning Version

Planningbucket profile


Page 37

Resources

• The resources are used to define – Capacities of equipment, machines, personnel, means of

transport, warehouses

• Resource data are relevant for planning order dates, taking working time and the capacity of the resources into account

• Resource types:– Bucket Resources– Single activity/ Multi-activity resource– Single Mixed and Multi-Mixed Resource


Page 38

Planning Parameters

• Parameters in the resource that are relevant to scheduling, which the system uses in PP/DS and SNP.

• Used to control in detail how the system schedules orders to resources.

• Examples: Overload, Bottleneck resource, activity overlap periods, etc.


Page 39

Shift sequences

Quantities / rates

A

A BCD $

Volume Weight Costs

Shift sequences

Breaks Shiftfactors Shifts

Day number

Validity

Time

Capacity Models


Page 40

Planning Order

Process

Production Process Model (PPM)

• PPM summarizes – Routing– BOM

• PPM supports– Location-dependent (PPM ID) and

location-independent (Plan No.)– Min/max lot sizes for the master material– Operations

• Set of different process steps Bucketed time intervals for each process step

• Resources assigned to production steps– Validity periods


Page 41

Production Process Model

Operations:Supply materialPre-assemblyFinal assemblyInspection

Activities: Setup Produce Tear down Queue time

Product Relationship Resources

In / Out

Resource consumption

Header: Costs, Lot size range $

Sequence


Page 42

Forecasts,Forecasts,Customers ordersCustomers orders

Sourcing Sourcing Production &Production &PurchasingPurchasing

Requirements,Requirements,Inventory levelsInventory levels

APO

Liste

Definition

Incoming quotas Production lead times Transportation lead times

Supply Chain ModelSupply Chain Model

40%60%

What is a Quota arrangement ?


Page 43

Quotas

40%60%30%

70%

Outgoing Quotas

Production Location Distribution Center (DC)

QuotaQuota

??Supplier

60% 40%

Product

Incoming Q

uotas


Page 44

Planning Process

Forecast

Supply Network Pl.

Demand Planning

Production PlanningDetailed Scheduling

Transport Load Builder

RecommendedTransportOrders

Deployment

TransportOrders

PlannedOrders

Confirmed Transport Orders

Transportation Planning& Vehicle Scheduling


Page 45

SNP Heuristic

Heuristic is an algorithm that has pre-defined set of parameters and workflow to influence the creation of a solution

The plan is not necessarily feasible

Planner must use capacity leveling to formulate a feasible plan


Page 46

SNP Run Using Heuristics:

• Heuristic is used as part of a repair-based planning process consisting of the Heuristic, Capacity Leveling, and Deployment

• The Heuristic processes each planning location sequentially and determines sourcing requirements

• Heuristic processing lumps all requirements for a given material in the location into one requirement for the period

• Heuristic determines the valid sources and quantity based on pre-defined percentages for each source, then passes the requirements through the supply chain to calculate a plan


Page 47

SNP Run Using Heuristics:

• Heuristic plans all distribution requirements for all locations in the distribution network before exploding the BOM and processing dependent demand in the production locations

• System explodes the BOM only when the Multi-level Heuristic run option is chosen

• Scope of the planning run– Multi-level– Network– Location


Page 48

Factors Considered in the Heuristic Run

• Transportation lanes• Lead Times• Quota arrangements• Lot sizing• Scrap• Component

Availability• PPMs

• Location Products• SNP Demand Profile• SNP Supply Profile• Demand Profile


Page 49

Heuristic Processing - Results

• Replenishment Plan– list of procurement– production orders– transportation orders

• Results can be viewed in the interactive planning table

• If the Level ID option is used, the Heuristic calculates an intersection of the following entries– Model Version– Products and Locations– Level ID from product-location hierarchy


Page 50

Capacity Leveling

• Capacity leveling is a function within Interactive Planning

• Enables to smooth production schedule• Manual or using a methods-based approach• Provides opportunity to build up inventory or increase

capacity • Alternatives can be easily analyzed • Re-plan even re-forecast before putting the plan into

production.


Page 51

Optimization-Based Planning Models

• In constraint-based planning, production processes can be represented as optimization models.

• A production model based on optimization consists of Objective Function(s), Decision Variables, and constraints based on market conditions, physical processes, and resources/capacity.

• These kinds of models are usually called mathematical programs.


Page 52

Optimization - Components

• Decisions variable are the independent variables of the problem

• The Objective Function is the single benchmark for evaluating all combinations of decisions that satisfy the constraints

• Constraints represent limitations on which decision can be made and how decisions can be made

F(x,y2)=


Page 53

Optimization of the Network

ForecastsCustomers orders

Sourcing production &purchasing

requirements

Priorities for:demand typesdefined viacosts

Control costsPenalty costs

$

Goal: Minimize costs

Goal: Maximize Profits*


Page 54

Optimization Methods

• Linear Programming– Continuous Linear Optimization Problems

• Primal Simplex Method• Dual Simplex Method• Interior Point Method

– Discrete Linear Optimization Problems• Mixed Integer Linear Programming

• Prioritization• Decomposition• Vertical Aggregated Planning• Horizontal Aggregated Planning• Discretization


Page 55

• Decision Variables– Production lot sizes– Transportation lot sizes– Capacity increase

• Objectives– Lateness – Storage costs – Transportation costs – Production costs– Penalty for increasing

capacity– Penalty cost for not

maintaining safety stock*– Penalty cost for late or non

delivery*

Optimization Parameters

• Constraints– Production capacities– Transportation capacities– Handling capacity– Due dates (demands)– Safety stock– Discrete Values

• Production Lot Size• Transportation Lot Size


Page 56

Optimization Relevant Profiles

• SNP Optimization Profile– Specifies the linear programming method to be used and

the constraints to be considered during the Optimization run

• SNP Cost Profile– Specifies the weight given to different categories of costs

in the objective function

• Optimization Bound Profile*– Specifies the time buckets where the new plan is

constrained by an upper and lower limit on the allowable change


Page 57

Optimization Total Costs

Total Cost (Sum Total) Source of cost dataProduction PPM

Storage Resource

Production resource expansion Resource

Storage expansion Resource

Penalty cost for safety stock Cost Profile

Transport cost Resource

Transport capacity expansion Resource

Penalty for non-delivery Master data

Handling capacity expansion Resource

Procurement costs Master data

Delay Penalty Master data


Page 58

SNP Optimization Run Results

• Distribution Plan• Production Plan• SNP Resulting Costs• Alerts


Page 59

CTM Process

DemandPrioritization

SupplyCategorization

CTM Engine

Phase 1Build CTMapplication model

Phase 2Match supply

to demand

Orders inliveCache

• Constraint-based heuristics to conduct multi-site checks of production/ transportation capabilities

• Supply categorization• Demand prioritization• CTM Engine

– Create CTM application model

– Match supply to demand using the CTM algorithm

CTM Process (Overview)


Page 60

• Demands– forecasts

(from APO Demand Planning)

– sales orders

• Prioritization based on • order type• customer priority• product priority• due date

• Defined in Sort Profile*

Prioritizeddemands Demands

1.2.3.4.5.6.

7.8.9.

10.11.

Demand Prioritization


Page 61

CategorizedSuppliesSupplies

excess

normal

target

Supply Categorization

• Supplies include– inventory– purchase orders

• Categorization is based on supply limits

– for each location– for each product


Page 62

1.2.3.4.5.6.

7.8.9.

10.11.

CTM Results

CategorizedSupplies

PrioritizedDemandsCapable to Match

Multi-site capacity and transportation capability check


Page 63

Rule Based Capable to Match*

• Allows to influence the supply and demand matching process, depending on a demand’s specific attribute

• Determines the following based on the attributes of individual demands– find the product/location substitutes for the particular

demand with substitution– influence the solution process for a particular demand using

demand-dependent constraints


Page 64

Sales and Operations Planning: Overview

• Supports interactive SNP Planning– create a feasible plan for the entire supply chain

• Considers resource capacities (Production and Transport)

• Flexible Controlling via quotas, priorities,and cost• Parameters are time dependant• No lot size Planning• No optimization• No Orders


Page 65

Sales and Operations Planning:Overview

Product

Location x Product

Location x Product x Channel

SNP: Propagation (finite, multi-level)

DP: (dis-) A

ggregation


Page 66

Sales and Operations Planning:Features• Pre-configured Planning Environment• Bucketed Planning based on Time Series• Aggregated Planning possible• Fully integrated with Demand Planning

– statistical forecasting– promotion planning

• Planning books can be configured to compare real world (OLTP) with tactical plan


Page 67

Conversion of SNP Orders to PP/DS Orders

• System automatically stores the final supply network plan in liveCache

• Converting SNP Orders to PP/DS orders makes it available for finite scheduling

• PP/DS enables to synchronize production planning with execution

• PP/DS creates a viable plan• Two ways of conversion

– Conversion of SNP Orders in Production Horizon– Conversion of Individual SNP Orders


Page 68

Collaborative Supply Planning - Overview

• The goal of collaborative supply planning is the exchange of materials requirements at an early stage between manufacturers and suppliers so that all parties involved can adjust their supply and production plans

• Partners can exchange data in two ways: automatically using time series data exchange between SAP

systems manually via a web browser for collaboration between SAP

and non-SAP systems


Page 69

Collaborative Supply Planning

Business Benefits

Better Transparency

Reduced costs by less inventory

Better Service level

More stability in demand

Easy communication with all related parties

Front End Agreements

Exchange Component

requirements/constr-aint or unconstraint

forecastSupply NetworkPlanning/Production

Planning

Supply NetworkPlanning/Production

Planning

Collaborate on exceptions


Planning


Planning

Business Benefits

Better service level of supplier

More accurate supply

Reduced costs by less inventory

Easy communication with all related parties

Supplier Both Manufacturer


Page 70

Collaborative Supply Planning - Process Flow

SupplyPlanner

AccountManager

Alertbroadcasting

(email)

Manufacturer Supplier

APO SNP

R/3

plan replan SupplierSystem

Reviewrequirements

Purchase order

Committed supply planData change information (alert,email)

Internet


Page 71

Planning Process & Time Buckets




Detailed Scheduling (DS)

Deployment

Transport Load Builder (TLB)

Year, Quarter, Month

Year, Quarter, Month, Week

Week, Day, Hour, Minute

Month, Week, Day, Hour

Week, Day, Hour, Minute

Quarter, Month, Week, Day

Network Design (ND)

Vehicle Scheduling


Page 72

Which products should be planned using PP/DS?

• Externally procured products with long replenishment lead time

• All in-house products produced on a bottleneck resource


Page 73

What PPDS delivers?

• Consistent Model through Production Process Model• Simultaneous Capacity (CRP) and Material (MRP)

Planning• Creation of Feasible Production Plans• Multi-level Forward and Backward Scheduling• Automatic Multi-level Transfer of Changes (e.g.

orders)


Page 74

Why perform Production Planning/ Detailed Scheduling?

• Improve customer response (due date performance)• Improve throughput• Reduce inventory/Reduce WIP• Reduce overtime expense• Increase asset utilization• Reduce cycle time


Page 75

How SAP delivers this solution?

• Built for quick response (liveCache)• Designed for a multi-plant heterogeneous

environment• Packaged to support R/3 implementations • Simultaneous material and capacity planning• Constraint solving & optimization engine• Simulation and what-if analysis• Exception driven decision support tool


Page 76

Differences between SNP and PPDS

SNP• Capacity and product receipts

and requirements are considered in a bucketed fashion

• No material problem if all receipts until a certain bucket equal all requirements until that bucket

• Sequence not relevant• LP-optimizer can:

– generate orders – optimize lot size– best for sourcing problems

PPDS• Both, capacity and

product are checked with exact time.

• It is a potential problem if material receipt is 1 second after requirement.

• Sequence relevant• Optimizer can:

– alter sequence of existing orders


Page 77

SCC

AlertsQ

ueriesMas

terD

ata

Sourcing,Confirmation

(SC)

HistoricalData

Uncon- strainedDemand

Plans

DP SNP PlannedOrders PP Feasible

Schedule DS

Inventory TransportOrdersATP

FinalProduction

Plan

Deploy-ment

ActualSales

Orders

SC


Page 78

Total Process Flow: APO

Production Process Model Evaluation / Selection

APO

PPM Explosion

Creating Order Network

Availability check against unassigned receipts

Core

TransferOrders

MRP

PP

OrderStock

Planning Functionality

Order Creation(OLTP, SNP, PP/DS)


Page 79

PP/DS Setup and Planning Cycle


Page 80

Features in PP

• Automatic planning– When integrated with R/3 continually, this can provide the

most up-to-date information

• Manual planning– to handle critical products that require particular attention

when planning

• Interactive planning– Planning board feature

• Cross-plant planning– Stock transfer or between parties in a supply chain


Page 81

Features in PP - (Continued)

• Lot-size calculation– Lot-for-Lot Order Quantity– Fixed Lot Size– Rounding profile

• Scrap calculation– Scrap at assembly level– Scrap at activity level

• Calculation of Days’ Supply• Planning with shelf-life data


Page 82

Features in PP - (Continued)

• Production Process Model (PPM) explosion• Pegging (Fixed and Dynamic)• Evaluation Options• Execution Functions in APO and R/3


Page 83

Pegging

• Pegging network:– Used for

supply/demand allocation

– Changes have to be propagated to all dependent orders

– Find unassigned order quantity

• APO offers fixed and dynamic pegging– Global setting in

APO

50 50

80 20Pegging edge

Input nodeOutput node

Demand

Order

50 2030


Page 84

Strategy Profile& Scheduling Modes


Page 85

• Infinite Scheduling– Schedule an operation without considering resource

capacity

• Finite Scheduling – Schedule an operation only when resource capacity is

available– Alert is only generated when planning using finite

scheduling only

Scheduling Strategy Profile

FiniteFinite StrategyStrategy

InfiniteInfiniteStrategyStrategy

Finite SchedulingFinite SchedulingFinite scheduling

Alert displayInfinite scheduling Alert display

Infinite SchedulingInfinite Scheduling No Alert display No Alert displayInfinite scheduling Infinite scheduling


Page 86

Scheduling Modes

• Insert operation to close gaps in schedule

• Squeeze-in operation

• Add an operation at end

• Dispatch to non-working time

• Infinite loading• Finite loading only

forward • Finite loading only

backwards • Finite loading with

direction switch• Search for a slot in

schedule


Page 87

Finite Scheduling - Backward Only

t

Resource 1

Resource 2

Resource 3

Available

Occupied

Customer OrderDesired dateand quantity

New Order

today confirm


Page 88

Finite Scheduling - Forward Only

t

Resource 1

Resource 2

Resource 3

Available

Occupied

Confirmed dateand quantitytoday


New Order


Page 89

Finite Scheduling - Backward with Reverse

t

Resource 1

Resource 2

Resource 3

Available

Occupied Confirmed dateand quantity

Final loading

today


1st Loading attempt


Page 90

Simultaneous Material and Resource Planning

Due Date

1st loadingInfinite planning strategy

today

Material 1 Delivery time

t

Available

Occupied

Resource 1

Resource 2 (bottleneck)

Resource 3 ALERTS:Resource OverloadSupplier Delivery Time Violated


Page 91

today

Material 1 Delivery time

t

Resource 1 (finite scheduling)

Resource 2 (bottleneck)

Resource 3

Available

Occupied

1st loadingInfinite planning strategy Due Date2nd LoadingFinite planning strategy Feasible Due Date

Simultaneous Material and Resource Planning


Page 92

Features in Detailed Scheduling

• Determine production date by taking account of the constraints entered in the strategy profile and in the resources for scheduling

• Changes in DS (e.g. orders, operations, …) will automatically propagate through to all relevant objects

• Taking sequence-dependent set-up times and/or costs into consideration

• If a constraint is relaxed (this is not considered in optimization run) and it is violated in scheduling, the system creates alerts


Page 93

Real Time Planning vs Batch Optimization

Real Time Planning– supports interactive scheduling

– finds a feasible solution

– real time answering

– Examples • backward/forward propagation

• scheduling in free slots

• simultaneous material and capacity planning

Batch Optimization– takes into account

complete situations

– optimizes feasible solutions

– answering time depends on user-defined run time

– Examples • complete rescheduling

of planning window


Page 94

Resource 6

Resource 3

Resource 5

Resource 4

Resource 2

Resource 1

Concept: Optimization

Order 1 Order 4Order 3Order 2

time


Page 95

Resource 6

Resource 3

Resource 5

Resource 4

Resource 2

Resource 1

Concept: Optimization

Order 4Order 3 Order 1 Order 2

time


Page 96

Foundation of Optimizer in DS: Metaheuristics

• Objective– obtain quality solution for given time frame

(scalability for a given problem size)• Purpose

– simplify the problem• Metaheuristics consists of

– time decomposition– resource decomposition– constraint relaxation

• Local improvement Strategy– Focus on a sub-problem and optimize

planning window

Objects (resources, orders, ops, constraints, …)

Time

Reducedopt model


Page 97

Metaheuristics - Time Decomposition

Resources

TimeCurrent window

Sliding window (Rolling time Horizon)1. Optimize only in window2. Move window by a time delta3. Go to first step


Page 98

Metaheuristics - Bottleneck Decomposition

Bottleneck1. Determine bottleneck2. Schedule bottleneck resources only3. Fix sequence on bottleneck resource4. Schedule all resources

Time

Resources

Bottleneck


Page 99

• A way to make a constraint less restrictive or remove certain constraints

• 5 choices– Remove max time constraint– Set resource utilization rate to 100%– Remove calendar– Do not consider sequence-dependent setup– Undo fixing of orders/operations/activities

Metaheuristics - Constraint Relaxation


Page 100

Non-work timesFixed operationsOperations, that may be rescheduledRelationshipsOptimization range Transferred Resources B, C, D

Start End

Scope and Size of Optimization: An Example


Page 101

Optimization Model in PPDS: Scheduling

• Decision Variables– Resource Allocation

• Alternative Machines• Alternative Storage

– Start dates– End dates

• Constraints– Time Constraints

• Maximal (Shelf Life)• Minimal

– Deadlines– Production and Storage

Capacities– Calendar (Shifts and Breaks)– Sequence- and Resource-

dependent Setup times– Resource Network– Breakable activities– Effectivity of BOM’s and

Routings– Productivity (per Shift)

• Objective Functions (Minimize)– Total Lateness – Maximum Lateness– Total Leadtime– Total Setup Times– Total Setup Costs


Page 102

Two Ways to Perform DS

• Optimization Procedures

– Constraint Programming• Constraint propagation• Branch and bound

– Genetic Algorithm• Priority rules• Sequencing


Page 103

Constraint-Based Programming

• Approach/method– checks hard constraints during scheduling– propagates constraints during scheduling

• additional reduction of the search space• early detection of dead ends • reduces back tracking

• Tradeoff– Dynamic propagation needs time– but improves quality of search decisions

• Advantages– High-performance constraint propagator (iLog)– Dedicated to complex scheduling problems

• Example: shelf life / expiration


Page 104

Constraint-based Programming: Example

Initial Solution

Solution with Changed Variables

• Propagate consequence of each decision– Dynamic constraint

propagation– checks hard constraints

during scheduling• Prune the search tree

– Remove unfeasible solution

– Remove worse solution


Page 105

• Based on Concept of Evolution• Population of candidate solutions• New candidate solutions by

– Crossover/Recombination– Mutation

• Exchanging sequence of activities• Change resource allocation of activities

• Preferring the better ones as parents• Eliminating the worse ones

Genetic Algorithms (GA)


Page 106

GA - Procedures

Generate initial schedules

Selection of “good” schedules

Generate new schedules by mutation and recombination

Evaluate new schedules

Survival of new schedules


Page 107

GA- Advantages

• High-performance in sequencing• Dedicated to not too complex scheduling

problems – Feasibility should be not the problem– Example: no shelf life / expiration


Page 108

Optimization Profile

• Maintain in Customizing

• Settings– Optimizing procedure

• Constraint-based Scheduling

• Genetic Algorithm– Runtime– Objective Functions and its

Weights• Total lead-time• Set-up times• Set-up costs• Maximum delay• Average delay

– Constraint Relaxation• Remove maximum time

constraints• Set utilization rate of all

resources to 100%• Remove calendar• Do not consider set-up

times/costs• Undo fixing of activities


Page 109

Techniques in Solving Complex Production

Processes and Optimization Models in

APO


Page 110

APO SolversA wide variety of computational solvers

applied to specific planning functions with industry-specific variations

DemandPlanning

Heuristic Methods

Linear Programming /Mixed Integer LinearProgramming

Genetic Algorithms, Constraint-based

Programming

Exponential SmoothingHolt WintersMultiple Linear Regression

SupplyNetwork Planning

ProductionPlanning &Scheduling

APO Computational Solvers


Page 111

Complexity in Real-life Production Models

• They are too complex to solve. For example, we may have nonlinear equation(s), integer decision variables, scale, ...

• In such cases, we will have to rely on algorithm, heuristics, and other “intelligent” methods.

• Most APS systems mix optimization and heuristic methods.


Page 112

Where is Constraint-based Planning Used in APO?

• Creating Production Process Models (PPMs) in SNP and PP/DS are based on Theory of Constraint and Constraint-based Planning

• Where are the decision variables? Where are the constraints identified? Where are the objective identified?

• For example, in PPDS, the decision variables are given in the planning table (which resource is used to produce a given order and its start and end times); objective function is given in the optimizer screen; and the constraints are stated in master data.


Page 113

Start Time

End Time

Example: Decision Variables in DS


Page 114

Example: Objective Functions in DS

Optimization methods

Obj. functions and its weights


Page 115

Optimization Models in APO

sap apo overview

Documents