Oracle Advanced Planning &...

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1-1 Copyright © Oracle Corporation, 1999. All rights reserved. Oracle Advanced Planning & Scheduling Plan Execute Measure Improve

Transcript of Oracle Advanced Planning &...

1-1 Copyright © Oracle Corporation, 1999. All rights reserved.

Oracle Advanced Planning & Scheduling

Plan

Execute

Measure

Improve

1-2 Copyright © Oracle Corporation, 1999. All rights reserved.

The Tool we used MRP

• Sequential process

• Unconstrained production, storage, and transportation capacity

• Not optimized

• No capacity decision support

• Re-planning is not triggered by events

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Evolution of Advanced Planning Systems

• MRP / MRP II

• Fast, cheap computing power

• Finite capacity scheduling

• Supply chain

• Optimization

MRP MRP II APSAPSfor

e-business

1970 1980 1990 Y2k

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History of Oracle APS

• Manufacturing and supply chain planning products

• Third generation of memory based planning products

• Release 11, simultaneous material and capacity planning, online simulation

• Release 11i, optimization and constraint based planning

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What is Optimization?• A model represents the essential elements of a

system• Objective criteria are used to evaluate alternative

feasible solutions & feasible solutions satisfy all of the system constraints

• Constraints specify feasibility boundaries for the solution to the model, for example: – resource constraints limit the ability to produce

• Assumptions state conditions that must be met for the solution to be valid

• The optimal solution to a model best satisfies the objective criteria

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Optimization Example

• Wire World - World Leader in– Dip Sticks – Croquet Wickets

• Material Constraint = 24 meters of wire– .6 meters for each product

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Material Constraint Optimized

OA

B

Wickets

Sticks

FeasibleSolutions

P10

20

30

10 20 30 40 50

X

Material

40

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Optimization Example

• Wire World– Make Dip Sticks and Croquet

Wickets• Material Constraint = 24 meters of wire

– .6 meters for each product• Resource Constraint = 300 minutes

– 10 minutes for Dip Stick– 6 minutes for Wickets

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Material and Resource Constraint Optimized

O A

B

Wickets

Sticks

FeasibleSolutions

P

S

W

10

20

30

A10 20 3040 50

40

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Objective: Maximize Revenue

Point

A

P

S

Wickets@ $0.10

40

25

0

Sticks@ $0.15

0

15

30

Total Revenue

$4.00

$4.75

$4.50

•Objective = Maximize Revenue From Sales

–.15 for Dip Sticks–.10 for Wickets

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Optimal Solution

• Use the available resources to produce 25 wickets and 15 sticks

• Assumptions: – Constraints are linear and continuous– Single, linear objective– Only two decision variables (sticks

and wickets)

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Objectives of APS Systems

• Global visibility

• Rapid planning cycles

• Better plans

• Fewer plans to synchronize

• Rapid return on investment

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Non-Synchronized Architecture

Aggregatedata

Transactiondata

High levelplan

Transactiondata

Transactiondata

Transactiondata

Semi-aggregate

data

Detaileddata

Mid rangeplan

Near termplan

Source: AMR Research

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Best in Class APS Architecture

Transactiondata

High levelplan

Transactiondata

Transactiondata

Transactiondata

Aggregatedata

Semi-aggregate

data

Detaileddata

Mid rangeplan

Near termplan

Source: AMR Research

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Single Common Data Model

Multi-levelsupplychainplan

Aggregatedata

Semi-aggregate

data

Detaileddata

Common data

model

Common memory model

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Big Picture

D2Miami

Supplier

D1Singapore

M2Boston

S1

Chicago

Supply ChainPlanning Engine

Supply ChainAvailable to Promise

BIS, SCPPI

Demand Planning

TransactionSystems

R11i

R10.7

R11

Other

D1

D2

M2

S1

Supl.

Multi- Org.PlanningServer

Net C

hange Snapshot

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Holistic Planning, Scheduling and Optimization

• Features

One plan

Suppliers ConsumersMFG

partner

Virtual manufacturer

Logistics partner

Internet

InternetInternet

Internet

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Oracle APS

Global ATPServer

ManufacturingScheduling

DemandPlanning(OLAP)

AdvancedSupply Chain

Planning

Internet-basedcollaboration