Optimization of the mid-term master production schedule using SAP-APO

(1)

BBS-IBS-SCM-D&NP • Dr. U. Neuhaus, Dr. M. Storz • 25.04.2008

Optimization of the mid-term

master production schedule

using SAP-APO

Dr. Ulf Neuhaus, Dr. Markus Storz

Bad Dürkheim, 25.04.2008

GOR-Arbeitsgruppentreffen

(2)

Agenda

01

Introduction

02

Planning problem

03

Model overview

04

Numerical results

05

(3)

Agenda

01

Introduction

02

Planning problem

03

Model overview

04

Numerical results

05

(4)

Bayer Business Services in the Bayer Group

Service Areas

Business Areas

Currenta

Bayer Technology

Services

Bayer

HealthCare

Bayer

CropScience

Bayer

MaterialScience

Bayer Business

Services

Bayer AG

(5)

Bayer Business Services is the

Bayer Group’s international

competence center for

IT-based services.

Our Service – Your Advantage

~ 5,000 employees

*

~ 5,000 employees

*

~ EUR 1 billion in sales

*

~ EUR 1 billion in sales

*

(6)

BPA = Business Planning & Administration

HR = Human Resources

BBS GmbH Struktur

ITO = IT Operations

IBS = IT Business Solutions

BC = Business Consulting

L&P = Law & Patents

FAS = Finance & Accounting Services

IES = Integrated Employee Services

BBS

ITO

IBS

S&T

FAS

IES

P&T

BC

L&P

MS

IT

Services

Specialty

Services

Integrated

Services

Central Functions

BPA

HR

M&S

BPA

HR

COM

(7)

Agenda

01

Introduction

02

Planning problem

03

Model overview

04

Numerical results

05

(8)

Introduction

Environment:

Pharmaceutical industry

Capacitated lot sizing

Horizon:

up to 36 months

Scope:

API-production

¼

Formulation

¼

Packaging

Tool:

(9)

Step 1: (SNP heuristics)

Step 2: (SNP optimization)

Step 3: (PPDS)

Step 4: (SNP deployment)

Supply Chain Planning Process

Sub-contractor

Production

Afilliate

DC

(10)

Structure – Production Plant

FormSol

PackSol

FormLiq

PackLiq

MatPrima

Formulation

Packaging

Solida

Liquida

Materia

Prima

Bulk

API

FG

(11)

SNP optimization - Overview

Output

Process

Input

Function

•

Bucket based rough cut planning

•

Determine optimized campaign sizes

(considering setup-, inventory holding costs)

Features

•

Monthly buckets

•

Multi-level simultaneous

(formulation, packaging)

•

Dynamic (real demands)

•

Finite capacity planning

•

Only bottleneck resources and

major items considered

•

Usage of alternative resources

•

No rounding values and minimal lot sizes

Erase not fixed PP/DS orders

Perform SNP-Optimization

Demand

(customer VMI orders, Distr. demands)

Actual Plan

(PP/DS planned orders)

SNP

(12)

MRP (12 m)

Unit

Hierarchical planning approach

SNP (36 m)

Det. Scheduling (6 m)

SNP Pl.-ord.

finite

PP/DS Pl.-ord.

infinite

prescheduled

PP/DS Pl.-ord.

finite scheduled

Time

month M month M+1

…

Horizons

SNP

PP/DS

month M+2

J

Campaign sizes

J

Capacity utilization

J

Dep. demands for

major components

J

Campaign sizes

J

Dep. demands for

all components

J

Campaign sizes

J

Dep. demands for

all components

Information

content of the

planning levels

(13)

Former methodology

Annually determination of the „optimal” lot-size based on Andler

→ Used as fixed lot size in MRP

Assumptions:

Date: Depending on demand/capacity

Quantity: Dynamic

Date: Depending on demands

Quantity: Fixed

Receipts

Simultaneous

Iterative

Determination

Finite

Infinite

Resources

N

One

Products

Single- or Multi-level

Single-level

Production process

Capital lockup (product specific)

Storage costs

Average value (product specific)

Set-up costs

Real demands

Æ

i.e. seasonal fluctuations

Constant rate

Demands

SNP-Opt.

Andler

(14)

Agenda

01

Introduction

02

Planning problem

03

Model overview

04

Numerical results

05

(15)

Actual Plan

Demands

Fixed PP/DS planed orders

Updated Plan

SNP-Planed orders

Constraints

Production capacity

Production calendar

Due dates

Safety stock

Technical Settings

Optimization profile

Cost model

Storage

Set-up

Penalties

Priorities

Overview: SNP optimization

(16)

SNP optimization - Constraints

Hard

Production capacity

Calendar

Pseudo Hard

Material availability

Soft

Due dates (demand)

Safety stock

Shelf life

(17)

SNP cost profile

The following cost model is used

The following additional penalties are considered

3) penalty for safety stock violations

4) penalty for non delivery

5) penalty for shelf life (maximum range of coverage)

1) inventory holding cost

(based on standard price R3)

2) fixed production cost

(based on cost calculation for

(18)

Storage cost

Inventory holding costs based on standard price R3 for plant location product

12345 Sample

APO

100

12345 Sample

R/3

(19)

Setup cost

Calculate set-up costs, using cost estimation for each material, based on the standard

production version

Identify relevant cost elements by a set of activity types (per plant)

Product calculation (R/3):

Sample

Machine set-up

Pers. set-up

Pers. clean-out

Mach. clean-out

QA

Changeover-costs (APO):

(20)

Maximum range of coverage

Soft shelf life (with continued using of expired product)

Range of

coverage

Max. Range of coverage

Storage costs

per BMU

and bucket

(21)

CIF

Supply of relevant costs (1)

APO

Planning

Customizing Table

Additional

Parameter

R/3

Transaction

Master data

maintenance

Master-Data

Transactional-Data

(22)

Supply of relevant costs (1)

X

Manually

(Mass)

Automatic

(CIF)

Cust.

table

R/3

X

Plant

X

Safety stock

Product group

X

Shelf Life

Plant

Location product

X

Non Del.

Location product

X

Storage

Location product

X

Set-up

Detail

Supply

Source

Cost element

(23)

Agenda

01

Introduction

02

Planning problem

03

Model overview

04

Numerical results

05

(24)

Sub-model 2

Sub-model 1

Decomposition sub-plants

FormSol

PackSol

FormLiq

PackLiq

MatPrim

Formulation

Packaging

Solida

Liquida

Materia prima

Parallel processing of disjunctive sub-models:

(25)

Product-Decomposition

Solution step 2

Definition of sub-models for connected components

Sequential solution of the sub-models

¼

Local optimization

Packaging

Formulation

Total model

Sub-model 2

Packaging

Formulation

Sub-model 1

Solution step 1

Determination of the global solution

(26)

Numerical results Solida

Model data

Sub-plants

SolPack

+SolForm

+MatPr

Products

2.148

PDSe

3.978

Demands

14.081

Resources

63

Horizon (Months)

24

Binary variables

64.371

Solution indicator

CPU

Intel Xeon (Netburst)

4 X 3 Ghz

CPU-time (h)

~ 2:50

Planned orders

1.688

Service level

~ 98 %

Opt. Gap

~ 0,005 %

Opt. Gap*

~ 3,3 %

(27)

Agenda

01

Introduction

02

Planning problem

03

Model overview

04

Numerical results

05

(28)

Benefits

Capacitated, dynamic lot sizes

Based on real demands

Consideration of production capacities

Multi level optimization (packaging <> formulation)

¼

Generation of a feasible and optimized medium term plan

¼

Reduction of manual planning activities

¼

Alert based planning

Decision support

Capacity planning

(29)

Challenges

High expectations

Short term planning horizon

Optimization based on real

costs

User acceptance

Transparence of solution

Global vs. local view

Quality of input data

Master data

¼

Reviewing

process

Transactional data

Problem complexity

CPU-time vs. level of detail

¼

Appropriate modeling

approach

(30)

Vielen Dank für Ihre Aufmerksamkeit

Dr. Franz-Josef Toelle

Manager of the Department “Supply Chain Management”

Telefon: +49 214 30

71346

E-Mail:

[email protected]

Dr. Markus Storz

Team Lead “Demand & Network Planning”

Telefon: +

49 214 30 56855

E-Mail:

[email protected]

Dr. Ulf Neuhaus

Supply Chain Consultant

Telefon: +49 214 30 72736

E-Mail:

[email protected]

Bayer Business Services GmbH

Customer & Sales Service Center

51368 Leverkusen

E-Mail: [email protected]

Internet: www.BayerBBS.com