advanced m&s applied to analysis techniques for supporting decision makers … ·...

Advanced M&S applied to

Analysis Techniques for

Supporting Decision Makers in

Multi-Job Management in an

Aeronautical IndustryAuthor: Enrico Briano

Advisor: Prof. Agostino G. Bruzzone

MISS Genoa Center - DIPTEM

Co-Advisors: Matteo Cecada

Giorgio Garassino

Piaggio Aero Industries

Francesco Longo

University of Calabria

Goals of the Research

The main goal of the Research is to reduce

the Assembling Line Lead Time. In order to

reach this goal is requested to:

• Identify and Analyze Criticalities

• Reorganize all the Phases of the

Production Process

• Evaluate the Impact of all the Stochastic

Phenomena

First Hypothesis of Assembling

Line Lead Time Reductionoperai/giorni 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 6 6 6 6 6 6 11 11 17 17 23 23 23 23 23 23

2 8 8 8 10 10 10 11 11 17 17 23 23 23 23 23 23

3 9 9 9 9 9 9 9 9 9 9 9 9 18 18 18 18 18 18

4 12 12 13 13 13 13 24 24 24 24 23 23 23 23 23 23

5 12 12 13 13 13 13 24 24 24 24 23 23 23 23 23 23

6 15 15 15 10 10 10 14 14 14 14 14 14 34 34 34 34 34 34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

6 1 1 1 1 1 1

8 1 1 1

9 1 1 1 1 1 1 1 1 1 1 1 1

10 2 2 2

11 2 2

12 2 2

13 2 2 2 2

14 1 1 1 1 1 1

15 1 1 1

17 2 2 1 1 1 1 1 1

18

23 4 4 4 4 4 4

24 2 2 2 2

34 1 1 1 1 1 1

6 6 6 6 6 6 4 4 0 0 4 4 6 6 6 6 6 6 6 6

Squadra B

Garbati

Salvador

Cecchini

Assy portelli principali e posteriori

Squadra A

Bidone

D'Agostino

Zone

TE

ST

WE

E

Canepa

Astengo

Gaggero

Fois

Macciò

Palladino

TE

ST

WE

E

Installazione portello bagagliaio

Installazione particolari fuori scalo

Installazione alettoni

2°Turno

Predisposizione Modifiche

Installazioni Portelli

Installazione Canard

Installazione antenne

installazione pinne

Raccordo ala fusoliera

1°Turno

50 ore 1pers

Predisposizione bulbo deriva

Predisposizione poppino

25 ore 1 pers

110 ore 1pers

50 ore 2 pers

35 ore 2pers

25 ore 2 pers

60 ore 2 pers

installazione Flap

persone per giorno

200 ore 4pers

60 ore 2 pers

58 ore 1persVerniciatura basico

50 ore 1 pers

24 ore 1 pers

35 ore 2 pers

50 ore 1 pers

•Data have been Modified due to their Confidential Nature

Methodology

• Build Simulators and Models devoted to analyzeRisks and Criticalities

• Development and Analysis of the Assembling LineSystems in order to:– Reduce the Aircraft Mean Lead Time from 6 to 4 Months

– Reduce WIP

– Decrease the Number of Aicrafts simoultaneously present inthe Assembling Line

– Save a significant amount of Money in terms of BankingInterests

– Distribute better Resources on Planes

– Have a Positive impact on the Company Cash Flow

Station 8

Slipway

Station 8 bis

Initial

Arrangements

Station 7

Assembling

Station 7 bis

Moving Parts

and Fillings

Station 8 ter

Interiors

Trimming

Final Tests

2 Stations

Station 4

Final Interiors

Assembling

Station 5 bis

Functional Tests

Station 5

High Value

Components

Station 6 bis

Painting

Station 6

Installations

The Present Productive

Processes

Total: 12

Phases

LT = 24

Weeks

New Assembling Line

Station 8

Slipway

Final Tests

Station 1

Station 2

Final Interiors

Assembling

Station 3

Functional Tests

Station 4

High Value

Components

Station 5

Painting

Station 6

Installations and

Fillings

Total: 8 Phases

LT = 16 / 18 Weeks

Departments to beReengineered

• Assemblers’ Dept.: Code 742– Carpenters

– Fillers

– Commanders

– Planters

– HVAC

– Assemblers

• Electricians’ Dept.: Code 744

• Interiors’ Dept.: Code 745

• Painters’ Dept.: Code 743

Data Collection

Data were acquired by the

Authors using the

LAN-Based Company

Informative System (CX)

The Main Functions are:

• Inventory Status

• Bills Control

• Job Progress Control

• Worked Hours Control

Performance Analysis(Dept. 742)Att. Rep. 742

0%

50%

100%

150%

200%

250%

300%

0% 50% 100% 150% 200% 250% 300%

Media / Ass.to

Ma

x / M

in

•Data are Modified for Privacy Reasons

Mean Extra-cost for 742 Dept. Is 30%compared with Scheduled


“Solar” Simulator

•VBA Simulator based on the real Job Completion Time

•Dates extracted from the Bills start and finishing time(CX)

•Mean Airplane Lead Time overestimated based onstatitistical analysis

•Necessity to validate data and to develop a more

detailed model

M.A.C.A.C.O. Simulator

• Stochastic Discrete Event Simulator

• Job Duration-Based historical data (from Aircrafts NC

1077 to NC 1086) and experts estimation by beta

distribution

• Production Process Model using concurrent PERT for

each plane considering resources and constraints

• C++ built and animated

• Stochasticity provided by different probabilitydistribution; deterministic case is also allowed

• Allows formulating What-If Analysis on Criticalitiesand Bottlenecks by variating Input Data

Modelling Air Craft Analysis for

Construction process and

OrganizationInterface

allows toevaluate:

• Job Status• Production

• Real TimeLead Time

• Resource

SaturationLevel

• UtilizationCoefficients

• PositionsSaturation

Bottleneck 49 Analysis(Test Press)

Sensitivity Analysis: Bottleneck 49

A

B

C

D

AB

AC

AD

BC

BD CDABC

ABD

ACD

BCD

0.0001

0.001

0.01

0.1

1

10

Eff

ec

ts

Input Factors

A: 46 Activity

B: 47 Activity

C: 48 Activity

D: 52, 53 Activities

•F/F

tab

Activity 49 is a Bottleneck in the process: the causes

of this phenomenon are the criticality of activities 52 e

53 and the influence of the sinergy of activities 46 and

48


Sensitivity Analysis on

Criticalities (1/2)

• 26 Factorial Project based on Critical Path Activities

Duration and on the Number of Fillers andAssemblers

1814F: N° OF ASSEMBLERS

64E: N° OF FILLERS

140%60%D: CRITICALITY DURATION COEFF. Station 5

140%60%C: CRITICALITY DURATION COEFF. Station 6

140%60%B: CRITICALITY DURATION COEFF. Station 7

140%60%A: CRITICALITY DURATION COEFF. Station 8

MAXMINFACTOR

Sensitivity Analysis on

Criticalities (2/2)Sensitivity Analysis: Criticalities

A

B

CD

E

F

AB

AC

AD

AE

AF

BC

BD

BE

BF

CD

CE

CF

DE

DF

EF

ABC

ABD

ABE

ABF

ACD

ACE

ACF

ADE

ADF

AEF

BCD

BCE

BCF

BDE

BDF

BEF

CDE

CDF

CEFDEF

ABCD

ABCEABCF

ABDE

ABDFABEF

ACDE

ACDF

ACEFADEF

BCDE

BCDFBCEF

BDEF

CDEF

ABCDEABCDF

ABCEF

ABDEF

ACDEFBCDEF

0.000001

0.00001

0.0001

0.001

0.01

0.1

1

10

100

1000

Eff

ec

ts

Input Factors

A: St. 8 Criticalities

B: St. 7 Criticalities

C: St. 6 Criticalities

D: St. 4 Criticalities

E: # of Fillers

F: # of Assemblers

•F/F

tab

Lead Time is strongly affected by criticalities on

Stations 8, 6 and 4


DOE & RSM Application

16.0

16.3

16.6

16.9

17.2

17.5

17.8

0.30

0.50

0.70

0.90

1.10

14.414.715.015.315.615.916.216.516.817.117.417.718.018.318.6

Plane Lead Time [weeks]

F

B

Response Surface Methodology: Lead Time

18.3-18.6

18-18.3

17.7-18

17.4-17.7

17.1-17.4

16.8-17.1

16.5-16.8

16.2-16.5

15.9-16.2

15.6-15.9

15.3-15.6

15-15.3

14.7-15

14.4-14.7

Local Best is at theminimum duration of

Station 7 criticalities

and at the maximumnumber of

Assemblers

ANN Methodology Applied to thePlane Delivery Date Analysis

• Full Connected FeedForward Architecture

• Back Propagation Algorythm

• 23 runs during Training

• 23 runs during Test

• 10 inputs: from job 49 to 58

(Station 6)

• 2 levels hidden layers

• 1 output: Delivery Date

ANN Methodology Results

(1/3)

M ax

Average

Min

Training

Test0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

Err

or

[%]

Error Type

Set

Errors for Predition of Plane Delivery in the Different Sets

Training

Test

7.96 %3.12 %TEST SET

0.11 %≅≅≅≅ 0TRAINING

SET

MAX

ERROR

AVG

ERROR

SET

ANN Methodology Results

(2/3)ANN Error in Estimating Plane Delivery

on Training/Test Data

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

0 23 46

Run No.

Err

or

[%]

Test SetTraining Set

Error is low also

during the Test

Set

ANN Methodology Results (3/3)

•Data are Modified for Privacy Reasons

Max Error on a 600 hours’delay (over 40 working days

variability) of about 3 days on the Completion Time Forecast

Delivery Times

0

1

2

1000 1100 1200 1300 1400 1500 1600 1700 1800 1900

Delivery Time

Se

t T

yp

e

Training Set

Test Set

Conclusions• Developed Simulation has been successfully validated

on the P180 Assembling Line Scenario

• Simulation was able to identify a solution to guarantee

18 Weeks Lead Time without Manpower and

Machinery Costs increase

• This Analysis has demonstrated the possibility of:

• 15% WIP Reduction

• 25% – 33% Off Planes inside the Assembling Line

• Saving 21.5k€/Plane on financial fees

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