Department Industrial Management Ghent University - itl.rtu.lv · TEMPUS project with Latvia) ... Lean manufacturing projects in Romanian SME’s LEONARDO projects)Remote training

Industrial Management© R. Van Landeghem, 2003.

Department Industrial ManagementDepartment Industrial ManagementGhent UniversityGhent University

2Industrial Management© R. Van Landeghem, 2003.


Brussels

Gent


Ghent University Ghent University

1 of 2 major universities in Flanders

Faculty of Engineering (1 of 10)Department of Industrial Management (1 of 15)

– Rik Van Landeghem - Logistics & information systems

– El-Houssaine Aghezzaf - Operations Research & Quality Management

– Hendrik Vanmaele - Simulation & Operations Research

– Filip Gheysens - Operations Research

– Peter Ottoy - Applied Statistics

– Dirk Matthys - Industrial management & expert systems

– Carmen Bobeanu - Multi-formalism modelling, Petri-Nets

– Dirk Van Goubergen - Operations Management & Manufacturing systems

– (em) Henri Muller - Business game• plus 5 teaching & research assistants

• yearly 3 to 5 visiting students (Socrates, from Riga, Barcelona, Grenoble)

industrial engineering, logistics & operations research for engineering and economy faculties


Teaching programsTeaching programs

EngineeringMaster in Operations Research & Industrial Engineering (start: oct. 2004)

Postgraduate Master in Industrial Managementengineers with more than 3 years of work experience

technical MBA

Educational programs to industryAPICS certification

Lean manufacturing workshops

Logistic training sessions


Teaching assignmentsTeaching assignments

Introduction to Industrial Management (FTW + FLBW + FW)

Operations Research techniques (FTW + FEB)

Design of Quality Systems (FTW + FEB)

Production and inventory control (FTW)

Simulation of socio-economic systems (FTW)

Supply Chain Management (FEB)

Design of Production and Distribution systems (FTW)

Operations Management (FTW - GGS IB)

Integral logistic management (FTW - GGS IB)

Expert systems in logistics (FTW - GGS IB)

Industrial applications of statistics (FTW - GGS IB)

Information management (FTW - GGS IB)


International teachingInternational teaching

Socrates exchanges (staff & students) withRiga Technical University, Latvia

Zaragoza University, Spain

Ecole Nationale d’ingenieurs Grenoble, France

Polytechnical University Barcelona, Spain

University of Bucharest, Romania

Collaboration agreement withVirginia Polytechnic Institute & State University, Blacksburg, USA (IE department)


Research themesResearch themes

1995-now: 149+ publicationsBenchmarking model and international research on Simultaneous Engineering (1998)Causal model for benchmarking of logistic organisation (1999)Cost model for supply chains (1999)Optimal boarding patterns for airline passengers (2000)Use of Monte Carlo simulation to characterize logistical networks (2000)Definition and introduction of the concept of “tactical robust planning” in logistical networks (2001, 2002)Robust inventory-routing solutions (2002)Setup optimization of multi-machine lines (2002)


International ProjectsInternational Projects

TEMPUS project with Latviafounding of Industrial Logistics degree in Engineering Faculty at Riga Technical University (prof. Yuri Merkuryev)

ADAPT project with 7 european partnersCost Model for Textile Supply Chains

IMPROMAN projectLean manufacturing projects in Romanian SME’s

LEONARDO projectsRemote training in operations management using simulation games

with Zaragoza University & Uni. Karlsruhe

Virtual engineering workbenchAlfa Project with Spain, Portugal, Brasil, Cuba, Mexico, Belgium


How to reach usHow to reach us

Website: http://tw18v.UGent.be

email: [email protected]

address: Technologiepark, 903, 9052

Gent/Belgium

phone: +32-9-264.55.02


Rik Van LandeghemIndustrial Management

Ghent University

[email protected]

http://tw18v.UGent.be

Riga Technical UniversitySocrates Exchange 2004

Simulation as tool to bridge the gapSimulation as tool to bridge the gapbetween between scientistsscientists and practitionersand practitioners


Researchers are “Optimizing” creaturesResearchers are “Optimizing” creatures

Researchers and scientists …have to publish newly discovered findingslook for microscopically defined unique problemslike to solve those problems to optimalityevery result must be validated by sound scientific methodsthe result is written down in an extensive report, with lot’s of symbols and jargonreading this report requires being familiar with a dozen or so prior publications from different (often unavailable) sourcespublication follows 6 months to 2 years after the facts


Practitioners are “Satisficing” creaturesPractitioners are “Satisficing” creatures

Practitioners and their managers …think in terms of business results and financial returnswant answers now, and impact next quarteronly read summary reports with resultsor only attend a PowerPoint presentationdo not like too revolutionary new concepts or solutions they hardly understand

unless their competitors all do it (e.g. ERP, APS)unless they are desperate (e.g. e-commerce hype)

look at resident scientists/experts as cost factorsand prefer to outsource knowledge to consultants, sacrificing continuity and uniqueness


Practitioners versus scientists…..Practitioners versus scientists…..


Case 1 (1990)Case 1 (1990)

Simulate operations of post-manufacturing test cells at Compressor Factory

our research showed better performance without any infrastructure expansion, and with limited tooling investments (based on simulation model)

managers wanted to use model for daily operational scheduling, requiring extensive programming of user interface (costing and during 2 times as much as original study)

union members wanted to now why and what this model would do to the work conditions

finally, model was built but hardly used afterwards


Case 2 (1992)Case 2 (1992)

Optimize mixed model assembly line at Airconditioning Factory

research into optimal parameters of line operation (batch size, operator assignment) and environment (rework %), using simulation and Taguchi-DOE, leading to 5% increase in throughput

by time study was presented, and model implemented, business had doubled and Japan Kobe plant destroyed by earthquake. Assembly lines were tripled in number and study was obsolete. Moreover, new lines were designed totally different, so conclusions of study were no longer useful.


Main effect lot size

100%

104%

98%

95%

16500

17000

17500

18000

18500

19000

19500

1 5 20 100

Lot size

Th

rou

ghp

ut

Main effect Product Mix

77%

90%

100%

119%

14000

15000

16000

17000

18000

19000

20000

21000

22000

23000

100S/0L 66S/34L 27S/75L 0S/100L

Product Mix (% Small/% Large)

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ut

Main effect Rework level

99%

100%

18050

18100

18150

18200

18250

18300

18350

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Rework level (%)

Th

rou

ghp

ut

Main effect Resource level

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100%

17400

17600

17800

18000

18200

18400

18600

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Resource level

Th

rou

ghp

ut


Case 3 (2000)Case 3 (2000)

Analyze marine tank farm operationmodel built by operations manager himself (part of thesis)

based on extensive data collection over 1 year

results very useful and model accepted readily by the firm

but…model validation not totally correct (arrival distribution of ships)

0

20

40

60

80

100

0 1 2 3 4 5

days

%

Real Simulation



Berth allocation and blockingBerth allocation and blocking

Tank farm

Adjacent plant

MP2 MP3 MP4

MP1 MP5MP6

MP7

MPn = mooring point n

Cat. 4


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400

500

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Demurrage costs in each scenarioDemurrage costs in each scenario


What did we learn from this?What did we learn from this?

Any applied research must be FAST and EFFECTIVE

as opposed to thorough, complete and scientifically validated

Real life problems evolve relatively quicklyso real risk of missing the mark altogether


How to do useful research then?How to do useful research then?

Anticipate problems, or identify long-standing problems with little or no workable solutions

airline passengers’ boarding process

deterministic planning in an uncertain environment

causal model linking logistic best practices with performance measures

Try to find a “class” of problems and develop flexible and/or modular solution methods, after which you can try to “sell” your solution to potential users

business process simulation

supply chain cost model for textile/apparel industry


Case 4: airline passenger boardingCase 4: airline passenger boarding

Increased air traffic puts pressure on turnaround time for airplanes at the gate (“block time”)

maximum 40 minutes (10 + 20 + 10)boarding often takes longer than 10’

Customer service becomes importantwaiting and queuing times are important dissatisfiersnarrow aisles are very uncomfortable (cabin luggage, temperature, interference with other passengers, …)

Different Passenger call systems currently in usewhich one is better?


Arena modelArena model


Global Boarding time

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10

15

20

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30

35

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45

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100% occupation

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By block By half-block By row By half-row By seat


Case 5: robust Supply Chain planningCase 5: robust Supply Chain planning

Uncertainty is essential ingredient (and disturbing factor) in supply chains

Most planning approaches are purely deterministic

based on averages and worst case scenario

Causes:uncertainty is a blind spot with most managers

it is hard to understand, let alone to model it (statistics!)

almost no tools available


OCT variation - minimum, average and maximum: where does it come from?

Decrease of maximum OCT from 60 days to

35 days


Order fullfilment lead time distributionsOrder fullfilment lead time distributions

MISTRAL supply chain simulator

Using a Monte Carlo simulator approach



and always think of a way to make it useableand always think of a way to make it useable

simulation with embedded algorithms/methods

data-interface problemlink into company data

user-interface problemintuïtive, shielding complexity and “science”

know-how transfer


Case 6: business process modellingCase 6: business process modelling

© Möbius Research & Consulting, 1998.


Visual model for validation and acceptance purposeVisual model for validation and acceptance purpose


Interactive input screens for parametersInteractive input screens for parameters


Performance metrics reveal uncertaintyPerformance metrics reveal uncertainty


Input and output reporting via ExcelInput and output reporting via Excel


Reporting includes simulationReporting includes simulation--generated costs and generated costs and financialsfinancials


Simulation can reach every segment in the companySimulation can reach every segment in the company

Visual

Metrics

$ Decision makers

Method engineersBusiness analists

Process owners

C


Simulation technologySimulation technology

Visually strong and realistic

Fast modelling and remodelling

Extensive data handling

Support for validation and interpretation of output

Dynamic intervention into scenario’s during runs possible

Reasonable acquisition cost


Visualisation of loading process (simulation)Visualisation of loading process (simulation)


Simulation in TeachingSimulation in Teaching

Simulation based learning: gameslearn students (and practitioners) that the intuitive way is often wrong, takes longer, and does not converge

expose them to scientific methods, and methodology (analyse, compare, select, validate)

make them realize that this approach is not “complex”, “difficult” or “only for large companies”

e.g. Box-Jenkins in forecasting

throughput analysis of logistic chains

probability calculation in planning

use of simulation and LP tools


Our gamesOur games

LOGDIS: manual “beer game”

JET+ game: hands-on industrial engineering game (workshop simulation)

STOCKSIM: statistical inventory control

KANBANSIM: Kanban simulator

DICXIM: multi-level inventory simulator (supply chain)

SIMRPC: MRP simulator using spreadsheet

ORSIAM: business game, simulating career path of engineer towards top management


SIMRPC


OPTSIM


STOCKSIM




DICXIM


LOGDIS

Our version of the “Beer Game”


JET+ hands-on workshop simulation



SpinSpin--off company:off company:Möbius Research & Consulting nvMöbius Research & Consulting nv


Case: optimizing Belgian Cargo RailCase: optimizing Belgian Cargo Rail

Optimize flow by allocating train cars to cargo transport requests

Check on impact of traffic on total rail networkinterference with passenger trains

measure punctuality of cargo transport


ConclusionConclusion

Scientists and practitioners are of a different breed

but share a common knowledge platform

should cooperate to convince “management”

Simulation is a powerful toolto visualize and enable understanding

to teach even complex dynamics and systems

is gaining recognition in companies

The bridge is built ….So do not hesitate to cross it !


Questions?Questions?

Documents

Department Industrial Management Ghent University - itl.rtu.lv · TEMPUS project with Latvia) ... Lean manufacturing projects in Romanian SME’s LEONARDO projects)Remote training