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HYBRID OPTIMIZATION/SIMULATION APPROACHFOR FLEXIBLE SUPPLY CHAIN MANAGEMENT

Dissertação realizada no âmbito do Mestrado Integrado em Engenharia Electrotécnica e de Computadores

Supervisors: Jorge Pinho de Sousa / Samuel de Oliveira Moniz

Narciso Manuel Sobral Couto Caldas

LIST OF CONTENTS

• 1 - Introduction

• 2 - Case Study

• 3 - Methodology

• 4 - Models

• 5 - Implementation

• 6 - Results

• 7 - Conclusion

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1 - INTRODUCTION

CONTEXT

[1] S. H. Khajavi, J. Partanen, and J. Holmström, "Additive manufacturing in the spare parts supply chain," Computers in Industry, Article vol. 65, no. 1, pp. 50-63, 2014.[2] D. J. Garcia and F. You, "Supply chain design and optimization: Challenges and opportunities," Computers and Chemical Engineering, Article vol. 81, pp. 153-170, 2015.[3] S. Moniz, "Operations management, Lecture TP9 – Managing inventories " November, 2016.

•Market competitivity [1]

• Importance of supply chain design [2]

•Good stock management [3]

•Use of additive manufacturing [1]

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1. INTRODUCTION

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MOTIVATION

• Evaluate new supply chain configurations

•Develop a decision support tool for supply chain managment

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1. INTRODUCTION

OBJECTIVES

•Develop of a optimization/simulation model•Consider the use of 3D printers• Evaluate new supply chain configurations

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1. INTRODUCTION

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2 - CASE STUDY

CASE STUDY

Inserted in the European project FASTEN

A company intends to use 3D printers and evaluate the impact of it’s use

External suppliers Remote stations Final clients

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Supply chain

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CASE STUDY2

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Don’t belong to the company

Supply parts

Inserted in the European project FASTEN

A company intends to use 3D printers and evaluate the impact of it’s use

External suppliers Remote stations Final clients

Supply chain

CASE STUDY

Inserido no projeto europeu FASTEN

Empresa pretende utilizar impressão 3D e avaliar o impacto do seu uso

External suppliers Remotes stations Final clients

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Supply chain

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Where 3D printers will be installed

Distribution Centers

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CASE STUDY

Inserido no projeto europeu FASTEN

Empresa pretende utilizar impressão 3D e avaliar o impacto do seu uso

External suppliers Remotes stations Final clients

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Supply chain

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Where elevators are installed

CURRENT SUPPLY CHAIN

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2. CASE STUDY

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SUPPLY CHAIN TO BE IMPLEMENTED

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2. CASE STUDY

3 - METHODOLOGY

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METHODOLOGY3

Conceptualization Modelation Validation and verification

Run optimization Run simulation Results analysis

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Implementation

METHODOLOGY3

Conceptualization Modelation Validation andverification

Run optimization Run simulation Results analysis

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Define simulation model requirements and integration with optimization

Implementation

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METHODOLOGY3

Conceptualization Modelation Validation andverification

Results analysis

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Build the simulationmodel

Run optimization Run simulation Implementation

METHODOLOGY3

Conceptualization Modelation Validation andverification

Results analysis

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Test the simulation model na the integration with the optimization

Run optimization Run simulation Implementation

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METHODOLOGY3

Conceptualization Modelation Validation andverification

Results analysis

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Run optimization Run simulation

Running the optimization model for the case study

Implementation

METHODOLOGY3

Conceptualization Modelation Validation andverification

Results analysis

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Run optimization Run simulation

Evaluate the performance of the supply chain

Implementation

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METHODOLOGY3

Conceptualization Modelation Validation andverification

Results analysis

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Run optimization Run simulation

Analysis and comparison of the results of both models

Implementation

METHODOLOGY3

Conceptualization Modelation Validation andverification

ImplementationResults analysis

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Run optimization Run simulation

3D printers, stock levels, capacities

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4 - MODELS

OPTIMIZATION

Number of 3D printers

Parts produced

Stock levels

Supplying parameters

Suppliers

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4. MODELS

Decision variables

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OPTIMIZATION

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Constrains

• Number of 3D printers >= 0

• A remote station can only provide parts that produce

• A remote station can only produce for its own consumption and/or supply

• A station can’t produce above its capacity

• For a part type, a station can only be supplied by a supplier or remote station

Costs

Objective function

4. MODELS

SIMULATION

Simulation

Simulation

Monitorization

Dashboards and tables

Number of 3D printes

Parts produced

Stock levels

Supplying parameters

Suppliers

Optimization

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4. MODELS

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SIMULATION MODEL

Configuration 1

• 3 Part types (A, B & C)

• 3 Remote stations (A, B & C)

• 1 External supplier (1)

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4. MODELS

SIMULATION MODEL

Configuration 2

• 5 Part types (A, B, C, D & E)

• 3 Remote stations (A, B & C)

• 3 External supplier (1, 2 & 3)

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4. MODELS

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COSTS DASHBOARD

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4. MODELS

COSTS DASHBOARD

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4. MODELS

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INVENTORY/PRODUCTION DASHBOARD

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4. MODELS

5 - IMPLEMENTATIONOF THE METHODOLOGY

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IMPLEMENTATION DA METHODOLOGY

Inputs Optimizationmodel

Supply chain Results

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Simulation model

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IMPLEMENTATION DA METHODOLOGY

Inputs Optimizationmodel

Supply chain Results

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Simulation model

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Inputs (costs, demand, times, etc.) given by Excel

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IMPLEMENTATION DA METHODOLOGY

Inputs Optimizationmodel

Supply chain Results

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Simulation model

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Running the optimization models with the inputs

IMPLEMENTATION DA METHODOLOGY

Inputs Optimizationmodel

Supply chain Results

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Simulation model

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Suppliers, number of 3D printers, stock levels, capacities, etc. saved on Excel

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IMPLEMENTATION DA METHODOLOGY

Inputs Optimizationmodel

Supply chain Results

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Simulation model

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Simulation model executed withthe optimization results

IMPLEMENTATION DA METHODOLOGY

Inputs Optimizationmodel

Supply chain Results

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Simulation model

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Results visualization

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6 - RESULTS

TEST SCENARIOS

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6. RESULTS

Scenario 3D printers type Configuration Localization

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TEST SCENARIOS

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6. RESULTS

Scenario 3D printers type Configuration Localization

1 Actual 1 A

TEST SCENARIOS6

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RESULTS

Scenario 3D printers type Configuration Localization

1 Actual 1 A

2 Advanced* 1 A e C

*2x faster and half of the cost

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TEST SCENARIOS

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6. RESULTS

Scenario 3D printers type Configuration Localization

1 Atual 1 A

2 Advanced* 1 A e C

3 Actual 2 A

*2x faster and half of the cost

TEST SCENARIOS

Scenario 3D printers type Configuration Localization

1 Atual 1 A

2 Advanced* 1 A e C

3 Actual 2 A

4 Advanced* 2 A

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6. RESULTS

*2x faster and half of the cost

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RESULTS DO CASE STUDY

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6. RESULTS

Lead time

Scenario

1 2 3 4

5,1h 4,7h 4,8h 4,8h

RESULTS DO CASE STUDY

Service level

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6. RESULTS

Scenario

1 2 3 4

96% 97% 98% 98%

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RESULTS DO CASE STUDY

Scenario 1: Inventory of part B

in remote B

Inventory status

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6. RESULTS

RESULTS DO CASE STUDY

Total cost

Scenario Optimization Simulation

1 € 225 430 € 237 514

2 € 192 242 € 200 849

3 € 346 291 € 348 844

4 € 288 111 € 297 303

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6. RESULTS

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RESULTS DO CASE STUDY

Scenario 1 costs

Costs Optimization Simulation

Fixed € 66 000 € 66 000

Orders € 159 135 € 162 594

Inventory € 295 € 440

Stockout € 0 € 8 480

Total € 225 430 € 237 514

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6. RESULTS

7 - CONCLUSION

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CONCLUSIONS

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• The tool developed (which integrates optimization and simulation) helps design supply chains

• The obtained results allowed to verify that well located 3D printing improves the lead time and the service level

7. CONCLUSION

FUTURE DEVELOPMENTS

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• More detailed transports simulation

• Consideration of maintenance teams

7. CONCLUSION

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THANK YOU

Narciso Manuel Sobral Couto Caldas