RESEARCH JOURNALCLASS OF 201

Summaries of select research projects by graduates of the MIT-Zaragoza Master of

Engineering in Logistics and Supply Chain Management (ZLOG)

Master of Engineering in Logistics and Supply Chain Management

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Introduction

Welcome to the 201 MIT Zaragoza Master of Engineering in Logistics and Supply Chain Management (ZLOG) Research Journal!

The papers included in this journal were chosen from the theses submitted by the ZLOG class of 201 at the Zaragoza Logistics Center. The articles are written as executive summaries and are intended for a business, rather than an academic audience.

The purpose of the executive summaries is to give the reader a sense of the business problem being addressed, the methods used to analyze the problem, and the relevant results, conclusions and insights gained. The complete theses are, of course, much more detailed. We have also included a complete list of this year’s ZLOG theses with short descriptions at the end of this journal.The articles in this publication cover a wide range of interests, approaches, and industries. This variety of topics illustrates one of the hallmarks of the ZLOG program: the students’ ability to focus their course work and research on topics that most interest them.

The ZLOG program is designed for early to mid-career supply chain professionals who want a more in-depth and focused education in supply chain management, transportation and logistics.

All projects are conducted in conjunction with the Zaragoza Academic Partner (ZAP) Program, an initiative to enhance applied research and closer industry-academia relationships in the field of supply chain management.

The ZAP Program gives ZLOG students the opportunity to work closely with industry professionals on actual supply chain problems, and gives companies an opportunity to interact with a student or student team along with a professor as expert thesis advisor who together bring new insights and approaches to a current supply chain project.

We hope you enjoy the articles. If you wish to discuss any other aspect of the ZLOG program or wish to find out how your company can interact with ZLOG students, please do not hesitate to contact me directly.

Happy reading!

DirectorZaragoza Logistics Centerm @zlc.edu.eswww.zlc.edu.es

KEY INSIGHTS

Introduction

Problem Definition

Methodology

Preliminary Process Study & Product Family Selection

Value Stream Mapping & Examining the "As-Is" state

Data Collection & Cleaning; Statistical Data Analyses

Scenario Simulation

Conclusions & Key Findings

Linking Lead Time and Inventoryin Pharma Businesses

Summary:

M.E. in Logistics & Supply Chain Management,MIT-Zaragoza International Logistics Program

Bachelor of Technology in Mechanical Engineering,VIT University, Vellore, India

M.E. in Logistics & Supply Chain Management,MIT-Zaragoza International Logistics Program

Bachelor of Technology in Chemical Engineering,VIT University, Vellore, India

Supply Chain Framework

Data Analysis

Analysis- Inventory

Analysis- Lead Time

Analysis- between Inventory & Lead Time

Analysis across SKUs

Mat ID Source Dest. LT LT Var Inv Var DOC ID 3 DS Site3 DS Hub2 29 119% 125% 61 ID 1 DS Site1 DP Site1 29 85% 51% 77 ID 3 DS Site3 DS Hub1 30 76% 101% 59

Statistical Correlation

Avg LT Avg Inv Avg LT 1 Avg Inv 0.33 1

LT Var Inv Var LT Var 1 Inv Var 0.47 1

Avg Inv LT Var Avg Inv 1 LT Var 0.43 1

Regression Analysis

Analysis over Time

Statistical Correlation

Avg LT Avg Inv Avg LT 1 Avg Inv 0.67 1

Regression Analysis

Simulation

Conclusions and Recommendations

Cited Sources

Avg 31 41% Variability

(I/P) (O/P)

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50

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1 5 913 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

101

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113

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Day

s

Period No

FG Stage - Lead Time

$175

$10

Split b/w Holding & StockOut Cost per period

Holding CostStockOut Cost

99% 100%

0%

10%

20%

30%

40%

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Service Level & Fill Rate

2,300

2,350

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2,600

2,650

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kg A

PI

LT Reduction (x%)

Avg Inventory BehaviorDS LT Reduction DP LT Reduction FG LT Reduction DS,DP,FG LT Reduction

1,600

1,800

2,000

2,200

2,400

2,600

2,800

3,000

kg A

PI

LT Var % Reduction (x%)

Avg Inventory BehaviorDS LT Var% Reduction DP LT Var% ReductionFG LT Var% Reduction DS, DP, FG LT Var% Reduction

KEY INSIGHTS

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Introduction

Adding Value through Collaboration: Big Data Analysis and Opportunities

Summary:

Romina Garcia Soriano graduated from MIT-ZLOG. Garcia Soriano has degrees in industrial engineering from the UPV and the ETHZ. Specialized in supply chain management, she has been working in the automotive and energy sectors in various countries.

Greivin Rodriguez, graduated from the MIT-ZLOG. Prior to ZLOG, Rodríguez worked for more than eight years in different companies in the logistics field,including as aLogistics Engineer for Canon-Germany where he finished his MBA in Logistics and Leadership.

Beverly Osborngraduated from MIT-ZLOG. Osborn has experience working in the private sector and the United Nations in sourcing, supplier performance management and negotiations. Shehas worked in petroleum and construction. She will start a PhD this year.

Methodology

Figure 1 - Opportunity identification

Figure 2 - As-is approach

Results

Figure 3 - Opportunities identified

Recommendations

Cited Sources

Int. J. ProductionEconomics

•••••

yellow

blue

green

purple

• Literature review of Bullwhip Effect causes and effects• Analysis of emerging Internet of Things technologies in the

supply chain sphere

1. Background

Study

• Study of prior attempts at quantifying the Bullwhip Effect• Observations of Dynamic & Static Aspects of the Beer

Game necessary for simulation performance

2. Past Simulations

• Decide relevant Observation Metrics, and understand how to isolate them in Arena environment

• Determine relevant IoT Tools for future implementation

3. Narrow Down Focus

• Development of Beer Game simulation• Make decision on Ideal Ordering Model based on prior

Beer Game Results• Visualization of Key Performance Indicators through

tracking of significant metrics

4. Initial Simulation

• Implement new technologies in a well defined manner• Create Metric Scorecard to track and analyze output data

of simulations• ANOVA Testing to confirm independence of data sets

5. IoTSimulations

MIT SloanManagement Review

The bullwhip effectin supply chains

Simulation Average Total Supply Chain Cost

With 3D printing 61,622.79$ Without 3D Printing 194,658.89$

ZLOG CLASS OF 201

THESIS PROJECTS

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Team: Advisor: Susana Val

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Team: Advisor: Luca Urciouli

Team: Advisor: Çagri Gürbüz

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“Construyendo Europa desde Aragón”

FONDO SOCIAL EUROPEO

www.zlc.edu.es/zlog