Financial Benchmarking Financial Benchmarking o of f Transportation Companies Transportation Companies

(1)

Financial Benchmarking

o

_o

f

_f

Transportation Companies

in the

New York Stock Exchange (N

YSE

)

Through

Data Envelopment Analysis

(

DEA

)

a

nd

Visualization

Firdevs Ulus,

(2)

Introduction

n

Benchmarking

B

enchmarking

study of industrial transportation

study

companies traded in the New York Stock Exchange

(NYSE)

n

Two distinguishing aspects of our study:

n

Using financial data in DEA

n

Visualizing the efficiency scores of the

companies in relation to the subsectors and the

number of employees.

(3)

Introduction

n n

Logistics:

n Movements of goods n Field of operations: Transportation planning, warehousing, inventory management, etc. n n

Financial benchmarking

_{Financial benchmarking}

:

n Comparing companies in an industry based on their financial statements n n

Data Envelopment Analysis (DEA):

n A nonparametric technique which can be used to compare a set of “decision making units” (DMUs) amongst each other

(4)

Introduction

n n

Our study:

_Our study:

n Financial statements for 2005 of the industrial transportation companies traded in New York Stock Exchange (NYSE) n Benchmarking through DEA n Results of DEA visualized n Miner3D n Detecting patterns n Deriving useful insights.

(5)

(6)

(7)

Literature Search

n

Min and Joo

Min and

Joo (2006)

(2006):

:

n A benchmarking study of six third party logistics (3PL) providers using DEA and financial data n n

Literature on DEA aplications in logistics:

_{Literature on DEA aplications in logistics:}

n For comparing container ports’ efficiencies n Estimating productivity of the trucking industry in U.S. n Deriving efficiency scores of urban rail firms n n

Scatter plot visualizations

_{Scatter plot visualizations}

:

n Efficiency scores of warehouses v.s. warehouse sizes and material handling systems investments

(8)

Methodologies

n n

Data Envolopment Analysis:

_{Data Envolopment Analysis:}

n Approach to measure efficiency of decision making units (DMUs) in comparison to each other n “Weights” for multiple inputs and outputs assigned automatically within DEA n No need to have congruent units n Determination the “efficient frontier”, “relative efficiencies” and “reference sets”

(9)

(10)

Methodologies

n n

Data Visualisation:

_{Data Visualisation:}

n Detecting outliers n Finding patterns n Coming up with new hypotheses n Visualizations include: n Quantile plots, histograms, box plots, symmetry plots, scatter plots, quantilequantile plots, etc.

(11)

Analysis and Results

Data Collection

Visual Analysis

Marine Transportation Subsector

Railroad Transportation Subsector

(12)

Data Collection

n n

Subsectors:

_Subsectors:

n Marine transportation (19) n Transportation (9) n Trucking (2) n Delivery (3) n Railroad (10) n n

Total of 39 companies benchmarked

_{Total of 39 companies benchmarked}

(13)

Data Collection

n n New York Stock Exchange (NYSE) and New York Stock Exchange (NYSE) and company websites company websites n

n A_Annual _nnualreports_reports: the income statements & the _:

balance sheets

DMU

Total Operating Expenses Total Assets Total Liabilities Total Revenue Net Income Total Shareholders Equity

(14)

Visual Analysis

n

Efficiencies of each DMU computed using

DEASolver software

n

Visualization of efficiency scores done by

Miner3D software

(15)

DEA

(16)

(17)

(18)

(19)

Marine Transportation

n

Considered 18 companies

n

Better understanding of this subsector’s own

boundaries for being efficient

n

Possible to see the companies that should be taken

as role models by the inefficient companies

n

Reference sets found by DEA enable us to identify

such patterns

(20)

Marine Transportation

Reference Sets

0.19 SFL 0.47 GMR 0.34 DSX TNP 0.06 TK 0.66 SFL 0.08 OSG 0.14 GMR 0.06 FRO OMM 0.03 SSW 0.04 GMR 0.93 DSX EXM 0.13 SFL 0.08 SSW 0.79 DSX NAT 0.60 SFL 0.40 DSX TUG 0.02 TK 0.17 SFL 0.46 HRZ 0.35 GMR KSP 0.03 SFL 0.77 DSX 0.20 ATB DHT 0.66 SFL 0.34 DSX USS

(21)

Railroad Transportation

Reference Sets

0.76 GWR 0.17 CP 0.02 CNI 0.05 BNI KSU 0.02 UNP 0.31 CP 0.30 CNI 0.36 BNI CSX 0.04 UNP 0.03 CP 0.73 CNI 0.20 BNI NSC 0.99 GWR 0.01 CP 0.00 BNI RRA