INTRODUCTION
Regarded as the third profit source, logistics is playing an extremely important
role in the process of the modern industrial production. At present it becomes
more and more difficult that the enterprises only rely on the ability to create
ways to obtain the sustainable competitive advantage. Therefore, the enterprises
begin to turn their attention to the logistics field, which is called "the third
profit source" of the enterprises.
In the past decades, a lot of research on the logistics capability had been
made by many experts and scholars at home and abroad. Shang
et al. (2009) summarized the logistics capability from four aspects
and evaluated the logistics capability of a case based on the fuzzy evaluation
method. Logistics capability should include processing ability and the valueadded
capacity and the relationship between them was analyzed (Kumar,
2008). Morash et al. (1996) made a study
on strategic logistics capabilities for competitive advantage and firm success.
Huang (2008) put forward some problemsolving measures
to improve the logistics capability. Daugherty and Pittman
(1995) studied the logistics capability from the speed of product distribution,
information exchange and flexibility. Daugherty et al.
(1998) thought that logistics capability should been studied from five performance
measures, including the response speed of the customer, the level of customer
service, delivery on time, stability of the quality and advance notice of delay
or shortage.
Generally speaking, a lot of experts and scholars performed some empirical
studies on logistics capabilities via some evaluation methods, including the
Analytic Hierarchy Process (AHP), fuzzy evaluation, Principal Component Analysis
(PCA) and entropy theory and so on (Shi et al.,
2009; Yu et al., 2010). At the same time,
every evaluation method has its advantages and disadvantages. Using the model
of AHP and fuzzy evaluation, the study establishes logistics capability evaluation
system from four aspects and tests it via a case study in the manufacturing
enterprise.
TO CONSTRUCT THE EVALUATION SYSTEM OF LOGISTICS CAPABILITY
Different industries have different priorities about the evaluation indicator
of logistics capability. Based on the results of previous research, according
to the comparison and summary, this study intends to establish a more scientific
indicator system from the following five aspects and every aspect can be divided
into different components.
Ability to control the logistics cost:
• 
Supply logistics cost: It mainly refers to forecast
cost of logistics, projected cost of logistics and preparation cost of logistics 
• 
Production logistics cost: It mainly refers to a variety of productive
logistics cost, including loading and unloading, transportation, processing,
storage and transportation, etc 
• 
Sales logistics cost: It mainly refers to the sales service cost
of the logistics, including the storage, packaging, service fees, etc 
• 
Return logistics cost: It mainly refers to the logistics cost for
the return and exchange 
• 
Abandoned logistics cost: It mainly refers to the logistics cost
produced by waste, substandard products 
Ability of logistics service:
• 
Time efficiency elements: It mainly refers to order
processing speed, delivery accuracy and flexibility 
• 
Information elements: It mainly refers to informatization level,
complete information and visibility 
• 
Customer element: It mainly refers to the goods availability, complaint
handling, personalized response 
Ability of logistics elements:
• 
Logistics equipment: It mainly refers to the machinery
and equipment needed for various logistics activities and logistics operations 
• 
Logistics facilities area: It mainly refers to the facilities area
needed for various logistics activities and logistics operations 
• 
Logistics capital: It mainly refers to the capital needed for various
logistics activities and logistics operations 
Ability of logistics organization and management:
• 
Management ability: It mainly refers to the management
ability of logistics administrators 
• 
Operation ability: It mainly refers to the operation ability of
logistics activity operators 
• 
Technical level: It mainly refers to the technical level of logistics
technical personnel 
Considering the principles of purpose, scientific, adaptability, comparability
and overall system about indicators, an evaluation indicator system of logistics
capabilities that contains 4 firstlevel indicators, 14 secondlevel indicators
is established (Table 1).
TO DETERMINE THE MODEL INDICATORS VIA AHP
The Analytic Hierarchy Process (AHP) is a functional decision process proposed
and gradually improved by the American mathematician Saaty T. L. in the 1970’s
(Saaty, 1990). It is appropriate to use the AHP method
to determine weights among the secondlevel indicators and weighted calculation.
Finally, this study uses the AHP method to establish a model, whose main steps
are as follows (Duan et al., 2011).
To construct the judgment matrices of each level: After the model of
AHP is established, the judgment matrices of each level will be easily constructed.
According to the questionnaires survey and expert opinion, the relative important
each other will be easily expressed. Then the data matrixes that represent respectively
the judgment matrices of the secondlevel indicators will be constructed (Chen
and Li, 2011).
To perform hierarchical single sorting and consistency check: As some
program can be written simply and run in the Matlab software, the hierarchical
single sorting and the consistency check can be easily solved. And the equation
of the consistency check is as follows:
Where:
λ_{max} 
= 
The maximum eigenvalue 
n 
= 
The rank of judgment matrix 
CI 
= 
Consistency of judgment matrix deviation 
CR 
= 
Random consistence rate 
RI 
= 
The average random consistency of different rank judgment matrix 
Table 1: 
Evaluation indicator system of logistics capabilities 

To adjust the judgment matrix and hierarchical ranking model: After
the calculation of the judgment matrix via the Matlab software, CI and CR will
be easily obtained. If necessary, the judgment matrix and hierarchical ranking
model may be corrected and adjusted. If CR is less than 0.1, the results of
hierarchical sorting will satisfy the requirement for consistency, otherwise
the judgment matrix will need to be adjusted.
TO ESTABLLISH EVALUATION SYSTEM OF LOGISTICS ABILITY VIA FUZZY EVALUATION
To determine the evaluation set: In the model of the fuzzy evaluation,
the evaluation set of the logistics capability is C_{i }(i = 1, 2, 3,
4, 5) that express, respectively five grades, including the higher, high, general,
low and lower. And the above five grades are given the assignment V = {95, 85,
75, 65, 30}.
To establish evaluation membership matrix μi:
Where:
i 
= 
No. of the firstlevel indicators 
u_{mm} 
= 
The membership degree of No. m responding to No. n 
n 
= 
No. of evaluation grades in the evaluation set 
m 
= 
No. of the evaluated factors 
To make the fuzzy comprehensive evaluation:
• 
To make the secondlevel fuzzy comprehensive evaluation: 
Where:
i 
= 
Number of the firstlevel indicators 
wi 
= 
Internal weight of the firstlevel evaluation index U_{i} (i =
1, 2, 3) 
μ_{i} 
= 
Evaluation matrix of level i 
• 
To make the firstlevel fuzzy comprehensive evaluation: 
Where:
w 
= 
Relative weights among the firstlevel indicators 
T 
= 
Membership vector of the factor U corresponding to the evaluation set
V 
A 
= 
Comprehensive evaluation vector 
• 
To determine the grade of comprehensive evaluation: 
Where:
F 
= 
Final evaluation score 
d 
= 
Rating scores matrix 
A 
= 
Comprehensive evaluation vector 
Case study: In this study, the logistics capability of a manufacturing
enterprise in 2011 is evaluated by the models of AHP and fuzzy evaluation.
To determine weights among the secondlevel indicators via AHP: According
to the expert scoring and results of the questionnaires, the hierarchical analysis
matrix will be built so as to determine the internal weights of evaluation index
level. The maximum eigenvalue of all judgment matrices is as follows and all
the results of hierarchical sorting can satisfy the requirement for consistency
check:
• 
The calculation of the judgment matrix U_{1} (Table
2) 
Remarks: λ_{max }= 5.2826, CI = 0.0706, CR = 0.0631<0.10
w_{1 }= Relative weights of the firstlevel indicators U_{1}.
• 
The calculation of the judgment matrix U_{2} (Table
3) 
Remarks: λ_{max }= 3.0385, CI = 0.0193, CR = 0.0332<0.10
w_{2 }= Relative weights of the firstlevel indicators U_{2}.
• 
The calculation of the judgment matrix U_{3} (Table
4) 
Remarks: λ_{max }= 3.0383, CI = 0.0091, CR = 0.0158<0.10
w_{3 }= Relative weights of the firstlevel indicators U_{3}.
• 
The calculation of the judgment matrix U_{4} (Table
5) 
Remarks: λ_{max }= 3.0765, CI = 0.0368, CR = 0.0634<0.10
w_{4 }= Relative weights of the firstlevel indicators U_{4}.
• 
The calculation of the judgment matrix U (Table
6) 
Remarks: λ_{max }= 4.1807, CI = 0.0602, CR = 0.0669<0.10
w = Relative weights among the firstlevel indicators.
Table 2: 
Judgment matrix U_{1} 

Table 3: 
Judgment matrix U_{2} 

Table 4: 
Judgment matrix U_{3} 

Table 5: 
Judgment matrix U_{4} 

Table 6: 
Judgment matrix U 

To make the fuzzy comprehensive evaluation of logistics capability:
According to questionnaires survey and Eq. 3, the membership
of indicators μi (i = 1, 2, 3, 4) will be obtainedwhich is as follows:
According to the Eq. 4, fuzzy comprehensive evaluation of
subspace dimension can be obtained:
T_{1} = w_{1}×μ_{1} = (0.2366,
0.2591, 0.3072, 0.1129, 0.0841)
Where:
w_{1} = (0.1140, 0.2711, 0.5016, 0.0708, 0.0425)
Similarly:
T_{2} = w_{2}×μ_{2} = (0.3154,
0.2339, 0.2927, 0.0871, 0.0710)
T_{3 }= (0.3756, 0.2680, 0.2122, 0.0721, 0.0721)
T_{4} = (0.3117, 0.3161, 0.2275, 0.0816, 0.0631)
According to the Eq. 5, the evaluation values of the firstindicator
can be obtained:
According to the Eq. 6, value of logistics capabilities of
every level can be obtained:
The score of the firstlevel indicator: F_{1} = T_{1}×(95
85 75 65 30)^{T }= 77.402 Similarly:
F_{2} = 79.588
F_{3} = 81.226
F_{4} = 80.739
The score of comprehensive evaluation of logistic capability in manufacturing
enterprise:
F = A×d = A×(95 85 75 65 30)^{T} = 78.73
The evaluation of calculation results: By the above calculation, all
of the final evaluation score is between 85 and 75. So the conclusion can be
drawn that the comprehensive evaluation of logistic capability in manufacturing
enterprise is at the secondary level and all of the capability of four aspects
is in general, which is consistent with the actual situation.
CONCLUSION
In this study, an evaluation system of logistics capabilities is established
from four aspects. A model of AHP and fuzzy evaluation is used to analyze the
logistics capabilities of manufacturing enterprise. And the result of an empirical
analysis proved to be valid.
ACKNOWLEDGMENTS
The study is supported by the outstanding Youth Science and Technology Innovation
Team funded projects of Hubei Polytechnic University under Grant No. 13xtr03
and the Laboratory Open Foundation of Hubei Polytechnic University Grant No.
201343.