INTRODUCTION
A growing number of participants in transport operations have led to increasingly
complex distribution setups with augmented needs for integration. The complex
services that are given and the problems of exchange rate fluctuations, supply
disruptions, transportation capacity constraints, order cancellations and a
series of factors make the execution of valueadded services difficult. To be
able to solve some of the shortcomings in many of today’s logistics, SL
system has been developed (Lumsden and Mirzabeiki, 2008).
SL is the oversight of materials, information and finances as they move in a
process from supplier to manufacturer to wholesaler to retailer to consumer.
It involves coordinating and integrating these flows both within and among companies.
Recent studies are investigating the benefits from SL are being applied for
increasing the accuracy and timeliness of the information and reducing the costs
in the logistics and transportation operations.
VALUECHAIN ANALYSIS
Valuechain analysis includes the whole cycle of the organization, production
and delivery of products from inception to use and recycling, provides a tool
for mapping these crucial domains of supply chain to value chain. There are
five major business challenges in the supply chain: Cost Control, Process Visibility,
Risk Management, Increasing Customer demands and Globalization according to
“the world’s chief supply chain officer survey” of IBM In 2009. Valuechain
analysis is used to deal with these challenges to continually evaluate the current
performance, identify areas for improvement and eventually set goals for the
future. SL satisfaction measurement model is setup based on QFD methodology,
originally from new product design, proved useful when applied to SL implementation.
We take the material flow of the most excellent enterprise all over the world,
such as the famous enterprise Apple, DELL and IBM as the benchmark to analyze
logistics demands of Chinese enterprises. Through the quality of house, the
demands of logistics can be transformed into the corresponding process design
features that can be measurable, actionable and potential improved shown in
Fig. 1.

Fig. 1: 
Quality function deployment house 
In this hypothesis, m stands for the number of Logistics Demands (LD) and n
stands for the design features corresponding to the process (DF), among which:
Wi 
= 
(w_{i})_{mx1}: w_{i }is the degree
of importance of number i logistics demand 
LI 
= 
(l_{i})_{mx1}: l_{i} = w_{i}xP_MSI_{i}xIF_{i}x100/Σ
(w_{i}xP_MSI_{i}xIF_{i}), l_{i} is the relative
importance of number i logistics demand, P_MSI = MAX_MSICUR_MSI, MAX_MSI
is the maximum market satisfaction degree under the cost constraint in enterprises,
CUR_MSI is the current market satisfaction degree and P_MSI is the potential
for process improvement. IF is the influence of the demand on the product
value: generally (IF = 1.0), larger (IF = 1.5), largest (IF = 2.0) 
PR 
= 
(R_{ij}): R_{ij} is the quantitative relation value between
the numbers i logistics demand and design feature of number j process 
AM 
= 
(r_{ij})_{nxn}; r_{ij} is the association degree
between design feature of number i and j process and d_{i} and d_{j;} 
DW 
= 
(dw_{i})_{mx1:} dw_{i} is the demand weight of
number i logistics 
RDW 
= 
(rw_{i})_{mx1:} rw_{i} is the relative demand
weight number i logistics demand; AT = (at_{j})_{1xn}: at_{j
}is the absolute technical weight of number i logistics demand 
RT 
= 
(rt_{j})_{1xn}: rt_{j} is the absolute technical
weight of number j logistics demand 
DOV is the design value. The relations of variables are as follows:
The purpose of value analysis is to finally get RDW and RT, as a value measurement reference for logistics demands and processes design features. In this way, we can find out the major valueadded and non valueadded chain process and determine the objectives and priorities of the process integration. LD is derived from study on the benchmarking and DF is gotten from the pointed activities decomposition. W_{i}, MAX_MSI, CUR_MSI, IF and AM is designated by the logistics experts, where DI can be identified.
It is the most important thing now to determine the PR. The relationship intensity
Rij in PR is determined by logistics expert who comes from different enterprises
in different areas of expertise with different experiences. However, different
team members tend to give their individual preferences in multiform at according
to their different knowledge, backgrounds, abilities and experiences. It is
a group decisionmaking problem in nature, by defining corresponding transforming
functions, preferences in multiformat can be uniformed to fuzzy judgment matrices.The
group preference aggregation and weights determination methods are adopted on
the PR. M refers to a number of experts to assess the relationship intensity
R_{ij}: (Pan, 2001).
Definition 1: Evaluation aggregate K = (k_{n}), 0 <n = 4, k_{n} stands for the result of the evaluation degree including strong, medium, weak and irrelevant.
Definition 2: Evaluation matrix P_{mxn}= { p_{mn} }, m, n∈N, p_{mn} stands for the membership of evaluation on the R_{ij }on k_{n }by the m people. Because there is no obvious division on the relationship intensity, there is a certain ambiguity. It is difficult to give a clear judge on the relationship intensity, whether strong, medium, weak or irrelevant. Thus the probability should be allowed, that is, the various levels of membership. In the evaluation matrix Pmxn, the fuzzy membership function replaces the single absolute value nature, in line with people's logical thinking.
Definition 3: The evaluators weight aggregate A_{1xm }= {a_{i}}, 0<i = m ,a_{i} stands for weight of the number i.
Because the decisionmaking team members come from the various SL areas, they
may differs in knowledge and experience on certain category of the problems.
Thus, the reliability and the degree of influence on the conclusions are different,
that is, with different degree of importance. Therefore, they are allocated
to different weight values. Here, we use full weight method, which the team
members assign weight to others and themselves. Each person assigns the scores
with maximum of 100 scores to others according to his or her view on the importance
of the evaluation results by others. Then the total score of each people is
normalized as the weight of the member. That is S_{ij} stands for the
score to the number i person by the number j person. The fuzzy evaluation set
can be got after comprehensive evaluation on various decisionmakers:
Complying with the maximum membership degree method, the k_{g }corresponds to maximum max (b_{j}) as the final judge value of R_{ij}. There may be a certain degree of dependency between the design features of the process. In order to weaken the influence of AM and increase the independence of AT, linear plan process is made: AT = AT*AM^{1}. F rom Rij, DI and AT, RDW and RT can be found.

Fig. 2: 
Activities of SL business process 
MULTIOBJECTIVE SL MODEL
The SL business process can be divided into activities relevant with value
in Fig. 2. It is the oversight of materials, information and
finances as they move in a process from supplier to manufacturer to wholesaler
to retailer to consumer. SL involves coordinating and integrating these flows
both within and among companies. It is said that the ultimate goal of SL is
to reduce inventory (with the assumption that products are available when needed).
As a solution for successful supply chain management, sophisticated hardware
and software systems based on Internet of Things (IOT) are competing with Cloud
Computing based application service providers who promise to provide part or
all of the SL service for companies who rent their service. SL can be divided
into three main flows: product flow, information flow and finances flow. The
product flow includes the movement of goods from a supplier to a customer, as
well as any customer returns or service needs. The information flow involves
transmitting orders and updating the status of delivery. The financial flow
consists of credit terms, payment schedules and consignment and title ownership
arrangements. 4PL is the core platform to integrate and coordinate supplier,
manufacturer and consumer and 3PL is responsible for providing a supply network.
According to benchmarking law principle, we form the quality house of demands
and activities to analyze the difference between outstanding SL process and
typical logistics process in China. From the multiobjective planning theory,
we use mathematical method to analyze its value: Supposing y_{i} reflects
the value of the number i logistics demand, x_{j }reflects the value
of j for achieving y_{i}, f_{i} reflects the function relations
between x_{j} of logistics procedures and logistics demand y_{i},
g_{j }reflects the function relations between x_{j} and x_{1},x_{2},…,x_{j1},x_{j+1},…,x_{m}.
F stands for the value of total logistics demands which is decided by each logistics
demand membership, w_{i} is the weight (degree of importance) of the
number i logistics demand membership. At this point, the process integration
can be described as a multiobjective optimization problem: In the functional
relationship Eq. 3 and other conditions (such as the time
constraints of transport, transportation costs, etc.), the process optimization
can change the value of activities (x_{1},x_{2},…,x_{m})
in order to make the maximum total value of f. (ShiHua and
YongLin, 2000).
Fuzzy evaluation on SL: According to the RDW and RT, we can determine
the SL optimization goals and priorities and then SL solution is given in Table
1. As the solutions meet the optimization objectives differently, the evaluation
on the various solutions is therefore multiobjective decisionmaking. A_{i}
(i = 1, 2... m) stands for the number i program and X_{j} stands for
optimization objectives. Then the multiobjective decision making problem can
be expressed as M_{ij }= (x_{ij}). X_{ij }means that
the number i solution contributes to the number j objective. It can be specified
in the similar way as PR. The following is the value evaluation method for SL
solution (Pan, 2004):
• 
According to the results of value analysis, hierarchical evaluation
indicator system of objectives is setup as follows and shown in Table
1. 
• 
Using Hierarchy Analysis Process (AHP) to determine the weight
of optimization objectives with all levels: 
Table 1: 
SL optimization goal and priority 

• 
Determine the best and worst indicator set (E^{+}
and E^{}) of optimized objectives of each level 
There are two types of the indicators’ nature: numerical and nonnumeric. As for nonnumeric indicators (such as: the timely maintenance services and security etc.), the evaluation indicator is the fuzzy logic language such as satisfaction or dissatisfaction. We use scores ranging from 1 to 19 to describe the degree of satisfaction. As for the numerical indicators (such as: the procurement cycle), the limits can be used as best and worst value (if an indicator is efficiencyoriented, the maximum value is the best; if it is costoriented, the minimum value is the best).
As the physical meaning, dimensionless and magnitude of the evaluation indicators
are different and the effect on the evaluation object is also inconsistent,
we must make nondimensional processing for multiindex comprehensive evaluation.
The dimensionless value should reflect the advantages and disadvantages of each
indicator value. At the same time, it is difficult to quantify the effect of
the indicators value strictly with ambiguous nature. Thus the membership function
μ stands for the effect of indicator value after processing the dimensionless.
μi stands for the scores or degree of satisfaction of the process design
parameter corresponding to the number i evaluation objective, which can be defined
as:
Among them, e_{i} is number i indicator value of the program. The singleobjective evaluation matrix which is composed of membership function as follows:
According to the maximum membership principle, the best program is maxb_{i}, (1 = I = p):
The symbol “○” stands for the fuzzy generalized operators, such as taking maximum operation, minimum operation, ordinary addition, general multiplication and so on, which make rational choice according to the characteristics of evaluation problems.
Based on the fuzzy theory, the method unifies the qualitative and quantitative indicators and the membership function with optimal value, so that it solves the multiobjective decision making problem on optimization programs and lays theoretical basis for optimization of process integration.
CONCLUSION
With economic globalization, the modern logistics industry has been considered as a key of the national economic development. SL is conducive to faster and better realization of development goals in the “Logistics Industry Restructuring and Revitalization Plan” of China. However, SL is a large and complex dynamic system based on IOT and Cloud Computing technology involving many factors. There are still many indepth studies needed to be further explored including SL performance evaluation, association between SL and modern logistics, the effect of technological progress and knowledge progress on the development of SL. SL is bound to become the direction for Chinese transport enterprises and SL will be promising in future. Government should give priority to SL development in the logistics industry.
ACKNOWLEDGMENT
The authors wish to thank the Humanities and Social Sciences Foundation of
Chinese Education Ministry (2324), Science and Technology Major Soft Science
Foundation (2008GXS1D042), Ningbo Natural Science Foundation (2011A610106) Zhejiang
philosophy and social sciences key research baseport modern service and creative
industries research center, under which the present work was possible.