The Research on Scheduling Model of Online Service in Electronic Commerce
：Perspective of the Service Profit Chain
Mi-yuan Shan, Xue-lin Zhou*, Ren-long Zhang
School of Business Administration, Hunan University, Changsha, China
(*zhouxuelin0309@163.com)
Abstract - The emergence of electronic commerce (ecommerce) has changed the consumption concept of
customers. They usually pay more attention to the customer
service quality of Network platform apart from the
convenience and risk of shopping environment. With the
characteristics of virtuality and information sharing under
the e-commerce environment, buyers and sellers cannot
contact face to face. It makes enterprises a lot of difficulties
such as low customer satisfaction, lack of loyalty and weak
profitability following the communication barrier. This
paper first applies the theory of Service Profit Chain (SPC)
to solve the problem of online service scheduling in ecommerce and establishes a mathematical model in which
the whole customers’ service value can be maximized. Then
via numerical test, we verified the effectiveness and
feasibility of the model. The results show that this research
has good practicability and generalization for improving
customer satisfaction in e-commerce.
Keywords –Electronic commerce, online service,
scheduling model, Service Profit Chain, service value
I. INTRODUCTION
The homogenization of products and technology
innovation intensifies the fierce competition for market
share of electronic commerce (e-commerce) businesses
thus force them to focus on online service management.
However, it is a common phenomenon that service
resource shortages, low efficiency and effectiveness of
services, and poor responsibility often occur in the
existing e-commerce platforms, the above defects of
online service management may lead to a loss of
customers due to the dissatisfaction. This affects the
profitability of enterprises severely.
Current researches on customer satisfaction focus on
the integration and innovation of customer service mode,
service strategies optimization in e-commerce, papers
considering the issue of online service scheduling are still
rare. Jun Xu made detailed analysis about the progress of
customer service and incisively summarized the
suggestions to improve service management that
enterprises should equip some service auxiliary tools to
support service measures [1]. Qingjiang Wei formulated
the service strategies of pre-sale, sale and after-sale stages
to improve customer satisfaction and customer loyalty [2].
Quan Fang studied the customer service center of network
platform [3]. Firstly he analyzed the existing problems and
weaknesses, on the basis he set up the structure of service
center and reviewed the functional module and business
flow. Although the elements were systematic and
comprehensive, the model was still a conceptual one and
lack of optimization approaches. Xiaohong Liu and
Xianyi Zeng presented a model of Customer Satisfaction
Index in e-commerce using fuzzy techniques to evaluate
the quality of service [4]. The paper did not suggest how to
improve customer satisfaction. In recent years, foreign
researchers have achieved some success. Sungjoo Lee,
Yongtae Park studied on the classification of e-commerce
service and the strategic management based on customers’
perception [5]. For the purpose, online services were
identified from Korean Portal Sites and classified by 11
variables representing customer perceptions about service
characteristics in the e-commerce context. Godwin J.Udo,
Kallol K.Bagchi and Peeter J. Kirs examined the
dimensions of web service quality based on e-customers’
expectations and perceptions after that they developed
operational web service quality construction, and
analyzed their relationships between customer satisfaction
and behavioral intentions in an e-business environment [6].
The research showed that the direct cause influencing
customer satisfaction was the contact progress of online
service in e-commerce. So studies on e-commerce service
system have important significance in-depth. Basic
queueing systems involve organized queues where units
are dealt with according to their order of arrival [7].
Nonetheless, this traditional first-come first-served rule
isn’t applied to the service strategy in improving customer
satisfaction; we should seek a new service rule on
customer priorities to optimize the combination order
which contribute the improvement of e-commerce’s
customer satisfaction. With regret, there is no one
submitting an effective intelligent optimization model to
solve the online service scheduling because of the
limitations of previous studies.
As there is much room for improvement for the
theories and methods in online service scheduling, we will
introduced the theory of Service Profit Chain(SPC) into
the customer services management, and design a
scheduling model based on the customer value equation in
order to help enterprises improve the customer
satisfaction and achieve sustained profits. We hope to
provide reference and guidance for e-commerce’s online
service management.
Internal
External
Customer
Business strategy
and service
delivery system
Concept of
service
Target market
Employees
Loyalty
Service
Satisfaction efficiency
Ability
Service
quality
Service value=
（process
quality +
result）/
（price paid +
service cost）
Employee
Customer
satisfaction
Profit
The lower cost of service
The higher efficiency of service
The less probability of
customer loss
The greater probability of
service contact success
The less loss of service
Profitability
Customer
loyalty
The better quality of service
The higher price paid
The more agile service response
mechanism
The more scientific and
reasonable order of service
The higher profitability of
enterprises
Fig.2 the mapping relationship of online service value in e-commerce
Fig.1 the Service-profit chain
II. THEORY OF SERVICE-PROFIT CHAIN
The theory of SPC was put forward by James L.
Heskett and so on in 1997[8]. After integrating the
Strategic Services theory and the relationship between
customer loyalty and employee loyalty as well as
corporate profits [9] [10], SPC systematically describes the
circular relationship with enterprises, customers,
employees and profits. The logical connotation is
described as: growing enterprise profit and rising
profitability mainly come from the improvement of
customer loyalty; customer loyalty is the direct result of
customer satisfaction and the satisfaction degree depends
on the perceptive service value which is created by the
staff; in the end the internal service quality determines
their satisfaction. The structure of SPC is shown as figure
1.
There are two circles, one is staff circle, the other
one is customer circle that are linked up by delivery
services (ie, service value equation). The entire dynamic
interaction process of service value delivering is the key
to service, called "service encounter". American scholar
Keaveney conducted a survey of 838 cases leading the
customers to choose competitors in e-commerce [11]. The
results found that the failure of service encounter is the
second reason after a critical service mistake, accounting
for 34%, other reasons including deception, unfair
pricing, service failure dissatisfaction. These findings are
in accordance with Godwin J.Udo that the customers’
satisfaction depends on the online service in e-commerce.
Enterprises should pay attention to the value of delivery
services during the service encounter that passed to
customers for each service process, so it can experience
the high-quality service, the higher the value of services
the more satisfaction, thus the enterprises will gain more
profits. After reviewing the theory of SPC, this paper
proposes a mapping model of online service value based
on the characteristics of e-commerce and online service
factors as figure 2.
The SPC theory is very useful to solve online service
scheduling problem for e-commerce enterprises which
gives important enlightenment. The service value
equation directly affects customers’ conversion in the
state of satisfaction and dissatisfaction. This suggests that
enterprises must give full play to the regulatory role of the
service value equation. If e-commerce enterprises provide
customers with the greater value of services, the more
satisfaction customers gaining, there must be a higher
profit value generated for enterprises. Therefore, this
paper designs a scheduling model of e-commerce’s online
service from the perspective of SPC to improve customer
satisfaction.
III. MODEL BUILDING OF E-COMMERCE ONLINESERVICE SORTING
A. Problem Description
Many service systems require customers to make an
appointment prior to receiving service [12]. In a period of
time, service requests of customers on the platform form
the tasks of online service personnel. Call center must
allocate reasonable tasks to the online service staff as
soon as possible under the condition of limited resources
in order to meet the need of customers. The expression of
service scheduling is shown as figure 3 below. Customer
service scheduling must pay attention to the value of
services for customers. Improving the whole value of
services will be able to improve customer satisfaction
from an overall point. So we can select different customer
service staff and optimize the combination of customers’
order to maximize the service value of all customers in a
specified period of time. The following from resources
environment, the action mechanism, target of online
service scheduling to describe the operation of ecommerce online customer service.
customer1i
……
customer12
customer11
service personnel 1
customer2j
……
customers in
queue
customer22
customer21
service personnel 2
customern 2
C. Parameters and Variables Description
……
……
……
……
……
customernj
4 ） Each customer can’t accept multiple clients’
service requests simultaneously. Only when one online
service personnel finishes the task for a specific customer
he or she can continue to the next task and each service
process is not allowed to interrupt;
 i =the service outcomes and process quality for
customern1
service personnel n
Fig.3 The expression of service sorting
1) Resources environment: In general, in order to
meet the different scales of customers, the call center
requires a certain number of online service personnel at
the same time whose efficiencies are different to handle
the requests for the purpose of realizing the parallel
service assignments.
2) Action mechanism: The service value is
constrained by transaction cost, service process quality
and the results. We try to introduce the service value
equation into the scheduling model to make the
integration of the SPC and the service scheduling
management method. The aim of customer satisfaction
maximization promotes more customers to be satisfied
also reduces the cases of customer dissatisfaction.
3) Target of online customer service scheduling: In
the process of online service encounter, efficient and agile
service channel and response rate can improve the quality
of service. If customers do not get services in their
acceptable time, it will cause hysteresis loss denoted by
Tij which greatly affects the quality of service. Tij and  i
have obvious decreasing correlation.  i represents the
service outcome and process quality for one customer.
Equally,  i can be expressed by Tij , the greater  i the
smaller Tij . Meanwhile, the time cost and service price i
is directly reflected in transaction price Pi and hysteresis
cost a j  Tij , the importance of transaction relies on the
client himself. All the factors of the service value
equation and Tij establish a quantitative relationship. The
purpose of online service scheduling is maximizing the
service value   i
of overall customers in a waiting
i
time.
each customer;
i = the time cost and transaction price;
m = the quantity of service requests at a specific
moment;
i = the number of service requests;
n = the quantity of online service personnel;
j = the number of online service personnel;
Sij = the beginning time of online service
personnel j handling service request i ;
Tij = the delay time of online service personnel
j handling service request i ;
d i = the waiting time of customer i acceptable;
Pi = the transaction price of customer i ;
a j = the unit time cost of delay for online service
employee j ;
x(t )ij = 0 or 1, online service personnel j provides
service for customer i at time t .
D. Model Construction
Objective function：
i
i 1  i
m
f  max 
i  M , M  1, 2,
i  S
 T
ij
t  0 j 1
 x(t )ij  1
i M
Smax
(3)
n
i  Pi   a j  Tij  x(t )ij
i M
(4)
t  0 j 1
Smax
n
t  0 j 1
1）One service request corresponds to one customer;
2）All the online service staff are vacant at initial
moment;
3 ） The service personnel handle the requests
submitted during the same period, and each customer’s
service waiting time is limited. Beyond the waiting time it
means hysteretic and affects the service value;
, m and x(t )ij  1 (2)
1
n
max
x
B. Mode hypotheses
(1)
Constraints：
Tij  max[( Sij  di )  x(t )ij , 0]
Smax m
( t ) ij
1
n
 x
t  0 i 1 j 1
( t ) ij
i  M (5)
m
(6)
x(t )ij 0,1
i  M ; j  N , N  1, 2,
, n
(7)
From the general perspective of enhancing
customers’ satisfaction, the objective function (1) denotes
that the goal of online service scheduling model is to
maximize overall customers’ service value. When the
objective value is a maximum, all the values equaling to 1
are the optimal combination results of customer service
scheduling.
The constraint condition (2) denotes the delay time
of online service personnel j handling service request i .
In the equation, x(t )ij =1 fixes on the service task which
customer i is assigned to service personnel j . If the time
when customer service personnel j receive the request of
customer i exceeds the acceptable waiting time, the
service delays and the value of Tij is equal to the
difference of service beginning time and waiting time. If
within the customer’s expectation, the service doesn’t
delay and the value of Tij is 0.
The constraint condition (3) represents that Tij
measures the service outcome and process quality  i for
customer i . The longer Tij , the smaller  i .
The constraint condition (4) denotes each customer’s
transaction price and time cost due to service delay.
The constraint condition (5) ensures that a specific
customer can only be one customer service personnel
service once, which S max says the beginning time of
service for the last one, x(t )i1 , x(t )i 2 , , x(t )in only one
value is 1, all the rest are 0.
The constraint condition (6) ensure that all customers
have access to services, the amount equal to the total
number of m .
The constraint condition (7) gives the range of
decision variables x( t )ij , when x( t )ij =1, it suggests that
online-service employee j services customer i at time t ,
otherwise 0.
IV. NUMERIC EXPERIMENTS
the calculation number of the optimal solution based on
their average values of 10 repeated tests, and in Table II,
we show the optimal outcome for each problem size.
Table Ⅰ Statistical analysis of test results
（10,4）
The
optimal
solution
0.0651
The
worst
solution
0.0185
（30,4）
0.1273
0.0635
（20,5）
（40,5）
0.1172
0.1663
0.0508
0.0917
Problem
size
Average
value
Number
0.03419
7
0.09717
5
0.06602
0.12295
5
4
Table Ⅱ The optimal sort results
results
（10,4）
j
（30,4）
（20,5）
（40,5）
1
7,4,6
7,28,17,4
15,11,5
3,37,29,1,6,26,38
2
3,9,2,1
27,22,23,26,14,
18,13,11,5,24
17,19,2
4,28,25,9,35,16,
8,24,30,21
3
5,10,8
19,9,10,16,6,2,
25,8,20,15,1
7,20,8,4
13,27,23,36,7,15
21,29,3,30,12
10,16,1,9,18
2,34,22,20,14,12,
33,17,11,10,5
5,14,12,3,13
40,31,18,19,39,32
4
5
Fig. 4
the Gantt chart of problem size (10,4)
In this section we take the following four groups of
problem size ( m , n ) to compare the performance of the
scheduling model. The testing parameters are randomly
generated for each problem size. Each transaction price
requested by the corresponding customer service obeys
uniform distribution in the interval [25,150]; the service
beginning time Sij obeys uniform distribution in the
interval [0, 10]; and the acceptable waiting time d i for the
waiting customer obeys uniform distribution in the
interval [3, 10]. Each unit time cost a j (unit: RMB/time)
of lag service by the corresponding service personnel is
set to be 1, 2,…, n respectively. To avoid each customer
service staff have different beginning time, the initial time
of service task received by all service staff are set to 0.
All of the scheduling model are implemented by
Matlab language, and were run on a PC with Windows
XP system, where CPU is 1.60 GHz and the memory is
2GB. In Table I, for each problem size, we report the
optimal solution, the worst solution, the average value and
Fig. 5
the Gantt chart of problem size (30,4)
Fig. 6
the Gantt chart of problem size (20,5)
Fig. 7
the Gantt chart of problem size (40,5)
The above Gantt charts show the optimal
combination result for each problem size. Such results
guarantee that the model can obtain a general service with
maximum value. The horizontal results show the special
order of a number of customers assigned to each customer
service personnel, and the length of stripes denotes the
length of time, which implies that each customer has
different service duration. Especially, the first Gantt chart
shows that for the case of ( m , n ) = (10, 4), the fourth
customer service staff is free, which implies that he is not
assigned any tasks.
In this experiment, the results of solving the model
appear to be reasonable and reliable, except for the first
case, that is, one customer service personnel is not
assigned any tasks, but other’s task distribution is more
evenly distributed. These shows that the optimal results
can be a reasonable allocation of resources, avoid waste
of resources, and each customer service personnel has
clear division of labor. In specific, as the customer
number increase, the objective function value under the
corresponding problem size increases approximately vs.
the number of customers, while the calculation number of
optimal solution decreases as the number increase. It may
be explained by that as the increase of customer number,
the computational complexity of the solving the model is
also increase, but this also can be accepted.
In conclusion, the model could be used to solve the
e-commerce online customer service scheduling problem
in a certain data scale, and the results of the model have
certain validity and feasibility. Optimal customer service
scheduling combination for improving customer
satisfaction has a certain reference value.
V. CONCLUSION
Due to the significance both in real world and theory,
scheduling has always been an important branch in the
research of combinatorial optimization. The paper
attempts to study the online service scheduling problem in
e-commerce and apply the SPC theory to the
circumstance for the purpose of improving and perfecting
the management of online service. With maximizing the
whole customers’ service value as goal, we have designed
the model. Then the validity and applicability of the
model have been verified by taking a numerical example.
Therefore, the scheduling model research of online
service has both theoretical and practical significance in
improving customer satisfaction to e-commerce
enterprises. Above work provided a foundation for further
studies into online service scheduling. However, the
scheduling model in the paper only considered the best
combination of stable quantity and scale in a closed state,
but ignored that the number of customers’ service request
is dynamic in the situation of real world. Therefore,
further research will continue to enrich the variables and
parameters for establishing a more realistic scheduling
model and utilize a new algorithm to enhance the
effectiveness and robustness of model by improving the
solution speed.
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