Optimal Shipping Strategy and Return Service
Charge Under No-Reason Return Policy
in Online Retailing
Abstract
Under no-reason return policy in an online setting, online retailers must
determine whether to offer free shipping services when delivering products to
consumers, and how to charge consumers for returns. To address such decisionmaking challenges, we develop a theoretic model to derive the optimal shipping
strategy and return service charge (RSC) for an online retailer under two decision
scenarios, in which decisions on shipping strategy and RSC are made either jointly
or separately. We find that the retailer is better off in joint decision scenario than in
separate decision scenario; a shipping free strategy is usually accompanied by a
higher RSC, while a shipping fee strategy is typically accompanied by a lower
RSC. We also find that, market parameters (e.g., the base return quantity, product
price, consumers’ sensitivities of shipping fee on demand, RSC on demand and
return quantity) have important effects on the retailer’s decisions on shipping
strategy and RSC. Our findings suggest that the retailer can benefit from taking
positive actions toward influencing the market to determine the favorable shipping
strategy and RSC. Furthermore, our results can provide theoretical explanations for
widely used shipping strategies and RSCs within the context of no-reason return
policies in online settings. In particular, our analytical results explain why some
real-world online retailers offer both free shipping and free return services to
certain consumers.
Architecture:
Front End (MVC RAZOR)
Back End (SQL Server)
Software Tools
(Visual Studio 2012, SQL 2008).
User:
1. User login to the System.
2. Users Search to the product.
2. Users buying a product.
3. User return the product.
Admin
1. Admin add Main category and sub Category.
2. Admin Store and view All User Details.
3. View in all product details.
4. Admin view product status.
5. Admin removes the product.
Owner
1. Owner uploads all the Products.
2. Owner view All Product Details.
3. Owner view product status.
1. Database
-> Online Social (As My Database)
->I am using entity framework
Controller
1. Admin controller
2. Owner controller
3. User controller
There are 3 views have been created based on the Action method.
SYSTEM ANALYSIS
EXISTING SYSTEM
We use the insights from all three literature streams to develop our demand
framework. To this end, shipping strategy, i.e., SFRS or SFES, is directly
incorporated into the demand function.
PROPOSED SYSTEM
Proposed an analytical model to derive the optimal pricing and restocking
fee policy by taking consumer preferences into consideration. Consumer trials into
their proposed analytical optimization models identified the retailer’s optimal
pricing and refund policies for advance-selling fashionable products. In their work,
full and partial refund policies are both examined. Similar work related to return
policy on fast fashion products is also found.
Algorithm:
Polynomial Algorithm
Polynomial algorithm that is guaranteed to terminate within a number of steps
which is a polynomial function of the size of the problem. See also computational
complexity, exponential time, nondeterministic polynomial-time.
SYSTEM SPECIFICATION
HARDWARE REQUIREMENTS:
System
: Pentium IV 2.4 GHz.
Hard Disk
: 40 GB.
Floppy Drive
: 1.44 Mb.
Monitor
: 14’ Colour Monitor.
Mouse
: Optical Mouse.
Ram
: 512 Mb.
SOFTWARE REQUIREMENTS:
Operating system
: Windows 7 Ultimate.
Coding Language
: MVC 4 Razor
Front-End
: Visual Studio 2012 Professional.
Data Base
: SQL Server 2008.
CONCLUSION
As observed in real online retailing settings, many retailers provide free shipping
strategy and allow dissatisfied customers to return products with no reason to boost sales.
However, such a strategy, in turn, increases operational costs for retailers. In such a
circumstance, it is important for retailers to determine whether to offer free shipping
service when delivering products, and how to charge consumers for returns. To address
these two issues, in this article, we consider that one retailer sells a particular product to a
group of consumers online, and we develop a theoretical model to examine the optimal
shipping strategy and RSC under no-reason return policy in this article has been accepted
for inclusion in a future issue of this journal. Content is final as presented, with the
exception of pagination. HUA et al.: OPTIMAL SHIPPING STRATEGY AND RSC
UNDER NO-REASON RETURN POLICY IN ONLINE RETAILING online retailing.
Using the introduced model, we first examine the optimal shipping strategy by fixing
RSC, and the optimal RSC by fixing shipping strategy, respectively. We then explore the
optimal shipping strategy and RSC jointly.
The retailer’s optimal shipping strategies and RSCs as well as the corresponding
conditions are derived. We then conduct a numerical study to illustrate the advantage of
joint decision of the optimal shipping strategy and RSC. Some key findings and insights
are obtained and are summarized as follows. The retailer’s optimal shipping strategy and
RSC interact with each other, and such decisions also depend on market parameters. The
retailer is better off when determining the optimal shipping strategy and RSC jointly than
that determining them separately.
Our numerical study supports this finding. This finding importantly shows that the
retailer can always set the optimal combinations of shipping strategies and RSCs
according to market changes. Our theoretical results show that, the retailer typically uses
SFRS accompanied by a higher RSC and SFES accompanied by a lower RSC. However,
when consumers are sufficiently sensitive to RSC, the retailer will offer free return
service regardless of whether providing free shipping service. The retailer may charge a
higher fee for return service when the base return quantity is relatively high and a lower
fee when the base return quantity is relatively low. In particular, when the base return
quantity is sufficiently high, the retailer may charge the maximum fee for return service.
In contrast, when the base return quantity is sufficiently low, the retailer may offer free
return service. Interestingly, the base return quantity has a significantly positive effect on
decisions regarding SFRS. Specifically, as the base return quantity increases, the retailer
has more incentive to offer free shipping service, and vice versa.
Surprisingly, when the effect of RSC on net demand is greater than that on return
quantity, the retailer charges less for return service when the product price is higher, and
vice versa. Conversely, the retailer charges more for return service when the product
price is higher, and vice versa.
The three market parameters have significant impacts on the optimal decisions on
shipping strategy and RSC as well as the retailer’s profits. When exceeds a particular
threshold, the retailer offers free shipping service and his profit remains constant when
changes; otherwise, the retailer will charge for shipping service, and in this case his profit
decreases in . Our numerical study shows that the threshold increases in but decreases in .
This relationship will influence the retailer’s choice of SFRS or SFES and RSC.
Furthermore, the retailer’ optimal profit increases in and decreases in , when they exceed
their particular thresholds. These results can help to guide online retailers in managing
their shipping and return services. In other words, to obtain higher profits, managers can
strive to influence the market to increase while decrease.
This paper presents key findings that shed light on retailers’ joint decisions on the
optimal shipping strategies and RSCs that can help online retailers to improve their
operational performance in terms of shipping and return service management. However,
this paper also presents some limitations that may serve as future research topics. First,
we develop our model based on the assumption that the demand function is deterministic.
Our model may generate different results when stochastic demand is considered. Second,
in online settings, consumers have to pay shipping fees when retailers do not offer free
shipping services. In such context, shipping fees can be regarded as sunk costs when
consumers decide to return products. In fact, such sunk costs may prevent consumers
from returning products and thus decrease returns. Consideration of such sunk costs in
our model may provide more insights for retailers to better manage their shipping and
return services. Third, issues such as seasonal discounts and limited time free shipping
services are also important, and may have significant impact on shipping strategy and
RSC decisions.
These issues can also be seen as important extensions in future studies. More
important, we consider only one retailer in this paper, and it would be interesting to
examine the optimal decisions made under competitive market environments. We can
illustrate this issue by using a simple example. We consider two retailers, A and B, in the
same setting. On the one hand, when retailer B adopts SFRS while retailer A does not,
some of retailer A’s consumers may move to retailer B. In such a case, retailer A may
then also adopt SFRS, which exactly depends on how much of his demand has shifted.
Thus, we can characterize conditions under which whether retailer A may choose SFRS.
On the other hand, when retailer B adopts more generous returns policy, some of retailer
A’s consumers may also move to retailer.
We can thus identify conditions when retailer A tends to decrease his RSC. Note
that, in this case, competition between retailers A and B will lead to an equilibrium of
shipping strategies and RSCs. Thus, study on this issue may also help online retailers to
better manage their shipping and return services in practice. This issue will constitute an
important topic in our future research.