A Component-based Approach to Realize Order Placement
and Processing in MSMEs
M. Saravanan
and J. Venkatesh
Ericsson Research India, Chennai, India
Department of Information Technology, MSE College, Chennai, India
Keywords: Micro Small and Medium Scale Enterprises, Middlemen, Order Placement and Processing, JSON, Android,
and Cloud Environment.
Abstract: Micro, Small and Medium scale Enterprises (MSMEs) hold an unfailing distinction of being pillars of
equitable economic growth. Lack of proper business platforms and knowledge of marketing strategies
render MSMEs vulnerable to middlemen exploitation. In view of the advancements and customers’ growth
in the telecommunications field, we utilize the mobile platform to offer trading solutions to MSMEs. In this
paper, we propose a mobile phone-based Order Placement and Processing components for MSMEs that can
achieve disintermediation and is developed as an android application integrated with cloud services to
provide easy access - anytime, anywhere. Our proposed component-based framework encompasses essential
trading operations and extends 24 x 7 supports to MSMEs. An economic order calculator and order
parallelizer sub-components helps limited budget MSMEs with small warehouse to survive the market by
efficiently managing the warehouse, scheduling payments and parallelizing the order depending on its
requirements. The other two sub-components custom specific negotiator and effective Order tracker helps in
customizing the product and keeps track of the parallelized order respectively, thus assisting buyers in
tracking their order to give an end-to-end solution. The envisioned framework will boost MSME margins,
build healthy business-ties and transform MSMEs into self-sufficient establishments equipped with full-
fledged trading systems that operate in mobile distributed environment.
Micro, Small and Medium Scale Enterprises
(MSMEs) hold a trustful distinction of being pillars
of equitable economic growth and account for 90%
of global businesses. The current MSME market
requires small-scale manufacturers to depend on
group of middlemen like wholesalers, distributors,
agents and brokers to carry out essential trading
tasks. These middlemen hold business-ties with
various consumer MSMEs in order to negotiate and
sell products on behalf of the seller MSMEs and in
return they charge a per cent of the MSME's revenue
as commission. Hence these intermediaries primarily
focus on identifying interdependencies in the
market, in terms of 'exchange opportunities' between
MSMEs, so that they can misuse their market
knowledge and business leads to rather reap
excessive profits, than to promote small-scale
businesses. Realizing this, MSMEs often lay claim
of middlemen pocketing their margins besides
indulging in unfair trading practices like adulteration
and hoarding. This middlemen involvement not only
has an effect on the revenues of MSMEs, but they
also affect the cost of the end product. The only
solution to the above problem is to eliminate
middlemen completely to boost MSME revenues,
which is assumed to be possible through several
existing web based and E-commerce solutions. But
in reality, MSMEs turn to another class of
middlemen to carry out their online trading.
Consequently, MSMEs end-up paying two different
classes of middlemen; hence they fail to obliterate
intermediation. However, small business owners are
unable to comprehend and use PC-based Internet
solutions due to limited knowledge of technologies.
Also, these web based solutions for order placement
and processing do not specifically solve the
problems of small-scale manufacturers or MSMEs
with a small warehouse and limited financial budget.
All the above said issues were addressed in our
proposed solution for a new order placement and
Saravanan M. and Venkatesh J..
A Component-based Approach to Realize Order Placement and Processing in MSMEs.
DOI: 10.5220/0005105002580265
In Proceedings of 3rd International Conference on Data Management Technologies and Applications (DATA-2014), pages 258-265
ISBN: 978-989-758-035-2
2014 SCITEPRESS (Science and Technology Publications, Lda.)
processing component that automates the activities
performed by middlemen and extend 24 × 7 support
to MSMEs through mobile phones. Hence we have
provided a specific solution for emerging
organization with a small budget to survive at the
market and achieve greater profits. For this, we have
developed an android application along with the
mobile cloud to support the calculation of the
optimal order quantity and the minimal buffer
quantity of raw materials using the Optimal
Inventory Calculator sub-component. When the
inventory nears the minimal buffer quantity, the
system suggests the reorder quantity from statistical
analysis of purchase history. The system also
provides provisions to parallelize order to efficiently
use the small warehouse, though they have a limited
budget using a Just-in-time inventory technique, thus
reaping extra profits.
Custom-specific negotiator component is used to
help buyers order custom-made products according
to their interests and requirements. We also have an
order tracker which keeps track of the undelivered
and pending orders, making it easy for the MSMEs
to keep track of them. The invention on the whole
provides a never before seen platform for MSMEs to
hold direct negotiations with each other, thereby
eliminating need for intermediation.
The recent past has witnessed development of a
multitude of applications and services to assist
MSMEs. Popular web applications provide CRM
systems (Achuama and Usoro, 2010) and human
resources management (Andersen, 2003) solutions.
Research has also been carried out to introduce ERP
systems for MSMEs (Upadhyay and Dan, 2010).
Most of the web solutions aim at providing a
business platform for the small-scale firms to sell
their produces in the online market. E-commerce
applications have been developed to cover business
transactions (Olatokun and Kebonye, 2010). In
reality, MSMEs are unable to comprehend the
technology involved. They turn to another class of
middlemen to carry out their online trading (Cooke,
2000). Consequently, MSMEs end-up paying two
different classes of middlemen.
A method (Nakamoto et al., 2002) processes a
simplified order placement and reception in a system
comprising a host computer and a PDA. The method
includes storing stocked-product data and estimation
data in an order placement and reception information
database provided in the host computer, then
transmitting from the host computer to the PDA, and
placing an order from the screen on which the
stocked-product data are displayed in the PDA.
These features support the order processing and in
addition, it allows the buyer to choose the most
nearby MSME to prioritize the orders depending on
the cost, thus reducing the delivery time when the
product is needed immediately without any
compromise on the price as well.
Mobile cloud, considered as the next generation
technology, is extensively used to provide services
to mobile phone networks (Taylor et al., 2011).
Location-based services are highlights of mobile
cloud applications. Main reason for utilizing mobile
clouds for businesses is the ability to carry out
remote computing. Mobile phones have limited
processing and storage capabilities. Hence storage
and computing tasks are delegated to remote Virtual
Machines (VMs) on mobile cloud that provide
Infrastructure As A Service (IAAS) (Sushil et al.,
Moreover, cloud developing platforms like
Eucalyptus and OpenStack (Pepple, 2011) extends
infinite scalability in processing of orders.
Nowadays, mobile phones are increasingly used as
entry points to cloud services (Giurgiu et al., 2012).
The Amazon EC2 is one of the recent developments
in the field of cloud computing that offers many
cloud related solutions as web services (Varia,
2010). The other paper describes a highly scalable
system developed for MSMEs using the ontology
engineered framework that uses cloud for data
storage and processing (Saravanan et al., 2012). The
android application has remote access to framework
components that run on cloud, for effective and
efficient processing.
The proposed order placement and processing
component has the following four sub-components
under them which executes linearly for each
product’s order placement and processing as shown
in Fig 1. The involvement of these components
improves the order processing system in MSME
sustainable development.
Figure 1: Order Placement & Processing.
3.1 Optimal Inventory Calculator
Unlike any online website this in-built component
calculates profitable order quantity based on
statistical analysis of stocking, ordering and holding
costs. And whenever the MSME inventory is in the
verge of being empty, a re-order point is set and
helps in advising the MSME that it has to order new
inventories when the re-order point is reached. Thus
it protects the warehouse with undisrupted
continuous business as shown in Fig 2.
We first obtain user input for annual demand
quantity, fixed cost per order, annual holding cost,
daily demand quantity, lead time, and safety stock to
calculate economic Order quantity and re-order
point quantity. We then order the optimized quantity
from the preferred supplier MSME through the
Order Parallelizer component. The MSME then
starts the manufacturing and sale of finished
products, and after that it verifies whether the
remaining available quantity is less than or equal to
the re-order point quantity. If re-order point is
reached, MSMEs re-order new set of products
(optimal quantity). Else, continue with manufacture
and sale of finished products.
Figure 2: Workflow of Optimal Inventory Calculator.
3.1.1 Economic Order Quantity
Economic order quantity is the order quantity that
minimizes total inventory holding costs and ordering
costs. It is one of the oldest classical production
scheduling models (Hax and Candea, 1984). Thus
we determine the optimal number of units to order
so that we minimize the total cost associated with
the purchase, delivery and storage of the product.
EOQ applies only when demand for a product is
same throughout the year.
New order is delivered in full when inventory
reaches zero or when the re-order point is
Fixed cost for ordering
Cost for storage (% of purchase cost)
Lead time is fixed
Only one product is involved
Purchase price should be constant
The following variables were used for calculation
P = Purchase Price
Q = order quantity
Q*= optimal order quantity
D = annual demand quantity
S = fixed cost per order (not per unit, typically cost
of ordering and shipping and handling)
H = annual holding cost per unit (also known
as carrying cost or storage cost)(warehouse space,
refrigeration, insurance, etc)
Total Cost = purchase cost + ordering cost + holding cost
- Purchase cost: This is the variable cost of goods:
purchase unit price × annual demand quantity. This
is P×D
- Ordering cost: This is the cost of placing orders:
each order has a fixed cost S, and we need to order
D/Q times per year. This is S × D/Q
- Holding cost: the average quantity in stock
(between fully replenished and empty) is Q/2, so this
cost is H × Q/2
TC = PD + (DS/Q) + (HQ/2) (2)
To determine the minimum point of the total cost
curve, partially differentiate the total cost with
respect to Q (assume all other variables are constant)
and set to 0:
0 = - (DS/Q
) + (H/2) (3)
Solving for Q gives Q* (the optimal order quantity):
Q* is independent of P; it is a function of only S, D,
3.1.2 Calculation of Re-Order Point
Another important technique used along with the
economic order quantity is the Re-order Point (ROP)
by maintaining safety stock.
ROP quantity reflects the level of inventory that
triggers the placement of an order for additional
The quantity associated with safety stock
protects the company from stock outs or
backorders. Safety stock is also known as a
ROP= Daily usage*Lead time (in days) (6)
When a safety stock is maintained, then the
reorder point is written as the following :
ROP = [Demand (Daily usage)*Lead time
(in days)] +safety stock
Demand - Quantity of inventory used or sold each
Lead Time - Time (in days) it takes for an order to
arrive when an order is placed
Safety Stock - The quantity of inventory kept on
hand in case there is a unpredictable event like
delays in lead time or unexpected demand.
3.2 Order Parallelizer
This sub-component helps in parallelizing the order,
thus obtaining different quantities of same product
from different sources. It helps the firms with small
warehouse and limited budget who immediately
want to purchase raw materials and start
manufacturing their products by partially ordering
an initial quantity of raw materials from nearby
sellers (priority is given in the order of distance, cost
and quality) and then order the remaining quantities
of the same product from different distant sellers
(priority is given in the order of quality, cost and
distance). Quality depends on whether the two
MSMEs have had previous business transactions.
More priority is given to MSMEs with whom the
buyer MSME has had previous transactions, as it
makes that MSME transaction more reliable
compared to the rest. This also reduces the burden
on the buyer MSME, as he need not pay the cost of
buying all the products from all sellers at the same
time and is a cost-effective measure. He has to pay
only for the product he buys from a particular seller
as he has now parallelized the order. MSMEs with a
small warehouse can use order parallelization as
they will not have sufficient space to store all the
required quantities in their warehouse by
parallelizing the order quantity depending on the
space available at the buyers warehouse. This saves
the buyer from renting a separate warehouse to store
the products. The component is also designed in
such a manner that the time required to empty the
warehouse (manufacturing and sale of initially
bought products) is the time to deliver the next set of
parallelized products from another seller. Hence
small firms which use this system can efficiently
utilize the warehouse though it is small.
Figure 3: Work flow of Order Parallelization.
Let’s consider the following variables for
explaining the flow of events shown in Fig 3.
T = Total no of products the warehouse can hold
H = No of products the ware house is already
A = No of products that have space for storage at
warehouse (A
A = T-H (8)
IQ = Initially Required Quantity to start manufacture
in case of immediate delivery
RQ = Required Quantity (Calculated by EOQ
= Time to deliver vector = t
, t
, t
, ..t
(Time to sell/produce vector = ts
, ts
, ts
, ..ts
where m<<n
= Quality bought vector = q
, q
, q
P = Product Vector/Product Measurement
In this component we first check whether the
order requires immediate delivery to start production
at the earliest. If yes, choose MSMEs using a Filter
Function F (Distance, Cost, Quality) which gives
high priority to distance (nearest MSME), then to
cost and then to quality. Then we further narrow
down the search to find MSMEs whose available
number of products for immediate sale, is greater
than or equal to the IQ (Initial required quantity).
After narrowing down, we order q1 quantity of
products at the chosen nearest MSME. Note that q1
shall be lesser than or equal to A
. If q1 is
equal to required quantity, then the order placement
process is complete. If not then we choose another
set of MSMEs using a different Filter Function F
(Quality, Cost, Distance), which gives high priority
to MSMEs with whom there has been previous
contracts, whose quality is good though they are at a
farther distance. Also choosing MSMEs also has
another criteria which specifies that the time to
deliver the present ordered quantity (t
+1) (t
) time
to sell previous ordered quantity. This type of time
constraint helps in efficient use of the inventory and
is called the Just-in-time Inventory. The MSMEs are
listed in ascending order of the time to deliver value,
where (i=1) if q1 exits, else (i=0 and t
=0). Parallel
orders are placed at different MSMEs, with variable
quantities q1, q2, q3. Check if sum of (q1, q2, q3…)
required quantity. If yes, repeat the above process,
if not then the required quantity is ordered and the
parallelized order placement component is complete
the process.
Table 1: Product-wise Ordering.
MSME Product
Distance Price Quality
3.2.1 How the MSMEs are Filtered
Let m
, m
, m
, m
, .., m
be the MSMEs with
available requested products.
Product availability (m
, m
, m
, m
, .., m
Product count (p
, p
, p
, p
, ..., p
Distance (d
, d
, d
, d
, ..., d
Price of product (pr
, pr
, pr
, pr
, ..., pr
In Table 1, quality of service is defined as ‘0’
and ‘1’ depending on whether the buyer MSME has
had previous business transaction with them or not
respectively. From the above table using query
processing, the information is filtered depending on
the following condition:
When the order is immediate,
o D = User specified distance
o P = User specified price
o F (distance, cost, quality)
Filter depending on distance < D
Filter the resulting rows depending on
price < P
Display the MSMEs with quality of
service = 1
When the Order is not Immediate,
o D = User specified distance
o P = User specified price
o F (quality, cost, distance)
Filter the MSMEs with quality of
service = 1
Filter the resulting rows depending on
price < P
Filter the resulting depending on
distance < D
3.3 Custom Specific Negotiator
This is a platform to negotiate product
customizations, price and time-to-deliver. Predefined
customizations are available along with their price
and time to deliver. If the predefined customization
is not suitable to the buyers customization then this
platform helps MSMEs to hold direct negotiations
with each other about the buyers custom-specific
products, thereby eliminating need for
intermediation. In order to hold direct negotiations,
the component helps in making direct phone
conversation with other MSME, thus making
negotiations easier and eliminating intermediation
According to Fig 4, this component first checks
if the user wishes to customize the product. If yes,
then the component checks if seller MSME’s
predefined customization is suitable to the buyer’s
customization. If yes the buyer chooses the
predefined customization, the buyer MSME should
also be fine with the price and time to deliver of the
predefined customized product. If not, then the
component realizes that the predefined
customization is not suitable to the buyer’s
preferences, so now the buyer MSME is given the
details of the seller MSME so that direct
negotiations of custom specific products can be done
through the phone conversations. Once it is done, a
customized product is successfully negotiated and
Figure 4: Workflow of Custom Specific Negotiator.
3.4 Order Tracker
Track pending orders module notifies the associated
MSMEs and assist in easy management of bulk
orders. The immediate available product quantity is
dispatched by the supplier and the remaining
quantity is calculated and is tracked by the order
tracker. The order tracker holds information about
the remaining amount of products yet to be
delivered, along with the time taken to deliver the
remaining product, thus summoning the MSME if
the order is not delivered in time.
A detailed workflow of the Order tracker
component is specified in Fig 5, here the component
first checks whether the order has been parallelized,
if yes the component keeps track of the order by
storing MSME Name, last date for delivery of order,
remaining quantity to be delivered of each supplier
MSME in the parallelized order. If not then the
component just keeps track of the order by storing
only the single supplier MSME’s Name, last date for
delivery of order and remaining quantity to be
delivered. The system continuously checks if the
delivery time has reached, if yes then both the seller
and buyer MSMEs are alerted. The algorithm used
by order tracker is defined below:
If (order has been parallelized)
Track MSME Name, Delivery Time,
(Remaining Quantity = Total Ordered
Quantity-Delivered Quantity) for each
MSME in the parallelized order
Track that single MSME Name, Delivery
Time, (Remaining Quantity = Total Ordered
Quantity-Delivered Quantity)
If (Current Date Delivery Date-3)
Alert both buyer and seller MSMEs that the
delivery date is nearing
If (Delivery Date Current Date)
Alert both buyer and seller MSMEs that the
delivery has not yet arrived
A detailed sketch of the workflow is depicted in Fig
6. MSMEs have to register with the service provider
by submitting appropriate identity proof. Once their
identity is verified, the MSME can download the
application on their android phones. A registered
MSME has to undergo Password authentication.
Once authenticated, the MSME can search for raw
materials/products. Optimal order quantity
calculation is carried out by employing Economic
Order Quantity (EOQ) (Hax and Candea, 1984)
concepts and techniques. The order quantity
calculation performs statistical analysis of previous
purchases and suggests the profitable order quantity.
Besides, MSME can parallelize the order and split
the quantity required to many divisions and purchase
from a combination of MSMEs – similar to
purchasing from assorted stock owned by
distributors. Once the order is parallelized, then
using the custom specific negotiator, the order can
be customized according to the buyer’s need. After
that the order is placed at the click of a button. Later
the ordered quantity, quantity delivered, last date of
delivery of ordered components are all self managed
by the order tracker, which continuously tracks these
details and alerts the MSME if it is not delivered on
time. For example, consider a buyer MSME who
would like to purchase logs of Burma Teak wood,
the MSME first logs into the application and
specifies the inputs to calculate the economic order
quantity. He also specifies whether the order is
urgent or not. Then the application displays all
details of MSMEs whose available quantity is
greater than the required economic order quantity;
the MSMEs are listed depending on whether the
order is urgent or not. Then order quantity is
automatically initialized by the system, which can be
changed if the buyer wishes. Once the order quantity
is finalized, at the click of a button the order is
placed, then the MSME is taken to the product
customization where he can customize his order.
The remaining process of tracking the order is
completely taken care by the Order Tracker.
Figure 5: Workflow of Order Placement and Processing.
The proposed component-based approach executes
on a mobile cloud for efficient information
processing and storage. The architecture of the
mobile cloud environment is very similar to that of
EC2 (Varia, 2010) cloud. The mobile cloud extends
various services to the mobile phone registered with
the system. The cloud spawns a new virtual machine
containing an instance of the framework for every
new session. This ensures a highly scalable multiple-
user environment. The entire system, with the
framework on cloud accessible from android phone,
is said to provide Infrastructure As A Service
(IAAS) (Sushil et al., 2010). Infrastructures provided
as service include persistent remote storage and
remote computing.
The android application acts as an entry point to
the mobile cloud. Using the android application,
MSMEs can search for products, take advice on
optimal order quantity, parallelize order quantity to
purchase from a combination of different MSMEs,
customize order specifications to suit the ever-
changing needs. Hold negotiations and transactions
directly with the concerned MSMEs to track
products and obtain delivery updates as simple text
This system completely removes intermediation and
has automated the order placement and processing
phase in the MSME business transaction cycle. It
makes the whole process easier for those involved
MSMEs because of its automation and user friendly
where it uses the android environment.
All the extra costs incurred in paying
intermediaries can be avoided as this component
helps in direct business between the MSMEs.
The component automatically calculates the
EOQ, ROP values, making it easier for the
MSMEs to make decisions when it comes to
deciding the order quantity. It also alerts the
MSME regarding when it is supposed to re-
order the quantity.
This invention supports order parallelization.
Through Order parallelization, even MSMEs
with a very small financial budget can survive
the market as they parallelize the order and need
to make only partial payments of the products
they buy. They are not burdened to pay the
entire amount; hence even an industry with a
small warehouse can be lead in the market.
Custom Negotiator helps in producing
customized products, making it more satisfying
to the buyer as he gets the product with all his
custom requirements installed. Also direct
negotiations are win-win model, satisfying both
the parties and there is no room for confusions
in customization as it is specified directly to the
manufacturer and not through any middlemen.
Order Tracker helps the MSME in tracking the
order. As everything is automated, the MSME
can be tension free. The delivery date alert,
along with the entire order is all maintained by
the order tracker and can be accessed by the
buyer MSMEs, even remotely using his mobile
phone and the android application.
Our proposed component-based approach makes
MSMEs independent on middlemen during order
placement and processing that has been analyzed
and provided with an mobile phone-based
implementation. It is evident from the
implementation with mobile cloud and android
application surely avoids the vital role played by
middlemen in the order processing task. From the
experiments conducted, we understand that the
proposed approach comparatively outperforms the
other traditional systems and give an end-to-end
solution in order placement and processing.
Introduction of this type of component-based
approaches improves the MSME business to new
level and creates sustainable development in B2B
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