Prettysecrets.com- A Women Fashion Ecommerce Private Limited

Fashion E-commerce-
Inventory & Planning
Management
To study the inventory planning process in an Online Fashion company- MTC
Ecom Pvt Limited
SUBMITTED BY: ANKUR SUYAL; Roll No. 56 ; Retail Management-Operations

[FASHION E-COMMERCE- INVENTORY & PLANNING
MANAGEMENT] March 20, 2017
Welingkar Institute of Management, Research and Development, Mumbai
Page1
Table of content
INTRODUCTION……………………………………………………………………………………………………………………..3
THE FASHION ECOMMERCE MARKET…………………………………………………………………………………….3
E-COMMERCE LINGERIE MARKET…………………………………………………………………………………………..4
MTC ECOM PVT LIMITED………………………………………………………………………………………………………..6
PRETTYSECRETS……………………………………………………………………………………………………………………6
THE USER JOURNEY MAP……………………………………………………………………………………………………….7
COMPETITORS OF PRETTYSECRETS ……………………………………………………………………………………...9
GOODS AND SERVICES TAX…………………………………………………………………………………………………….9
IMPACT OF GST ON SELLER AND BUYER………………………………………………………………………………10
TAXES TO BE SUBSUMED UNDER GST………………………………………………………………………………..…11
GST ANALYSIS BASED ON PRICE AND INCOME……………………………………………………………………..13
ISSUES OF ECOMMERCE AGAINST GST………………………………………………………………………………….14
MANAGEMENT INFORMATION SYSTEM……………………………………………………………………………….15
TO MAINTAIN THE MIS OF PRETTYSECRETS…………………………………………………………………….…15
CALCULATIONS……………………………………………………………………………………………………………………16
RESULTS……………………………………………………………………………………………………………………………...17
THE DEMAND FORECASTING……………………………………………………………………………………………….20
ARIMA MODEL……………………………………………………………………………………………………………………..20
ARIMA IN R…………………………………………………………………………………………………………………………..21
EXAMPLE………………………………………………………………………………………………………………..……………21
HOLTS-WINTER IN R……………………………………………………………………………………………………………22
EXAMPLE……………………………………………………………………..………………………………………………………23
CONCLUSION……………………………………………………………..…………………………………………………………24
REFERENCES AND ATTACHMENT…………………………………………………………………………………………25

Page2
Acknowledgement
I wish to express my profound gratitude and sincere thanks to my internship mentor
Ms. Sonal Bisen, Senior Manager, PrettySecret for her sincere exhortation, meticulous guidance
and sustained interest, constant encouragement and constructive criticism and painstaking efforts
throughout the course of investigation and preparation of the project.
I would like to make a special mention of Mr. Suraj Dhende and Ms. Rashmi Ubhe
for attending to my innumerable doubts and queries. I would like to thank all the other members of
the Inventory & Planning Management (IPT) team for taking out time to help me with the finer
details of the project.
I would also like to sincerely thank my college mentor Prof. Kavita
kalyandurgmath, Operations Management, Welingkar Institute of Management,
Development and Research, Mumbai, Maharashtra for providing her knowledge on the nuances
of Operations management and continuous motivation during and before the project. At last, special
thanks to my parents for their moral support and warm blessings.
Bandra-Kurla Complex, Mumbai ANKUR SUYAL ( Intern)
31st March, 2017 MTC Ecom. Pvt Limited

Page3
Introduction
E‐commerce stands for electronic commerce. It means dealing in goods & services through the
electronic media & Internet. The rapid growth of e-commerce in India is being driven by greater
customer choice & improved convenience with the help of internet the vendor or merchant
who sells products or services directly to the customer from the portal using a shopping basket
system or digital cart & allows payment trough debit card, credit card or electronic fund
transfer payments. In the present scenario e-commerce market & its space is increasing in
demand as well as an impressive display or range of a particular type of services. E-commerce is
already appearing in all areas of business, customer services, new product development &
design. E‐commerce business is growing in India because of wide range of product with
minimum price, wide range of suppliers & customers internet. In this modern era every
business units want to join online business because increasing ratio of internet users in India.
Ecommerce in India is still in growing stage but it offers considerable opportunity. India had an
internet user base of about 354 million as of June 2015and is expected to cross 500 million in
2016. Despite being the second‐largest user base in world, only behind China (650 million, 48%
of population), the penetration of e‐commerce is low compared to markets like the United
States (266 million, 84%), or France (54 M, 81%), but is growing at an unprecedented rate,
adding around 6 million new entrants every month. The industry consensus is that growth is at
an inflection point. In India, cash on delivery is the most preferred payment method,
accumulating 75% of the e‐retail activities. Demand for international consumer products
(including long‐tail items) is growing much faster than in-country supply from authorized
distributors and e‐commerce offerings. In 2017, the well-established e‐commerce companies in
India were Flipkart, Amazon India, Limeroad, Tata CLQ, Ajio etc.
The Fashion Ecommerce Market
The Indian textiles industry, currently estimated at around US$ 108 billion and it is estimated
that by 2020, Indian textile and apparels industry- $220 billion. According to Forrester
Research, web rooming will result in $1.8 trillion in sales globally by 2018.By stressing emphasis
on physical stores, e-commerce players aim to draw customers by creating product and service
awareness. Like all the products, purveyors of tactile and personal products like clothing,
eyewear and jewelry, selling stuff in person has an obvious appeal.
In-store experiences need to grow and evolve in order to continue to compete with the
convenience of online shopping. The biggest challenge lies in creating a flawless integrated
system to incorporate online and in-store shopping together. Those brands that can discover a
seamless way to make shopping both online and offline will be the most successful.

Page4
Another importance of flagship store is that they are great marketing tools. If it works well, not
only it tends to be economically successful but also they generate a huge lift in incremental
shopping to the online store. We also see a halo effect where stores themselves become a
great generator of awareness for the brand and drive a lot of traffic to its website, as well and
accelerate the e-commerce sales.
Also, the advent of more offline retail stores will bring more business for the real-estate sector.
Property is an evergreen investment and increases the shelf life of these brands. Any
investment done affects the entire ecosystem of industries and e-commerce can certainly
mutually benefit from and to other sectors as well.
For greater access to market and especially rural areas, e-tailers are tying up with local retailers
which also bring a local flair for greater business. For instance, Amazon India introduced an
initiative named ‘Udaan’ back in 2014, in an attempt to increase its reach in small towns across
the Indian hinterland. Under it, it tied up with local entrepreneurs to run physical outlets which
could also guide consumers over shopping online
E-Commerce Lingerie Market
India is one of the most attractive retail targets globally and represents a huge untapped
market for lingerie. The term lingerie is mainly used for fashionable and alluring
undergarments. Novelty, an evolving fashion industry, and growing need for comfort have
resulted in increased demand for lingerie across geographies. The concept of online lingerie
stores has enhanced the growth prospects of the lingerie market as it offers a wide range of
international and private labels on a single platform.
The online lingerie market in India is estimated to grow at a CAGR of 42.32% over the period
2014-2019.
Key vendors
• Gokuldas Intimate wear (GI, Enamor)
• Jockey International (Jockey)
• Lovable Lingerie (Lovable)
• MAS Holdings (Amante)
• MTC Ecom (PrettySecrets)
Other prominent vendors
• Baci Lingerie

Page5
• Calvin Klein
• Chantelle
• Cloe
• Etam
• Hanes Brands
• La Maison Lejaby
• Laceandme
• Lindex
• Lise Charmel
• Triumph
• Wacoal
• Wolf Lingerie
• Wolford
Market driver
• Changing consumer preferences
• For a full, detailed list, view our report
Market challenge
• Difficulty in choosing right product
Market trend
• Increased penetration in smaller towns

Page6
MTC Ecommerce Pvt. Ltd
MTC Ecom Pvt. Ltd. operates an online store under the brand name “PrettySecrets” that offers
lingerie and apparel for women. It offers a collection of bras, panties, lingerie sets, nightwear,
work-outs, swimwear, active wear, and dresses; and accessories, such as straps, stickers and
pads, leggings, and stockings. MTC Ecom Pvt. Ltd. was incorporated in 2011 and is based in
Mumbai, India.
PrettySecrets is a lingerie brand targeting young Indian Women who are confident, stylish,
happy & know what they want. PrettySecrets retails through its own website
www.prettysecrets.com as well as leading online fashion marketplaces like Myntra, Amazon,
Flipkart, Limeroad, Ajio, Tata Cliq, etc. Enjoying a loyal customer base, the brand has reached 1
million plus customers over the last 4 years. PrettySecrets started their operation in 2012 and
currently with the help of different franchising model PrettySecrets is going to follow Omni
channel strategy.
In the year 2016, PrettySecrets has raised $6 Mn (INR 40 Crore) from RB investments. Existing
investor Orios Venture Partners also participated in the round. The raised funds will be utilized
on adding new products and categories to its platform. The company will also invest in
improving its supply chain and for marketing campaigns. There are also plans to achieve break
even by the end of this financial year
Initially, PrettySecrets used to sell its products on its own platform but soon faced heavy
competition from bigger online marketplaces such as Amazon, Myntra, Snapdeal and Zivame.
Since last three years, it has started selling on 18 other ecommerce platforms including
Bengaluru-based lingerie marketplace Zivame, and enjoys 15 per cent market share in the
online lingerie market. Till early 2015, PrettySecrets has garnered INR 2.5 crore monthly
revenue which comes from marketplaces like Jabong, Myntra, Flipkart, and Amazon. Also, it
holds 20-25% share of Jabong and Myntra’s lingerie sections.
PrettySecrets offers the following product categories:
 Nightwear
 Bra
 Panty
 Swim wear
 Beach
 Blouse
 Shapewear
 Stocking

Page7
 Workout wear
 Dresses
 Accessories
The “Nightwear” is the flagship product category of PrettySecrets, occupying approximately
15% of the total sales annually. Similarly, the Bra and Panty categories are cash cow products
for PrettySecrets. The price range varies between Rs 300-2100 depending upon the different
product category.
The User Journey Map
Customer
Wants/
Needs
• On the basis of fashion Trend
• On the basis of customer requirement
Online
Searching
• Through google chrome, mozilla firefox
• Through Application based platform
Website/App
Platform
• Marketplaces website/App
• PrettySecrets Website (PrettySecret doesn't have app based platform)
Product
Category
• Nightwear,Bra,Panty,Dresses,Swim wear, workout
wear,accessories,Beach,Blouses,Stocking, Shapewear
Product Sub-
Category
• Babydolls,Pajamas,shorts,Tops,Wraps,Sleepshirt,lace,t-
shirts,Pushup,sports,strapless,wireless,seamless,lace,Bikni,Brief/Hipster,Thong/G-
string,swimsuits,sarong/coverups,Tanks,leggings

Page8
Product
• Its a subjective step because sometime colour is preferred over product
Colour
• Blue,Pink,Black,Green,Yellow etc
Size
• S,M,L,XL,XXL,Bra Size-30,32A,32B,34C,34D,D6A,36,B,36D,etc
AddTo
Cart/Wishli
st
• Add To wishlist option is helpful in maintaining loyal customer
Checkout
• After adding product into cart, customer do click on checkout to find the net
amount
Transaction
• Basically done through third party such as PayU,Paytm,jio money etc
• Net Banking
• Cash on delivery
Order
Placed
• Invoice generated
• Along with invoice, order Id is generated so that custommer can track down their
order
Table1

Page9
Competitors of PrettySecrets:
Goods and Services Tax:
The Goods and Services Tax (GST) is a summation of all the indirect taxes such as central
indirect taxes like excise duty, countervailing duty and service tax, state levies like value added
tax, octroi and entry tax, luxury tax, etc., but petroleum products, alcohol for human
consumption and tobacco have been kept out of the purview of the GST. GST is levied at the
point of sale or provision of service. It is also known as national level VAT on goods and
services. It is believed to be the major reform change after independence. At the time of sale of
goods or providing the services, the seller or service provider can claim the input credit of tax
which he has paid while purchasing the goods or procuring the service.
If a business is registered for GST it must include GST in the price of goods, services and other
things they sell to others in the course of business. These are called ‘taxable sales’. There are
other types of sales where GST is not included in the price. These are either ‘input taxed’ sales
or ‘GST-free’ sales. GST may be included in the price of purchases (including importations)
made by a business, and it’s a good idea to allow for it when setting prices. When a business
is registered for GST, they can generally claim a credit for any GST included in the price they pay
for things purchased by the business. This is called a GST credit.
Zivame
Clovia
Enamor
N-Gals
Original
BwitchFabsdeal
Shyawa
y
Amante
Lovable
PrettySecrets
FIG.1

Page10
In this system, the credit of GST paid is claimed on the basis of invoice. It is claimed when the
invoice is received. It is immaterial whether payment is made or not. The GST (Output) is
accounted for when invoice is raised. The time of receipt of payment is immaterial. The
advantage of invoice system is that the input credit can be claimed without making the
payment. The disadvantage of the invoice system is that the GST has to be paid without
receiving the payment.
The impact of GST on seller and Buyer
Party
Seller
Current Regime
Charges VAT/CST to the
customer depending on
the movement of goods
The service tax charged
by the online
marketplace (listing fee,
facilitation fee, etc.)
becomes a cost since the
same cannot be utilized
GST Regime
Seller will either charge
CGST and SGST or IGST
and additional tax,
depending on the nature
of the transaction
All input taxes(including
marketplace) will now be
available as credit,
leading to efficiency in
costs.
Impact Under GST
Possible higher tax
rate on the output
side
The seller can claim
credit of all taxes on the
input side except
additional tax (currently,
excise duty, CST and
service tax on
procurements have
become a cost)
Impact on pricing to be
analyzed keeping in
mind the interplay
between tax rate and
credits.
Table 2

Page11
Taxes to be subsumed under GST
Party
Online Marketplace
Current Regime
The online
marketplace charges
service tax to the
vendor for providing
facilitation services.
It can avail itself of
service tax credit of
input services.
However, any VAT
credit on the purchase
of goods becomes a
cost
GST Regime
The online
marketplaces will
either charge CGST
and SGST or IGST,
depending on the
nature of
transaction. Such
taxes will be now
available as credit to
the vendor
Further, the
marketplace can
now also avail itself
of full credit of all
inputs and input
services
Impact Under GST
Possible higher tax rate on the
output side
All output taxes to be creditable
on provision of services to seller
The online marketplace can also
claim credit of all taxes on the
input side ( currently taxes on
procurements becomes a cost)
No concept of centralized
registration: multiple
registrations maybe required
Impact on pricing to be analyzed
keeping in mind the interplay
between tax rate and credits.
Central levies
Additional Customs duty
(ACD)
Special Additional duty of
customs (SAD)
Excise duty
Service tax
Central Sales Tax (CST)
Central-levied surcharge and cess related to
supply of goods and services
State levis
Value Added Tax (VAT)
Other state levies such as luxury tax, octroi,
entry tax and purchase tax
State-levied surcharge and cess related to the
supply of goods and services
Taxes on lottery, betting and gambling
Table 4
Table 3

Page12
Estimated share of textile segments in tax base
Cascading Effect of Present Tax system
Producer/
Manufacturer
Cost of
Input
Value of
Output
Tax Rate Selling
Priceinclusing Tax
Rate
Tax
Burden
Producer A - 100 10% 110 (100 + 10% of
100)
10
Producer B 110 150 10% 165 (150 +10% of
150)
15
Producer C 165 200 10% 220 (200 + 10% of
200)
20
Table 6
Table 5

Page13
Price and Income Effects
Assuming the GST rate to be 12% and let’s analyse the impact of GST on the price of textile
product.
Calculation Formulae:
Increase in price (Percentage) = ___________________________*100
Change in demand due to price (Percentage) = _________________________________*100
Net Change in demand (Percentage) = [(Increase in price)*(Change in demand due to
price)*(change in demand due to income)]
Table 7
(Base+GST) - (Base+PT)
(Base+PT)
(Base+PT) - (Base+GST)
(Base+GST)*Average change in price
percent

Page14
Findings from the above data:
• Overall current RNR (Revenue Neutral Rate) lower than the sum of lower CGST and SGST
rates (12%); implies additional tax burden
• Blocked input taxes are relatively more for State VAT since output tax rates are zero for
most categories compared to Cenvat where Input Cenvat on goods and service tax on
service inputs are both rebated
• Category-wise RNR is highest for readymade garments and artificial silk and synthetic
fibre textiles
• Key concern – Increase in tax burden may lead to a reduction in demand
• However overall impact may not be negative
• Greater efficiency in production – may lead to downward movement of prices
• Exports may go up due to true zero rating
• A major reform like GST will lead to higher GDP and higher disposable incomes
• Price and Income elasticity of demand may compensate for each other
Issues with the e-commerce against GST
• Ambiguity about the GST rate applicable in the apparel sector (Approx.20%)
• GST is applicable on MRP. Hence, the e-retailor will not be able to give discounts and
cash back offers to the customer
• The return of goods procedure will be more complicated because GST is applied on the
invoice generation. Therefore, once the invoice is generated then GST need to be filled.
• The TCS (Tax Collected at source) will also be charged at 1% resulting increase in the
price value of the goods and services
• Customer delight offers such as Buy 1 Get1, Buy2 Get3 etc., will need to re-evaluate in
the GST system

Page15
GST rates on textiles in some international jurisdictions
• South Asia: Pakistan (5%), Bangladesh (15%), Sri Lanka (12%)
• Developed nations: Australia (10%), New Zealand (15%), Japan (5%, 8% from 1
April 2014 and 10% from 1 Oct 2015), UK (20%), Germany (19%), France (19.6%)
• China: 13%, 3% for SMEs without input tax credit
To Maintain the MIS report for an online retail store “PrettySecrets”:
The Management Information System (MIS) has to prepare weekly inventory review report in
order to keep the track on the cover stock, sales performance, cut size percentage and channel
wise inventory performance.
Inventory Cover
Inventory cover means the number of month for which the current inventory will suffice as per
the average consumption. The ideal cover is considered to be of 5 months. The cover less than
3 month is a sign of danger which can lead to under stock resulting in a loss of sale. The cover
more than 8 month is also a sign of danger which can lead to overstock resulting in a loss of
margin. The motive of successful company is to keep the stock cover in between 4 and 6
months.
It is estimated with the help of ABC model of inventory categorization. ABC model classifies
inventory items based on their overall consumption (sales of last 30 days, Rate of sale of last 15
days, Rate of sale between last 45-60 days) value. Category A are goods that contribute to
about 80% of the total consumption value, Category B to the about 15% and Category C to 5%.
Sales Performance
It is used to measure the sales performance of old and new stock. Also, it is significant in
estimating the discounting criteria for different product category. It is determined with the help
of cover report excel sheet.
Cut Size Percentage
It helps in analyzing the depth of assortment by categorizing each style on the basis of size and
quantity. This helps in knowing the product category level depth of the inventory on hand. It is
determined through inventory SKU Bin report which is being taken out from ERP system known
as Vineretail.com

Page16
Channel wise inventory performance
It helps in tracking down the stock sales or return at different market place channels such as
amazon, flipkart, myntra, snapdeal, etc. It also helps in analyzing different marketplace
performance so as to determine in which marketplace we should keep our inventory more or
less. It is determined by the channelwise SOR (Sale or Return) data provided by marketplace
merchandiser.
Calculation:
1. Inventory Cover
After grading inventory on the basis of ABC model, the inventory cover is determined using the
following formula,
Inventory Cover = ___________________
2. Sell through Rate (STR)
It is defined as a percentage, comparing the amount of inventory a retailer/e-tailer receives
from a manufacturer or supplier against what is actually sold to the customer.
STR= LAST 30 DAYS SALE
3. Contribution Margin (CM)
Every business sustains only when it makes profit or at least reach their break-even point.
Determining profitability at product level, we use contribution margin of each product.
Contribution Margin of a particular product is determined by subtracting all expenses from net
sales. It plays a major role while implementing discounts and other offers. In order to do
sustainable business, E-commerce player always maintain their contribution margin between 5-
10%.
Contribution = Net Sales – Expenses including taxes
Contribution Margin (percentage) =_____________
Stock ON HAND (SOH)
LAST 30 DAYS SALE
SOH + LAST 30 DAYS SALE
SALE
Contribution
Net Sales

Page17
Result:
1. Inventory cover*
Values Feb Week-2
Range New Total
SOH 84,159 1,15,130
SOH(MRP) 7,32,02,688 9,85,89,684
Week’s Actual Sale (MRP) 22,91,994 46,59,823
Sales last 30 days(MRP) 1,31,53,963 2,25,26,682
Current Cover(MRP) 5.82 4.86
*Digits are changed for confidential purpose
2. New Vs Old product categories performance
Points of Comparison New Old New (%) Old (%)
Last Week Revenue 22,91,994 23,67,829 49% 51%
Last Week Sales (Units) 2,556 2,721 48% 52%
SOH Qty 83,502 31,628 73% 27%
SOH MRP 7,26,72,395 2,59,17,289 74% 26%
Revenue Sale-ALL
Weekly Sales New Old New (%) Old (%)
Bras 8,29,857 4,98,030 62% 38%
NW 7,43,158 8,00,620 48% 52%
Panty 3,46,631 3,46,999 50% 50%

Page18
MRP SOH
Cat New Old New (%) Old (%)
Bras 2,82,75,901 45,97,380 86% 14%
NW 2,69,53,002 64,25,312 81% 19%
Panty 79,71,277 39,72,076 67% 33%
3. Current Category Cover
Category STOCK% (MRP)
MONTHLY
SALE% (QTY)
MONTHLY SALE%
(MRP)
CURRENT COVER
(MRP)
BRA 33% 39% 34% 4.76
NIGHTWEAR 34% 22% 34% 4.79
PANTY 12% 26% 14% 4.15
SWIM 11% 6% 11% 4.87
Others 10% 6% 7% 6.51
Grand Total 9,85,89,684 22,967 2,04,40,077 4.82
4. PrettySecrets Website Performance (www.prettysecrets.com)
(X) Week Revenue Sale Qty Sale
Weekly
Sales
New Old New (%) Old (%) New Old New (%) Old (%)
Bras 8,29,857 5,03,730 62% 38% 1093 670 62% 38%
NW 7,39,912 8,01,769 48% 52% 488 681 42% 58%
Panty 3,46,631 3,46,999 50% 50% 719 751 49% 51%

Page19
5. SKU Width Categorization
SKU –Width 4 & above units/style
4 & above
Total
Season Cat 0-25% 25-50% 50-75% 75-80% 80-90% 90-100%
Old 24 45 115 25 63 439 711
Sep'16
BRA 2 65 67
NW 1 113 114
PANTY 2 76 78
SWIM 4 35 39
Sep'16 Total 5 4 289 298
Grand Total 24 45 120 25 67 728 1009
(X-1)
Week
Revenue Sale Qty Sale
Weekly
Sales
New Old New (%) Old (%) New Old New (%) Old (%)
Bras 19,22,175 3,15,430 86% 14% 2,475 420 85% 15%
NW 12,08,925 4,78,866 72% 28% 775 432 64% 36%
Panty 5,19,785 2,61,993 66% 34% 1,165 557 68% 32%

Page20
6. Channel wise SOR inventory
SOR PORTAL LAST 60 DAYS SALE CURRENT STOCK % STOCK (MRP)
REVENUE
EFFICIENCY
(x week)
REVENUE
EFFICIENCY
(x-1 week)
Amazon 207 642 22% 618 482
Flipkart 881 1577 40% 418 443
Reliance 710 1390 36% 424 376
Snapdeal 76 60 2% 398 411
Souq 395 2662 - -
Total SOR 1874 (4%) 3669 (4%) 33,77,475 (4%) 437 418
PS warehouse 50,301 1,15,130 9,85,89,684 14,560 15,702
Demand Forecasting Methods
In ecommerce e-retail, demand forecasting is done using R programming language. R- Language
is an open source programming language and software environment for statistical computing
and graphics that is supported by the R Foundation for Statistical Computing. The R-Language is
widely used by statisticians and data miners for developing statistical tools and software data
analysis. There are different demand forecasting model in the R-language and the user just
required to upload the past sales data into the software and on the basis of the programming,
R-Language yields the required output.
These are the various methods being used in PrettySecrets to estimate demand forecasting:
1. ARIMA Model
The Autoregressive integrated moving average (ARIMA) method is used for the dynamic data
set, meaning where the data is highly non- stationary and the demand of the product changes
in short periods. The AR part of ARIMA represents that the evolving variable of interest
is regressed on its own lagged values. The MA part signifies that the regression error is actually

Page21
a linear combination of error terms whose values occurred contemporaneously and at various
times in the past. The I (Integrated) represents that the data values have been replaced with
the difference between their values and the previous values (and this differencing process may
have been performed more than once). The purpose of each of these features is to make the
model fit the data as well as possible.
ARIMA IN R
Different definitions of ARMA models have different signs for the AR and/or MA coefficients.
The definition used here has
X[t] = a[1]X[t-1] + … + a[p]X[t-p] + e[t] + b[1]e[t-1] + … + b[q]e[t-q]
and so the MA coefficients differ in sign from those of S-PLUS. Further, if include.mean is true
(the default for an ARMA model), this formula applies to X - m rather than X. For ARIMA models
with differencing, the differenced series follows a zero-mean ARMA model. If am xreg term is
included, a linear regression (with a constant term if include.mean is true and there is no
differencing) is fitted with an ARMA model for the error term.
The variance matrix of the estimates is found from the Hessian of the log-likelihood, and so may
only be a rough guide.
Optimization is done by optim. It will work best if the columns in xreg are roughly scaled to zero
mean and unit variance, but do attempt to estimate suitable scalings.
Applying ARIMA in R
The exact likelihood is computed via a state-space representation of the ARIMA process, and
the innovations and their variance found by a Kalman filter. The initialization of the differenced
ARMA process uses stationarity and is based on Gardner et al (1980). For a differenced process
the non-stationary components are given a diffuse prior (controlled by kappa). Observations
which are still controlled by the diffuse prior (determined by having a Kalman gain of at
least 1e4) are excluded from the likelihood calculations. (This gives comparable results
to arima0 in the absence of missing values, when the observations excluded are precisely those
dropped by the differencing.)
Missing values are allowed, and are handled exactly in method "ML".
If transform.pars is true, the optimization is done using an alternative parametrization which is
a variation on that suggested by Jones (1980) and ensures that the model is stationary. For an
AR(p) model the parametrization is via the inverse tanh of the partial autocorrelations: the
same procedure is applied (separately) to the AR and seasonal AR terms. The MA terms are not

Page22
constrained to be invertible during optimization, but they will be converted to invertible form
after optimization if transform.pars is true.
Conditional sum-of-squares is provided mainly for expositional purposes. This computes the
sum of squares of the fitted innovations from observation n.cond on, (where n.cond is at least
the maximum lag of an AR term), treating all earlier innovations to be zero.
Argument n.cond can be used to allow comparability between different fits. The ‘part log-
likelihood’ is the first term, half the log of the estimated mean square. Missing values are
allowed, but will cause many of the innovations to be missing.
When regressors are specified, they are orthogonalized prior to fitting unless any of the
coefficients is fixed. It can be helpful to roughly scale the regressors to zero mean and unit
variance.
Examples
arima(lh, order = c(1,0,0))
arima(lh, order = c(3,0,0), method = "CSS")
arima(USAccDeaths, order = c(0,1,1), seasonal = list(order = c(0,1,1)))
arima(USAccDeaths, order = c(0,1,1), seasonal = list(order = c(0,1,1)),
method = "CSS") # drops first 13 observations.
# for a model with as few years as this, we want full ML
arima(LakeHuron, order = c(2,0,0), xreg = time(LakeHuron) - 1920)
## presidents contains NAs
## graphs in example(acf) suggest order 1 or 3
require(graphics)
(fit1 <- arima(presidents, c(1, 0, 0)))
nobs(fit1)
tsdiag(fit1)
(fit3 <- arima(presidents, c(3, 0, 0))) # smaller AIC
tsdiag(fit3)
BIC(fit1, fit3)
## compare a whole set of models; BIC() would choose the smallest
AIC(fit1, arima(presidents, c(2,0,0)),
arima(presidents, c(2,0,1)), # <- chosen (barely) by AIC
fit3, arima(presidents, c(3,0,1)))
## An example of ARIMA forecasting:
predict(fit3, 3)

Page23
2. Holts-Winter Method
It is also known as double exponential method. This method of forecasting considers both trend
and random variable while estimating demand.
Holts-Winter model in R-Language
The additive Holt-Winters prediction function (for time series with period length p) is
Yhat[t+h] = a[t] + h * b[t] + s[t - p + 1 + (h - 1) mod p],
where a[t], b[t] and s[t] are given by
a[t] = α (Y[t] - s[t-p]) + (1-α) (a[t-1] + b[t-1])
b[t] = β (a[t] - a[t-1]) + (1-β) b[t-1]
s[t] = γ (Y[t] - a[t]) + (1-γ) s[t-p]
The multiplicative Holt-Winters prediction function (for time series with period length p) is
Yhat[t+h] = (a[t] + h * b[t]) * s[t - p + 1 + (h - 1) mod p],
where a[t], b[t] and s[t] are given by
a[t] = α (Y[t] / s[t-p]) + (1-α) (a[t-1] + b[t-1])
b[t] = β (a[t] - a[t-1]) + (1-β) b[t-1]
s[t] = γ (Y[t] / a[t]) + (1-γ) s[t-p]
The data in x are required to be non-zero for a multiplicative model, but it makes most sense if
they are all positive.
The function tries to find the optimal values of α and/or β and/or γ by minimizing the squared
one-step prediction error if they are NULL (the default). Optimize will be used for the single-
parameter case, and optim otherwise.
For seasonal models, start values for a, b and s are inferred by performing a simple
decomposition in trend and seasonal component using moving averages (see
function decompose) on the start. periods first periods (a simple linear regression on the trend
component is used for starting level and trend). For level/trend-models (no seasonal
component), start values for a and b are x[2] and x[2] - x[1], respectively. For level-only models
(ordinary exponential smoothing), the start value for a is x[1].

Page24
Examples
## Seasonal Holt-Winters
(m <- HoltWinters(co2))
plot(m)
plot(fitted(m))
(m <- HoltWinters(AirPassengers, seasonal = "mult"))
plot(m)
## Non-Seasonal Holt-Winters
x <- uspop + rnorm(uspop, sd = 5)
m <- HoltWinters(x, gamma = FALSE)
plot(m)
## Exponential Smoothing
m2 <- HoltWinters(x, gamma = FALSE, beta = FALSE)
lines(fitted(m2)[,1], col = 3)
Conclusion:
1. The impact of GST is seen as negative in short term but positive in long term. Hence, GST can
create trouble for online e-retailers in its initial stage of implementation. The ambiguity
regarding discounts, cash backs and other customer delighting offers should be resolved before
July 1st, 2017 (expected implementing date according to GOI).
2. The Management Information System (MIS) is a crucial part of inventory & planning
management. Fashion e-retailers are in highly dynamic market and everyone is in a race to
occupy the market share. By keeping track on our inventory movement, we can leverage good
margin by promoting products which are performing really well. Also, we can determine
discounting on the right item in the right time at the right channel.
3. The Demand Forecasting Method is applied with the help of R-language. The R-Language is
really helpful and its forecasting estimation is approx. 90% accurate of the actual demand. The
inventory planning team always does experiment with different forecasting models.

Page25
References & Document Attached
Cover Report.xlsm
GST Sheet.xlsx
InventoryBySkuBin.xlsx
http://stats.stackexchange.com/questions/78287/what-are-disadvantages-of-state-space-
models-and-kalman-filter-for-time-series-m
www.prettysecrets.com
https://www.tutorialspoint.com/hadoop/hadoop_introduction.htm
http://retail.economictimes.indiatimes.com/re-tales/why-omni-channel-remains-largely-
misunderstood-in-india/2190
http://retail.economictimes.indiatimes.com/re-tales/why-omni-channel-remains-largely-
misunderstood-in-india/2190
http://www.indiaretailing.com/2017/01/10/fashion/heres-why-the-garment-industry-is-
upbeat-despite-demonetization/
http://www.gstindia.com/most-companies-may-be-struggling-with-gst/
http://www.gstindia.com/gst-tax-structure-ready-how-will-it-impact-economy-and-various-
sectors/
http://www.gstindia.com/indias-warehousing-market-after-gst-industry-perceptions/
http://www.gstindia.com/textile-mills-write-to-pm-on-gst/
http://www.gstindia.com/gst-no-respite-for-flipkart-amazon-on-tax-collection-at-source/
http://www.gstindia.com/trump-effect-on-gst-u-s-businesses-in-india-might-get-effected/

Prettysecrets.com- A Women Fashion Ecommerce Private Limited

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Prettysecrets.com- A Women Fashion Ecommerce Private Limited

Similar to Prettysecrets.com- A Women Fashion Ecommerce Private Limited (20)

Prettysecrets.com- A Women Fashion Ecommerce Private Limited