Improvising the Sales of Garments by Forecasting Market ...EngyShafik [14 ] presented a Time series...

Improvising the Sales of Garments by Forecasting Market

Trends using Data Mining Techniques

S.Vinod Kumar1and S.Poonkuzhali2

1 Associate Professor, Department of Computer Science & Engineering,

Rajalakshmi Engineering College, 2Professor & Head, Department of Information Technology, Rajalakshmi Engineering College

Abstract: The Textile industry plays a major role in International trade which improves our

country’s economy by 30 % due to its contribution in employment generation and earnings in

foreign exchange. India ranks world’s second in the production of textiles and garments

which leveraged the rapid economic growth. Sales forecasting is very crucial to improve the

demand and supply chain process in this garment industry. The large scale of data involved in

very dynamic e-commerce or offline business is not available at one click from available

software. Extracting relevant data from various business units quickly and modeling them

using visual analytics to make effective decisions using data mining techniques which

maneuver the textile industry to stay competent with world leaders. The proposed work

provides analytical inferences from e-commerce data for garment industry and proposes a set

of business drivers by predicting trends in e-commerce through trend analysis. These

analytics helps garment industry to improve the turn over, stay competitive against global

recession, fluctuating raw material cost and balance supply chain management.

Keywords: data mining, garment Industry, prediction, sales, trend analysis

I INTRODUCTION

At present Indian Textile Industry economy is growing at an enormous rate by taking competitions and innovation seriously though there is a crisis in global economy. There is a significant contribution to the global market by the Unique Multifarious Indian Ethnic Wears, apparels and accessories. India has traditional design patterns and emerging fashion trends also on par with the fashion etiquettes of the world. This along with the liberalization trends followed by Indian government has given rise to a huge market space for export, innovation in design and fashion trends, retail and wholesale trade in garments industry. Garments Industry has become a billion dollar context now. This business is now looked upon more seriously and more professionally with multi-national companies expanding sales and production territories. More sustained growth in the industry can be achieved by application of technology for classification, clustering and prediction of sales, marketing, production of clothing apparels by means of state of art machine learning algorithms. This can help us to manipulate the global market and synthesize opportunities for success. The huge data coming out of the manufacturing units, sales and marketing hubs, point of sale units spread across various locations in the world needs to be organized and analyzed to throw light on the trends of business in and around the company. The growing e-commerce trends have made scope for data analytics very wide. With sub-second response to online transactions, data also is available for analysis very widely. Recommendation and Customer Preferences based on correlation, classification and prediction can be used to identify the trends that are famous for the day, week, month or year, even for decades if the data is present at disposal.

The data mining algorithms for regression analysis, predictive classification, time series analysis,

forecasting, recommendation, customer preference or sentiment analysis, etc., have made possible analytics a inseparable part of the garments industry.

International Journal of Pure and Applied MathematicsVolume 119 No. 7 2018, 797-805ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu

797

The proposed work is focused on sales forecasting using trend analysis of garments industries based on the e-commerce data collected from Coimbatore region which is called Manchester of Tamil Nadu region in India due to its cotton production and the density of textile industries present in the region. Time series analysis is performed on the data and forecasting on the trend of the sales is derived from the data.The paper is divided as Introduction in first chapter, related works in the second chapter, Methods and Materials for performing trend analysis using Time series based forecasting in textile industry and Results and Discussion in the final chapter.

II. Review of Literature

Data Mining has helped derive deep insights into the trade if not available by manual analysis in the areas

of customer buying pattern analysis, customer relationship management based decision making,

managing the operations of wide spread units efficiently, make projections for future trading. Many

works have been considered in the proposed work for ground work. The literature survey is briefed in

the section below.

S.K.Tyagi and B.K Sharma[1] presented the role of quality control and usage of data mining tools and

techniques in Textile Industry. They gave the Computerization through Context-Analysis diagram of the

quality control Laboratory in Textile Industry.Xia Li et al [2] applied DEA method to analyze the

technological innovation of China’s top ten textile and apparel provinces in goods exported, and propose

the countermeasures to improve the efficiency of input and output in technological innovation.

PromodRaichurkar and RamachandranManickam[3] provided a detailed survey about Problems and

challenges for rapid growth, Optical fiber application in textiles, Marketing Initiatives, Scheme for

Integrated Textile Parks, Textile Education and Skill Development, and also reflects that wholehearted

joint efforts from manufacturers, buyers, suppliers, government, and other stockholders are highly

expected to accomplish the development of potential and sustainable textile industries growth in India.

Raj Kumar Somani and ReenaDadhich[4] gave new technologies to improve their MIS system by cloud

computing and mobile computing using various factors for single case company’s implementation project.

NahidaParvin et al,[5] presented a comparative analysis from accuracy to explore the knowledge by

classification techniques to make automated decision for reducing the rate of casualty of industrial

misshapes.They used Textile and Garment accident dataset. Vittorio Murino et al[6] addressed the defects

in textile manufacturing by new classification scheme with various extracted features using support

vector machines and made an accurate analysis on two different dataset.

SébastienThomassey[7] proposed a review of the different constraints related to sales forecasting in the

fashion industry to design a sale forecasting system.The different constraints taken to design the

forecasting system is Horizon, Life Cycle, Aggregation,Seasonality,Exogenous VariablesM.MartinJeyasingh

et al [8] attempted to predict clothing insulation factors by Linear regression to achieve the goal of

understanding the computational character of learning.

M.K. Gandhi et al,[9] achieved operational excellence and got the best on Return on Investment.They

proposed an add-on modules in ERP to fulfil the needs of the organization by eliminating the Risk factor.

M.K Gandhi andK.Sarukesi addressed the issues for of ERP Applications and identifies the solutions for

implementation in Apparel Industry[11]. Avonigharde[13] conducted a study on forecasting of clothing

sales using regression and time series model. EngyShafik [14] presented a Time series Forecasting Model

for US Winter Season Apparels based on Seasonality, Economic Condition, Fashion Trends and

Consumer Behavior.

Based on the background work the proposed work is focused on data mining using statistical modeling of

data in time series and forecasting based on the data.

III. Methods and Material The basic process flow for the analysis process carried out through the proposed work is detailed in this section.

International Journal of Pure and Applied Mathematics Special Issue

798

This section is divided into Process flow Model and the Time Series Analysis algorithm used for analysis is briefed in the subsequent section.

A. Process Flow Model

The data is acquired from various units of sales considered herein. The data from these units is organized, pre-processed by means of smoothing and normalization. Smoothing reduces the variation present in the data collected from various units. Normalization helps to retain the attributes obtained from feature relevance fall within a specified range. The time series based forecasting and trend analysis is performed using RStudio and R tools. Based on the predicted trends the report is generated from the tool under the context. The fig 1 illustrates the flow pictorially.

Figure 1: Process Flow for Data Analysis

B. Dataset Description

The data acquired from various units of sales for the two companies ABC and XYZ is pre-processed, feature selection is performed and the following attributes are chosen for forecasting purpose. The attributes are Month. Number of Pieces sold &Amount sold for both companies,Grand Total pieces sold, Grand Total Amountsold.The data is accumulated for three years and then assembled for analysis.

C. Time Series Analysis

The time series analysis helps in identifying the forces and structure that affect the data and it helps in order to fit the data to predict the data for future analysis. Time series analysis applied herein is Holt-Winters exponential smoothing technique.

D. Holt – Winters Analysis

Holt Winters performs smoothing of even widely varying data to retain a particular range of the variables projected in the analysis and then goes for forecasting, which is the most sensible way for prediction.

Holt Winters Exponential Smoothing: Algorithm 1. The data is ordered chronologically to prepare a time series which is the base for the Holt- Winters Analysis perceived in two dimensional order.

[12]

2. Smoothing is performed and the generated series is names St

whereSt=yt + (1 - )St-1 , S1 = y1 and theis the smoother used in the function. The exponential smoothing formulae applied to a series with a trend and constant seasonal component using the Holt-Winters additive technique are:

)b)(a1()s(a 11 ttpttt Y

11 )b1()a(ab tttt

ptttt Y )s1()a(s

where:

E-commerce Dataset of

Garment Industry

Data Preprocessing

Smoothing

Normalization

Data Analytics using R tool

Prediction of Trends

Report Generation


799

, and are the smoothing parameters at is the smoothed level at time t bt is the change in the trend at time t st is the seasonal smooth at time t p is the number of seasons per year

3. The Holt-Winters algorithm requires starting (or initialising) values. Most commonly:

)(1

a 21 pp YYYp

p

YY

p

YY

p

YY

p

ppppp

p 22111

b

ppppp YsYsY a , ,a ,as 2211

4. The Holt-Winters forecasts are then calculated using the latest estimates from the appropriate exponential smooths that have been applied to the series.

So we have our forecast for time period T :

TTTTy sba

where: Ta is the smoothed estimate of the level at time T

Tb is the smoothed estimate of the change in the trend value at T

Ts is the smoothed estimate of the appropriate seasonal component at T

IV. Results and Discussions

For experimental analysis and study, an input dataset is collected from a garment industry in

Coimbatore, India for three years. For this analysis only two major vendors of that garments

company are considered and for confidentiality purpose, the companies names are not disclosed in

this paper and they are mentioned as company XYZ and ABC for referral throughout the paper. The

following are the components of analysis in this work.

A. Trend analysis of sales (in Rupees) for companies XYZ and ABC on monthly basis

B. Time series based forecasting of sales for companies XYZ and ABC for years in future

A. Trend analysis of sales (in Rupees) for companies XYZ and ABC on monthly basis

The sales is analyzed in terms of comparison over days, months and years. The trend analysis work based on

the dataset considered, led to the following inferences as summarized in fig 2, fig 3, fig 4 and fig 5.

Figure 2: Trend of number of pieces sold by Company ABC in last three years ( in months )

0

50

100

150

200

250

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

ABC-13

ABC-14

ABC-15


800

Figure 3: Trend of number of pieces sold by Company XYZ in last three years ( in months )

Figure 4: Trend of amount equivalent to sales made by Company ABC in last three years ( in months )

Figure 5: Trend of amount equivalent to sales made by Company XYZ in last three years ( in months )

0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10 11 12

XYZ-13

XYZ-14

XYZ-15

0

20000

40000

60000

80000

100000

120000


ABC-13

ABC-14

ABC-15

0

50000

100000

150000

200000

250000

300000

350000

400000


XYZ-13

XYZ-14

XYZ-15


801

Company XYZ is the most potential client.

Company ABC has shown seasonal trends in sales. It was highly hit after the seasons. In normal times

the sales has dropped considerably.

Company XYZ has not been prone to seasonal sales. During festive seasons they seem to give good

deals to customers earlier to ABC and sales pick up earl during the festivities. The customers are not

only regional for XYZ it seems. There are others also using XYZ , this could be a potential reason why

seasonal festivities are not only the factors contributing to sales.

Sales has been good on last weeks of the month except for festive season sales during the mid weeks or

vacation time sales.

Sales has improved for the Company compared to the last year , but pattern of sales has not been same.

Online users have increased maybe because of usage of Mobile Apps along with computers and

laptops.

Weekend of last weeks of the month normally show good sales. In case of festivities or any other

reasons if sales has been good in mid weeks then the last weeks sales have not been to the mark.

The vacation time weekdays sales are improving during March ,April and May.

The sales has been good for XYZ most of the days in the month. There have been very few no sales

days compared to ABC.

Earlier ABC seems to have not been getting sales on Sundays. This trend has changed. Sales of

weekends have improved.

B. Time series based forecasting of sales for companies XYZ and ABC for years in future

Based on the data, time series analysis and prediction is made and the prediction forecast for sales of both

companies ABC and XYZ are presented in fig 6 and fig 7 till 2050 A.D.

Fig 6: Forecast for Company ABC


802

Fig 6: Forecast for Company XYZ

The above figures show that the sales of the company XYZ is likely to boom around 2025-28 and 2031-33 and it is likely to increase through till 2050 with hikes in the afore mentioned years.

Conclusion

A Time series analysis and trend analysis of sales of vendors XYZ and ABC who are part of a

Garments Company is performed in the proposed work. The Business forecasting of sales till 2050

A.D is achieved from the existing sales data based on which the customer buying pattern and

Customer Relation based reasoning are achieved using mathematical models on the data. Based on

this work, the training and testing of the data with classical data mining algorithms can help to present

short-term or long-term forecasting and evaluation can be done on various parameters.

References

[1]. S.K.Tyagi and B.K Sharma,” Data Mining Tools and Techniques to Manage the Textile Quality

Control Data for Strategic Decision Making”, International Journal of Computer Applications

(0975 – 8887) Volume 13– No.4, January 2011 pp .26-29

[2]. Xia Li, Yingchun Liu, Tingting Wang and Fanxing Kong,” Evaluation and Analysis on Textile

Industry Technological Innovation Ability based on DEA”, International Conference on

Information Management and Engineering, V52.46 2012, IACSIT Press, Singapore.

[3]. PromodRaichurkar and RamachandranManickam,”Recent Trends and Developments in Textile

Industry in India”, International Journal on Textile Engineering and Processes, Vol 1, Issue 4,

October 2015.

[4]. Raj Kumar Somani and ReenaDadhich,” Design of Cloud Computing Based MIS Model for Textile

Industries”, Design of Cloud Computing Based MIS Model for Textile Industries, Volume 2, Issue

11, May 2013.

[5]. NahidaParvin,Ayesha Aziz Prova andMehnazTabassum,” Comparative Analysis of Industrial

Mishaps Based on Classified Prediction”, IJSRSET , Volume 1 , Issue 6 ,2015

[6]. Vittorio Murino, ManueleBicego and Ivan A. Rossi,” Statistical classification of raw textile defects”,

Proceedings of the 17th International Conference on Pattern Recognition, 2004


803

[7]. SébastienThomassey,” Sales Forecasting in Apparel and Fashion Industry: A Review”, T.-M. Choi

et al. (eds.), Intelligent Fashion Forecasting Systems: Models and Applications, Springer-Verlag

Berlin Heidelberg 2014.

[8]. M.MartinJeyasingh, KumaravelAppavoo andP.Sakthivel,” Data Mining for Predection of Clothing

Insulation”, International Journal of Modern Engineering Research, Vol.2, Issue.2, Mar-Apr 2012

pp-001-005.

[9]. M.K. Gandhi ,K. Sarukesi and S.Poonkuzhali,” Road Map for add-on Modules for Sustainable ERP

Solution For Apparel Industry”, GE-International Journal Of Engineering Research Volume -3,

Issue -3 (March 2015).

[10]. M.K Gandhi, andK. Sarukesi,” Change Management Challenges in ERP Implementation in Apparel

Industry”,IJSR-International Journal of Scientific Research Volume-4 Issue:4 April 2015. [11]. T.K.Sethuramalingam, andB.Nagaraj,”A Comparative Approach On Pid Controller Tuning Using Soft

Computingtechniques”,International Journal of Innovations in Scientific and Engineering Research

(IJISER),Vol.1,No.12,pp.460-465,2014.

[12]. White paper on “A Teacher’s Guide to the Models Used In Time Series Module Of iNZight”

available online.

[13]. AvoniGharde, “Influence of Factors on Clothing Sales and Its Future Trend: Regression Analysis

and Time Series Forecast of Clothing Sales”, Journal of Textile and Apparel, Technology and

Management,”Vol 10, Issue-2, pp.1-11,2016.

[14]. EngyShafik,”Predictive Modeling of US Winter Apparel Sales Using Time Series Forecasting”,

Journal of Textile and Apparel, Technology and Management,”Vol 10, Issue-2, pp.1-11,2016.


804

Improvising the Sales of Garments by Forecasting Market ...EngyShafik [14 ] presented a Time series...

Documents

Transcript of Improvising the Sales of Garments by Forecasting Market ...EngyShafik [14 ] presented a Time series...