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The textile industry plays a major role in international trade which improves our country’s economy, due to its contribution in employment generation and earning in foreign exchange. India stands 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 clothing 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 modelling them using visual analytics to make effective decisions using data mining techniques which manoeuvre 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.
Data are raw facts or measurements that can be recorded about event and aspects, and indirectly meaningful. The data needs to be collected, organized, summarized, analysed and synthesized for decision-making purposes. Data analysis requires a systematic approach involving several important steps.
Statistics are defined as the art of learning from data. In its most general form, statistics deals with the collection of data, the description of data following it and the analysis of data that often leads to the conclusion. Data mining is a scientific discipline that takes the origin of statistics. Although data mining is basically a statistical application, the methods of data mining are somewhat different from the statistical method. The most obvious difference is that, unlike data mining method, it is not easy to analyse the large-scale data with traditional statistical method.
Data mining is widely used in diverse and interdisciplinary fields. In recent years, data mining has gained great deal of important due to the large amount of data in different applications belonging to various fields. This case study centres around displaying the data mining application with model in material industry the examination comprises a general diagram of information mining and talks about the fundamental fields for which information mining connected.
What is TECHNOLOGY?
Technology is a body of knowledge devoted to creating tools, processing actions and the extracting of materials. The term “Technology” is wide, and everyone has their way of understanding its meaning. We use technology to accomplish various tasks in our daily lives, in brief; we can describe technology as product and processes used to simplify our daily lives.
Many industries are using technology to stay competitive, they create new product and services using technology, and they also use technology to deliver those products and services to their customers on time and within budget.
What is INDUSTRY?
An industry is a group of manufacturing or businesses that produce a kind of goods or services. For example, workers in the textile industry design, fabricate, and sell cloths. The word industry come from the Latin “Industria”, which means “diligence, hard work.”
Industrial system has input, processes and output. Input can be the raw materials need to make something. Input can also be labour, building, capital and machinery. Processes are the things which go on within the factory. This is usually the manufacturing of goods. It can also be design and research-anything needed to make something. Outputs are the things which leaves the factory. This can include the finished product, profit or even waste.
There are four types of industry.
- Primary industry involves getting raw materials e.g. mining, farming and fishing.
- Secondary industry involves manufacturing e.g. making cars and steel.
- Tertiary industries provide a service e.g. teaching and nursing.
- Quaternary industry involves research and development industries e.g. IT.
Definitions of Data mining methods:
According to Kleinberg, data mining is an interesting pattern extraction process from the raw data”. According to the Gartner group, data mining is the process of “discovering new correlations, pattern and trends that are meaningful by passing a large number of data stored in a vault”.
Data mining consists of seven steps as a part of knowledge discovering process. The first step is data cleaning step, missing data is completed by removing noisy, erroneous and inconsistent data; data integrating is the second step, in which the different data sources are combined by adding there plurality of different data source; next step is followed by data selection and transformation step, in this step the data is determined and taken from the database to analysis and conversion is performed. Intelligent method is applied to extract data pattern in data mining; pattern evaluation step that defines the correct and interesting pattern representing the information obtained according to the measurements; knowledge is the final step; information method is used to obtain information presented in the given data base.
The ways that technology effects and changes the clothing industry.
It is been observed that data mining in textile industry has been used in many studies in the textile industry. Data mining is used very extensively to reduce the complexity of the management of the data transaction study in the industrial sector. This type of technology is used in clothing industry. Data mining is now being applied at every stage of industry-working out which is the most popular products by predicting the trends, forecasting the demands, optimizing the price, identifying the customers, forecasting the market.
- Predicting trends.
These days, the fashion industry have the wide range of tools available to them like the trend forecasting algorithms comb social media posts and web browsing habits to work out what is causing increasing the trend, and the market department will push the trends by the analysed ad-buying data.
- Forecasting demands and optimizing prices
By data mining one can understand what people are and will be buying, by gathering demographic data and economic indicators the textile industry understands to build a picture of spending across the targeted market.
Datamining is also playing a major part in determining when prices should be dropped. The analytics has shown that a more steeply decrease in cost, from the moment demand starts to reduce, usually leads to increase in revenues when reduction of price at the end of a sales season a products demand has almost gone.
In these years, technology has been increasing rapidly in fashion world. Technology’s impact has been difficult to ignore especially in e-commerce in full potential.
The clothing industry is considering these technologies revolutionizing according to the businesses operation, with the help of data analytics, artificial intelligence etc leading to efficient processes, as well as they need to acquire new landscape and embrace the turning point of new fashion world.
Some all technologies are making the big impact on fashion world.
- Online Shopping
The online shopping is winning this day, since traditional fashion companies have been decreasing due to the online marketing for fashion, and this space of shopping is filled by online retailers such as Amazon, style bop, Jabong etc.
This online retailer not only providing an online platform for selling fashion products, and at the same time they also offering a great customer service and experience. These types of retailers very efficient to acquire and understand what the consumer want, and this is possible by using different means, such as social media, advance data analysing tools, and artificial intelligence. they immediately react to the costumer’s insights collecting through digital media and incorporate them into their decision-making process. The below figure shows the increase of online shopping in past few years.
Fig:1 Number of online shoppers from 2006 to 2017 (in million)
- Artificial intelligence
Present days every sector is using AI and fashion industry is no strange to this. In fact, every online retailer has jump started onto the digitalization and are using these machines to their advantages.
In present world, social media is ruling the web, and providing consumers with the suitable platform to mend and shape trends. But the customers don’t know that they are providing the company’s the valuable data that can be used to predict trends and by this brand can estimate the gap between what to be produced and sold. Rather than collecting the required data manually, the AI gather, organise analyse, and sort data into relevant categories that can be used to predict and understand the pattern of the upcoming trends.
- Using of virtual reality in clothing industry
In this present modern world mobile and e-commerce totally changed the fashion industry. Now we are at the next possibility of fashion sector by the virtual reality technology.
By using mobile customers can easily browse their required items with one click. Sometimes brands have suffered the backlash from the customers who gets items that don’t match with the items they ordered. But, now the game changer technology is uprising, new VR platform is merging the physical and online world. Apps like Dressing Room are utilizing the augmented reality technology to try outfits on an avatar- customized to the correct measurements before purchasing any item. The VR platform totally changes the online shopping experience for the better.
The factors that effects the online shopping.
The online shopper is speculative at the best as delving in to the thought processes of each individual consumer is hard to estimate. The behaviour of online consumer is as complicated as socio-technical phenomenon and from the last decade it is in range of the focus of the researchers. It is very hard to judge the psychological state of the different customers while they are purchasing items. Because it is hard to generalize the conclusion, number of studies that have the hypothesizing different factors.
- Financial problems
The financial risk is the primary concern of individual who are doing online shopping. Financial risks in online shopping is defined as the loss of some money while purchasing any goods. Some age groups are more concern with their security and privacy of the bank information.
- Product issues
In the traditional and regular way of shopping the products can have in front of the customer this gives the more options to manage the expectations that a customer has when they are buying a product. Due to the limited option and information that given e-commerce business like description of the product and the ability to zoom in on the product pictures to give the client an expectation of the product. Consumer loss the ability to evaluate the quality of the product.
- Delay delivery and not delivered
Although this factor is not a commonly occurred while shopping online, not receiving their products after purchasing them online is a very common fear among shoppers. Loss or damage of ordered goods and the products are not received in agreed time frame is the factors effects on line shopping.
- Return policy
The policy’s like return policy is the very important thing that gives customers the chance to return the unwanted items or goods that doesn’t suites up to their needs and expectations. The customer’s shopping behaviour may change with out any proper return policy because the faith of the customers is forced to put on the e-commerce business, which is very hard to achieve due to falsely & deception products and consumer will fell like their money was wasted because the products don’t match up to the mark of expectations.
The process involves in data mining through online shopping.
In actual shopping the process involved is very simple and from the beginning the customers are doing their shopping in this method only. The below figure the simple process.
And the process model diagram shows the clean step by step process, activities, tasks, mainly decision making of the Actual traditional shopping.
This kind of shopping is done by the customers before development in technology. In this type of shopping the data mining is not possible for clothing industry because the customers trends are not able to follow effectively.
Due to the increase of technology the data mining became very simple through online shopping platform. These days everyone is using cellular devises like cell phone, PC etc everything like shopping is one click away. We can understand the below figure.
Online shopping has major role in data mining in clothing industry, this type of technology helps to gather the required data for forecasting the upcoming fashion, by this the profits of the industry increases exponentially. The below process model diagram shows the steps, involving in online shopping.
The impact of data mining on the clothing industry has a significant role, because the development of this kind if industries are on data that collect from the customers only. The collection of data varies from industry to industry, some factors have significant effects on the data mining.
Due to the increase of technology the data mining changed from the past decades, data mining is used to done manually by people for clothing industry. But in present days software’s, artificial intelligence playing the important role to collect the required data through online shopping for industries.
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