Data-driven Problem Solving in the Ready-Made Garments Industry

-By Tushar Soliwal
IE Supervisor at Pratibha Syntex Ltd.
Pithampur, Indore
Email: tusharsoliwal01@gmail.com

1. Introduction:
In this article, we will understand brief about data driven problem solving in ready-made garments industry, as we all aware garment industry is one of those industries who generate more employment, after the agriculture industries. The apparel and textiles are the second largest industries which generate more employment and direct- indirect jobs to needy. It places an important role to improvement in significance in global economy. Garment and textile industries contribute 2.3% to the GDP of India. So in many different areas ready-made garment industries of fashion goods plays an important role. There are so many different challenges and complexities faced by these industries. Mainly the government policies, political decisions, weak supply chain management, increasing in labour rate, labour availability, quality of raw material and also equipment, machineries. There are some new and difficult challenge like technology, and automations. These are the few challenges and complexities faced by every garment industries. There are some other factors which also affects on fashion manufacturing industries like fashion trades, fashion forecasting, trade barriers, import and export rules-regulations and many more. So, these factors have direct impact on consumer buying behavior. In today’s world the technology is became more familiar with the common man. And the effective technology requires huge data set and information. So, in this article we will study how the data driven techniques will solve the problems of ready-made garment industries.

2. Data-driven Problem Solving:
It is defined as, the approach of problem solving with the help of data and makes an informational decision. Nowadays, many different business sectors adopted data driven problem solving techniques to earn the profit through customer satisfaction by delivery them what they actual want. In data driven problem solving huge amount of data is required which is related with the buying habit of customer and many different things. This kind of information will help to the organization to take better and effective decisions according to their goals. As a data plays an important role to understand the buying behaviour of customer [2].

There are some other objects of the same are mention below.

2.1 Objective of Data-driven Problem Solving

Collecting the data and information
Analyzing the data
Help to understand customer wants and needs
To improve operational efficiency
To do the forecasting
Predict the fashion trends and future trends
Improve the information technology system of the organisation
Enhance customer experience
At end to satisfy the customer through solving the problems

2.2 Sequence of data driven problem solving approach

Understanding the problem
Collecting relevant data
Organising the data
Analysing the data
Understand the data though statistical tools and techniques
Solve the problem by data driven problem solving skills / methods

2.3 Need of data driven problem solving approach

It fulfill the objectivity and accuracy in terms of results
Utilise the cost and time efficiency of manufacturing process
To be a perfect competitor at market place
Solve the complexity
For better understanding of results and feedback
Improve overall customer experience
To provide better service
To increase the profitability of the organisation [2].

3. Common Problems Faced by the Garment Industry:
As I discussed earlier there are several different problems which have to face garment industry to meet the demand of customers at various stages of manufacturing of fashion goods, starting from sourcing of raw material to the final shipment of goods to the customer. Here I listed some common and major problems which will easily solved by data driven techniques in garment Industries [1].

Environmental & social impact of fashion
Copycats & product counterfeiting
Inefficient supply chain &distribution
Adaption to changing consumer demands
All buzz & no business from fashion shows
Quality control
Production Process optimization
Inventory Management

4. Data Collection and Analysis:
In today’s world all the information, data, network, and programming procedure are available on internet. Some information is confidential which includes mobile details, email address, passwords, photographs, fingerprints, face identification techniques and other personal details which are available on internet, but do not accessed easily by any hacker due to the security and data privacy. To collect the data from various sources skilled manpower and more time is required. This data is collected on the basis of requirement and organising the data is carried out. After organising the data analysis is done. Organizing the data is very important part because after organising the data analysis is carried out and statistical results are marked and created for better understanding. Data collection mainly carried out by from customer end. Like sales record, production related data and information, market trends and it also including some major data related with review of customers, feedback of customer which respect product, feedback of customer in terms of service, negotiation details, buying habit of customer, and some details which includes site of visit, type of information they search, type of product they researched, time of visit, total duration for browsing, category of product and service they want, what are the requirements and demands, query or question asked by the customer on the official site and other various kinds of data. This data will help for marketing team to understand customer better. Collection of various kinds of data and analysis need and wants of customers are understand and problem solution is identified an implemented so, this data helps to reduce decision making risk. These decisions are carried out on the basis of complete and correct information and dataset.

4.1 Types of Data Analysis Techniques
Let’s take a overview on types of techniques of data analysis.

As we seen earlier collecting the useful information is very essential. The data analysis will help to solve the thing based on the information and data. It starts with inspection then cleaning and organising the data, then transforming the data. And then based on the type of data analysis technique data is modified. The technologies enhance decision-making processes and provide competitive advantages. Here I mention updated method of data analysis technique which includes two types [3].

Based on mathematical and statistical approach of data analysis
Based on machine learning and artificial intelligence of data analysis

(Note: These are some basic and important techniques, and the choice of method and technique of data analysis is based on the requirements of the manufacturing industry we cannot define the best technique for the data analysis)

4.1.1 Based on mathematical and statistical approach of data analysis

Descriptive Analysis
Regression Analysis
Dispersion Analysis
Factor Analysis
Time Series

4.1.2 Based on machine learning and artificial intelligence of data analysis

Decision Trees
Neural Networks
Evolutionary Algorithms
Fuzzy logic

In the competitive place of garment industries, meaning this is start adopting new technologies and applications of artificial intelligence in their rooting production process. Artificial intelligence, machine learning, IoT, automations, robotics and many more advanced technologies which plays an important role to reduce the manpower, to save the time, to improve the effectiveness as well as quality of work with shortest lead time. It provide many more different advantages to the manufacturing industries [ 3].

5. Data Analysis and Problem Solving:
In garment industries the main challenge is to meet the customer demands at shortest lead time. So here data driven technologies are comes under the picture. There are two different methods used by the manufacturing industries which are statistical method of analysis or artificial intelligence method of analysing to understand the data effectively and clearly. As we know merchandising and marketing department are one of those departments who take care of all the things related form sourcing of raw material up to the communicating with final customer and providing the goods within the deadlines asked by them. In garment industries there are various departments uses different software and technologies for betterment of their work. Many small scale organizations purchase 5 years subscription and others purchase the software’s subscription for the lifetime use, like ERP, MRP, SAP, and other. This system required some inputs in the form of a data. Here I mentioned department wise basic data through which manufacturing industries work effectively.

5.1 Raw material details
Type of raw material, quantity of raw material, size, and style of raw material, contact details of supplier, shipment details, Raw material testing reports and other things. Category of pro material depends on type of manufacturing system it includes trims and accessories departments, fabrication department, sourcing department, storage, testing department and remaining. So this are the departments who requires huge amount of data, the collect the data and then store in their system so they use different kinds of software to boost up their productivity overall efficiency of work.

5.2 Manufacturing departments
After the raw materials are collected and tested then actual manufacturing process of goods is beings. In manufacturing process of garments there are common departments like fabric store department, pattern making department, designing department, sampling department, cutting department, sewing department, finishing department, maintenance department, and remaining. So these are the main departments which plays and very important role for the manufacturing of goods. Collection of data is based on the type of department for example like the fabric store department required different details like amount of fabric, weight of roll, shade details, CSV details, fabric quantity reports, style number, lot number, all the things are noted in one sheet. So this data helps to understand and the requirements related to the fabric. Similarly cutting, sewing and finishing department required the respective data as per their department and responsibilities respectively. Sampling, pattern making and merchandising departments are comes under those department which required different variety of data Like details of tech pack, emails, photographs, meeting schedule, some confidential data like contact details, details of buyer, forecasted garments, style, designs, brand logos, and many other things. So this various kinds of data is stores department wise by individual team and it uploaded on the cloud type of software they are using in their organization. So this data plays an important role for both customer and manufacturers

5.3 Other departments
After the manufacturing of goods is completed then remaining departments are responsible for shipment of goods with the final customer. This department’s includes quality department, garment stores department, marketing department, retail stores or company outlets and remaining. So, as per the departments various data is required and collected from the respective departments with help of team members. This data is similarly feed into the type of software they are using in their organization.

Finally the third party team or the organizational team of data analysis and trade analysis are comes under the picture of data driven technologies. These people are responsible for the collection organization and analysing of data, understand the need of customers, finding out the problems statement, creation of trades, forecasting, prediction of upcoming trades, and finally provide results to meet market size and demands. So these people are responsible for various kinds of activities. In today’s worlds this task is quite easy because of advanced technologies, tools and various software we are adopting. The various applications are responsible for these kinds of activities. On one click some major details are shared with other person who is directly or indirectly linked with that platform. By browsing on internet, purchase some goods through online, visiting retail stores, e-commerce websites, online shopping applications, online shopping websites, following the groups or profiles. So there are many ways through which we can access the data of people. This data is collected and analysed and store as input data or information. And finally this data is used as a data driven problem solving tool. In this stage various ads, videos, advertisements, offers, details of products and particular suppliers details are pop-up on our windows or mobile screens. These all only done by Artificial Intelligence. This technique automatically analyses, filter, store and recollect the data from cloud system.

But there are different challenges and obstacles are present. Which includes Data storage, data privacy and security, data quality issues, skill gaps, and cultural change within organizations, overlapping the data and many more things.

6. Benefits and Impact of Data-Driven Problem Solving:
After understanding all the methods objectives techniques and many more things related with the garment industries let’s go through various benefits of data driven problem solving in ready-made garment industry

Help to understand the customer
Increase efficiency and productivity
For proper management
At and enhance customer satisfaction through better service
Provide accurate forecasting
Analyse barriers and problem and solve them
Improve business model
Improved practices of manufacturing
Increase profitability of the organization

So there is a very huge scope and opportunities for readymade garment Industries [4] [5].

7. Conclusion:
In conclusion, data-driven problem solving has become increasingly important in the ready-made garment industry. With the use of data and information, organizations are able to make informed and effective decisions to meet customer needs and improve operational efficiency. By collecting and analysing data, companies can understand customer wants and needs, forecast fashion trends, and enhance the overall customer experience. Additionally, data analysis techniques such as descriptive analysis, regression analysis, and artificial intelligence methods provide competitive advantages and increase profitability. Despite challenges such as data storage, privacy, and skill gaps, data-driven problem solving offers numerous benefits for the industry.

8. References:

Somani, V. (2022, March 25). 5 common problems faced by the fashion industry (with solutions!). WFX. https://www.worldfashionexchange.com/blog/5-common-problems-faced-by-the-fashion-industry-with-solutions/(Assessed on 7 July, 2023)
Nieuwenburg, P. (n.d.). Problem solving with a data-driven approach. Digital Government. https://www.nldigitalgovernment.nl/overview/new-technologies-data-and-ethics/data-agenda-government/problem-solving-with-a-data-driven-approach/(Assessed on 6 July, 2023)
Types of Data Analysis Techniques | Methods of Data Analysis Techniques. (2019, October 7). EDUCBA. https://www.educba.com/types-of-data-analysis-techniques/(Assessed on 7 July, 2023)
The problem-solving process: A modern, data-driven approach. (n.d.). https://traccsolution.com/blog/problem-solving-process/(Assessed on 6 July, 2023)
Calzon, Bernardita. “Learn Here Different Ways of Data Analysis Methods & Techniques.” BI Blog | Data Visualization & Analytics Blog | Datapine, 3 Mar. 2023, www.datapine.com/blog/data-analysis-methods-and-techniques/. (Assessed on 8 July, 2023)

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Mazharul Islam Kiron

Founder & Editor of Textile Learner. He is a Textile Consultant, Blogger & Entrepreneur. He is working as a textile consultant in several local and international companies. He is also a contributor of Wikipedia.