V P Anjana Prabhu, Post Graduate Academic Scholar, Department of Fashion Management Studies, National Institute of Fashion Technology, Daman.
Dr. Rahul Kushwaha, Assistant Professor, Department of Fashion Management Studies, National Institute of Fashion Technology, Daman.
Abstract
This study compares digital and manual status tracking techniques, emphasizing how each is used in a buying house’s merchandising department. Based on an internship at Winsum Brands Private Ltd., a tech-driven fashion manufacturing platform that provides end-to-end solutions from design to delivery, the study investigates the effects of each tracking strategy on the garment supply chain’s accuracy, efficiency, and visibility. The accessibility and flexibility of manual tracking methods—which depend on spreadsheets, paperwork, and direct communication—are evaluated, but they are found to be constrained by inefficiencies, human error, and limited real-time visibility. Digital tracking systems, on the other hand, offer scalability, automation, improved transparency, and real-time information, facilitating quicker and more accurate decision-making. According to the findings, digital solutions are becoming more and more necessary for contemporary buying houses looking to streamline operations, lower errors, and satisfy the expectations of a dynamic and competitive market environment, even while manual methods may be more appropriate for smaller businesses or low-volume orders.
Introduction
The study was done at Winsum Brands Private Limited in Bangalore, a Buying House, which specializes in sourcing, supplier assessment, and supply chain management, serves as a vital middleman between manufacturers and retailers. It determines the needs of the client, bargains with suppliers, manages product development, and guarantees timely delivery and quality compliance. As a sourcing agency, it streamlines stakeholder communication by managing logistics, quality assurance, and vendor relationships. It is a vital partner in the fast-paced clothing sector because of its proficiency in procurement and operational effectiveness. Buyer is an individual or organization who is involved in acquiring good and services on the behalf of the company. The buyer makes purchasing decisions for the company or retailers. They evaluate and select items to stock in the store for sale or for product development. The buyer is also responsible for maintaining store records and forecasting customer demand. Buying houses serve as vital intermediaries connecting international buyers and manufacturers like Winsum Brand Private Ltd., streamlining order execution through cost-effective sourcing, price negotiation, and quality assurance. By managing logistics, compliance, and supplier matching, they reduce operational burdens and production timelines. Their rapid growth strengthens Bangladesh’s economy by channelling export orders to factories, creating jobs in technical and quality control roles, and enhancing global market access. This ecosystem supports Bangladesh’s ambition to achieve $100 billion in RMG exports while bridging cultural and logistical gaps for international buyers and empowering small factories through global connectivity. “Merchandising” refers to professionals managing export-focused garment trade operations, derived from “merchandise” (goods bought/sold). It involves procuring raw materials, producing quality garments, and ensuring timely exports. This role demands technical expertise in production oversight, quality control, and logistics, requiring broad trade knowledge and coordination skills.
Literature Review
Ashley Taylor(2024), H&M also adopted just-in-time inventory to boost its ability to adapt to rapidly evolving fashion trends and streamline operations. H&M is able to meet customer demand throughout its worldwide network of shops and online platforms while maintaining a lean inventory by utilizing retail automation and smart data analytics. To control its inventory levels, H&M combines data-driven forecasting with automated solutions. In order to make prompt, well-informed judgments regarding new launches and restocking, the brand constantly gathers data on consumer preferences, sales trends, and worldwide trends. As a result, H&M can decrease expenses, get rid of extra inventory, and prevent overproduction.
Ashley(2023) Zara’s implementation of Just-in-Time (JIT) systems minimizes excess inventory and markdowns by combining real-time data analytics with vertical integration, which controls design, manufacturing, and distribution, to manufacture small batches in line with demand. New designs can arrive in stores within weeks thanks to advanced IT systems that facilitate quick communication between manufacturing and stores, such as consumption tracking and handheld devices. Agile supply chain management, technology integration, and customer-centric response are critical success elements that shorten lead times and boost fast fashion competitiveness.
Methodology
This study adopts a quantitative research approach, utilizing a structured Google Form questionnaire to collect primary data from participants working in the apparel industry. The survey is designed to compare manual and digital status tracking methods by gathering responses on various aspects such as time efficiency, data retrieval ease, and perceived reliability. Data collected through the form will be systematically analyzed using descriptive and inferential statistical techniques to draw meaningful comparisons between the two tracking methods.
Objectives
To analysis Customize manual data retrieval processes for each department or role.
To examine Leverage staff experience to improve manual data retrieval.
To analysis manual tracking tools based on specific departmental needs.
To Integrate manual and digital workflows for faster data processing.
Research Design
A comparative cross-sectional research design is employed to assess and contrast the effectiveness of manual versus digital status tracking methods at a single point in time. The survey instrument comprises both closed-ended and Likert-scale questions to ensure standardized data collection, enabling a direct comparison of respondents’ experiences and perceptions regarding each method.
Sampling Design (Probability Sample)
The study utilizes a probability sampling technique, specifically simple random sampling, to ensure that every individual working in the apparel industry has an equal chance of being selected for participation. This approach enhances the representativeness of the sample and allows for generalization of the findings to the broader population within the industry.
Data Analysis & Interpretation
Pearson’s Correlations-1
H0: There is no significant relation between Easiness to retrieve data (manual tracking) and Department/Role.
H1: There is significant relation between Easiness to retrieve data (manual tracking) and Department/Role.
Interpretation
The Pearson’s correlation results indicate a significant positive relationship between Department/Role and the easiness to retrieve data through manual tracking. The correlation coefficient (r = 0.374) suggests that as Department/Role changes, the easiness to retrieve data manually also increases or decreases in a moderately positive manner. The p-value (0.017) is less than the typical significance threshold (0.05), indicating that the observed correlation is statistically significant.
Pearson’s Correlations-2
H0: There is no significant relation between Easiness to retrieve data (manual tracking) and years of experience.
H1: There is significant relation between Easiness to retrieve data (manual tracking) and years of experience.
Interpretation
The Pearson’s correlation results indicate a significant positive relationship between Years of Experience and the easiness to retrieve data through manual tracking. The correlation coefficient (r = 0.328) suggests that as Years of Experience increase, the easiness to retrieve data manually also increases in a moderately positive manner. The p-value (0.039) is less than the typical significance threshold (0.05), indicating that the observed correlation is statistically significant.
Pearson’s Correlation-3
H0: There is no significant relation between Easiness to retrieve data (manual tracking) and frequency of manual methods.
H1: There is significant relation between Easiness to retrieve data (manual tracking) and frequency of manual methods.
Interpretation
The Pearson’s correlation results indicate significant positive relationships between the Frequency of manual methods and easiness to retrieve data (manual tracking). r = 0.524,
indicating a strong positive relationship between the frequency of manual methods and the easiness of retrieving data through manual tracking.p-value < 0.001, indicating that the observed correlation is highly statistically significant.
Pearson’s Correlation-4
H0: There is no significant relation between frequency of manual methods and the quickness in digital works.
H1: There is significant relation between frequency of manual methods and the quickness in digital works.
Interpretation
The Pearson’s correlation results indicate significant positive relationships between frequency of manual methods and the quickness in digital works. r = 0.487, indicating a moderate positive relationship between the frequency of manual methods and the speed of digital systems.p-value = 0.001, indicating that the observed correlation is highly statistically significant.
Analysis of variance
H0: There is no difference between Tools used for manual tracking and the Department/Role
H1: There is difference between Tools used for manual tracking and the Department/Role
Interpretation
The ANOVA results examine the relationship between Department/Role and the Tools used for manual tracking.F-statistic: F = 5.237, indicating a significant effect. p-value: p = 0.028, which is less than the typical significance threshold (0.05), indicating statistical significance. Degrees of Freedom: df = 1 (between groups) and 38 (residuals).There is a statistically significant difference in the tools used for manual tracking across different departments or roles (p = 0.028). Department or role differences significantly explain some of the variation in tools used for manual tracking.
Findings & Recommendation
A significant positive relationship exists between an individual’s department/role and their perceived ease of retrieving data through manual tracking (r = 0.374, p = 0.017). Years of experience positively correlate with the ease of manual data retrieval (r = 0.328, p = 0.039). A strong positive correlation is present between the frequency of using manual methods and the perceived ease of data retrieval via manual tracking (r = 0.524, p < 0.001). The frequency of using manual methods shows a moderate positive relationship with the perceived quickness of digital systems (r = 0.487, p = 0.001). There is a statistically significant difference in the tools used for manual tracking across different departments or roles (F = 5.237, p = 0.028). Digital Solutions are Needed: Encourage contemporary buying houses to adopt digital solutions to streamline operations, reduce errors, and satisfy market demands. Training Programs: Provide training programs for employees to improve their skills in digital status tracking methods. Systematic Data Analysis: Implement systematic data analysis to compare the effectiveness of manual versus digital status tracking methods.
Conclusion
This study highlights that while manual status tracking methods are still widely used and can be effective with experienced staff, they have inherent limitations such as inefficiency, human error, and lack of real-time visibility. Digital tracking methods, on the other hand, offer greater accuracy, speed, and transparency, enabling faster decision-making and improved supply chain management. The findings suggest that adopting digital solutions is essential for buying houses in the apparel industry to remain competitive, streamline operations, and meet dynamic market demands. However, manual methods may still be suitable for smaller operations or low-volume tasks. Overall, integrating digital tracking systems will enhance efficiency and accuracy, driving better performance in the apparel supply chain.
References
Sabbir Hussain, Daffodil internation University Dhaka, Bangladesh (2012)https://www.academia.edu/31281810/STUDY_ON_GARMENTS_BUYING_HOUSE_MERCHANDISING?email_work_card=thumbnail
Gereffi, G., &Memedovic, O. (2003). The global apparel value chain: What prospects for upgrading by developing countries? Retrieved from https://www.unido.org/uploads/tx_templavoila/Global_apparel_value_ch ain.pdf on 10/ 11/ 2022
Ashlet Taylor, Product Manger in Clieverence has Published on 30 April 2025 Retail Automation Success Stories: How Zara and H&M Master Just-in-Time Inventory https://www.cleverence.com/articles/business-blogs/retail-automation-success-stories-how-zara-and-handm-master-just-in-time-inventory/#:~:text=With%20Zara’s%20JIT%20strategy%2C%20products,of%20just%20a%20few%20weeks.
