Erameha and Odoh (2021) noted that manual inventory management is still prevalent
in many businesses and presents challenges in effectively monitoring inventories. To
address this issue, they designed and implemented a web-based inventory control
system using a small medium enterprise (SME) as a case study. The system was
designed with tracking methods such as QR codes and barcodes to monitor products
from point of purchase to point of sale, between stores, and directly to customers. The
system provided precise up-to-date information on inventory levels to the corporate
office and allowed authorized users to access the system to perform different functions.
The authors found that the inventory control system was a tremendous boost to store
managers and the corporate office, as it helped them efficiently monitor transactions
across multiple stores, locate items that should be ordered first, and spend less time on
inventory management functions. These benefits ultimately led to improved customer
service and satisfaction, resulting in increased revenue for the company.
CITATIONS: Erameha, K. B., & Odoh, B. I. (2021). Design and Implementation of a
Web-Based Inventory Control System Using a Small Medium Enterprise (SME) as
a Case Study. NIPES Journal of Science and Technology Research, 3(3), 211-219.
The study by Amora et al. (2021) presents the Digital Attendance and Accomplishment
Report Monitoring System (DigiAtt), which is designed to address the limitations of the
current online attendance system using Google Forms for recording daily time records
(DTR) and accomplishment reports. The authors highlight the difficulty of tracking DTR
and accomplishment reports with the current system, which records separate responses
daily. To overcome this challenge, DigiAtt features a mobile application that allows
employees and supervisors to conveniently submit both DTR and accomplishment
reports before logging out, facilitating better monitoring of employee performance.
Additionally, DigiAtt includes a web application with a dashboard for easy monitoring
and printing of reports needed by the administration for work from home arrangements.
The researchers employed the Agile: Scrum methodology of software engineering to
break user stories/tasks according to priority, conduct daily standup meetings, and
communicate consistently with stakeholders until a fully shippable product was
delivered. The authors conclude that the convenience provided by DigiAtt in time-in and
time-out processes, submission of accomplishment reports, attendance monitoring, and
report generation and printing can increase the productivity of office workers, reduce
stress and burden on individuals, and fill in the gaps of the current online attendance
system.
CITATIONS: AMORA, E. N. O., ROMERO, K. V., AMOGUIS, R. C., BERNALES, A. M.
J., & ROMERO, P. J. B. (2021). Digital attendance and accomplishment report
monitoring system (digiatt). Ioer International Multidisciplinary Research
Journal, 3(2), 123-133.
The problem of customer acquisition and retention in retail marketing has been
extensively studied, with various soft techniques proposed, but they often lack the ability
to achieve higher performance and require a strategic approach. Data mining
techniques have emerged as a promising solution for addressing these limitations and
improving retail marketing performance. As Kumar, Venkatesh, and Rahman (2021)
stated in their study on data mining and machine learning in retail business, "the
application of data mining techniques has great impact in the development of retail
marketing."
In their study, they presented a novel customer interest prediction algorithm that uses
Multi Variant K-means clustering and pattern mining techniques to support E-Commerce
systems. The algorithm identifies purchase histories and enquires of various users and
clusters the logs using the Multi Variant K-means clustering algorithm. The method then
identifies the list of purchase patterns and determines the concrete interest of the user
and similar interested users. Based on this, a set of recommendations is generated for
the user to improve customer retention rates. As the authors note, "by identifying the
user interest according to their purchase pattern and by generating the
recommendations based on the logs of similar interested users, the method supports
the customer retention in higher ratio" (Kumar et al., 2021, p. 4).
This study highlights the importance of using data mining techniques in retail marketing
to improve customer retention and ultimately, business performance. By utilizing these
techniques, businesses can gain insights into customer behavior and preferences, and
tailor their marketing strategies accordingly.
Kumar, M. R., Venkatesh, J., & Rahman, A. M. Z. (2021). Data mining and machine
learning in retail business: developing efficiencies for better customer
retention. Journal of Ambient Intelligence and Humanized Computing, 1-13.
The development of a web-based system for automatic content retrieval database as
presented by Korotun, Vakaliuk, and Oleshko (2020) is relevant to the topic of a webbased management system for Nerissa's Grocery Store utilizing data mining and QR
code technology. In both cases, the development of a web-based system is necessary
to manage and retrieve data efficiently. Additionally, the use of algorithms and patterns
to process and analyze data is essential to ensure that the system functions effectively.
The implementation of the Template Method architectural pattern in the automatic
content filling system can also be applied in the development of a web-based
management system for Nerissa's Grocery Store to ensure efficient data processing
and management. While the focus of the web-based automatic content retrieval
database is different from that of the proposed web-based management system for
Nerissa's Grocery Store, the principles and techniques presented in the former can be
applied to the latter to improve its functionality and efficiency.
CITATIONS: Korotun, O., Vakaliuk, T., & Oleshko, V. (2020). Development of a web-based
system of automatic content retrieval database. Available at SSRN 3719834.
FOREIGN STUDY
This review of literature discusses several studies that are relevant to the development
of a web-based management system for Nerissa's Grocery Store utilizing data mining
and QR code technology.
First, the study by Erameha and Odoh (2021) presents a web-based inventory control
system that utilizes QR codes and barcodes to monitor products from purchase to sale,
allowing for precise and up-to-date information on inventory levels. This study highlights
the importance of efficient inventory management in improving customer service and
satisfaction, leading to increased revenue for the business.
Second, Amora et al. (2021) designed a Digital Attendance and Accomplishment
Report Monitoring System (DigiAtt) to address the limitations of the current online
attendance system, allowing for convenient submission of daily time records and
accomplishment reports. This study emphasizes the importance of convenient and
efficient systems in improving productivity and reducing stress for office workers.
Third, Kumar et al. (2021) presented a customer interest prediction algorithm that
utilizes data mining techniques to improve customer retention rates in e-commerce
systems. This study highlights the importance of utilizing data mining techniques to gain
insights into customer behavior and preferences, allowing for tailored marketing
strategies and ultimately, improved business performance.
Finally, Korotun, Vakaliuk, and Oleshko (2020) developed a web-based system for
automatic content retrieval database utilizing algorithms and patterns for efficient data
processing and management. This study emphasizes the importance of efficient data
management in developing effective web-based systems.
Overall, these studies provide valuable insights and techniques that can be applied in
the development of a web-based management system for Nerissa's Grocery Store
utilizing data mining and QR code technology. By utilizing efficient inventory
management, convenient and efficient systems, and data mining techniques,
businesses can gain insights into customer behavior and preferences and tailor their
marketing strategies accordingly, ultimately leading to improved business performance.