BESTLINK COLLEGE OF THE PHILIPPINES

RESEARCH PAPER

Enhancing School Resource Management with AI in Books and Uniform

Inventory at Bestlink College of the Philippines

CHAPTER 1: THE PROBLEM AND ITS BACKGROUND

INTRODUCTION

In recent years, educational institutions face increasing pressures to manage their resources
efficiently. Effective management of books and uniforms is critical to the success of
educational programs, impacting not only operational efficiency but also student satisfaction.
This research investigates how artificial intelligence (AI) can enhance the management of
these essential resources in schools, specifically focusing on Bestlink College of the
Philippines. By examining the existing challenges and potential AI solutions, this study aims to
provide a comprehensive understanding of how technology can transform resource
management in educational settings.

Bestlink College of the Philippines, like many educational institutions, relies on a manual
system for managing its book and uniform inventories. This system can lead to various issues,
including inaccurate stock levels, loss of items, and inefficient tracking of resource usage. As
the college continues to grow, these challenges become more pronounced, affecting both
staff productivity and student satisfaction.

Recent advancements in AI technologies offer promising solutions to these challenges. AI can

automate inventory management processes, optimize resource allocation, and provide real-
time data analytics to support decision-making. By integrating AI into the inventory systems
for books and uniforms, educational institutions can enhance their resource management
strategies, leading to improved efficiency and effectiveness.
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THEORITICAL FRAMEWORK
This study is grounded in two primary theories: Resource-Based Theory (RBT) and Automation
Theory.

Resource-Based Theory (RBT) suggests that organizations can achieve a competitive

advantage by efficiently managing their resources. In the context of Bestlink College, the
effective management of books and uniforms, essential resources for students, can enhance
the institution's operational efficiency (Barney, 1991).

Automation Theory posits that the implementation of automated systems, such as AI in

inventory management, can streamline processes, reduce human error, and enhance
productivity (Groover, 2008). This theory supports the idea that AI technologies will address
inefficiencies, real-time data shortages, and staff limitations in managing resources.

These theories provide the foundation for understanding how AI can be effectively integrated
into resource management systems to improve outcomes.

Citations:

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of

Management, 17(1), 99-120.

Groover, M. P. (2008). Automation, Production Systems, and Computer-Integrated

Manufacturing. Pearson.
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CONCEPTUAL FRAMEWORK

INPUT
Challenges in Resource Management

- Inefficient inventory management

- Lack of Real-Time Data

- Increase Operational Costs

- Staff shortages and long wait times

PROCESS
Theoritical Exploration of AI Solutions
- Predictive Analysis
- Real-Time Data Tracking
- Automated Ordering Systems

OUTPUT
Theoritical Implications for Resource Management Practices
- Enhanced Inventory Accuracy
- Reduced Operational Costs
- Faster Processing Times
- Better Decision-Making

OUTCOME
- Increased Operational Efficiency
- Higher Student Satisfaction
- Streamlined Staff Processes
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RESEARCH PAPER
The conceptual framework provides a structured approach to understanding how the
integration of artificial intelligence (AI) can improve the management of resources
(books and uniforms) in educational institutions. It outlines the key components of the
research and their interconnections, emphasizing the theoretical exploration rather
than the development of a system.

1. Input
The Input section identifies the challenges faced in resource management at Bestlink College.
These challenges include:

Inefficient Inventory Management: Traditional methods may lead to inaccuracies, resulting in

either overstock or shortages of books and uniforms.

Lack of Real-Time Data: Without immediate insights into inventory levels, it becomes difficult
for the institution to make informed decisions about restocking and resource allocation.

Increased Operational Costs: Inefficient management practices can lead to higher costs related
to excess inventory and lost opportunities due to stockouts.

Staff Shortages and Long Wait Times: Insufficient staff in the resource store can lead to long
lines for students, causing frustration and delays in obtaining necessary materials.

2. Process
The Process section explores theoretical AI solutions that can address the challenges identified
in the input stage. This does not involve system development but rather examines how AI
technologies could be applied:

Predictive Analytics: This involves using historical data to forecast future inventory needs,
allowing for more accurate restocking.

Real-Time Data Tracking: Implementing systems that provide immediate insights into inventory
levels, enabling quick adjustments to meet student needs.

Automated Ordering Systems: Theoretical exploration of systems that could streamline the
ordering process, reducing manual workload and errors.

3. Output
The Output section focuses on the theoretical implications of applying AI solutions to improve
resource management practices. These implications include:
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Enhanced Inventory Accuracy: Improved data tracking could lead to a more accurate reflection
of available resources.

Reduced Operational Costs: More efficient management practices could lower costs associated
with overstocking and shortages.

Faster Processing Times: Automation and real-time data could speed up the purchasing process
for students.

Better Decision-Making: Access to accurate and timely data would enable staff and
administrators to make informed decisions regarding resource management.

4. Outcomes
The Outcomes section highlights the long-term benefits that could arise from implementing the
theoretical AI solutions:

Increased Operational Efficiency: Overall improvement in the functioning of resource

management systems within the institution.

Higher Student Satisfaction: Enhanced access to books and uniforms could lead to a better
experience for students, positively impacting their academic performance.

Streamlined Staff Processes: Reducing the burden on staff members, allowing them to focus on
more critical educational tasks rather than manual inventory management.

Conclusion
This conceptual framework illustrates how addressing the challenges in resource management
with AI solutions can lead to improved practices and positive outcomes at Bestlink College of the
Philippines. By understanding these relationships, your research can provide valuable insights
into the potential benefits of integrating AI into educational resource management, paving the
way for more effective and efficient practices.
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STATEMENT OF THE PROBLEM

Despite the advancements in technology, educational institutions continue to rely on
outdated methods for resource management. The problems identified in managing books and
uniforms include:

Inefficient Inventory Management: Traditional systems often result in inaccuracies,

leading to overstock or shortages of resources.

Lack of Real-Time Data: Without real-time insights into inventory levels, institutions
struggle to make informed decisions about restocking and resource allocation.

Time-Consuming Processes: Manual tracking of resources is labor-intensive, diverting

attention from more critical educational tasks.

Increased Operational Costs: Inefficient inventory management can lead to increased costs
associated with excess inventory and lost opportunities due to stockouts.

Staff Shortages and Long Wait Times: A lack of sufficient staff in the resource store leads
to long lines for students when purchasing books and uniforms. This not only inconveniences
students but also exacerbates the inefficiencies in the management process.

HYPOTHESIS

Inefficient Inventory Management: AI-driven inventory systems can reduce inaccuracies in

stock levels and optimize resource allocation, leading to more efficient management of books
and uniforms.

Lack of Real-Time Data: Integrating AI technologies into inventory management will provide
real-time data, enabling timely restocking and improved decision-making for resource
allocation.

Time-Consuming Processes: AI-based automation will streamline resource tracking and

management processes, significantly reducing the time spent on manual tasks by staff.

Increased Operational Costs: Implementing AI in inventory management will reduce

operational costs by minimizing overstock, stockouts, and inefficient use of resources.

Staff Shortages and Long Wait Times: AI can help address staff shortages by automating
inventory processes, leading to reduced wait times for students when purchasing books and
uniforms.
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RESEARCH PAPER

SCOPE AND LIMITATIONS

Scope
This research focuses on the management of books and uniforms at Bestlink College of the
Philippines, exploring how artificial intelligence (AI) can improve inventory management
practices. The study will target students and staff directly involved in managing and utilizing
these resources. It will examine specific AI technologies such as predictive analytics and real-
time data tracking to address key challenges, including inefficiencies, lack of real-time data,
operational costs, and staff shortages leading to long wait times for students. Data will be
gathered through surveys, interviews, and case studies from relevant stakeholders.

Limitations
The research is limited to the management of books and uniforms and does not cover other
types of resources such as equipment or facilities. It also does not involve the development or
implementation of an AI system but will focus on theoretical insights and recommendations.
The study is specific to Bestlink College of the Philippines and may not reflect the experiences
of other institutions. Additionally, it may not capture the perspectives of all stakeholders, such
as external suppliers or partners involved in resource management.

SIGNIFICANCE OF THE STUDY

This research holds significant importance for several stakeholders:

Bestlink College of the Philippines: The findings will provide valuable insights into how
artificial intelligence (AI) can enhance resource management, improving operational efficiency
and ensuring that students and staff have timely access to essential materials.

Students: As primary users of books and uniforms, students will benefit from improved
inventory management through greater accessibility and reduced wait times, leading to
enhanced academic experiences and satisfaction.

Staff and Administrators: The study will help staff and administrators recognize the
advantages of AI-driven solutions, potentially reducing their workload, minimizing errors, and
streamlining processes involved in managing resources.

Future Researchers: This study will serve as a foundational reference for further research on
AI applications in educational resource management, paving the way for future innovations
and enhancements in this area.
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DEFINITION OF TERMS

Real-Time Data: Real-time data is information that is processed immediately, allowing

institutions to monitor stock levels and make timely decisions regarding resource allocation.

Predictive Analytics: Predictive analytics uses statistical methods to forecast future

outcomes based on historical data, helping institutions optimize inventory levels and resource
use.

Operational Efficiency: Operational efficiency refers to delivering services cost-effectively

without compromising quality, which can enhance student satisfaction in resource
management.

Machine Learning (ML): A subset of AI where systems learn from data patterns and improve
their performance over time without explicit programming. In inventory management, ML can
predict trends and optimize stock levels based on historical usage.

Algorithmic Optimization: A process in AI where algorithms are designed to make the most
efficient decisions, often by analyzing vast amounts of data to find optimal solutions for
inventory management challenges like stocking and resource allocation.

Natural Language Processing (NLP): A field of AI that enables machines to understand and
interpret human language. NLP can be used in customer service applications within resource
management, such as AI-powered chatbots for student inquiries regarding book or uniform
availability.

Robotic Process Automation (RPA): A form of automation where software robots mimic
human actions to perform repetitive tasks, like tracking inventory, placing orders, and
managing stock levels without manual intervention.

Cognitive Computing: An advanced form of AI that simulates human thought processes to

solve complex problems. In inventory management, it can help in decision-making by
analyzing multiple factors like demand fluctuations and supplier reliability.

Big Data Analytics: The process of examining large and varied data sets to uncover patterns,
correlations, and insights. In the context of this study, big data analytics can help optimize
inventory control by providing deep insights into usage patterns and forecasting future needs.

Supply Chain Automation: The use of technology to automate the flow of goods from
suppliers to the end-users, minimizing human intervention in processes like ordering, tracking,
and replenishing stock.

Data-Driven Decision Making: The practice of basing decisions on the analysis of data
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RESEARCH PAPER
rather than intuition. In resource management, AI can facilitate data-driven decisions by
providing accurate insights on inventory status and needs.

Neural Networks: A model in AI that mimics the human brain’s structure to process data. In
inventory management, neural networks can be used to predict complex patterns such as
seasonal demand variations or supply disruptions.
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