report

1. SMART INVENTORY MANAGEMENT SYSTEM FOR
HOUSEHOLDS USING IOT
DISSERTATION PROJECT
DISSERTATION REPORT
BY GAUTAM CHANDRA

2. TABLE OF CONTENT
I. Introduction
II. Objective of Study
III. Need of Study
IV. Literature Review
V. Research Gap
VI. Research Methodology
VII. Research Model
VIII. Data Analysis
IX. Conclusion
X. Limitations and Future Decisions

3. I. INTRODUCTION
I. A Smart Inventory Management System (SIMS) is a cutting-edge cost-control tactic to satisfy rising
demand with fewer inventory. Planning and purchasing groceries will be made easier and more
effective with the help of this IoT-based solution. Users will be able to see how much inventory they
have left, and if there is a shortage, it will also automatically place orders for extra items. Through the
SIMS app, users can either manually order anything online, or ask their virtual assistant to order when
the inventory depletes till a particulate level and it will be ordered and delivered to their door.
II. The concept behind smart inventory management for grocery shopping at home is to use the capacity
of interconnected devices and sensors to automate the tracking and management of ordinary
commodities. By constantly monitoring inventory levels, this system provides comfort and simplicity of
use while avoiding shortages and waste. When stock levels are low or products are approaching
expiration dates, the system can be configured to send warnings and messages via an application,
ensuring that stock levels are replenished as soon as possible. Finally, the goal of this technology is to
optimize and streamline the management of household inventories, allowing homeowners to focus on
other important obligations.

4. II. OBJECTIVE OF STUDY
I. The goal of a research is to primarily focus on the adoption of the technology by customers or buyers
might be to see how consumers accept and use the technology. The research might seek to discover
characteristics influencing consumer acceptance and usage of technology, such as simplicity of use,
perceived utility, and confidence in technology. This data might be utilized to improve the technology's
design and execution in order to better match the demands and preferences of users.
II. In general, the study just might offer insightful information about how consumers are using the
technology and what aspects affect their adoption and use. To better match the wants and preferences
of customers, the design and use of the technology may be improved with the help of this information.

5. III. NEED FOR STUDY
I. A study on smart inventory management for household groceries using IoT is needed to create new
techniques and technology suited to managing household inventories.
II. Research into IoT-enabled smart inventory management for household groceries is essential for
successful household administration and ensuring people's safety during disasters.
III. IoT can increase efficiency, cut waste, and free up time, helping to reduce greenhouse gas emissions
and preserve resources.

6. IV. LITERATURE REVIEW
I. This paper aims to highlight the effects of IoT technologies on inventory management in supply chains and
conducts a thorough investigation to identify the research gap of applying IoT to inventory management 1. The
trend and potential opportunities of applying IoT to inventory management in the industry 4.0 era are explored by
analysing the literature. The results demonstrate that research on this topic is expanding in a number of industries.
The article comes to the following conclusions: It is advantageous to transform a supply chain into an integrated
supply chain 4.0, and standard inventory replenishment methods appear to be insufficiently responsive to new
technologies and unable to deal with IoT systems effectively. (Yasaman Mashayekhy, 2022)
II. This study details how ordinary freezer were modified to keep track of the contents by using machine learning.
The fridge can sense its contents, keep an eye on them, and warn the user when certain items are running low.
The authors of the paper claim to have created a system that, when used with a Smart Refrigerator system, can
turn any existing refrigerator into an intelligent device. This solution offers a simple "plug and play" way to add
smart features to conventional refrigerators without having to purchase a new one. On a Raspberry Pi Model 3B
with a webcam installed, the detection algorithms operate. This was mostly made possible by recent
developments in object detection algorithms like YOLO v3 and Tiny YOLO, which enable object identification
on tiny processors like the Raspberry Pi. (Debarghya Saha, 2020)

7. I. This paper described the solution in the study uses the Internet of Things (IoT) to provide smart grocery level
management. Sensor-equipped smart containers are used to gather information about the level of groceries inside
of them. With the assistance of the wireless protocol, this data is appropriately stored in the cloud platform. The
system's inspiration comes from the potential use of IoT for home automation. (K. Sakthisudhan, 2019)
II. For real-time, passive RFID tag-based Stock Keeping Units (SKU) tracking and predictive stock analysis, the
research suggests an IoT-cloud architecture. The SKUs are equipped with RFID tags, which are read upon entry
and leave from the warehouse. Real-time data is then transmitted through the internet to a cloud server. Following
data acquisition, an ML-based software engine processes the data using techniques like classification, training,
and testing. For the SKUs in the warehouse, the authors took into account ABC Inventory Classification, and for
the predictive analysis of SKUs in inventory, they used three ML algorithms: Support Vector Machine (SVM), K-
nearest Neighbours (KNN), and Bayes. The outcome showed that SVM is superior, with an accuracy rate of
84.8%. (Mishra & Mohapatro, 2020)

8. V. RESEARCH GAP
The lack of research on the installation and use of IoT technologies in the process of inventory management
may be the research gap for applying IoT to this discipline. There may be little research on the precise
application of IoT systems in different industries and supply chains, despite the literature's suggestion that
IoT technologies have the potential to revolutionize inventory management. Further research into the
efficiency and viability of various ML algorithms for predictive stock analysis and real-time SKU tracking in
warehouse inventory management may also be necessary. As well as analyzing the advantages and
restrictions of employing ML algorithms for predictive analysis, additional research might examine the real-
world ramifications and potential difficulties of incorporating IoT technology into the inventory
management process.

9. VI. RESEARCH METHDOLOGY
The descriptive research method is utilized in this study to examine the characteristics of a group of individuals, as
well as their perceptions about the use of online platforms for purchasing items and its positive/negative influence
on teenagers. The study is of a qualitative . This study included both primary and secondary data sources. While
collecting data, a sample survey approach was employed via digital methods via Google forms. Data analysis is
largely statistical with data cleaning methods like Pivot Tables, Rapid were utilized. Although the nature of the study
is largely direct data collecting, some secondary data has been utilized. The output is displayed directly from the
Google form, with many figures, one for each question asked.

10. Data Collection: A structured questionnaire will be used to gather the majority of the study's data. The purpose of the
questionnaire is to gather data on the advantages, drawbacks, and general efficacy of the IOT powered smart
inventory management system. A sample of retail businesses that have used the system will receive the questionnaire.
Sampling: A non-probability-based selection method will be used for this research. Consumer data was gathered to
learn more about how the system might affect customers' shopping experiences and buying decisions. The study aims
to assess the system's performance in automating inventory management, enhancing stock management precision,
decreasing stock counting errors, and reducing costs associated with human inventory monitoring by gathering data
from customers who have used the system.

11. VII. RESEARCH MODEL
I. H0: There is no Significant Relationship Between Independent Variables and The Adoption of Smart Inventory
Management for Households Using IoT.
II. H1: There is a Significant Relationship Between Independent Variables and The Adoption of Smart Inventory
Management for Households Using IoT.

12. VIII. DATA ANALYSIS
Tools used for analysis
I. Rapid Miner
II. Microsoft Excel
III. SPSS (Statistical Package for the Social Sciences)
Algorithms used for the analysis
I. Naïve Bayes
II. Deep Learning
III. Random Forest
IV. Correlation
V. Pivot Tables

13. A. CORRELATION

14. B. PREDICTIVE ANALYSIS
I. Naives Bayes
II. Deep Learning
Note - Remaining analysis is mentioned in the report

15. X. CONCLUSION
It is a creative solution that can assist homeowners in automating and improving their inventory management
procedure. This technology offers real-time monitoring of inventory levels, expiration dates, and usage trends
through the use of linked devices and sensors, leading to better planning, decreased waste, and increased
convenience. The research also highlighted some difficulties with system adoption, including worries about data
protection and the necessity for businesses to weigh the advantages of automation with a pinch of human
intervention The IoT powered Smart inventories management system, in conclusion, has the potential to have a
substantial impact on the retail sector by providing advantages including real-time inventory updates, customised
recommendations, and smooth shopping experiences. To preserve client privacy and ensure that the technology is
properly integrated into current operations, organisations must carefully weigh the possible risks and advantages of
adoption.

16. XI. LIMITATIONS AND FUTURE DIRECTION
Limitations -
The expense of installing the technology is one of the drawbacks of employing IoT for smart inventory management
of household groceries. Some households may not be able to afford the initial outlay needed to buy the hardware
and software components. Additionally, the system can be less efficient in households where consumption habits
are highly variable or inventory changes often.
Future Direction –
IoT-based smart inventory management for household groceries has a number of potential future avenues. The
incorporation of artificial intelligence and machine learning algorithms to increase the precision and effectiveness of
inventory management is one possible area for growth. The system might also be developed to incorporate other
household necessities besides groceries, like medicines, cleaning supplies, and personal care goods. Finally,
improvements in IoT technology may result in the creation of more accessible and affordable solutions, expanding
the availability of this technology to homes of all economic levels.