Inventory Management System with Sales Prediction

Inventory Management System with Sales Prediction

The retail sector has widely adapted different inventory management applications and some retail chains even employ prediction software to analyze future sales. However, a lot of day-to-day shopping in India happens through local shops. The owners of such mom-and-pop shops do not necessarily have the capital to invest in proprietary applications for setting up an inventory management system. Needless to say that same is the case for any sales prediction software. As a result, many of the shopkeepers end up hoarding a lot of irrelevant and nonprofitable products that lead to financial losses. A very cost-effective and accessible solution for this problem is a mobile application that provides all the features of a point-of-sale system as well as gives future sales insights. It will enable shopkeepers to manage their current product purchases and invoicing. The predictive sales analysis will help them to modify their investments on products and supplies thereby ensuring maximum profits. If a shop houses relevant products that cater to customer needs, its customer reach will increase. The Economic Times published an article in the May of 2019, which stated that the number of smartphone users in India is expected to rise by 84% to 859 million by 2022 from 468 million in 2017. It is safe to assume that a large population of shop owners will have smartphones in the following years. Hence, equipping the local shopkeepers with a mobile application will prove instrumental since it will give them exposure to all the aforementioned benefits. Inventory Management System with Sales Prediction

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Mobile phones have become a vital part of day-to-day life today. The e-commerce trend has taken businesses online and has proved to be beneficial for them. In a basic e-commerce system, the merchants put their products on display over the website and the customers searching for that product can placean order using the same website. Payment portals do the transactions, and then a delivery service delivers the products to the designated customer. E-commerce websites hold the potential to showcase a wide variety of products at once and therefore, are equally convenient to buyers and sellers. The sellers can generate a report of their sales or product demands either manually or through a data mining software. Even with so much backbone in the e-commerce industry, many shopkeepers in India have chosen to stay completely offline. This scenario raises the need to understand the reason for the
same. A major contributing factor for local shopkeepers to not take their businesses online is the lack of monetary funds and resources. It may seem a personal drawback from afar but if we aim at achieving social development, this problem needs to be addressed, and a viable solution must be found.
Accessing a point-of-sale system via their own mobile phones is definitely one of the most feasible solution to this problem. Android apps are free to download and can provide an equally good user interface like that of a computer-based inventory management system

A seller can manually list down all the products and investment and tally it with to total sales to produce a profit report but that is not to say tedious and monotonous. Through data mining techniques the same results can be achieved more quickly and one can even get a graphical representation for better understand makes the process more engaging. Traditionally to perform any product based analysis, different software is needed to be purchased. A mobile app with an amalgamation of both these trends will make the whole process more convenient.

he author describes demographics as a statistical representation of the characteristics of a population. These are mostly the socio-economic features of an individual. The age, educational level, occupation, income, marital status, the average size of family, etc. are all considered as demographics. In terms of the website, these demographics are extracted from the visitors. In the data analysis process, these demographics are categorized into groups and mapped with their frequent activities on the website. The paper follows a technique where the demographic clusters are combined with corresponding
transaction data clusters to generate input for the data prediction mode. The data mining task is done using the k-mean algorithm. K-means algorithm categories input item-set into ”k” number of clusters based on their similarity. The similarities are calculated using the Euclidean distance method. It is an effective algorithm where large input item-set are available.

To make the android app most compatible the minimum SDK version should be defined as 15 and the target SDK version should be defined as 28. A crucial part of this mobile app is sales prediction and analysis. This can be achieved by employing data mining algorithms on the customer data
collected as well as the temporal data fed in by the shopkeepers. The best-suited algorithm for the same is Regression Analysis.

A good percentage of people in India have access to smartphones and that percentage will greatly increase in the coming 2-3 years. With such favorable circumstances, anAndroid app is ought to flourish and attract a wider customer base over a period of time. Thus it is advantageous to have a mobile application that not only assists with inventory and invoice operations but also helps with sales analysis. Since sellers are presented with performance reports and product analysis, they can make necessary changes in their policies or way of operation after thoroughly studying and understanding
all the factors that impact their sales. Similarly, customers will avail of the experience of accessing the right products at the right time and will stay informed about new products simultaneously. With time, as more and more data is fed into the database the accuracy of the data mining model will improve and regression analysis will be presented with over a 96% accuracy. From the perspective of profits for the app vendor company, they can present in-app purchases to the shopkeepers like unlimited sales report storage, e-mail sharing of invoices with customers, etc. Therefore the app will not only assist in bringing about social empowerment and development but will also present profitable business opportunities to app development companies