Ration Guru: Implementation of an Advance Method to Simplified Stocking

Inventory management is a growing topic of concern and better software support is lacking for customers. All existing software applications that exist are expensive and thus can only be used by rich retail stores. There is no app that is powerful yet simplistic enough for anyone and everyone. To implement a project such as this it is important to understand the target audience; the human mind that lives and breathes through us. This project is based on concepts from behavioral economics about the human mind. Research sheds light on the topic of behavioral economics and how the different factors in different situations change how a customer perceives a situation which includes multiple decisions. One of the most important cases of behavioral modeling is to study and understand the rational mind. This project implements the module that deals with the case of a rational mind. A rational mind is one that makes decisions that always give the maximum output and benefit and reaps the best results. Behavioral economics also gives an insight on other cases such as uncertainty, prospect theory, temporal dimensions, social dimensions and ethics. Uncertainty is a case where the user cannot make up their mind about a choice. Prospect theory is how a simple, single question asked in two different perspectives can make the buyer pick another option. Temporal dimensions deal with how time plays a huge role in the choices a user makes and how the decisions depend on future events as well as present. Social dimensions include social norms and trust, dishonesty and such factors of a human mind that play a huge role while buying something. Lastly, ethics play a huge role on how a human mind perceives a situation. To implement another module of this, a smaller section of the temporal dimension, machine learning was studied. The main module that deals with the rational mind making rational choices was implemented. Before implemented, following the software development life cycle the design of the architecture was done and analysis of how the application will react to the user was conducted. Then all the nonfunctional requirements were defined, and the functional requirements were set. The algorithm to implement the project was developed to begin the implementation life cycle. A survey of two hundred people was conducted to be able to define defaults. The survey shed light on different sections of the module such as time interval for buying distinctly quantifiable items, like grains and pulses, and monthly expenditure. The module for a segment for future work including machine learning was generated. In conclusion, the project successfully deals with the rational mind and the rational choices. Behavioral modeling and predictive modeling play a huge role in this case and there is still more scope of adding more modules to this project to improve it.