Efficient Inventory Management System Inventory management is a systematic approach to sourcing, storing, and selling inventory - both raw materials (components) and finished goods (products). In business terms, inventory management means the right stock, at the right levels, in the right place, at the right time, and at the right cost as well as price. Poor inventory management can cost you time, money and your business. It is among the top reasons why small businesses fail.

1. Poor Forecasting
2. Excessive stock pile-up
3. Supply chain complexity
4. Inefficient order management
5. Managing warehouse space

1. Increased Costs
2. Unsatisfied Customer
3. Delayed delivery of orders
4. Imbalanced inventory

In order to avoid the poor inventory and to upscale the business, the machine learning comes into picture where the analysis is carried out based on the past sales information and with the help of algorithms to predict the future inventory.

1. Avoidance of stock-outs and excess stocks
2. Improved business negotiations
3. Ability to make more profitable business decisions
4. Reduced risk of overselling

This project will explain how to manage the inventory efficiently with the help of a sample dataset.

Poor inventory management leads to a loss in sales which in turn paints an inaccurate picture of lower demand for certain items, making future order predictions based on that past data inherently inaccurate. Instead smart retailers use real-time data to move inventory where it's needed before it's too late. The objective of this project is to make inventory management efficient using predictive analysis.

Two datasets has been considered for this project and they are Product details Product Revenue details

Product dataset consists of 1115 product type with cost per unit and time for delivery. The sample data of this dataset is given below Sample data for product dataset

Revenue dataset consists of 10,17,209 records with daily data of 1115 products. The dataset consists of other columns like revenue, store status, promotion applied, Generic holiday etc. The total records in revenue dataset will constitutes for 3 years 10months data.\

Features are explained below:

1. Store Status : It's a categorical feature which explains "Is the store open or closed..?". It has two unique values i.e., open and close.
2. Promotion Applied : It's a continuous feature which explains "Is the promotion applied for the particular product or not..?". 
3. Generic Holiday : It's a continuous feature which explains "Is the particular day is a general holiday or not..?".
4. Educational Holiday : It's a continuous feature which explains "Is the particular day is a educational holiday or not..?".

In order to build the predictive model, two datasets are merged with respect to product type and 'No of sales' new feature has created which represent the units sold.

i.e., No. of sales = Revenue / cost per unit

After merging two datasetIn general, the inventory of the shop will be refreshed every week or month or sometimes it will be quarterly. In order to analyze the data in terms of weekly, monthly and quarterly basis, the entire dataset is converted into weekly, monthly and quarterly aggregated values respectively.

From exploratory data analysis, it is observed that the educational holiday variable does not affect the revenue. Therefore, the educational holiday variable is neglected while building the model. The other variables are considered for the model building which is explained in next section.

To build the model, the entire dataset is separated into input and output variables.

In order to remove scaling effect from input variable, MinMax scalar is used which will convert every data point between 0 and 1 since there are many categorical variable.

The no. of units variable is considered to be output variable and Log transformation is applied to bring the variable towards the normal distribution.

After transformation the entire dataset is splitted into train and test data with 80% data as train and 20% data as test.

With the above transformed data different models have been built for weekly, monthly and quarterly datasets.

Root Mean Squared Error and R2 score is considered as evaluation metric to select the best model. All the models which are developed are optimized with hyper parameter tuning using GridSearchCV and evaluation metrics are calculated. Below table shows the RMSE and R2 score for different model used

Evaluation metric results for weekly dataset

XG Boost algorithm is considered as final model for weekly dataset.

Evaluation metric results for monthly dataset

XG Boost algorithm is considered as final model for monthly dataset.

Evaluation metric results for quarterly dataset

XG Boost algorithm is considered as final model for quarterly dataset.

Finalized model shown in previous section is considered for app deployment. The predicted stocks are added with 10% of buffer to compensate for growth in the industry and the population.
The Web API has been developed with the help of Flask framework and deployed in Heroku platform.\

https://efficient-inventory.herokuapp.com/

https://medium.com/@sumanthneechadi/efficient-inventory-management-system-eb48a6520326

Inventory management is a very tedious process if not done properly may lead to loss in sales, customer dissatisfaction etc. Smart retailers will use predictive analysis which will lead better management of stocks, centralized control which intern increase the productivity.