Supply-Chain-Analytics-Project

Goal

Demand Forecasting of products

Demand Forecasting is the art & science of predicting customer’s future demand  for  products.

DATASET

• Source: https://archive.ics.uci.edu/ml/datasets/online+retail
• Multivariate, Sequential, Time-Series dataset
• Contains all the transactions occurring between 2010-2011 for a UK-based and registered non-store online retail.
• Dataset is used from online retail data of a gift shop.
• Attributes are InvoiceNo, StockCode, Description, Quantity, InvoiceDate UnitPrice, CustomerID, Country.

Correlation

Created new features from text column "Description"

• Picked out the nouns from every rows using POS tagger for product name and created a new column named Product_type
• Picked out colour of the product from each rows and created a new column named Colour_type

Created new column revenue

• Revenue = UnitPrice * Quantity

A Machine-Learning Approach (3- steps Model)

• Data is first clustered (Clustering)
• Output from clustering is then used as labelled(with cluster no.) training data for classification (Classification)
• Then the no. of sales is predicted on the basis of regression model employing ‘cluster no.’ as one of the features. (Prediction)

CLUSTERING:

Challenge:

• Mixed Attributes : Numerical + Categorical
• Categorical were also important, couldn’t be removed !
• Converting to numerical would compromise with significance of categorical attributes

Solution:

• Algorithm which considers mixed attribute : K-Prototypes

CLUSTERING: k-prototypes

• Based on the k-means paradigm
• Works well with mixed data, preserving its efficiency.
• Maximises the intra cluster similarity of objects
• Object similarity measure is derived from both numeric and categorical attributes

Clustering output:

The output column from clustering of Train was used as Target variable for classification training set

CLASSIFICATION:

To build model for cluster classification Linear SVM of machine learning algorithm performed very well.

Prediction of demands

• Collected all features together and mark "Quantity" column as a target variable.
• Splitted the whole dataset into train test and predicted the quantity of the items
• Demand forecasting of quantity column, the machine learning algorithm random forest performed very well.

Conclusion

• The data pre-processing phase facilitates the formation of the inputs to the models.
• The feature engineering process helps create new variables that bring additional value to demand interpretation.
• The three-step model involving clustering, classification and prediction enables the company further to visualize the relationship between predictor variables and customize the forecasting approaches accordingly.