ANLY482 AY2017-18 T1 Group2
The use of programming to automate and cleanse the dataset has numerous benefits that improves the efficiency and productivity of doing things. Python, an object-oriented programming language, is often well-regarded for its ease-of-usage and large variety of standard libraries such as Pandas and Tensorflow.
In order to truly understand the data-automation and transformation process, a collaboration with Johnson & Johnson (JnJ) was made to work on a real-life project focusing on JnJ supply chain network. The objective of this project was to not only help the company understand its end-to-end supply chain network but to also offer insights from data through visualisations done on Tableau. This requires the raw data to be rigorously cleansed and transformed in order for any visualisation to be done, which was in line with our aim of understanding the data-automation and transformation process. Through Tableau, the different types of cost and plants were clearly visualised and represented, providing much insights and setting a foundation for an end-to-end supply chain flow for the company.
The tangible result from this project was the quick data cleaning and transformation process, that helped integrate the different Excel file and allowing JnJ to identify areas in which attention must be paid to improve its supply chain information accuracy.
