ANLY482 AY2017-18 T1 Group1/Midterm Progress
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INTEGRATION OF MORE DATA SETS INTO ANALYTICAL SANDBOX - As we have just received data on chemical classification, we will integrate it with the data on packaging material classification. With these data sets, we can further analyse the composition of finished goods based on the types of chemicals or packaging materials it contains, and possibly derive insights on which types chemicals or packaging materials will increase the manufacturing cost of a finished good.
We have also received more information regarding the putup (volume of the bottle) and variants of each finished good. With these additional columns, we can analyse if the costs required to manufacture or transport a larger finished good increase proportionately with those of a smaller finished good. We can also analyse the cost composition of different variants of the same volume and see if the additional chemicals required to produce the variants contribute to the manufacturing costs significantly.
We also expect to receive more data sets from our sponsor in the coming weeks which may include production volume and sales volume of the finished goods (PO). With these information, we could potentially analyse the gap between production and sales volume of a finished good, and derive insights on the popularity between different putups or variants of the same product.
IMPUTATION, CLARIFICATION AND REMOVAL OF NULL/NA VALUES - We aim to remove all instances of null and NA values from our MasterTable. By achieving this objective, we will identify gaps in data between different Excel files, so that we can reflect them to our sponsor and see if they are data quality issues, and remove obsolete records if necessary so that our analysis and visualizations will be focused on existing SKUs.
For the second half of our semester, we will be focusing on the end-to end perspective of the supply chain. We have further discussed with the sponsor s business team and realised that an end-to-end network analysis of operations is crucial in identifying the gaps in their supply chain.
We would also be focusing on checking the quality of data to ensure the accuracy of our analysis. As of now, our sponsor has responded to us that India’s data is not clear for analysis. We would be removing India from our scope of work due to the complexity of its supply chain and multiple gaps found in India’s extracted data, unless our sponsor gets back to us with updated information that can fully capture the costs involved in transporting goods between the Indian vendors and their buyers.
