ANLY482 AY2016-17 T2 Group19 Documentation

OVERVIEW	DATA	METHODOLOGY & ANALYSIS	FINDINGS	DOCUMENTATION	BACK TO COURSE

Key Deliverables

Reports and Minutes will not be available due to client confidentiality

Conclusion

A. LIMITATIONS

This project is set to be limited in span of roughly 6 months, from the sourcing of a data sponsor to the EDA then finally to the dashboard construction. With severe time constraints, the full capabilities of R and Shiny platforms could not be represented fully and accurately. Furthermore, insights generated from the sales data in this project is not exhaustive as not all relevant techniques and tools are used.

B. RECOMMENDATIONS / IMPLICATIONS

We recommend that future research or work be done in this field to consider exploring the use of clustering on top of geospatial analysis to provide a deeper understanding of the business. Furthermore, should the data set be operational in nature, it is critical to analyse stock movements considering holding and transportation costs. Predictive analytics can be employed to forecast demand and as such better allocate time for restocking. In regards to transportation costs, delivery schedule or routes can be analysed to increase operational efficiency to lower cost for the business. This applied research into R and Shiny platform would have implications on how businesses apart from large corporations can employ big data analytics in a more affordable way. With more businesses making use of their untapped wealth of data, greater value can be generated to benefit the end-consumers and the country’s overall productivity.

C. ENDING REMARKS

IVAD’s initial development was done with the data sponsor’s interest in mind, however, applications for this dashboard has potential to benefit SMEs who have business models based on wholesaling. IVAD’s open-sourced nature and its intermediate IT requirements presents a viable alternative for SMEs to leverage on the benefits of data analytics. The geospatial aspect of IVAD provides users with a new avenue to analyse their sales transactions, thus allowing them to improve upon logistical processes and the precision of marketing efforts. The combination of the geospatial, product and customer aspects along with its reactive charts and interactivity can be used to improve decision making through data-driven insights. However, a significant barrier that remains is nonchalance of many SMEs towards using newer technologies in driving productivity. Common concerns behind this nonchalance are related to ease of usage, which should be alleviated with the user-friendly interface and operation of IVAD.

Acknowledgements

Additions to the data, namely the demographic data and the subzone data, were sourced from Data.gov.sg, a publicly accessible database run by the Singapore Government. We would like to extend our gratitude to our data sponsor for entrusting us with the sales data as well as the efficient assistance rendered. We appreciate the timely correspondence with regards to our enquiries and clarifications. We would also like to extend our gratitude to Professor Kam Tin Seong, our project supervisor, for his valuable insights regarding dashboard construction and for guiding us throughout the entirety of the practicum.

References

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