Our Case Study developed for the module B.4 Operations Analytics & Application have won the Teradata University Network (TUN) Teaching Innovation Award 2013.The case study developed by Prof Michelle Cheong and Murphy Choy, together with our industry partner was selected unanimously as the winner by the TUN award committee, beating several other impressive submissions.They will be presenting the Case Study at the 2013 International Conference on Information Systems (ICIS 2013), to be held in Milano, Italy from December 15-18, 2013.
Some of the compliments given were:
"reflects considerable work, and provides a sophisticated set of teaching resources"
"had a well thought class activity with appropriate ancillary materials"
"I particularly liked the emphasis on identify the root cause of the problem rather than assuming the problem or being given the problem"
Congratulations to our Faculty members and thank you very much to our industry partner!Once again, we have proven to be the leader in Analytics education and training.
Brief Description of the Case Study:
Our submission consists of a short case and its accompanying teaching notes and laboratory guide, which is taught as part of the course "Operations Focused Data, Analytics & IT" in the Master of IT in Business (Analytics) programme, targeted to train business analytics professionals.
We attempt to use the case to expose our students to the Data and Decision Analytics Framework which helps the students identify the actual cause of business problems by collecting, preparing, and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to improve the order distribution for a logistics service provider. Due to the fluctuations in orders on a day to day basis, the logistics provider will need the maximum number of trucks to cater for the maximum order day, resulting in idle trucks on other days. By performing data analysis of the orders from the retailers, the inventory ordering policy of these retailers can be inferred and new order intervals proposed to smooth out the number of orders, so as to reduce the total number of trucks needed. An average of 20% reduction of the total number of trips made can be achieved. Complementing the proposed order intervals, the corresponding new proposed order size is computed using moving average from historical order sizes, and shown to satisfy the retailers’ capacity constraints within reasonable limits.
We have successfully demonstrated how insights can be obtained and new solutions can be proposed by integrating data analytics with decision analytics, to reduce distribution cost for a logistics company.
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Last updated on 02 Jan 2022 .