Difference between revisions of "ANLY482 AY2017-18 T1 Group2 Project EZLin Project Overview"
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3. To identify the clustering factors and determine their effects on each other <br/> | 3. To identify the clustering factors and determine their effects on each other <br/> | ||
4. To build a dashboard reporting system which helps with cleaning the data and doing data exploration in the future <br/> | 4. To build a dashboard reporting system which helps with cleaning the data and doing data exploration in the future <br/> | ||
| − | 5. | + | 5. To build a data automation process that can help to clean and transform the raw data <br/> |
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Revision as of 15:57, 15 October 2017
In any company that delivers a physical product or solution, supply chain has always been a huge challenge in the operations of the organisation. There are many established companies in the market that operate the supply chain in silos and treat it as another cost center without clearly identifying what constitutes the cost in the entire supply chain ecosystem. Our motivation therefore is to not only determine the various costs that are inherent in the client’s supply chain ecosystem. We are keen to illustrate and automate the entire data analysis process for the client in a sustainable and scalable manner.
With our background in Operations Management and Information Systems, we are eager to work on this project as it allows us to apply what we have learned to a real world business problem. In addition, this project requires us to constantly learn on the job as we do not possess actual knowledge of supply chain analytics or some of the tools required in carrying out the analysis. As daunting as this may be, it will be interesting to discover whether our learnings, analysis and application has a positive effect on a huge multinational organisation like our client.
The main aim of our project is to help XXX’s supply chain team explore any trends and patterns in their current supply chain spending when they produce their adult wash product. Through this trends and patterns identified, it is hoped that we will be able to ultimately help them improve their end-to-end supply chain understanding as well as automate their data extraction for future supply chain analysis.
Based on the patterns identified, a dashboard reporting system will also be developed simultaneously in this project to provide a visual interface for their future usage.
The objectives of this project are:
1. To summarise the information of the raw materials prices based on different criteria
2. To determine the most cost-efficient budget plan based on the criteria
3. To identify the clustering factors and determine their effects on each other
4. To build a dashboard reporting system which helps with cleaning the data and doing data exploration in the future
5. To build a data automation process that can help to clean and transform the raw data
Based on our literature review and secondary research, we have come to understand the following,
- the importance of continuously assessing the configuration of the supply chain network in order to ensure the organisation’s competitive priorities are in line (Shukla & Kiridena, 2016),
- the benefits of visualising the supply chain network, proven successful by HP in its usage of Geographic Analytics (Acksteiner & Trautmann, 2013),
- the benefits of how predictive analytics enables an organisation to be proactive and improve its supply chain performance (Stefanovic, 2014).
Over the course of the project, we will constantly be reviewing literature to give us a better understanding of different ways to look at this project.
