ANLY482 AY2017-18 T1 Group2 Project EZLin Project Overview
Cost optimization of the supply chain is a challenge for many companies, as the whole process of supply chain is a complex network. Between each point on this network, there are different cost components for different transactions. Currently, XXX’s analysis only focuses on separate parts of this whole supply chain network and look at them in silos, preventing them from seeing the whole picture. As such, our motivation is to use different analysis methods and visualization tools to map the end-to-end business process. Given the complex structure of the company’s supply chain network, we are keen to explore ways to map the flow from the plant to the final distribution centre. Through this, we are hoping that we would be able to discover what are the cost implications for different parts of the supply chain and how to potentially improve this whole network.
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 materials prices based on different criteria and condition type
2. To clearly map the process of the supply chain from the internal manufacturer to the final distribution centre with the transaction cost incurred at each point
3. Build a data automation process that can help to clean and transform the raw data required for visualisation
4. To identify the clustering factors and determine their effects on each other
5. Establish a dashboard reporting system which helps with data exploration and visualisation of the supply chain flow in the future
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.
