Development of A Smart Database for Construction Inventory Management Using Deep Learning to Eliminate Supply Chain Bottlenecks Post COVID-19
Authors: Unmesa Ray, Abdulaziz Banawi
Abstract: The COVID-19 pandemic has sent shockwaves down the supply chains of industries. The Architecture, Engineering, and Construction (AEC) industry is used to cyclical downturns, but the speed and strength with which COVID-19 has struck are unprecedented. Projects are being delayed or canceled. Supply chains are under threat. National and International policies are being revamped to deal with the transformed landscape. In 2019, the USA receives more than 530 thousand metric tons of steel from Russia, Germany, Italy, Canada, Mexico, and other such countries. The US building industry is dependent largely on other countries for the supply of raw materials, which make the construction industry at risk due to trade restrictions that have ensued in the post COVID world. One way to deal with such a changing environment is to diversify the dependence portfolio in supply chains to reduce shocks. The other alternative is to look to build an inventory based on predictive analytics. This research aims to implement the two reactionary measures of portfolio diversification and inventory infrastructure development by leveraging predictive analytics and big data. The project will be mainly divided into three phases – PHASE 1 will be mainly focused on the gathering of the relevant manufacturer and supplier data of construction materials both within and external to geographical borders of the USA. PHASE 2 will focus on the selection and integration of algorithms with the live database that has been created in phase 1. PHASE 3 will be devoted to the creation of custom-made user interfaces for the project owners. This phase will also focus on the automation of live reports, notifications,s, etc. to be sent to project owners. The deep learning algorithms would need continuous feedback and improvement to increase their credibility and reliance on a continuous basis. Thus, it will help to reduce the risks generated through uncertainties by developing a resilient smart responsive database that will provide stockholders accurate data and predictions in response to the market and industry behavior.
Keywords: COVID-19, Supply Chains, Time-series, Inventory, Predictive analytics, Integration, Deep-learning algorithms, Resilient, Smart Database
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