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This project will serve as the first of the many efforts which the company conducts regularly to constantly improve its entire logistics process via data analytics. Logistics process encompasses the entire flow of Stock Keeping Units (SKU), from the inflow of goods to the outflow of goods. The project is mainly aimed at providing in-depth SKU analysis based on different product brands which the company is handling. In this project, we will be looking into the products of 2 companies it is handling. The companies are:
# A company which manufactures and produces medical products.
# A leading duty free company headquartered in Singapore.
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The complication of big data resonates strongly with the company, much like many others. The Warehouse Management System (WMS) captures data of logistics supplies in the warehouses of the company, such as quantity of movement, time of movement and identification codes of the packages stored. With extensive data provided by the WMS, analyzing of spreadsheets can be cumbersome and inefficient due to visualization impediments. The company is unable to effectively analyze and sculpt solutions as a result. <br/>

In addition, the company is in the midst of implementing a vertical lift high-tech system  for its new warehouses in its upcoming Supply Chain City (SCC) project. In light of these recent developments, the company saw an opportunity to better analyze their WMS data to determine its Stock Keeping Unit (SKU)’s inbound rate, outbound rate, warehouse utilization trend, and ideally, by performing the aforementioned analysis, categorizing each SKUs into namely A, B, C categories. ‘A’ category refers to fast moving SKUs while ‘C’ category SKUs refers to slow moving SKUs. <br/>

With the analyzed results, the company hopes to determine the optimal warehouse location and vertical lift in which a particular SKU can be placed for picking. In addition, the company would also like to see the extent of productivity savings it can obtain with the adoption of a batch picking technique instead of an order picking technique. Order picking involves going into the warehouse to collect the supplies per order basis, while batch picking involves collecting for multiple orders in a batch.<br/>
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The main objective of the project would be to develop the following:
# Creation of an application to sort SKUs by A, B and C categories
#* To provide employees with a high level view of the flow and demand changes for different SKUs. This allows higher level management employees to make important decisions based on it (e.g. eliminate SKUs which are extremely slow moving as they are taking up warehouse space which can be otherwise optimized.)
# Dashboard for quick visualization of inbound and outbound rate for different SKUs
#* Inbound rate: X-axis and y-axis to be date against number of inbound in pieces, carton and pallets per day.
#* Outbound rate:  X-axis and y-axis to be date against number of inbound in pieces per day.
#* This reduces both the time and manpower needed for the manual analyzing of data as it provides a general trend and flow of SKUs. For instance, when an employee notices a particular product’s supply running low he will be able to call for a refill immediately. 
# Warehouse Utilization Tool
#* To help employees understand fill and flow rate of the warehouse based on historical data.
#* To determine pick rate of warehouse locations and identify most used and least used locations.

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The data will be obtained from the in-house Warehouse Management System (WMS). The company updates the database whenever goods are being received (inbound) and released (outbound). For the purpose of the project’s analysis, the team will be given one year worth of data for each of the companies' SKUs.  The size of the data is expected to be approximately 4.2 million rows of data (4 excel sheets) for each company. For each of the companies, there will be 3 sets of data provided, namely:
# Product master sheet
# Product inbound report 
# Product outbound report 

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