AY1516 T2 Team Skulptors - Project Description

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Skulptors-HomeIcon.png   HOME Skulptors-AboutIcon.png   ABOUT US Skulptors-OverviewIcon.png   PROJECT OVERVIEW Skulptors-ProjMgmtIcon.png   PROJECT MANAGEMENT Skulptors-DocIcon.png   DOCUMENTATION
Summary Description Methodology & Technology Limitations & Risks


Project Description


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:

  1. B. Braun Singapore - A company which manufactures and produces medical products.
  2. Heinemann Asia Pacific - A leading duty free company headquartered in Singapore.


Motivation


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.

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.

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.

Objective & Goals


The main objective of the project would be to develop the following:

  1. 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.)
  2. 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.
  3. 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.


Provided Data


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 - B. Braun Singapore and Heinemann Asia Pacific’s 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:

  1. Product master sheet
  2. Product inbound report
  3. Product outbound report


Methodology


Control Chart

The control chart is a graph used to study how a process changes over time. Data are plotted in time order. A control chart always has a central line for the average, an upper line for the upper control limit and a lower line for the lower control limit. These lines are determined from historical data. By comparing current data to these lines, we can draw conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).
With respect to our project, these control charts will provide us with insights on whether certain movements of SKUs are out of control e.g. too little movements which results in wastage of space in the warehouse. It also simplifies the performance to be easily read by the human eye. Our sponsor emphasizes on sustainability of our solution, which will be further tackled by this in our dashboard.

Time series line chart

A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. With relation to the inbound and outbound rate visualization for different SKUs, the time series line graph will cater to this purpose.

Treemap

A Treemap can convey our hierarchical data with 2 additional attributes via color and size, allowing us to dissect the relationship between the two. The size of each block represents the percentage of the warehouse utilized, and we can allow the user to fill in the colors with other indicators, such as the type of SKUs.


Technology


D3.js

D3.js is a Javascript library. As our client request for as low cost as possible, D3.js will be a good option. That is because it can work on websites. Thus, our client will be able to see the data visualization without paying and installing any software. Another benefit of D3.js is its flexibility. It allows control over the final result. D3.js will be used for the visualizations that was mentioned above.

JMP

JMP is developed by the JMP business unit of SAS Institute. It is the tool of choice for data explorers in every industry. We will be using JMP to perform Control Chart Analysis for the inbound and outbound rate so that we can classify the SKUs into the ABC.

SAS Enterprise Miner

SAS Enterprise Miner is a software developed by SAS Institute. We may be using SAS Enterprise Miner to perform other needed analysis.

Java & Bootstrap

Java will be used to develop the skeleton of the application while Bootstrap will be used to beautify the application. As our client wanted a sustainable analysis, we will need to do up a simple application whereby they can upload their data to be processed for analysis.

OpenShift

OpenShift is a product from Red Hat. It is an open-source platform as a service. It serves as a platform to launch our application for live deployment.


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