Difference between revisions of "AY1516 T2 Team Skulptors - Project Overview"

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|style="padding:0.4em; text-align:center; border-top:1px solid #ffffff; border-bottom:1.5px solid #005ae6; " width="10%" | [[AY1516 T2 Team Skulptors - Project Overview |<font color="#000a1a"><b>Summary</b></font>]]
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In recent years, data has evolved to provide answers and solutions to problems hidden from the surface. Analytics, the study of data, has evolved over the years to be an integral tool for many companies to further improve efficiency and sustainability. However, the ability to easily visualise data, quickly modify views and interrogate data can be difficult and time consuming due to the difficulty and effort required to update reports and dashboards. On top of that, in today's connected world there are many more data sources available to organisations, such as machinery sensors, mobile devices, wearables, web logs, etc. The Internet of Things (IoT) now allows us to collect and exchange data between systems, providing a rich set of data for analysis. Some of this data is unstructured, in siloes, making it difficult to analyse in its raw form let alone integrating it to gain valuable insights .<br/>
<div style="background: #d9d9d9; padding: 12px; font-family: Impact; font-size: 18px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #595959 solid 32px;"><font color="black">Project Description</font></div>
 
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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:
 
# B. Braun Singapore - A company which manufactures and produces medical products.
 
# Heinemann Asia Pacific - 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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Today, a local multi-national corporation faces the same adversity as many others – the plight of unworked data. Visualizations of the data provided by their in-house warehousing solution has proven to be difficult and much of the information is not placed to better use. The company hopes to solve 3 main issues, namely how they should categorize their warehouse supplies into ABC categories (each category refers to how fast the goods move) to store their goods in the best location available, how can their data be visualized better so that it is easier for employees to see, and how can their warehouses be utilized to the fullest to ensure minimal wastage.<br/>
  
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Our team, Skulptors, will be developing a dashboard application to solve the issues of this company. Control charts will be used to depict control of the movement of the SKUs, identifying which type of products have excessive or lack of movements from the warehouse. Treemaps will be used to identify products or SKUs that have minimal utilization of warehouses, and also to identify which warehouse location is not fully utilized. Java add-ons will be used to facilitate additional functions, such as allowing the upload of an excel file to update the database. This allows for our solution to remain sustainable. Time series line graphs will also be used for easy display of inflow and outflow rate of specified SKUs.<br/>
  
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With our solution, we hope to assist the company and the employees, relieving them from the hassle of manually analyzing the given data. Through our visualizations and analysis, we also hope to improve upon their warehousing solution, allowing the company to better utilize their warehouses and thus improving the business value.<br/>
  
 
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Latest revision as of 23:39, 12 January 2016

Skulptors-Logo.png

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 & ROI


In recent years, data has evolved to provide answers and solutions to problems hidden from the surface. Analytics, the study of data, has evolved over the years to be an integral tool for many companies to further improve efficiency and sustainability. However, the ability to easily visualise data, quickly modify views and interrogate data can be difficult and time consuming due to the difficulty and effort required to update reports and dashboards. On top of that, in today's connected world there are many more data sources available to organisations, such as machinery sensors, mobile devices, wearables, web logs, etc. The Internet of Things (IoT) now allows us to collect and exchange data between systems, providing a rich set of data for analysis. Some of this data is unstructured, in siloes, making it difficult to analyse in its raw form let alone integrating it to gain valuable insights .

Today, a local multi-national corporation faces the same adversity as many others – the plight of unworked data. Visualizations of the data provided by their in-house warehousing solution has proven to be difficult and much of the information is not placed to better use. The company hopes to solve 3 main issues, namely how they should categorize their warehouse supplies into ABC categories (each category refers to how fast the goods move) to store their goods in the best location available, how can their data be visualized better so that it is easier for employees to see, and how can their warehouses be utilized to the fullest to ensure minimal wastage.

Our team, Skulptors, will be developing a dashboard application to solve the issues of this company. Control charts will be used to depict control of the movement of the SKUs, identifying which type of products have excessive or lack of movements from the warehouse. Treemaps will be used to identify products or SKUs that have minimal utilization of warehouses, and also to identify which warehouse location is not fully utilized. Java add-ons will be used to facilitate additional functions, such as allowing the upload of an excel file to update the database. This allows for our solution to remain sustainable. Time series line graphs will also be used for easy display of inflow and outflow rate of specified SKUs.

With our solution, we hope to assist the company and the employees, relieving them from the hassle of manually analyzing the given data. Through our visualizations and analysis, we also hope to improve upon their warehousing solution, allowing the company to better utilize their warehouses and thus improving the business value.