Difference between revisions of "ANLY482 AY2016-17 T2 Group19"

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[[ANLY482_AY2016-17_T2_Group19|<font color="#a1212e"><b>OVERVIEW</b></font>]]
 
[[ANLY482_AY2016-17_T2_Group19|<font color="#a1212e"><b>OVERVIEW</b></font>]]
  
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[[File:Protege_data.svg|40px|link= ANLY482_AY2016-17_T2_Group19_Data ]] &nbsp;
 
[[File:Protege_data.svg|40px|link= ANLY482_AY2016-17_T2_Group19_Data ]] &nbsp;
 
[[ANLY482_AY2016-17_T2_Group19_Data| <font color="#000000"><b>DATA</b></font>]]
 
[[ANLY482_AY2016-17_T2_Group19_Data| <font color="#000000"><b>DATA</b></font>]]
  
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[[File:Protege_Methods.svg|40px|link= ANLY482_AY2016-17_T2_Group19_Methodology ]] &nbsp;
 
[[File:Protege_Methods.svg|40px|link= ANLY482_AY2016-17_T2_Group19_Methodology ]] &nbsp;
[[ANLY482_AY2016-17_T2_Group19_Methodology|<font color="#000000"><b>METHODOLOGY</b></font>]]
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[[ANLY482_AY2016-17_T2_Group19_Methodology|<font color="#000000"><b>METHODOLOGY & ANALYSIS</b></font>]]
  
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[[File:Protege_Analysis.svg|40px|link= ANLY482_AY2016-17_T2_Group19_Analysis ]] &nbsp;
 
[[ANLY482_AY2016-17_T2_Group19_Analysis| <font color="#000000"><b>ANALYSIS</b></font>]]
 
 
 
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[[File:Protegemaster-03.svg|40px|link= ANLY482_AY2016-17_T2_Group19_Findings ]] &nbsp;
 
[[File:Protegemaster-03.svg|40px|link= ANLY482_AY2016-17_T2_Group19_Findings ]] &nbsp;
 
[[ANLY482_AY2016-17_T2_Group19_Findings| <font color="#000000"><b>FINDINGS</b></font>]]
 
[[ANLY482_AY2016-17_T2_Group19_Findings| <font color="#000000"><b>FINDINGS</b></font>]]
  
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[[File:Protege_poster.svg|40px|link= ANLY482_AY2016-17_T2_Group19_Poster ]] &nbsp;
[[ANLY482_AY2016-17_T2_Group19_Poster| <font color="#000000"><b>POSTER</b></font>]]
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[[ANLY482_AY2016-17_T2_Group19_Documentation| <font color="#000000"><b>DOCUMENTATION</b></font>]]
  
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[[ANLY482_AY2016-17_Term_2| <font color="#000000"><b>BACK TO COURSE</b></font>]]
 
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==<div style="background: #4d4d4d; padding: 20px; line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family: Trajan Pro;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #FFF>Members</font></div>==
 
==<div style="background: #4d4d4d; padding: 20px; line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family: Trajan Pro;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #FFF>Members</font></div>==
 
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<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Myriad Pro Light, sans-serif; border-radius: 7px; text-align:center">
To be Updated
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[[File:Protegeteam.png|900px|center|link= ANLY482_AY2016-17_T2_Group19]]
 
</div>
 
</div>
  
 
==<div style="background: #4d4d4d; padding: 20px; line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family:Trajan Pro;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #ffffff>Project Background</font></div>==
 
==<div style="background: #4d4d4d; padding: 20px; line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family:Trajan Pro;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #ffffff>Project Background</font></div>==
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<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Myriad Pro Light, sans-serif; border-radius: 7px; text-align:justify">
Company Z is a medium-sized pharmaceutical product distributor and wholesaler in Singapore who caters to various healthcare institutes and clinics. Effective management of the supply chain and sales strategies is particularly important for Company Z as it deals primarily with large volume and high-value products at a rapid pace. Hence, even the smallest miscalculation in strategic management would result in significant losses. That said, there is huge potential for insights from the wealth of information that can be found in the sales data.
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SMEs employ about 70% of Singapore’s workforce while contributing more than 50% of the country’s economic output. However, most these SMEs are either slow or not engaged in adopting big data analytics despite government initiatives aimed at improving productivity. According to a survey conducted amongst 233 SMEs in April to June in 2016, cost was the main barrier for SMEs to invest in such capabilities, followed by a lack of in-house expertise to make full use of data analytics in improving business processes.
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The use of data analytics would allow SMEs to discover deeper insights regarding sales trends, customer spending patterns and operations. It also allows for newer dimensions of data to be analysed, for instance, spatial analytics could potentially allow SMEs to conduct a more refined market segmentation, consequently bringing about more precise identification of customers and targeting of sales promotions or products.
 +
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To assist SMEs in sharpening their competitiveness and to address their concerns regarding investment in data analytics, an interactive visual analytics dashboard (IVAD) was developed as a potential solution. The proposed IVAD considers the need for a holistic view of business and thus incorporates three perspectives that may prove useful in a business context: Spatial, Product, Customer; and aims to answer the questions of when, where, what, and who.
 +
 
 +
The IVAD was developed using transactional data in a pharmaceutical context however it is a framework that is highly generalizable to businesses with multiple points of sales and a range of customer segments. This is done by incorporating spatial analytics in the form of proportionate symbol web-maps, product tree-maps, customer segment tree-maps and purchasing pattern heatmaps, as well as reactive charting for trend analysis.  
 
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==<div style="background: #4d4d4d; padding: 20px; line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family: Trajan Pro;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #ffffff>Motivation & Objective</font></div>==
 
==<div style="background: #4d4d4d; padding: 20px; line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family: Trajan Pro;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #ffffff>Motivation & Objective</font></div>==
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<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Myriad Pro Light, sans-serif; border-radius: 7px; text-align:justify">
Insights generated with contextual knowledge reinforced by managerial experience and the understanding of the industry/business would ensure tangible positive impacts.
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Research and development efforts were motivated by the general sentiments of SMEs towards data analytics innovation across Singapore and the lack of professionals with deep computer language knowledge required to handle volume, variety, velocity of data. It aims to address the gap between research areas of institutes (e.g. University) and practical business needs through working closely with a local SME, in this case, specifically in the pharmaceutical field.  
While supply chain management is an area with great potential for the application of big data analytics, few firms have fully embraced it in the region. As analytics is increasingly adopted by organisations, it is our aim to apply big data analytics to an established pharmaceutical organisation’s wholesaling and distribution practices, thereby observing the benefits gained and learning more about the challenges faced in maintaining an effective and efficient supply chain.
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The objective of this project is to create value through the use of Sales data analytics to improve operational efficiency, reduce unnecessary losses and ultimately enhance profitability.
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By building an interactive visual analytical tool that does not require advanced computing knowledge for data exploration, we can enable business professionals with domain knowledge to leverage on the wealth of data. With deeper understanding of how nuances between sales, customer types, products and geographic segmentation, businesses can help to drive business sustainability with greater focus.
 +
 
 +
In relevance to the business, the visual dashboard attempts to support decision making process by fulfilling the following analysis requirements:
 +
 
 +
1. To be able to display massive sales data cartographically.
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 +
2. To provide a graphical visualization framework that can display distribution of product mixes in relation to customer types over time.
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3. To provide visual comparability between similar types of customers in a designated geographical context.
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4. To derive insights into customer spending trends based on their respective types and geographical locations
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<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Myriad Pro Light, sans-serif; border-radius: 7px; text-align:center">
Status:  Liaising with Client
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Status:  Completed. Refer to Methodology and Analysis to View.
 
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==<div style="background: #4d4d4d; padding: 20px; line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family: Trajan Pro;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #FFF>Timeline with Process Flow </font></div>==
 
==<div style="background: #4d4d4d; padding: 20px; line-height: 0.1em;  text-indent: 10px; font-size:20px; font-family: Trajan Pro;  border-radius: 7px; border-bottom:3px solid #ba3749"><font color= #FFF>Timeline with Process Flow </font></div>==
 
<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Myriad Pro Light, sans-serif; border-radius: 7px; text-align:center">
 
<div style="margin:0px; padding: 10px; background: #f2f4f4; font-family: Myriad Pro Light, sans-serif; border-radius: 7px; text-align:center">
To be Updated
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End of Project.
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Submitted Final Conference Paper and Artifacts. Handover of deliverables to Client.
 
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Latest revision as of 00:33, 24 April 2017



Protegelogo-01.svg

Protege overview.svg   OVERVIEW

Protege data.svg   DATA

Protege Methods.svg   METHODOLOGY & ANALYSIS

Protegemaster-03.svg   FINDINGS

Protege poster.svg   DOCUMENTATION

  BACK TO COURSE

Overview


Members

Protegeteam.png

Project Background

SMEs employ about 70% of Singapore’s workforce while contributing more than 50% of the country’s economic output. However, most these SMEs are either slow or not engaged in adopting big data analytics despite government initiatives aimed at improving productivity. According to a survey conducted amongst 233 SMEs in April to June in 2016, cost was the main barrier for SMEs to invest in such capabilities, followed by a lack of in-house expertise to make full use of data analytics in improving business processes.

The use of data analytics would allow SMEs to discover deeper insights regarding sales trends, customer spending patterns and operations. It also allows for newer dimensions of data to be analysed, for instance, spatial analytics could potentially allow SMEs to conduct a more refined market segmentation, consequently bringing about more precise identification of customers and targeting of sales promotions or products.

To assist SMEs in sharpening their competitiveness and to address their concerns regarding investment in data analytics, an interactive visual analytics dashboard (IVAD) was developed as a potential solution. The proposed IVAD considers the need for a holistic view of business and thus incorporates three perspectives that may prove useful in a business context: Spatial, Product, Customer; and aims to answer the questions of when, where, what, and who.

The IVAD was developed using transactional data in a pharmaceutical context however it is a framework that is highly generalizable to businesses with multiple points of sales and a range of customer segments. This is done by incorporating spatial analytics in the form of proportionate symbol web-maps, product tree-maps, customer segment tree-maps and purchasing pattern heatmaps, as well as reactive charting for trend analysis.

Motivation & Objective

Research and development efforts were motivated by the general sentiments of SMEs towards data analytics innovation across Singapore and the lack of professionals with deep computer language knowledge required to handle volume, variety, velocity of data. It aims to address the gap between research areas of institutes (e.g. University) and practical business needs through working closely with a local SME, in this case, specifically in the pharmaceutical field.

By building an interactive visual analytical tool that does not require advanced computing knowledge for data exploration, we can enable business professionals with domain knowledge to leverage on the wealth of data. With deeper understanding of how nuances between sales, customer types, products and geographic segmentation, businesses can help to drive business sustainability with greater focus.

In relevance to the business, the visual dashboard attempts to support decision making process by fulfilling the following analysis requirements:

1. To be able to display massive sales data cartographically.

2. To provide a graphical visualization framework that can display distribution of product mixes in relation to customer types over time.

3. To provide visual comparability between similar types of customers in a designated geographical context.

4. To derive insights into customer spending trends based on their respective types and geographical locations

Progress Dashboard

Status: Completed. Refer to Methodology and Analysis to View.

Timeline with Process Flow

End of Project.

Submitted Final Conference Paper and Artifacts. Handover of deliverables to Client.