KPI Dashboarding:KPI dashboarding refers to the process of visualizing and tracking key performance indicators (KPIs) in a dashboard format. KPI dashboards provide a clear and concise view of important metrics and enable stakeholders to quickly identify areas of concern or opportunities for improvement.
To create a KPI dashboard, it is important to first identify the key metrics that are most relevant to the business or organization. These metrics should align with the organization's goals and objectives and should be measurable and actionable.
Text analysis:Text analysis is the process of analyzing textual data to extract insights and information from it. Text analysis can involve various techniques, including natural language processing, machine learning, and statistical analysis.
Text analysis is used in a wide range of applications, including sentiment analysis, topic modeling, and entity recognition. Sentiment analysis involves analyzing text to determine the emotional tone or sentiment of the writer. This can be useful in understanding customer feedback, social media trends, and other areas where understanding sentiment is important.
2. KPI Dashboarding
Introduction to Dataset Why this dashboard?
Attributes
Warehouse ID, Customer Name, Customer Code, Customer Type, Vendor Name,
Booking Number, PO Number, Style, Item Qty, Total Packages, Package UOM,
Total CBM, Gross weight (kg), CHA Name, Shipping Bill Number, Shipping Bill
date, Warehouse Bay number, Consignee, Port of Discharge, VehicleSize,
Offloading Day, Offloading Date, Offloading Start Time, Offloading END Time,
Handover Date, Handover Time and others
This dashboard will provide a mistake proofing
mechanism, which acts as an additional check
points before loading the consignment into the
container.
Advatages of using this dashboard
Efficient visualization of historical and
present data
Ensures proper mapping of cargos in
designated bay area
Avoiding rework and time wastage due to
improper loading
Avoiding hefty fines due to unloading of
the container from port
What is it about?
This data is about tracking of all
the consignments from the
shippers to consignee
internationally for one of the
biggest vessel operating carrier.
We can see sample data the
company looks after to keep track
of the consignments.
3. Understanding Dashboarding
Here, we can
change the
offloading date
Here, we can
change the
shipper
For the selected
date/dates we
could see the
shipper details
For the selected
date/dates we
could see the
shipper details
For the selected
date/dates we
could see the
Cubic meters
Package weights
For the selected
date/dates we
could see the
Warehouse bay
the shipper
offloaded
Let’s see it working……….
4. Introduction to Dataset Text Analysis
For text analysis, we have selected three
files, they are from the research papers
from Scopus.
1. Characterizing supply chain visibility
- a literature review.
2. The future of operations
management: An outlook and
analysis.
3. The impacts of software product
management.
The pdf for these research paper were
converted to .txt files in order to perform
text analysis.
For this data, we are
going to see the text
analysis,
Commonalities and
comparison analysis.
Term frequency
Wordcloud 2
Wordcloud
Classify, sort, and extract information from text
to identify patterns, relationships, sentiments,
and another actionable knowledge.
Text Analysis
5. Text Analysis
Commonalities Comparison
Plot a cloud of words shared across documents
Code: commonality.cloud(term.matrix,)
Plot a cloud comparing the frequencies of words across
documents
Code: comparison.cloud(term.matrix,)
6. CREDITS: This presentation template was created by Slidesgo,
including icons by Flaticon, infographics & images by Freepik
Thanks!
Do you have any questions?
harender.singh@siom,in
+91 9812969049