No journey is alike, and neither is the timeline of climbing towards full AI adoption. With varying ranges of technical capability and business readiness, one thing is for certain, you need to see results, and fast! In this webinar, we will explore three client use cases from the manufacturing industry, to oil and gas, to education with examples of successful projects including: Sales Forecasting – We will share sales forecasting and market segmentation techniques in the manufacturing industry. Using historical sales data, we introduce fast and effective signal decomposition and clustering techniques to produce valuable customer insights. Inventory Management – We apply text analytics and natural language processing techniques for advanced and custom automation. This use case saves significant time for inventory managers and analysts by accurately and rapidly classifying their inventory based on each item description. Public Safety – We introduce a computer vision capability that can recognize firearms and trigger alerts. In this use case, we apply real-time object recognition technology for early detection of firearms for school safety. You’ll walk away with modern analytics and AI tools to benefit your organization’s immediate needs no matter where you are on your journey to AI adoption.

1. Small Investments; Big Returns
3 Data Science Use Cases

2. 2
Data Scientist
sshenoy@sensecorp.com
Shreya Shenoy
www.sensecorp.com/contact
@SenseCorp
facebook.com/SenseCorpSC/
linkedin.com/company/sense-corp
Data Scientist
sdevitt@sensecorp.com
Susan Devitt

3. 3
80% of analytics insights will
not deliver business outcomes
through 2022 and 80% of AI
projects will “remain alchemy,
run by wizards” through 2020
(Gartner 2019)

4. PROJECT DO’S AND DONT’S
Begin early, be agile, and start small
Timelines that deliver on weekly scales
Aim for “good enough’ & adding business value
4-6 person teams
Hyper-focused on the business problem
Co-developing with SMEs and stakeholders
Focus on fast mover strategyFocus on first mover strategy
Designing the ‘supreme’ solution
Timelines that deliver on monthly scales
Aim for perfect accuracy
Large, slow-moving teams
Hyper-focused on the solution
Developing in silos

5. Deliver Big Returns on your AI investment
5
START SIMPLE PRIORITIZE GENERATE VALUE

6. Inventory categorization1.
Small investment; Big returns
Sales forecasting; anomaly detection2.
Computer vision public safety3.

7. Inventory Categorization
2 weeks to delivery; 1 FTE

8. Inventory Categorization
8
PROCESS
Item Description Item Category
CHALLENGES
Manual Labor
• Speed
• Accuracy
• Niche Expertise
• Tedious
Dataset Size
• ~266k+ items
• 155 categories
• Continuously growing
APPLICATIONS
Cost Analysis
and Reporting
Predictive Maintenance
and Equipment Failure
Parts Optimization
and Savings

9. 9
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
25 45 65 85 105 125 145
ModelAccuracy
Number of Most Frequent Labels
Decision Tree
Naïve Bayes, Count Vectors
Naïve Bayes, Wordlevel TFIDF
Logistic Regression (softmax),
Count Vectors
Logistic Regression (softmax),
Wordlevel TFIDF
Random Forest, Count Vectors
Random Forest, Wordlevel TFIDF
XG Boost, Count Vectors
XG Boost, Wordlevel TFIDF
7/9 show extremely high accuracy (>90%) for
frequently occurring categories

10. 10
0
10
20
30
40
50
60
70
80
90
100
20 40 60 80 100 120 140 160
Number of Most Frequent Labels
Minimum Label
Frequency
Percentage of Total
Data (%)
Best Model Accuracy
(%)
Worst Model
Accuracy (%)
Total Data Accuracy
(%)
Modeling Results
Takeaways:
• 98% of the data can be classified with over 90% accuracy
• More labels => more learning => better accuracy

11. REDUCE COST:
98% of the data can be classified with over 90% accuracy1.
Use Case: Inventory Categorization
90% LESS TIME:
2-week process now takes <1 day2.
INCREASE DATA ACCURACY:
Accurate data now available for downstream applications in
automation, optimization, smart assistant etc.
3.

12. Forecasting and Market Segmentation
2-week Proof of Concept; 1 FTE

13. Daily Sales (450+ stores, 27 months, 7 million transactions)

14. Weekly Sales (450+ stores, 27 months, 7 million transactions)

15. Time Series Analysis
15
Exploratory Insights
• Time series decomposition for trend,
seasonality and anomaly detection for
each segment
Optimization &
Forecasting
Clustering & Market
Correlation
Objective Objective
• Apply machine learning techniques and
simulations to quantify uncertainty
• Apply mathematical models to
understand lag, growth rate and
trajectory during rebound & recovery
• Group stores/products based on
historical daily invoices to identify non-
obvious groupings
• Evaluate micro and macro economic
leading indicators to improve
predictions
Objective
What INSIGHTS can we derive
from our DATA ASSETS?
Identify non-obvious TRENDS
and ANOMALIES.
Which markets have
SIMILAR BEHAVIORS, and
why? Which markets may
IMPACT others?
How do we identify and
REDUCE UNCERTAINTY of
anomalous events? How do
we OPTIMIZE the climb back
to normalcy?

16. Time Series Decomposition to Isolate Key Events
Major Hurricane

17. Total Sales and Hurricane Rebound
1500000
1700000
1900000
2100000
2300000
2500000
9/22/17 12/31/17 4/10/18 7/19/18 10/27/18 2/4/19 5/15/19 8/23/19 12/1/19 3/10/20 6/18/20
EmeraldCoast - Total Sales Rebound Forecast
Hurricane

18. Multiscale Correlation for FORECASTING
18
1. Macro/regional 2. Micro/Local
30-year fixed mortgage average in US.
Source: Freddie Mac
Cross Correlation – 0.6226
(optimal at 50-week lag)
West Texas Intermediate (WTI) spot price
Source: US EIA
WTI Corr – 0.76
(no lag)
30-yr corr – 0.42
(max)

19. Nonobvious trends can be deconstructed and
RECONSTRUCTED1.
Use Case: Anomaly Detection and Forecasting
Anomalous events can be grouped to build forecasts that
REDUCE UNCERTAINTY2.
Time lags and micro/macro drivers improve accuracy of
forecasting
3.

20. Computer Vision for Public Safety
4 weeks; 1.5 FTE

21. Convolutional Neural Nets OpenCV, Tesseract Google Cloud, AWS, Azure
MED MED LOW
HIGH MED LOW
LOW MED HIGH
HIGH MED LOW
Computer Vision Options
Greater Complexity Less Complexity
Build from
Scratch
Open-Source
Library
Off the
Shelf API
Solutions
LOE and Time to
Implement
Data Size
Requirements
Long Term
Costs
Control Over
Accuracy

22. Public Safety: Security Camera to Detect Armed Individual

23. Computer Vision: Detecting Firearms

24. Successful Example
24
Person 1:
Asphalt
Person 2:
Gun Shooting range
Shooting sport
Shooting Recreation
Sport venue Air gun
Trigger Trap shooting
Person 3 skipped
Person 4 skipped
Person 5 skipped

25. Overall Performance
25
• End-to-end speed of 3-4 seconds from acquiring image to sending
notification of detected firearm
• 88% accuracy at detecting firearms with current algorithm
• Room for improvement in next steps

26. Time and resource savings with initial implementation
1.
Use Case: Computer Vision for Public Safety
Can be easily adapted for various specific use cases; all
collected data can further customization2.
Can deliver business value quickly with a focused problem3.

27. Inventory Categorization
98% accuracy; 90% reduction in effort
1.
Small investment; Big returns
Sales forecasting; anomaly detection
Macro/micro drivers of sales; quantify uncertainty
2.
Computer vision public safety
88% accuracy; end-to-end runtime of 3-4 seconds
3.

28. 28

29. Small investment; Big returns - Takeaway
29
START SIMPLE PRIORITIZE GENERATE VALUE
Use what you have;
no new data
High feasibility;
high return
Build momentum
with early success

30. Thanks For Joining Us
We hope you enjoyed the presentation.
If you’d like to learn more about how to stand up your
AI organization, download our eBook.
https://sensecorp.com/artificial-intelligence-ebook/
DOWNLOAD EBOOK
www.sensecorp.com | marketing@sensecorp.com

31. Thank you!