1. HIGH PERFORMANCE
ANALYTICS USING
SAP HANA
LOS ANGELES GOVERNMENT SPENDING DATA SET FOR THREE FINANCIAL
YEARS
PROJECT TEAM
(GROUP - 5 TRIDENT):
SMIT SHAH
SUMIT SAINI
SWAPNIL NAIR

2. OVERVIEW OF THE MODEL
•DATA
VISUALIZATION
•SAP LUMIRA
•FORECASTING
AND PREDICTION
•SAP PREDECTIVE
AND EXPERT
ANALYTICS
•DATA LOADING
AND ANALYSIS
•SAP HANA
•DATA CLEANSING
•MS EXCEL
LA GOVERNMENT
FLOATED TENDERS
BASED ON
REQUIREMENT
(GOVERNMENT
ACTIVITY,
DEPARTMENT, ITEM
CODE, QUANTITY)
VENDORS SUBMITTED
QUOTATIONS AND BID
FOR THE ORDERS
(VENDOR NUMBER,
VENDOR NAME, CITY,
UNIT PRICE)
GOVERNMENT
PLACED ORDER ON
THE LOWEST
BIDDER VENDOR
MEETING ALL THE
REQUIREMENTS
(ORDER NUMBER,
ORDER DATE)
GOVERNMENT BILLED
THE TOTAL AMOUNT
INCLUDING TAXES
(DOLLAR AMOUNT,
SALES TAX, PAYMENT
METHOD, VALUE OF
SPEND)

3. DATASET
• There are two tables in our dataset
• Table – 1 VENDOR MASTER
• This table consists of the vendor master data with all basic details about the vendor and the
governments different departments. Some of the attributes are Vendor Name, Department Name,
Account Name, Government Activity, Buyer Name, Supplier City, Vendor Number, etc.
• Table – 2 VENDOR TRANSACTION DATA
• This table consists of the normal day to day transactional data for the vendor which includes
attributes such as Transaction Number, Value of Spend, Unit of Price, Unit of Measure, Invoice
Number, Description etc.
• https://controllerdata.lacity.org/Purchasing/eCheckbook-Data/pggv-e4fn

4. VIEWS
• ATTRIBUTE VIEW • ANALYTIC VIEW

5. HYPOTHESIS
• Hypothesis 1 – The Government spending depends on the Vendor Name.
• Hypothesis 2 – It is difficult to determine the government spending pattern
as per the fiscal year/month.
• Hypothesis 3 – The vendor supplier of the materials for the government
spending should be as near as possible to Los Angeles.

6. THEOREMS
• Theorem I - We can see that the money spent by the Government is the highest for the
Vendor ‘Haaker Equipment Co. for the fiscal year 2015.
• Theorem II – We can see that the money spent by the Government is the lowest in the
Fiscal Month July- 2015 and highest in the Fiscal Month June-2015.
• Theorem III - We can see that the quantity of material purchased is the highest from
the Vendor ‘Konica Minolta Business’.
• Theorem IV – We can see that the value spent for the department ‘General Services’ for
the Government Activity ‘General Government’ is the highest overall.
• Theorem V - We can see that highest percentage of Sales Tax is levied for the Item
Code Name ‘MISCELLANEOUS OR UNCLASSIFIED’
• Theorem VI – We can see that the quantity of materials supplied is the highest from the
city of Los Angeles itself from the United States

7. THEOREM 1
THE MONEY SPENT BY THE GOVERNMENT IS THE HIGHEST FOR THE VENDOR ‘HAAKER
EQUIPMENT CO. FOR THE FISCAL YEAR 2015.

8. THEOREM 2
THE MONEY SPENT BY THE GOVERNMENT IS THE LOWEST IN THE FISCAL MONTH
JULY- 2015 AND HIGHEST IN THE FISCAL MONTH JUNE-2015

9. THEOREM 3
THE QUANTITY OF MATERIAL PURCHASED IS THE HIGHEST FROM THE VENDOR
‘KONICA MINOLTA BUSINESS’.

10. THEOREM 4
THE VALUE SPENT FOR THE DEPARTMENT ‘GENERAL SERVICES’ FOR THE GOVERNMENT
ACTIVITY ‘GENERAL GOVERNMENT’ IS THE HIGHEST OVERALL

11. THEOREM 5
HIGHEST PERCENTAGE OF SALES TAX IS LEVIED FOR THE ITEM CODE NAME
‘MISCELLANEOUS OR UNCLASSIFIED’

12. THEOREM 6
THE QUANTITY OF MATERIALS SUPPLIED IS THE HIGHEST FROM THE CITY OF LOS
ANGELES ITSELF FROM THE UNITED STATES

13. SAP PREDICTIVE ANALYTICAL TOOL -
MODELER
• We performed regression analysis and decision tree algorithm to build the
model and predict the values. Our target value is value of spend. Target
leakage was giving almost 100% KI and KR values.

14. SAP PREDICTIVE ANALYTICAL TOOL -
MODELER
• Predictive power KI is 94.07% i.e model predict the value of spending of LA
Government based on input features 94.07% accurately.
• Prediction confidence KR is 99.72% i.e model can be reliably used to
continue on the data set.

15. SAP PREDICTIVE ANALYTICAL TOOL -
MODELER
• Influence on target of input feature

16. SAP PREDICTIVE ANALYTICAL TOOL -
MODELER
• Maximum Variable contribution

17. SAP PREDICTIVE ANALYTICAL TOOL -
MODELER
• Regression Model

18. SAP PREDICTIVE ANALYTICAL TOOL -
MODELER
• Decision tree model

19. • Model simulation
SAP PREDICTIVE ANALYTICAL TOOL -
MODELER

20. SAP PREDICTIVE ANALYTICAL TOOL -
MODELER
• Model predicted values

21. SAP PREDICTIVE ANALYTICAL TOOL -
MODELER

22. SAP PREDICTIVE ANALYTICAL TOOL –
EXPERT ANALYTICS
• We have performed auto regression analysis in expert analytics keeping
value of spend as target variable.

23. SAP PREDICTIVE ANALYTICAL TOOL –
EXPERT ANALYTICS
• Feature selection

24. SAP PREDICTIVE ANALYTICAL TOOL –
EXPERT ANALYTICS
• Model Graph

25. RECOMMENDATIONS
• For LA Government –
• They should promote placing order on local vendor to save transportation
charge and other state taxes.
• They should plan their spending for the next fiscal year beforehand.
• They should have collected few more data about quality of item received,
number of day vendor took to deliver, and government and vendor
relationship behavior.

26. RECOMMENDATIONS
• For Vendors –
• They should focus more on Government’s general services requirement.
• They should highlight their discount method and provide some offer.
• They should maintain healthy relation with the Government and supply
goods with required quality on time.

27. TOOLS USED
• SAP HANA - This is used for data extraction and building views ie:- attribute view and
analytical views.
• SAP LUMIRA- This is used for building Visualizations.
• SAP Predictive and Expert Analytical tools – This is used for predictive analysis based
on the input features.