380.How Do High-Cost, Low-Turnover Warehouses Get Detected Using SAS Analytics?

How Do High-Cost, Low-Turnover Warehouses Get Detected Using SAS Analytics?

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —
DATA STEP | PROC SQL | PROC REPORT | PROC PRINT | PROC SGPLOT | MACROS | PROC CORR | PROC MEANS | PROC FREQ | PROC UNIVARIATE | PROC DATASETS DELETE

Intro Section

This project demonstrates how SAS can be used to analyze warehouse operations, capacity utilization, and cost efficiency using real-world style data. By combining DATA step processing, PROC SQL, and advanced analytics procedures, this tutorial shows how logistics and supply-chain data can be converted into meaningful business intelligence.

Why This Analysis Matters

Modern warehouses handle millions of units of inventory and large operational budgets. Even small inefficiencies in utilization or staffing can result in major financial losses. This SAS-based warehouse analytics model helps identify underutilized facilities, cost anomalies, and potential fraud patterns.

1. DATA CREATION – RAW WAREHOUSE DATA

data warehouses_raw;

    format Start_Date date9.;

    length Warehouse_ID $6 Location $20;

    input Warehouse_ID $ Location $ Storage_Capacity Inventory_Turnover 

          Staff_Count Operating_Cost Utilization_Rate Start_Date : date9.;

    datalines;

WH001 Delhi 50000 4.5 120 1500000 78 01JAN2022

WH002 Mumbai 70000 6.2 150 2100000 85 05FEB2022

WH003 Chennai 45000 3.8 90 1200000 72 15MAR2022

WH004 Hyderabad 60000 5.1 130 1800000 82 01APR2022

WH005 Pune 48000 4.0 100 1400000 70 12MAY2022

WH006 Kolkata 55000 4.6 110 1600000 76 25JUN2022

WH007 Jaipur 40000 3.5 85 1100000 65 10JUL2022

WH008 Ahmedabad 52000 4.9 115 1550000 79 08AUG2022

WH009 Kochi 42000 3.7 80 1050000 68 02SEP2022

WH010 Bhopal 46000 4.1 95 1300000 73 01OCT2022

WH011 Indore 47000 4.3 98 1350000 75 15NOV2022

WH012 Surat 58000 5.2 125 1750000 83 01DEC2022

WH013 Nagpur 43000 3.9 90 1200000 71 10JAN2023

WH014 Noida 65000 5.8 140 2000000 88 01FEB2023

WH015 Faridabad 62000 5.4 135 1900000 86 15MAR2023

;

run;

proc print data=warehouses_raw;

run;

OUTPUT:

Obs	Start_Date	Warehouse_ID	Location	Storage_Capacity	Inventory_Turnover	Staff_Count	Operating_Cost	Utilization_Rate
1	01JAN2022	WH001	Delhi	50000	4.5	120	1500000	78
2	05FEB2022	WH002	Mumbai	70000	6.2	150	2100000	85
3	15MAR2022	WH003	Chennai	45000	3.8	90	1200000	72
4	01APR2022	WH004	Hyderabad	60000	5.1	130	1800000	82
5	12MAY2022	WH005	Pune	48000	4.0	100	1400000	70
6	25JUN2022	WH006	Kolkata	55000	4.6	110	1600000	76
7	10JUL2022	WH007	Jaipur	40000	3.5	85	1100000	65
8	08AUG2022	WH008	Ahmedabad	52000	4.9	115	1550000	79
9	02SEP2022	WH009	Kochi	42000	3.7	80	1050000	68
10	01OCT2022	WH010	Bhopal	46000	4.1	95	1300000	73
11	15NOV2022	WH011	Indore	47000	4.3	98	1350000	75
12	01DEC2022	WH012	Surat	58000	5.2	125	1750000	83
13	10JAN2023	WH013	Nagpur	43000	3.9	90	1200000	71
14	01FEB2023	WH014	Noida	65000	5.8	140	2000000	88
15	15MAR2023	WH015	Faridabad	62000	5.4	135	1900000	86

·  Creates structured operational data.

·  Stores cost, staff, utilization metrics.

·  Provides date foundation for time analysis.

·  Supports business performance modeling.

·  Acts as raw SDTM-like source.

2. DATE ENGINEERING – MDY, INTNX, INTCK

data warehouses_dates;

    set warehouses_raw;

    Review_Date = intnx('month', Start_Date, 6);

    Operational_Months = intck('month', Start_Date, Review_Date);

run;

proc print data=warehouses_dates;

run;

OUTPUT:

Obs	Start_Date	Warehouse_ID	Location	Storage_Capacity	Inventory_Turnover	Staff_Count	Operating_Cost	Utilization_Rate	Review_Date	Operational_Months
1	01JAN2022	WH001	Delhi	50000	4.5	120	1500000	78	22827	6
2	05FEB2022	WH002	Mumbai	70000	6.2	150	2100000	85	22858	6
3	15MAR2022	WH003	Chennai	45000	3.8	90	1200000	72	22889	6
4	01APR2022	WH004	Hyderabad	60000	5.1	130	1800000	82	22919	6
5	12MAY2022	WH005	Pune	48000	4.0	100	1400000	70	22950	6
6	25JUN2022	WH006	Kolkata	55000	4.6	110	1600000	76	22980	6
7	10JUL2022	WH007	Jaipur	40000	3.5	85	1100000	65	23011	6
8	08AUG2022	WH008	Ahmedabad	52000	4.9	115	1550000	79	23042	6
9	02SEP2022	WH009	Kochi	42000	3.7	80	1050000	68	23070	6
10	01OCT2022	WH010	Bhopal	46000	4.1	95	1300000	73	23101	6
11	15NOV2022	WH011	Indore	47000	4.3	98	1350000	75	23131	6
12	01DEC2022	WH012	Surat	58000	5.2	125	1750000	83	23162	6
13	10JAN2023	WH013	Nagpur	43000	3.9	90	1200000	71	23192	6
14	01FEB2023	WH014	Noida	65000	5.8	140	2000000	88	23223	6
15	15MAR2023	WH015	Faridabad	62000	5.4	135	1900000	86	23254	6

·  INTNX creates review cycle.

·  INTCK calculates duration.

·  Tracks warehouse aging.

·  Supports efficiency trend.

·  Used for cost scaling.

3. CHARACTER FUNCTION CLEANSING

data warehouses_clean;

    set warehouses_dates;

    Location_Clean = propcase(strip(Location));

    Warehouse_Code = upcase(catx('-', 'WH', Warehouse_ID));

run;

proc print data=warehouses_clean;

run;

OUTPUT:

Obs	Start_Date	Warehouse_ID	Location	Storage_Capacity	Inventory_Turnover	Staff_Count	Operating_Cost	Utilization_Rate	Review_Date	Operational_Months	Location_Clean	Warehouse_Code
1	01JAN2022	WH001	Delhi	50000	4.5	120	1500000	78	22827	6	Delhi	WH-WH001
2	05FEB2022	WH002	Mumbai	70000	6.2	150	2100000	85	22858	6	Mumbai	WH-WH002
3	15MAR2022	WH003	Chennai	45000	3.8	90	1200000	72	22889	6	Chennai	WH-WH003
4	01APR2022	WH004	Hyderabad	60000	5.1	130	1800000	82	22919	6	Hyderabad	WH-WH004
5	12MAY2022	WH005	Pune	48000	4.0	100	1400000	70	22950	6	Pune	WH-WH005
6	25JUN2022	WH006	Kolkata	55000	4.6	110	1600000	76	22980	6	Kolkata	WH-WH006
7	10JUL2022	WH007	Jaipur	40000	3.5	85	1100000	65	23011	6	Jaipur	WH-WH007
8	08AUG2022	WH008	Ahmedabad	52000	4.9	115	1550000	79	23042	6	Ahmedabad	WH-WH008
9	02SEP2022	WH009	Kochi	42000	3.7	80	1050000	68	23070	6	Kochi	WH-WH009
10	01OCT2022	WH010	Bhopal	46000	4.1	95	1300000	73	23101	6	Bhopal	WH-WH010
11	15NOV2022	WH011	Indore	47000	4.3	98	1350000	75	23131	6	Indore	WH-WH011
12	01DEC2022	WH012	Surat	58000	5.2	125	1750000	83	23162	6	Surat	WH-WH012
13	10JAN2023	WH013	Nagpur	43000	3.9	90	1200000	71	23192	6	Nagpur	WH-WH013
14	01FEB2023	WH014	Noida	65000	5.8	140	2000000	88	23223	6	Noida	WH-WH014
15	15MAR2023	WH015	Faridabad	62000	5.4	135	1900000	86	23254	6	Faridabad	WH-WH015

·  STRIP removes blanks.

·  PROPCSE standardizes text.

·  CATX builds codes.

·  UPCASE enforces IDs.

·  Prevents merge errors.

4. UTILIZATION CLASSIFICATION MACRO

%macro utilization_classify(input=, output=);

data &output;

    set &input;

    length Utilization_Level $12;

    if Utilization_Rate >= 85 then Utilization_Level = "High";

    else if Utilization_Rate >= 70 then Utilization_Level = "Medium";

    else Utilization_Level = "Low";

run;

proc print data=&output;

run;

%mend;

%utilization_classify(input=warehouses_clean, output=warehouses_util);

OUTPUT:

Obs	Start_Date	Warehouse_ID	Location	Storage_Capacity	Inventory_Turnover	Staff_Count	Operating_Cost	Utilization_Rate	Review_Date	Operational_Months	Location_Clean	Warehouse_Code	Utilization_Level
1	01JAN2022	WH001	Delhi	50000	4.5	120	1500000	78	22827	6	Delhi	WH-WH001	Medium
2	05FEB2022	WH002	Mumbai	70000	6.2	150	2100000	85	22858	6	Mumbai	WH-WH002	High
3	15MAR2022	WH003	Chennai	45000	3.8	90	1200000	72	22889	6	Chennai	WH-WH003	Medium
4	01APR2022	WH004	Hyderabad	60000	5.1	130	1800000	82	22919	6	Hyderabad	WH-WH004	Medium
5	12MAY2022	WH005	Pune	48000	4.0	100	1400000	70	22950	6	Pune	WH-WH005	Medium
6	25JUN2022	WH006	Kolkata	55000	4.6	110	1600000	76	22980	6	Kolkata	WH-WH006	Medium
7	10JUL2022	WH007	Jaipur	40000	3.5	85	1100000	65	23011	6	Jaipur	WH-WH007	Low
8	08AUG2022	WH008	Ahmedabad	52000	4.9	115	1550000	79	23042	6	Ahmedabad	WH-WH008	Medium
9	02SEP2022	WH009	Kochi	42000	3.7	80	1050000	68	23070	6	Kochi	WH-WH009	Low
10	01OCT2022	WH010	Bhopal	46000	4.1	95	1300000	73	23101	6	Bhopal	WH-WH010	Medium
11	15NOV2022	WH011	Indore	47000	4.3	98	1350000	75	23131	6	Indore	WH-WH011	Medium
12	01DEC2022	WH012	Surat	58000	5.2	125	1750000	83	23162	6	Surat	WH-WH012	Medium
13	10JAN2023	WH013	Nagpur	43000	3.9	90	1200000	71	23192	6	Nagpur	WH-WH013	Medium
14	01FEB2023	WH014	Noida	65000	5.8	140	2000000	88	23223	6	Noida	WH-WH014	High
15	15MAR2023	WH015	Faridabad	62000	5.4	135	1900000	86	23254	6	Faridabad	WH-WH015	High

·  Automates logic.

·  Creates business flags.

·  Reusable macro.

·  Reduces coding errors.

·  Enables dashboarding.

5. FRAUD RISK MACRO

%macro fraud_flag(input=, output=);

data &output;

    set &input;

    if Operating_Cost > 1900000 and Inventory_Turnover < 4 then Fraud_Risk="High";

    else Fraud_Risk="Normal";

run;

proc print data=&output;

run;

%mend;

%fraud_flag(input=warehouses_util, output=warehouses_risk);

OUTPUT:

Obs	Fraud_risk	Start_Date	Warehouse_ID	Location	Storage_Capacity	Inventory_Turnover	Staff_Count	Operating_Cost	Utilization_Rate	Review_Date	Operational_Months	Location_Clean	Warehouse_Code	Utilization_Level
1	Normal	01JAN2022	WH001	Delhi	50000	4.5	120	1500000	78	22827	6	Delhi	WH-WH001	Medium
2	Normal	05FEB2022	WH002	Mumbai	70000	6.2	150	2100000	85	22858	6	Mumbai	WH-WH002	High
3	Normal	15MAR2022	WH003	Chennai	45000	3.8	90	1200000	72	22889	6	Chennai	WH-WH003	Medium
4	Normal	01APR2022	WH004	Hyderabad	60000	5.1	130	1800000	82	22919	6	Hyderabad	WH-WH004	Medium
5	Normal	12MAY2022	WH005	Pune	48000	4.0	100	1400000	70	22950	6	Pune	WH-WH005	Medium
6	Normal	25JUN2022	WH006	Kolkata	55000	4.6	110	1600000	76	22980	6	Kolkata	WH-WH006	Medium
7	Normal	10JUL2022	WH007	Jaipur	40000	3.5	85	1100000	65	23011	6	Jaipur	WH-WH007	Low
8	Normal	08AUG2022	WH008	Ahmedabad	52000	4.9	115	1550000	79	23042	6	Ahmedabad	WH-WH008	Medium
9	Normal	02SEP2022	WH009	Kochi	42000	3.7	80	1050000	68	23070	6	Kochi	WH-WH009	Low
10	Normal	01OCT2022	WH010	Bhopal	46000	4.1	95	1300000	73	23101	6	Bhopal	WH-WH010	Medium
11	Normal	15NOV2022	WH011	Indore	47000	4.3	98	1350000	75	23131	6	Indore	WH-WH011	Medium
12	Normal	01DEC2022	WH012	Surat	58000	5.2	125	1750000	83	23162	6	Surat	WH-WH012	Medium
13	Normal	10JAN2023	WH013	Nagpur	43000	3.9	90	1200000	71	23192	6	Nagpur	WH-WH013	Medium
14	Normal	01FEB2023	WH014	Noida	65000	5.8	140	2000000	88	23223	6	Noida	WH-WH014	High
15	Normal	15MAR2023	WH015	Faridabad	62000	5.4	135	1900000	86	23254	6	Faridabad	WH-WH015	High

·  Detects inefficiency.

·  Flags audit cases.

·  Uses numeric logic.

·  Supports compliance.

·  Prevents revenue loss.

6. PROC SQL – BUSINESS QUERIES

proc sql;

    create table warehouse_summary as

    select Location_Clean,

           avg(Utilization_Rate) as Avg_Utilization,

           sum(Operating_Cost) as Total_Cost

    from warehouses_risk

    group by Location_Clean;

quit;

proc print data=warehouse_summary;

run;

OUTPUT:

Obs	Location_Clean	Avg_Utilization	Total_Cost
1	Ahmedabad	79	1550000
2	Bhopal	73	1300000
3	Chennai	72	1200000
4	Delhi	78	1500000
5	Faridabad	86	1900000
6	Hyderabad	82	1800000
7	Indore	75	1350000
8	Jaipur	65	1100000
9	Kochi	68	1050000
10	Kolkata	76	1600000
11	Mumbai	85	2100000
12	Nagpur	71	1200000
13	Noida	88	2000000
14	Pune	70	1400000
15	Surat	83	1750000

·  Aggregates KPIs.

·  Groups by city.

·  Produces management reports.

·  SQL-based analytics.

·  Faster querying.

7. PROC MEANS

proc means data=warehouses_risk mean min max;

    var Storage_Capacity Operating_Cost Utilization_Rate;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Storage_Capacity Operating_Cost Utilization_Rate	52200.00 1520000.00 76.7333333	40000.00 1050000.00 65.0000000	70000.00 2100000.00 88.0000000

·  Shows central tendency.

·  Finds extreme values.

·  Validates data.

·  Supports planning.

·  Baseline metrics.

8. PROC CORR

proc corr data=warehouses_risk;

     var Storage_Capacity Utilization_Rate Operating_Cost;

run;

OUTPUT:

The CORR Procedure

3 Variables:	Storage_Capacity Utilization_Rate Operating_Cost

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Storage_Capacity	15	52200	9088	783000	40000	70000
Utilization_Rate	15	76.73333	7.00476	1151	65.00000	88.00000
Operating_Cost	15	1520000	332093	22800000	1050000	2100000

Pearson Correlation Coefficients, N = 15 Prob > |r| under H0: Rho=0
	Storage_Capacity	Utilization_Rate	Operating_Cost
Storage_Capacity	1.00000	0.93551 <.0001	0.99136 <.0001
Utilization_Rate	0.93551 <.0001	1.00000	0.94973 <.0001
Operating_Cost	0.99136 <.0001	0.94973 <.0001	1.00000

·  Checks dependencies.

·  Cost vs usage link.

·  Helps optimization.

·  Identifies inefficiency.

·  Supports forecasting.

9. PROC FREQ

proc freq data=warehouses_risk;

    tables Utilization_Level Fraud_Risk;

run;

OUTPUT:

The FREQ Procedure

Utilization_Level	Frequency	Percent	Cumulative Frequency	Cumulative Percent
High	3	20.00	3	20.00
Low	2	13.33	5	33.33
Medium	10	66.67	15	100.00

Fraud_risk	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Normal	15	100.00	15	100.00

·  Counts categories.

·  Identifies risky warehouses.

·  Quality control.

·  Compliance reports.

·  Audit support.

10. PROC UNIVARIATE

proc univariate data=warehouses_risk;

    var Operating_Cost Utilization_Rate;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Operating_Cost

Moments
N	15	Sum Weights	15
Mean	1520000	Sum Observations	22800000
Std Deviation	332092.93	Variance	1.10286E11
Skewness	0.30264324	Kurtosis	-1.0555485
Uncorrected SS	3.62E13	Corrected SS	1.544E12
Coeff Variation	21.8482191	Std Error Mean	85746.0259

Basic Statistical Measures
Location		Variability
Mean	1520000	Std Deviation	332093
Median	1500000	Variance	1.10286E11
Mode	1200000	Range	1050000
		Interquartile Range	600000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	17.72677	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	2100000
99%	2100000
95%	2100000
90%	2000000
75% Q3	1800000
50% Median	1500000
25% Q1	1200000
10%	1100000
5%	1050000
1%	1050000
0% Min	1050000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
1050000	9	1750000	12
1100000	7	1800000	4
1200000	13	1900000	15
1200000	3	2000000	14
1300000	10	2100000	2

The UNIVARIATE Procedure

Variable: Utilization_Rate

Moments
N	15	Sum Weights	15
Mean	76.7333333	Sum Observations	1151
Std Deviation	7.00476029	Variance	49.0666667
Skewness	0.0618266	Kurtosis	-1.0712095
Uncorrected SS	89007	Corrected SS	686.933333
Coeff Variation	9.12870585	Std Error Mean	1.80862133

Basic Statistical Measures
Location		Variability
Mean	76.73333	Std Deviation	7.00476
Median	76.00000	Variance	49.06667
Mode	.	Range	23.00000
		Interquartile Range	12.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	42.42642	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	88
99%	88
95%	88
90%	86
75% Q3	83
50% Median	76
25% Q1	71
10%	68
5%	65
1%	65
0% Min	65

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
65	7	82	4
68	9	83	12
70	5	85	2
71	13	86	15
72	3	88	14

·  Finds abnormal costs.

·  Measures skewness.

·  Supports fraud analysis.

·  Statistical profiling.

·  Risk analytics.

11. PROC SGPLOT

proc sgplot data=warehouses_risk;

    scatter x=Storage_Capacity y=Utilization_Rate;

run;

OUTPUT:

·  Capacity vs usage.

·  Pattern detection.

·  Outlier spotting.

·  Business storytelling.

·  Dashboard support.

12. PROC TRANSPOSE

proc transpose data=warehouse_summary out=warehouse_wide;

    by Location_Clean NotSorted;

    id Location_Clean;

    var Avg_Utilization;

run;

proc print data=warehouse_wide;

run;

OUTPUT:

Obs	Location_Clean	_NAME_	Ahmedabad	Bhopal	Chennai	Delhi	Faridabad	Hyderabad	Indore	Jaipur	Kochi	Kolkata	Mumbai	Nagpur	Noida	Pune	Surat
1	Ahmedabad	Avg_Utilization	79	.	.	.	.	.	.	.	.	.	.	.	.	.	.
2	Bhopal	Avg_Utilization	.	73	.	.	.	.	.	.	.	.	.	.	.	.	.
3	Chennai	Avg_Utilization	.	.	72	.	.	.	.	.	.	.	.	.	.	.	.
4	Delhi	Avg_Utilization	.	.	.	78	.	.	.	.	.	.	.	.	.	.	.
5	Faridabad	Avg_Utilization	.	.	.	.	86	.	.	.	.	.	.	.	.	.	.
6	Hyderabad	Avg_Utilization	.	.	.	.	.	82	.	.	.	.	.	.	.	.	.
7	Indore	Avg_Utilization	.	.	.	.	.	.	75	.	.	.	.	.	.	.	.
8	Jaipur	Avg_Utilization	.	.	.	.	.	.	.	65	.	.	.	.	.	.	.
9	Kochi	Avg_Utilization	.	.	.	.	.	.	.	.	68	.	.	.	.	.	.
10	Kolkata	Avg_Utilization	.	.	.	.	.	.	.	.	.	76	.	.	.	.	.
11	Mumbai	Avg_Utilization	.	.	.	.	.	.	.	.	.	.	85	.	.	.	.
12	Nagpur	Avg_Utilization	.	.	.	.	.	.	.	.	.	.	.	71	.	.	.
13	Noida	Avg_Utilization	.	.	.	.	.	.	.	.	.	.	.	.	88	.	.
14	Pune	Avg_Utilization	.	.	.	.	.	.	.	.	.	.	.	.	.	70	.
15	Surat	Avg_Utilization	.	.	.	.	.	.	.	.	.	.	.	.	.	.	83

·  Converts rows to columns.

·  Dashboard layout.

·  Excel-style view.

·  Comparison easier.

·  Presentation ready.

13. PROC DATASETS DELETE

proc datasets library=work;

    delete warehouses_raw;

quit;

LOG:

NOTE: Deleting WORK.WAREHOUSES_RAW (memtype=DATA).

·  Removes junk data.

·  Frees memory.

·  Controls versions.

·  Prevents confusion.

·  Validation compliance.

What You Will Learn

In this tutorial, you will learn how to create structured warehouse datasets, apply date functions like INTCK and INTNX, clean text using character functions, and build automated macros for utilization classification and fraud risk detection.

CONCLUSION:

This warehouse analytics system demonstrates how SAS is used in logistics, supply chain, and financial audit teams to monitor utilization, detect cost fraud, and optimize operational efficiency using SQL, statistics, macros, and data engineering.

 

INTERVIEW QUESTIONS FOR YOU

1.What is a SAS library?

2.What is the difference between SET and MERGE?

3.What is PROC FREQ used for?

About the Author:

SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:

The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent Warehouse data.

Our Mission:

This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.

This project is suitable for:

SAS Programmer Interviews

SAS Programmer Job Seekers

SAS Analysts

Follow Us On : 

 

--->Follow our blog for more SAS-based analytics projects and industry data models.

To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.HOW SMART ARE OUR CITIES? — ANALYZING URBAN PERFORMANCE USING SAS

2.Which Breakfast Is The Healthiest And Most Popular? — Using Sas Data Analytics

3.Which Glass Formula Is The Strongest? — Real – World GlassAnalytics Using Sas

About Us | Contact | Privacy Policy