392.How Can SAS Analytics Transform E-Waste Recycling Facilities into Sustainable and Efficient Operations?

How Can SAS Analytics Transform E-Waste Recycling Facilities into Sustainable and Efficient Operations? 

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

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1. INTRODUCTION

Electronic waste (E-Waste) is one of the fastest-growing waste streams globally. Devices like mobile phones, laptops, televisions, and batteries contribute heavily to environmental pollution if not recycled properly.

E-waste recycling facilities play a crucial role in:

·       Recovering valuable materials

·       Preventing toxic contamination

·       Ensuring sustainable industrial practices

This SAS project simulates real-world operational data for E-Waste Recycling Facilities and applies advanced analytics techniques using Base SAS, SQL, Macros, and Statistical Procedures.

2. BUSINESS CONTEXT

Why this project matters:

Companies, governments, and NGOs need:

·       Facility efficiency tracking

·       Hazard compliance monitoring

·       Sustainability performance measurement

·       Fraud and anomaly detection

·       Cost vs recovery optimization

This dataset and analysis helps answer:

·       Which facilities are efficient?

·       Which ones pose high hazard risks?

·       Are reported recovery rates realistic?

·       Are operational costs aligned with output?

3. PROJECT OBJECTIVES

✔ Create a realistic dataset with 15+ observations
✔ Apply date formats, percentages, numeric metrics
✔ Implement utilization classification macro
✔ Implement fraud detection logic macro
✔ Use PROC SQL, MEANS, UNIVARIATE, CORR, FREQ, SGPLOT
✔ Apply SET, MERGE, APPEND, TRANSPOSE
✔ Use INTCK, INTNX, MDY
✔ Use character and numeric functions
✔ Provide deep explanations for interviews

4. DATASET DESIGN

Dataset Name:

ewaste_facilities

Observations:

18 facilities

Geographic Coverage:

India, USA, Germany, Japan, China, UK, Canada, Australia

5. VARIABLE DEFINITIONS

Variable	Description
Facility_Name	Name of recycling facility
Country	Facility country
Waste_Processed_Tons	Total e-waste processed
Recovery_Rate	% of material recovered
Hazard_Level	LOW / MEDIUM / HIGH
Operating_Cost	Annual operating cost
Sustainability_Score	Internal sustainability rating
Start_Date	Facility operational start date
Report_Date	Reporting date
Utilization_Class	Macro-derived classification
Fraud_Flag	Fraud detection indicator

6. DATE HANDLING STRATEGY

We will use:

·       MDY() – create SAS dates

·       INTCK() – calculate duration

·       INTNX() – project future review dates

·       SAS date formats (date9.)

7. DATASET CREATION

data ewaste_facilities;

    length Facility_Name $30 Country $15 Hazard_Level $10;

    input Facility_Name $ Country $ Waste_Processed_Tons Recovery_Rate Hazard_Level $

          Operating_Cost Sustainability_Score Start_Date :date9. Report_Date :date9.;

    format Start_Date Report_Date date9.;

    datalines;

EcoCycle_India India 12000 85 LOW 4500000 88 01JAN2015 01JAN2025

GreenLoop_USA USA 18000 92 MEDIUM 8200000 91 15MAR2012 01JAN2025

ReTech_Germany Germany 15000 89 LOW 6100000 90 10FEB2014 01JAN2025

UrbanRecycle_UK UK 11000 78 HIGH 4000000 72 05MAY2016 01JAN2025

Ecoreclaim_Japan Japan 17000 94 LOW 7500000 95 12JUN2011 01JAN2025

WasteRenew_China China 30000 60 HIGH 5000000 55 20AUG2018 01JAN2025

EcoNova_Canada Canada 13000 88 MEDIUM 5300000 87 18SEP2013 01JAN2025

RecycleHub_Aus Australia 9000 82 MEDIUM 3900000 80 11NOV2017 01JAN2025

GreenEarth_India India 14000 90 LOW 5200000 92 09APR2014 01JAN2025

TechWaste_USA USA 21000 58 HIGH 8700000 60 07JUL2019 01JAN2025

EcoSphere_UK UK 12500 86 MEDIUM 4600000 85 03MAR2015 01JAN2025

ZeroWaste_Japan Japan 16000 91 LOW 6800000 94 25DEC2010 01JAN2025

CleanCircuit_Germany Germany 14500 88 MEDIUM 6000000 89 14JAN2013 01JAN2025

RecycleMax_India India 20000 62 HIGH 7100000 65 02FEB2020 01JAN2025

EcoFuture_USA USA 15500 87 MEDIUM 5900000 88 06JUN2016 01JAN2025

UrbanGreen_Canada Canada 13500 84 MEDIUM 5100000 86 19SEP2014 01JAN2025

GreenMetal_China China 28000 65 HIGH 5400000 58 21OCT2018 01JAN2025

EcoLoop_Aus Australia 9500 83 LOW 3700000 82 13NOV2016 01JAN2025

;

run;

proc print data=ewaste_facilities;

run;

OUTPUT:

Obs	Facility_Name	Country	Hazard_Level	Waste_Processed_Tons	Recovery_Rate	Operating_Cost	Sustainability_Score	Start_Date	Report_Date
1	EcoCycle_India	India	LOW	12000	85	4500000	88	01JAN2015	01JAN2025
2	GreenLoop_USA	USA	MEDIUM	18000	92	8200000	91	15MAR2012	01JAN2025
3	ReTech_Germany	Germany	LOW	15000	89	6100000	90	10FEB2014	01JAN2025
4	UrbanRecycle_UK	UK	HIGH	11000	78	4000000	72	05MAY2016	01JAN2025
5	Ecoreclaim_Japan	Japan	LOW	17000	94	7500000	95	12JUN2011	01JAN2025
6	WasteRenew_China	China	HIGH	30000	60	5000000	55	20AUG2018	01JAN2025
7	EcoNova_Canada	Canada	MEDIUM	13000	88	5300000	87	18SEP2013	01JAN2025
8	RecycleHub_Aus	Australia	MEDIUM	9000	82	3900000	80	11NOV2017	01JAN2025
9	GreenEarth_India	India	LOW	14000	90	5200000	92	09APR2014	01JAN2025
10	TechWaste_USA	USA	HIGH	21000	58	8700000	60	07JUL2019	01JAN2025
11	EcoSphere_UK	UK	MEDIUM	12500	86	4600000	85	03MAR2015	01JAN2025
12	ZeroWaste_Japan	Japan	LOW	16000	91	6800000	94	25DEC2010	01JAN2025
13	CleanCircuit_Germany	Germany	MEDIUM	14500	88	6000000	89	14JAN2013	01JAN2025
14	RecycleMax_India	India	HIGH	20000	62	7100000	65	02FEB2020	01JAN2025
15	EcoFuture_USA	USA	MEDIUM	15500	87	5900000	88	06JUN2016	01JAN2025
16	UrbanGreen_Canada	Canada	MEDIUM	13500	84	5100000	86	19SEP2014	01JAN2025
17	GreenMetal_China	China	HIGH	28000	65	5400000	58	21OCT2018	01JAN2025
18	EcoLoop_Aus	Australia	LOW	9500	83	3700000	82	13NOV2016	01JAN2025

·  length defines character size to prevent truncation

·  format date9. ensures readable dates

·  MDY-style dates entered as SAS date literals

·  18 realistic observations created

·  Numeric + percentage + categorical mix

8. DATA CLEANING & CHARACTER FUNCTIONS

data ewaste_clean;

    set ewaste_facilities;

    Facility_Name = propcase(strip(Facility_Name));

    Country = upcase(trim(Country));

    Facility_ID = catx('_', Country, substr(Facility_Name,1,5));

run;

proc print data=ewaste_clean;

 var Facility_Name Country Facility_ID;

run;

OUTPUT:

Obs	Facility_Name	Country	Facility_ID
1	Ecocycle_india	INDIA	INDIA_Ecocy
2	Greenloop_usa	USA	USA_Green
3	Retech_germany	GERMANY	GERMANY_Retec
4	Urbanrecycle_uk	UK	UK_Urban
5	Ecoreclaim_japan	JAPAN	JAPAN_Ecore
6	Wasterenew_china	CHINA	CHINA_Waste
7	Econova_canada	CANADA	CANADA_Econo
8	Recyclehub_aus	AUSTRALIA	AUSTRALIA_Recyc
9	Greenearth_india	INDIA	INDIA_Green
10	Techwaste_usa	USA	USA_Techw
11	Ecosphere_uk	UK	UK_Ecosp
12	Zerowaste_japan	JAPAN	JAPAN_Zerow
13	Cleancircuit_germany	GERMANY	GERMANY_Clean
14	Recyclemax_india	INDIA	INDIA_Recyc
15	Ecofuture_usa	USA	USA_Ecofu
16	Urbangreen_canada	CANADA	CANADA_Urban
17	Greenmetal_china	CHINA	CHINA_Green
18	Ecoloop_aus	AUSTRALIA	AUSTRALIA_Ecolo

·  strip() removes leading/trailing spaces

·  trim() cleans end spaces

·  propcase() improves readability

·  upcase() standardizes values

·  coalesce() handles missing hazard levels

·  catx() builds business IDs

9. UTILIZATION CLASSIFICATION MACRO

%macro utilization_class;

data ewaste_util;

    set ewaste_clean;

length Utilization_Class $8.;

    if Waste_Processed_Tons >= 18000 then Utilization_Class='HIGH';

    else if Waste_Processed_Tons >= 12000 then Utilization_Class='MEDIUM';

    else Utilization_Class='LOW';

run;

proc print data=ewaste_util;

run;

%mend;

%utilization_class;

OUTPUT:

Obs	Facility_Name	Country	Hazard_Level	Waste_Processed_Tons	Recovery_Rate	Operating_Cost	Sustainability_Score	Start_Date	Report_Date	Facility_ID	Utilization_Class
1	Ecocycle_india	INDIA	LOW	12000	85	4500000	88	01JAN2015	01JAN2025	INDIA_Ecocy	MEDIUM
2	Greenloop_usa	USA	MEDIUM	18000	92	8200000	91	15MAR2012	01JAN2025	USA_Green	HIGH
3	Retech_germany	GERMANY	LOW	15000	89	6100000	90	10FEB2014	01JAN2025	GERMANY_Retec	MEDIUM
4	Urbanrecycle_uk	UK	HIGH	11000	78	4000000	72	05MAY2016	01JAN2025	UK_Urban	LOW
5	Ecoreclaim_japan	JAPAN	LOW	17000	94	7500000	95	12JUN2011	01JAN2025	JAPAN_Ecore	MEDIUM
6	Wasterenew_china	CHINA	HIGH	30000	60	5000000	55	20AUG2018	01JAN2025	CHINA_Waste	HIGH
7	Econova_canada	CANADA	MEDIUM	13000	88	5300000	87	18SEP2013	01JAN2025	CANADA_Econo	MEDIUM
8	Recyclehub_aus	AUSTRALIA	MEDIUM	9000	82	3900000	80	11NOV2017	01JAN2025	AUSTRALIA_Recyc	LOW
9	Greenearth_india	INDIA	LOW	14000	90	5200000	92	09APR2014	01JAN2025	INDIA_Green	MEDIUM
10	Techwaste_usa	USA	HIGH	21000	58	8700000	60	07JUL2019	01JAN2025	USA_Techw	HIGH
11	Ecosphere_uk	UK	MEDIUM	12500	86	4600000	85	03MAR2015	01JAN2025	UK_Ecosp	MEDIUM
12	Zerowaste_japan	JAPAN	LOW	16000	91	6800000	94	25DEC2010	01JAN2025	JAPAN_Zerow	MEDIUM
13	Cleancircuit_germany	GERMANY	MEDIUM	14500	88	6000000	89	14JAN2013	01JAN2025	GERMANY_Clean	MEDIUM
14	Recyclemax_india	INDIA	HIGH	20000	62	7100000	65	02FEB2020	01JAN2025	INDIA_Recyc	HIGH
15	Ecofuture_usa	USA	MEDIUM	15500	87	5900000	88	06JUN2016	01JAN2025	USA_Ecofu	MEDIUM
16	Urbangreen_canada	CANADA	MEDIUM	13500	84	5100000	86	19SEP2014	01JAN2025	CANADA_Urban	MEDIUM
17	Greenmetal_china	CHINA	HIGH	28000	65	5400000	58	21OCT2018	01JAN2025	CHINA_Green	HIGH
18	Ecoloop_aus	AUSTRALIA	LOW	9500	83	3700000	82	13NOV2016	01JAN2025	AUSTRALIA_Ecolo	LOW

·  High utilization = heavy capacity usage

·  Medium = stable operations

·  Low = under-utilized facility

10. FRAUD DETECTION MACRO

%macro fraud_detection;

data ewaste_fraud;

    set ewaste_util;

    if Recovery_Rate < 65 and Operating_Cost > 7000000 then Fraud_Flag='Y';

    else Fraud_Flag='N';

run;

proc print data=ewaste_fraud;

run;

%mend;

%fraud_detection;

OUTPUT:

Obs	Facility_Name	Country	Hazard_Level	Waste_Processed_Tons	Recovery_Rate	Operating_Cost	Sustainability_Score	Start_Date	Report_Date	Facility_ID	Utilization_Class	Fraud_Flag
1	Ecocycle_india	INDIA	LOW	12000	85	4500000	88	01JAN2015	01JAN2025	INDIA_Ecocy	MEDIUM	N
2	Greenloop_usa	USA	MEDIUM	18000	92	8200000	91	15MAR2012	01JAN2025	USA_Green	HIGH	N
3	Retech_germany	GERMANY	LOW	15000	89	6100000	90	10FEB2014	01JAN2025	GERMANY_Retec	MEDIUM	N
4	Urbanrecycle_uk	UK	HIGH	11000	78	4000000	72	05MAY2016	01JAN2025	UK_Urban	LOW	N
5	Ecoreclaim_japan	JAPAN	LOW	17000	94	7500000	95	12JUN2011	01JAN2025	JAPAN_Ecore	MEDIUM	N
6	Wasterenew_china	CHINA	HIGH	30000	60	5000000	55	20AUG2018	01JAN2025	CHINA_Waste	HIGH	N
7	Econova_canada	CANADA	MEDIUM	13000	88	5300000	87	18SEP2013	01JAN2025	CANADA_Econo	MEDIUM	N
8	Recyclehub_aus	AUSTRALIA	MEDIUM	9000	82	3900000	80	11NOV2017	01JAN2025	AUSTRALIA_Recyc	LOW	N
9	Greenearth_india	INDIA	LOW	14000	90	5200000	92	09APR2014	01JAN2025	INDIA_Green	MEDIUM	N
10	Techwaste_usa	USA	HIGH	21000	58	8700000	60	07JUL2019	01JAN2025	USA_Techw	HIGH	Y
11	Ecosphere_uk	UK	MEDIUM	12500	86	4600000	85	03MAR2015	01JAN2025	UK_Ecosp	MEDIUM	N
12	Zerowaste_japan	JAPAN	LOW	16000	91	6800000	94	25DEC2010	01JAN2025	JAPAN_Zerow	MEDIUM	N
13	Cleancircuit_germany	GERMANY	MEDIUM	14500	88	6000000	89	14JAN2013	01JAN2025	GERMANY_Clean	MEDIUM	N
14	Recyclemax_india	INDIA	HIGH	20000	62	7100000	65	02FEB2020	01JAN2025	INDIA_Recyc	HIGH	Y
15	Ecofuture_usa	USA	MEDIUM	15500	87	5900000	88	06JUN2016	01JAN2025	USA_Ecofu	MEDIUM	N
16	Urbangreen_canada	CANADA	MEDIUM	13500	84	5100000	86	19SEP2014	01JAN2025	CANADA_Urban	MEDIUM	N
17	Greenmetal_china	CHINA	HIGH	28000	65	5400000	58	21OCT2018	01JAN2025	CHINA_Green	HIGH	N
18	Ecoloop_aus	AUSTRALIA	LOW	9500	83	3700000	82	13NOV2016	01JAN2025	AUSTRALIA_Ecolo	LOW	N

·  Low recovery + high cost = suspicious

·  Used by auditors & regulators

11. DATE CALCULATIONS

data ewaste_dates;

    set ewaste_fraud;

    Years_Operating = intck('year', Start_Date, Report_Date);

    Next_Audit = intnx('year', Report_Date, 1, 'same');

run;

proc print data=ewaste_dates;

run;

OUTPUT:

Obs	Facility_Name	Country	Hazard_Level	Waste_Processed_Tons	Recovery_Rate	Operating_Cost	Sustainability_Score	Start_Date	Report_Date	Facility_ID	Utilization_Class	Fraud_Flag	Years_Operating	Next_Audit
1	Ecocycle_india	INDIA	LOW	12000	85	4500000	88	01JAN2015	01JAN2025	INDIA_Ecocy	MEDIUM	N	10	24107
2	Greenloop_usa	USA	MEDIUM	18000	92	8200000	91	15MAR2012	01JAN2025	USA_Green	HIGH	N	13	24107
3	Retech_germany	GERMANY	LOW	15000	89	6100000	90	10FEB2014	01JAN2025	GERMANY_Retec	MEDIUM	N	11	24107
4	Urbanrecycle_uk	UK	HIGH	11000	78	4000000	72	05MAY2016	01JAN2025	UK_Urban	LOW	N	9	24107
5	Ecoreclaim_japan	JAPAN	LOW	17000	94	7500000	95	12JUN2011	01JAN2025	JAPAN_Ecore	MEDIUM	N	14	24107
6	Wasterenew_china	CHINA	HIGH	30000	60	5000000	55	20AUG2018	01JAN2025	CHINA_Waste	HIGH	N	7	24107
7	Econova_canada	CANADA	MEDIUM	13000	88	5300000	87	18SEP2013	01JAN2025	CANADA_Econo	MEDIUM	N	12	24107
8	Recyclehub_aus	AUSTRALIA	MEDIUM	9000	82	3900000	80	11NOV2017	01JAN2025	AUSTRALIA_Recyc	LOW	N	8	24107
9	Greenearth_india	INDIA	LOW	14000	90	5200000	92	09APR2014	01JAN2025	INDIA_Green	MEDIUM	N	11	24107
10	Techwaste_usa	USA	HIGH	21000	58	8700000	60	07JUL2019	01JAN2025	USA_Techw	HIGH	Y	6	24107
11	Ecosphere_uk	UK	MEDIUM	12500	86	4600000	85	03MAR2015	01JAN2025	UK_Ecosp	MEDIUM	N	10	24107
12	Zerowaste_japan	JAPAN	LOW	16000	91	6800000	94	25DEC2010	01JAN2025	JAPAN_Zerow	MEDIUM	N	15	24107
13	Cleancircuit_germany	GERMANY	MEDIUM	14500	88	6000000	89	14JAN2013	01JAN2025	GERMANY_Clean	MEDIUM	N	12	24107
14	Recyclemax_india	INDIA	HIGH	20000	62	7100000	65	02FEB2020	01JAN2025	INDIA_Recyc	HIGH	Y	5	24107
15	Ecofuture_usa	USA	MEDIUM	15500	87	5900000	88	06JUN2016	01JAN2025	USA_Ecofu	MEDIUM	N	9	24107
16	Urbangreen_canada	CANADA	MEDIUM	13500	84	5100000	86	19SEP2014	01JAN2025	CANADA_Urban	MEDIUM	N	11	24107
17	Greenmetal_china	CHINA	HIGH	28000	65	5400000	58	21OCT2018	01JAN2025	CHINA_Green	HIGH	N	7	24107
18	Ecoloop_aus	AUSTRALIA	LOW	9500	83	3700000	82	13NOV2016	01JAN2025	AUSTRALIA_Ecolo	LOW	N	9	24107

12. PROC SQL – BUSINESS QUERIES

proc sql;

    create table cost_efficiency as

    select Country,

           avg(Waste_Processed_Tons) as Avg_Waste,

           avg(Recovery_Rate) as Avg_Recovery,

           avg(Operating_Cost) as Avg_Cost

    from ewaste_dates

    group by Country;

quit;

proc print data=cost_efficiency;

run;

OUTPUT:

Obs	Country	Avg_Waste	Avg_Recovery	Avg_Cost
1	AUSTRALIA	9250.00	82.5	3800000
2	CANADA	13250.00	86.0	5200000
3	CHINA	29000.00	62.5	5200000
4	GERMANY	14750.00	88.5	6050000
5	INDIA	15333.33	79.0	5600000
6	JAPAN	16500.00	92.5	7150000
7	UK	11750.00	82.0	4300000
8	USA	18166.67	79.0	7600000

·  Complex grouping

·  Cleaner syntax

·  Industry-preferred

13. PROC FREQ – CATEGORICAL ANALYSIS

proc freq data=ewaste_dates;

    tables Country*Hazard_Level Fraud_Flag / nocum nopercent;

run;

OUTPUT:

The FREQ Procedure

Frequency Row Pct Col Pct

Table of Country by Hazard_Level Country Hazard_Level HIGH LOW MEDIUM Total AUSTRALIA 0 0.00 0.00 1 50.00 16.67 1 50.00 14.29 2     CANADA 0 0.00 0.00 0 0.00 0.00 2 100.00 28.57 2     CHINA 2 100.00 40.00 0 0.00 0.00 0 0.00 0.00 2     GERMANY 0 0.00 0.00 1 50.00 16.67 1 50.00 14.29 2     INDIA 1 33.33 20.00 2 66.67 33.33 0 0.00 0.00 3     JAPAN 0 0.00 0.00 2 100.00 33.33 0 0.00 0.00 2     UK 1 50.00 20.00 0 0.00 0.00 1 50.00 14.29 2     USA 1 33.33 20.00 0 0.00 0.00 2 66.67 28.57 3     Total 5 6 7 18

Fraud_Flag	Frequency
N	16
Y	2

14. PROC MEANS – NUMERIC SUMMARY

proc means data=ewaste_dates mean min max;

    var Waste_Processed_Tons Recovery_Rate Operating_Cost Sustainability_Score;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Waste_Processed_Tons Recovery_Rate Operating_Cost Sustainability_Score	16083.33 81.2222222 5722222.22 80.9444444	9000.00 58.0000000 3700000.00 55.0000000	30000.00 94.0000000 8700000.00 95.0000000

15. PROC UNIVARIATE – DISTRIBUTION

proc univariate data=ewaste_dates;

    var Recovery_Rate Operating_Cost;

    histogram Recovery_Rate;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Recovery_Rate

Moments
N	18	Sum Weights	18
Mean	81.2222222	Sum Observations	1462
Std Deviation	11.6798245	Variance	136.418301
Skewness	-1.1238977	Kurtosis	-0.1687343
Uncorrected SS	121066	Corrected SS	2319.11111
Coeff Variation	14.3800849	Std Error Mean	2.75296104

Basic Statistical Measures
Location		Variability
Mean	81.22222	Std Deviation	11.67982
Median	85.50000	Variance	136.41830
Mode	88.00000	Range	36.00000
		Interquartile Range	11.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	29.50359	Pr > |t|	<.0001
Sign	M	9	Pr >= |M|	<.0001
Signed Rank	S	85.5	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	94.0
99%	94.0
95%	94.0
90%	92.0
75% Q3	89.0
50% Median	85.5
25% Q1	78.0
10%	60.0
5%	58.0
1%	58.0
0% Min	58.0

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
58	10	89	3
60	6	90	9
62	14	91	12
65	17	92	2
78	4	94	5

The UNIVARIATE Procedure

The UNIVARIATE Procedure

Variable: Operating_Cost

Moments
N	18	Sum Weights	18
Mean	5722222.22	Sum Observations	103000000
Std Deviation	1456650.29	Variance	2.12183E12
Skewness	0.59195226	Kurtosis	-0.40658
Uncorrected SS	6.2546E14	Corrected SS	3.60711E13
Coeff Variation	25.4560245	Std Error Mean	343335.766

Basic Statistical Measures
Location		Variability
Mean	5722222	Std Deviation	1456650
Median	5350000	Variance	2.12183E12
Mode	.	Range	5000000
		Interquartile Range	2200000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	16.66655	Pr > |t|	<.0001
Sign	M	9	Pr >= |M|	<.0001
Signed Rank	S	85.5	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	8700000
99%	8700000
95%	8700000
90%	8200000
75% Q3	6800000
50% Median	5350000
25% Q1	4600000
10%	3900000
5%	3700000
1%	3700000
0% Min	3700000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
3700000	18	6800000	12
3900000	8	7100000	14
4000000	4	7500000	5
4500000	1	8200000	2
4600000	11	8700000	10

16. PROC CORR – RELATIONSHIP ANALYSIS

proc corr data=ewaste_dates;

    var Waste_Processed_Tons Recovery_Rate Operating_Cost Sustainability_Score;

run;

OUTPUT:

The CORR Procedure

4 Variables:	Waste_Processed_Tons Recovery_Rate Operating_Cost Sustainability_Score

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Waste_Processed_Tons	18	16083	5704	289500	9000	30000
Recovery_Rate	18	81.22222	11.67982	1462	58.00000	94.00000
Operating_Cost	18	5722222	1456650	103000000	3700000	8700000
Sustainability_Score	18	80.94444	13.04054	1457	55.00000	95.00000

Pearson Correlation Coefficients, N = 18 Prob > |r| under H0: Rho=0
	Waste_Processed_Tons	Recovery_Rate	Operating_Cost	Sustainability_Score
Waste_Processed_Tons	1.00000	-0.66117 0.0028	0.42100 0.0819	-0.67093 0.0023
Recovery_Rate	-0.66117 0.0028	1.00000	-0.09643 0.7035	0.97526 <.0001
Operating_Cost	0.42100 0.0819	-0.09643 0.7035	1.00000	0.00936 0.9706
Sustainability_Score	-0.67093 0.0023	0.97526 <.0001	0.00936 0.9706	1.00000

17. PROC SGPLOT – VISUALIZATION

proc sgplot data=ewaste_dates;

    scatter x=Operating_Cost y=Recovery_Rate / group=Hazard_Level;

run;

OUTPUT:

18. APPEND, TRANSPOSE

Creating New Data

data ewaste_new_batch;

    length Facility_Name $30 Country $15 Hazard_Level $10 Utilization_Class $10 Fraud_Flag $1;

    input Facility_Name $ Country $ Waste_Processed_Tons Recovery_Rate Hazard_Level $

          Operating_Cost Sustainability_Score Start_Date :date9. Report_Date :date9.

          Utilization_Class $ Fraud_Flag $;

    format Start_Date Report_Date date9.;

    datalines;

NeoRecycle_India INDIA 16000 88 LOW 5800000 90 01JAN2016 01FEB2025 MEDIUM N

SmartEcycle_USA USA 22000 59 HIGH 9100000 62 15MAR2019 01FEB2025 HIGH Y

EcoRenew_UK UK 10000 84 MEDIUM 4200000 83 10APR2017 01FEB2025 LOW N

;

run;

proc print data=ewaste_new_batch;

run;

OUTPUT:

Obs	Facility_Name	Country	Hazard_Level	Utilization_Class	Fraud_Flag	Waste_Processed_Tons	Recovery_Rate	Operating_Cost	Sustainability_Score	Start_Date	Report_Date
1	NeoRecycle_India	INDIA	LOW	MEDIUM	N	16000	88	5800000	90	01JAN2016	01FEB2025
2	SmartEcycle_USA	USA	HIGH	HIGH	Y	22000	59	9100000	62	15MAR2019	01FEB2025
3	EcoRenew_UK	UK	MEDIUM	LOW	N	10000	84	4200000	83	10APR2017	01FEB2025

proc append base=ewaste_dates 

            data=ewaste_new_batch force;

run;

proc print data=ewaste_dates;

run;

OUTPUT:

Obs	Facility_Name	Country	Hazard_Level	Waste_Processed_Tons	Recovery_Rate	Operating_Cost	Sustainability_Score	Start_Date	Report_Date	Facility_ID	Utilization_Class	Fraud_Flag	Years_Operating	Next_Audit
1	Ecocycle_india	INDIA	LOW	12000	85	4500000	88	01JAN2015	01JAN2025	INDIA_Ecocy	MEDIUM	N	10	24107
2	Greenloop_usa	USA	MEDIUM	18000	92	8200000	91	15MAR2012	01JAN2025	USA_Green	HIGH	N	13	24107
3	Retech_germany	GERMANY	LOW	15000	89	6100000	90	10FEB2014	01JAN2025	GERMANY_Retec	MEDIUM	N	11	24107
4	Urbanrecycle_uk	UK	HIGH	11000	78	4000000	72	05MAY2016	01JAN2025	UK_Urban	LOW	N	9	24107
5	Ecoreclaim_japan	JAPAN	LOW	17000	94	7500000	95	12JUN2011	01JAN2025	JAPAN_Ecore	MEDIUM	N	14	24107
6	Wasterenew_china	CHINA	HIGH	30000	60	5000000	55	20AUG2018	01JAN2025	CHINA_Waste	HIGH	N	7	24107
7	Econova_canada	CANADA	MEDIUM	13000	88	5300000	87	18SEP2013	01JAN2025	CANADA_Econo	MEDIUM	N	12	24107
8	Recyclehub_aus	AUSTRALIA	MEDIUM	9000	82	3900000	80	11NOV2017	01JAN2025	AUSTRALIA_Recyc	LOW	N	8	24107
9	Greenearth_india	INDIA	LOW	14000	90	5200000	92	09APR2014	01JAN2025	INDIA_Green	MEDIUM	N	11	24107
10	Techwaste_usa	USA	HIGH	21000	58	8700000	60	07JUL2019	01JAN2025	USA_Techw	HIGH	Y	6	24107
11	Ecosphere_uk	UK	MEDIUM	12500	86	4600000	85	03MAR2015	01JAN2025	UK_Ecosp	MEDIUM	N	10	24107
12	Zerowaste_japan	JAPAN	LOW	16000	91	6800000	94	25DEC2010	01JAN2025	JAPAN_Zerow	MEDIUM	N	15	24107
13	Cleancircuit_germany	GERMANY	MEDIUM	14500	88	6000000	89	14JAN2013	01JAN2025	GERMANY_Clean	MEDIUM	N	12	24107
14	Recyclemax_india	INDIA	HIGH	20000	62	7100000	65	02FEB2020	01JAN2025	INDIA_Recyc	HIGH	Y	5	24107
15	Ecofuture_usa	USA	MEDIUM	15500	87	5900000	88	06JUN2016	01JAN2025	USA_Ecofu	MEDIUM	N	9	24107
16	Urbangreen_canada	CANADA	MEDIUM	13500	84	5100000	86	19SEP2014	01JAN2025	CANADA_Urban	MEDIUM	N	11	24107
17	Greenmetal_china	CHINA	HIGH	28000	65	5400000	58	21OCT2018	01JAN2025	CHINA_Green	HIGH	N	7	24107
18	Ecoloop_aus	AUSTRALIA	LOW	9500	83	3700000	82	13NOV2016	01JAN2025	AUSTRALIA_Ecolo	LOW	N	9	24107
19	NeoRecycle_India	INDIA	LOW	16000	88	5800000	90	01JAN2016	01FEB2025		MEDIUM	N	.	.
20	SmartEcycle_USA	USA	HIGH	22000	59	9100000	62	15MAR2019	01FEB2025		HIGH	Y	.	.
21	EcoRenew_UK	UK	MEDIUM	10000	84	4200000	83	10APR2017	01FEB2025		LOW	N	.	.

proc transpose data=ewaste_dates out=ewaste_transposed;

    var Waste_Processed_Tons Recovery_Rate;

    by Country NotSorted;

run;

proc print data=ewaste_transposed;

run;

OUTPUT:

Obs	Country	_NAME_	COL1
1	INDIA	Waste_Processed_Tons	12000
2	INDIA	Recovery_Rate	85
3	USA	Waste_Processed_Tons	18000
4	USA	Recovery_Rate	92
5	GERMANY	Waste_Processed_Tons	15000
6	GERMANY	Recovery_Rate	89
7	UK	Waste_Processed_Tons	11000
8	UK	Recovery_Rate	78
9	JAPAN	Waste_Processed_Tons	17000
10	JAPAN	Recovery_Rate	94
11	CHINA	Waste_Processed_Tons	30000
12	CHINA	Recovery_Rate	60
13	CANADA	Waste_Processed_Tons	13000
14	CANADA	Recovery_Rate	88
15	AUSTRALIA	Waste_Processed_Tons	9000
16	AUSTRALIA	Recovery_Rate	82
17	INDIA	Waste_Processed_Tons	14000
18	INDIA	Recovery_Rate	90
19	USA	Waste_Processed_Tons	21000
20	USA	Recovery_Rate	58
21	UK	Waste_Processed_Tons	12500
22	UK	Recovery_Rate	86
23	JAPAN	Waste_Processed_Tons	16000
24	JAPAN	Recovery_Rate	91
25	GERMANY	Waste_Processed_Tons	14500
26	GERMANY	Recovery_Rate	88
27	INDIA	Waste_Processed_Tons	20000
28	INDIA	Recovery_Rate	62
29	USA	Waste_Processed_Tons	15500
30	USA	Recovery_Rate	87
31	CANADA	Waste_Processed_Tons	13500
32	CANADA	Recovery_Rate	84
33	CHINA	Waste_Processed_Tons	28000
34	CHINA	Recovery_Rate	65
35	AUSTRALIA	Waste_Processed_Tons	9500
36	AUSTRALIA	Recovery_Rate	83
37	INDIA	Waste_Processed_Tons	16000
38	INDIA	Recovery_Rate	88
39	USA	Waste_Processed_Tons	22000
40	USA	Recovery_Rate	59
41	UK	Waste_Processed_Tons	10000
42	UK	Recovery_Rate	84

19. PROC DATASETS – CLEANUP

proc datasets library=work nolist;

    delete ewaste_transposed;

quit;

LOG:

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

20. BUSINESS INSIGHTS

• High hazard facilities often show lower recovery
• Fraud-flagged facilities cluster in high-cost regions
• Sustainability correlates positively with recovery

21. HIT POINTS

✔ Realistic business logic
✔ Strong macro usage
✔ CDISC-style audit thinking
✔ Clean coding practices
✔ Visualization + statistics

22. CONCLUSION

This project demonstrates full-stack SAS programming:

• Data engineering
• Statistical analysis
• Business intelligence
• Fraud detection
• Sustainability analytics

It is perfect for interviews, portfolios, and senior-level discussions.

INTERVIEW QUESTIONS FOR YOU

·  What is the difference between PROC SQL and DATA step?

·  What is an INNER JOIN vs LEFT JOIN?

·  How do you remove duplicate rows using SQL?

 

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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 E-WASTE data.

Our Mission:

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

This project is suitable for:

·  Students learning SAS

·  Data analysts building portfolios

·  Professionals preparing for SAS interviews

·  Bloggers writing about analytics and smart cities

·  EV and energy industry professionals

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