395.Can smart SAS macros help identify high-risk safety incidents before they become disasters?

Can smart SAS macros help identify high-risk safety incidents before they become disasters?

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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 SORT | MERGE | PROC TRANSPOSE | PROC DATASETS DELETE | DATA FUNCTIONS

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INTRODUCTION

Workplace safety is one of the most critical areas for organizations across manufacturing, construction, healthcare, logistics, IT services, and energy sectors. Every safety incident not only affects employee health but also results in:

• Productivity loss
• Legal liabilities
• Insurance claims
• Increased compensation costs
• Damage to company reputation

Modern organizations use data analytics to monitor, analyze, and prevent safety incidents before they escalate.

BUSINESS CONTEXT

Imagine a multinational company with operations across multiple industries:

• Manufacturing
• Construction
• Healthcare
• IT Services
• Logistics

The Safety & Compliance Department wants answers to questions like:

• Which industries have the highest severity incidents?
• Are compensation costs aligned with severity?
• Are there suspicious (fraud-like) claims?
• Which root causes are most frequent?
• How effective are prevention measures?
• Can we classify incidents by risk level automatically?

TABLE OF CONTENTS

1. Dataset Design & Variables
2. Creating Base Incident Dataset
3. Date Handling using MDY, INTCK, INTNX
4. Character & Numeric Functions Usage
5. PROC SQL Analytics
6. PROC FREQ – Root Cause Analysis
7. PROC MEANS – Cost & Severity Summary
8. PROC UNIVARIATE – Distribution Analysis
9. PROC CORR – Relationship Analysis
10. PROC SGPLOT – Visualization
11. Macros for:
• Incident Severity Classification
• Fraud Detection Logic
• Date Derivations
12. SET, MERGE, APPEND, TRANSPOSE
13. PROC DATASETS DELETE

1. DATASET DESIGN

Variables Used

Variable	Description
Incident_ID	Unique incident identifier
Industry	Industry type
Incident_Date	Date of incident
Severity_Level	Low / Medium / High / Critical
Lost_Work_Days	Number of days lost
Compensation_Cost	Claim amount
Root_Cause	Cause of incident
Prevention_Score	Score (0–100)
Prevention_Percentage	Converted %
Reported_By	Supervisor / Worker
Region	Operational region

2. CREATING BASE DATASET

data safety_incidents;

    length Incident_ID $6 Industry $20 Severity_Level $10 

           Root_Cause $30 Reported_By $15 Region $15;

    format Incident_Date date9.;

    Incident_ID='INC001'; Industry='Manufacturing'; Incident_Date=mdy(1,15,2024);

    Severity_Level='High'; Lost_Work_Days=12; Compensation_Cost=45000;

    Root_Cause='Machine Failure'; Prevention_Score=60; Reported_By='Supervisor'; 

    Region='South'; 

    output;

    Incident_ID='INC002'; Industry='Construction'; Incident_Date=mdy(2,10,2024);

    Severity_Level='Critical'; Lost_Work_Days=45; Compensation_Cost=150000;

    Root_Cause='Fall Hazard'; Prevention_Score=40; Reported_By='Supervisor'; 

    Region='West'; 

    output;

    Incident_ID='INC003'; Industry='Healthcare'; Incident_Date=mdy(3,5,2024);

    Severity_Level='Medium'; Lost_Work_Days=6; Compensation_Cost=18000;

    Root_Cause='Needle Injury'; Prevention_Score=75; Reported_By='Worker'; 

    Region='North'; 

    output;

    Incident_ID='INC004'; Industry='IT Services'; Incident_Date=mdy(3,20,2024);

    Severity_Level='Low'; Lost_Work_Days=1; Compensation_Cost=3000;

    Root_Cause='Ergonomic Issue'; Prevention_Score=90; Reported_By='Worker';

    Region='East'; 

    output;

    Incident_ID='INC005'; Industry='Logistics'; Incident_Date=mdy(4,2,2024);

    Severity_Level='High'; Lost_Work_Days=18; Compensation_Cost=52000;

    Root_Cause='Vehicle Accident'; Prevention_Score=55; Reported_By='Supervisor';

    Region='South'; 

    output;

    Incident_ID='INC006'; Industry='Manufacturing'; Incident_Date=mdy(4,15,2024);

    Severity_Level='Medium'; Lost_Work_Days=8; Compensation_Cost=22000;

    Root_Cause='Chemical Exposure'; Prevention_Score=65; Reported_By='Worker';

    Region='West';

    output;

    Incident_ID='INC007'; Industry='Construction'; Incident_Date=mdy(5,10,2024);

    Severity_Level='High'; Lost_Work_Days=22; Compensation_Cost=78000;

    Root_Cause='Equipment Failure'; Prevention_Score=50; Reported_By='Supervisor';

    Region='North'; 

    output;

    Incident_ID='INC008'; Industry='Healthcare'; Incident_Date=mdy(5,22,2024);

    Severity_Level='Low'; Lost_Work_Days=2; Compensation_Cost=4000;

    Root_Cause='Slip'; Prevention_Score=85; Reported_By='Worker'; Region='East';

    output;

    Incident_ID='INC009'; Industry='Logistics'; Incident_Date=mdy(6,1,2024);

    Severity_Level='Critical'; Lost_Work_Days=60; Compensation_Cost=210000;

    Root_Cause='Fire Accident'; Prevention_Score=30; Reported_By='Supervisor'; 

    Region='West'; 

    output;

    Incident_ID='INC010'; Industry='IT Services'; Incident_Date=mdy(6,18,2024);

    Severity_Level='Medium'; Lost_Work_Days=4; Compensation_Cost=9000;

    Root_Cause='Stress Injury'; Prevention_Score=70; Reported_By='Worker'; 

    Region='South'; 

    output;

    Incident_ID='INC011'; Industry='Manufacturing'; Incident_Date=mdy(7,5,2024);

    Severity_Level='High'; Lost_Work_Days=15; Compensation_Cost=48000;

    Root_Cause='Electrical Hazard'; Prevention_Score=58; Reported_By='Supervisor'; 

    Region='East';

    output;

    Incident_ID='INC012'; Industry='Construction'; Incident_Date=mdy(7,22,2024);

    Severity_Level='Medium'; Lost_Work_Days=10; Compensation_Cost=25000;

    Root_Cause='Tool Misuse'; Prevention_Score=68; Reported_By='Worker';

    Region='South'; 

    output;

    Incident_ID='INC013'; Industry='Healthcare'; Incident_Date=mdy(8,8,2024);

    Severity_Level='High'; Lost_Work_Days=20; Compensation_Cost=60000;

    Root_Cause='Patient Handling'; Prevention_Score=52; Reported_By='Supervisor'; 

    Region='North';

    output;

    Incident_ID='INC014'; Industry='Logistics'; Incident_Date=mdy(8,20,2024);

    Severity_Level='Low'; Lost_Work_Days=3; Compensation_Cost=7000;

    Root_Cause='Manual Handling'; Prevention_Score=88; Reported_By='Worker';

    Region='East';

    output;

    Incident_ID='INC015'; Industry='IT Services'; Incident_Date=mdy(9,2,2024);

    Severity_Level='Medium'; Lost_Work_Days=5; Compensation_Cost=12000;

    Root_Cause='Eye Strain'; Prevention_Score=78; Reported_By='Worker'; 

    Region='West'; 

    output;

run;

proc print data=safety_incidents;

run;

OUTPUT:

Obs	Incident_ID	Industry	Severity_Level	Root_Cause	Reported_By	Region	Incident_Date	Lost_Work_Days	Compensation_Cost	Prevention_Score
1	INC001	Manufacturing	High	Machine Failure	Supervisor	South	15JAN2024	12	45000	60
2	INC002	Construction	Critical	Fall Hazard	Supervisor	West	10FEB2024	45	150000	40
3	INC003	Healthcare	Medium	Needle Injury	Worker	North	05MAR2024	6	18000	75
4	INC004	IT Services	Low	Ergonomic Issue	Worker	East	20MAR2024	1	3000	90
5	INC005	Logistics	High	Vehicle Accident	Supervisor	South	02APR2024	18	52000	55
6	INC006	Manufacturing	Medium	Chemical Exposure	Worker	West	15APR2024	8	22000	65
7	INC007	Construction	High	Equipment Failure	Supervisor	North	10MAY2024	22	78000	50
8	INC008	Healthcare	Low	Slip	Worker	East	22MAY2024	2	4000	85
9	INC009	Logistics	Critical	Fire Accident	Supervisor	West	01JUN2024	60	210000	30
10	INC010	IT Services	Medium	Stress Injury	Worker	South	18JUN2024	4	9000	70
11	INC011	Manufacturing	High	Electrical Hazard	Supervisor	East	05JUL2024	15	48000	58
12	INC012	Construction	Medium	Tool Misuse	Worker	South	22JUL2024	10	25000	68
13	INC013	Healthcare	High	Patient Handling	Supervisor	North	08AUG2024	20	60000	52
14	INC014	Logistics	Low	Manual Handling	Worker	East	20AUG2024	3	7000	88
15	INC015	IT Services	Medium	Eye Strain	Worker	West	02SEP2024	5	12000	78

·  Used to manually create mock data

·  Common in POCs, interviews, demos

·  length ensures proper character storage

·  mdy() ensures correct SAS date values

3. DATE CALCULATIONS USING INTCK & INTNX

data safety_dates;

    set safety_incidents;

    Days_Since_Incident = intck('day', Incident_Date, today());

    Review_Date = intnx('month', Incident_Date, 1, 'same');

run;

proc print data=safety_dates;

run;

OUTPUT:

Obs	Incident_ID	Industry	Severity_Level	Root_Cause	Reported_By	Region	Incident_Date	Lost_Work_Days	Compensation_Cost	Prevention_Score	Days_Since_Incident	Review_Date
1	INC001	Manufacturing	High	Machine Failure	Supervisor	South	15JAN2024	12	45000	60	755	23421
2	INC002	Construction	Critical	Fall Hazard	Supervisor	West	10FEB2024	45	150000	40	729	23445
3	INC003	Healthcare	Medium	Needle Injury	Worker	North	05MAR2024	6	18000	75	705	23471
4	INC004	IT Services	Low	Ergonomic Issue	Worker	East	20MAR2024	1	3000	90	690	23486
5	INC005	Logistics	High	Vehicle Accident	Supervisor	South	02APR2024	18	52000	55	677	23498
6	INC006	Manufacturing	Medium	Chemical Exposure	Worker	West	15APR2024	8	22000	65	664	23511
7	INC007	Construction	High	Equipment Failure	Supervisor	North	10MAY2024	22	78000	50	639	23537
8	INC008	Healthcare	Low	Slip	Worker	East	22MAY2024	2	4000	85	627	23549
9	INC009	Logistics	Critical	Fire Accident	Supervisor	West	01JUN2024	60	210000	30	617	23558
10	INC010	IT Services	Medium	Stress Injury	Worker	South	18JUN2024	4	9000	70	600	23575
11	INC011	Manufacturing	High	Electrical Hazard	Supervisor	East	05JUL2024	15	48000	58	583	23593
12	INC012	Construction	Medium	Tool Misuse	Worker	South	22JUL2024	10	25000	68	566	23610
13	INC013	Healthcare	High	Patient Handling	Supervisor	North	08AUG2024	20	60000	52	549	23627
14	INC014	Logistics	Low	Manual Handling	Worker	East	20AUG2024	3	7000	88	537	23639
15	INC015	IT Services	Medium	Eye Strain	Worker	West	02SEP2024	5	12000	78	524	23651

·  INTCK → time difference (used in SLA checks)

·  INTNX → future review scheduling

·  Very common in compliance analytics

4. CHARACTER & NUMERIC FUNCTIONS

data safety_clean;

    set safety_dates;

    Industry_Upper = upcase(Industry);

    Industry_Lower = lowcase(Industry);

    Industry_Proper = propcase(Industry);

    Root_Cause_Clean = strip(trim(Root_Cause));

    Incident_Label = catx('-', Incident_ID, Industry);

    Prevention_Percentage = Prevention_Score / 100;

    Cost_Adjusted = round(Compensation_Cost * 1.05, 1);

run;

proc print data=safety_clean;

run;

OUTPUT:

Obs	Incident_ID	Industry	Severity_Level	Root_Cause	Reported_By	Region	Incident_Date	Lost_Work_Days	Compensation_Cost	Prevention_Score	Days_Since_Incident	Review_Date	Industry_Upper	Industry_Lower	Industry_Proper	Root_Cause_Clean	Incident_Label	Prevention_Percentage	Cost_Adjusted
1	INC001	Manufacturing	High	Machine Failure	Supervisor	South	15JAN2024	12	45000	60	755	23421	MANUFACTURING	manufacturing	Manufacturing	Machine Failure	INC001-Manufacturing	0.60	47250
2	INC002	Construction	Critical	Fall Hazard	Supervisor	West	10FEB2024	45	150000	40	729	23445	CONSTRUCTION	construction	Construction	Fall Hazard	INC002-Construction	0.40	157500
3	INC003	Healthcare	Medium	Needle Injury	Worker	North	05MAR2024	6	18000	75	705	23471	HEALTHCARE	healthcare	Healthcare	Needle Injury	INC003-Healthcare	0.75	18900
4	INC004	IT Services	Low	Ergonomic Issue	Worker	East	20MAR2024	1	3000	90	690	23486	IT SERVICES	it services	It Services	Ergonomic Issue	INC004-IT Services	0.90	3150
5	INC005	Logistics	High	Vehicle Accident	Supervisor	South	02APR2024	18	52000	55	677	23498	LOGISTICS	logistics	Logistics	Vehicle Accident	INC005-Logistics	0.55	54600
6	INC006	Manufacturing	Medium	Chemical Exposure	Worker	West	15APR2024	8	22000	65	664	23511	MANUFACTURING	manufacturing	Manufacturing	Chemical Exposure	INC006-Manufacturing	0.65	23100
7	INC007	Construction	High	Equipment Failure	Supervisor	North	10MAY2024	22	78000	50	639	23537	CONSTRUCTION	construction	Construction	Equipment Failure	INC007-Construction	0.50	81900
8	INC008	Healthcare	Low	Slip	Worker	East	22MAY2024	2	4000	85	627	23549	HEALTHCARE	healthcare	Healthcare	Slip	INC008-Healthcare	0.85	4200
9	INC009	Logistics	Critical	Fire Accident	Supervisor	West	01JUN2024	60	210000	30	617	23558	LOGISTICS	logistics	Logistics	Fire Accident	INC009-Logistics	0.30	220500
10	INC010	IT Services	Medium	Stress Injury	Worker	South	18JUN2024	4	9000	70	600	23575	IT SERVICES	it services	It Services	Stress Injury	INC010-IT Services	0.70	9450
11	INC011	Manufacturing	High	Electrical Hazard	Supervisor	East	05JUL2024	15	48000	58	583	23593	MANUFACTURING	manufacturing	Manufacturing	Electrical Hazard	INC011-Manufacturing	0.58	50400
12	INC012	Construction	Medium	Tool Misuse	Worker	South	22JUL2024	10	25000	68	566	23610	CONSTRUCTION	construction	Construction	Tool Misuse	INC012-Construction	0.68	26250
13	INC013	Healthcare	High	Patient Handling	Supervisor	North	08AUG2024	20	60000	52	549	23627	HEALTHCARE	healthcare	Healthcare	Patient Handling	INC013-Healthcare	0.52	63000
14	INC014	Logistics	Low	Manual Handling	Worker	East	20AUG2024	3	7000	88	537	23639	LOGISTICS	logistics	Logistics	Manual Handling	INC014-Logistics	0.88	7350
15	INC015	IT Services	Medium	Eye Strain	Worker	West	02SEP2024	5	12000	78	524	23651	IT SERVICES	it services	It Services	Eye Strain	INC015-IT Services	0.78	12600

·  strip/trim → remove messy spaces

·  cat/catx → labeling

·  upcase/lowcase/propcase → reporting consistency

·  Numeric functions → cost projections

5. PROC SQL – INDUSTRY LEVEL SUMMARY

proc sql;

    create table industry_summary as

    select Industry,

           count(*) as Incident_Count,

           sum(Lost_Work_Days) as Total_Lost_Days,

           mean(Compensation_Cost) as Avg_Compensation format=dollar12.,

           mean(Prevention_Score) as Avg_Prevention

    from safety_clean

    group by Industry;

quit;

proc print data=industry_summary;

run;

OUTPUT:

Obs	Industry	Incident_Count	Total_Lost_Days	Avg_Compensation	Avg_Prevention
1	Construction	3	77	$84,333	52.6667
2	Healthcare	3	28	$27,333	70.6667
3	IT Services	3	10	$8,000	79.3333
4	Logistics	3	81	$89,667	57.6667
5	Manufacturing	3	35	$38,333	61.0000

·  Preferred in corporate analytics

·  Easy joins, grouping, aggregations

·  Similar to real databases

6. PROC FREQ – ROOT CAUSE ANALYSIS

proc freq data=safety_clean;

    tables Root_Cause Severity_Level / nocum nopercent;

run;

OUTPUT:

The FREQ Procedure

Root_Cause	Frequency
Chemical Exposure	1
Electrical Hazard	1
Equipment Failure	1
Ergonomic Issue	1
Eye Strain	1
Fall Hazard	1
Fire Accident	1
Machine Failure	1
Manual Handling	1
Needle Injury	1
Patient Handling	1
Slip	1
Stress Injury	1
Tool Misuse	1
Vehicle Accident	1

Severity_Level	Frequency
Critical	2
High	5
Low	3
Medium	5

·  Identifies most common accident causes

·  Helps management prioritize safety training

7. PROC MEANS

proc means data=safety_clean mean min max sum;

    var Lost_Work_Days Compensation_Cost Prevention_Score;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum	Sum
Lost_Work_Days Compensation_Cost Prevention_Score	15.4000000 49533.33 64.2666667	1.0000000 3000.00 30.0000000	60.0000000 210000.00 90.0000000	231.0000000 743000.00 964.0000000

·  Fast statistical summary

·  Used in almost every SAS job

8. PROC UNIVARIATE

proc univariate data=safety_clean;

    var Compensation_Cost;

    histogram Compensation_Cost;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Compensation_Cost

Moments
N	15	Sum Weights	15
Mean	49533.3333	Sum Observations	743000
Std Deviation	58703.695	Variance	3446123810
Skewness	1.91950422	Kurtosis	3.48959698
Uncorrected SS	8.5049E10	Corrected SS	4.82457E10
Coeff Variation	118.513516	Std Error Mean	15157.2289

Basic Statistical Measures
Location		Variability
Mean	49533.33	Std Deviation	58704
Median	25000.00	Variance	3446123810
Mode	.	Range	207000
		Interquartile Range	51000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	3.267968	Pr > |t|	0.0056
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	210000
99%	210000
95%	210000
90%	150000
75% Q3	60000
50% Median	25000
25% Q1	9000
10%	4000
5%	3000
1%	3000
0% Min	3000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
3000	4	52000	5
4000	8	60000	13
7000	14	78000	7
9000	10	150000	2
12000	15	210000	9

The UNIVARIATE Procedure

·  Detects skewness

·  Identifies extreme claim amounts

9. PROC CORR

proc corr data=safety_clean;

    var Lost_Work_Days Compensation_Cost Prevention_Score;

run;

OUTPUT:

The CORR Procedure

3 Variables:	Lost_Work_Days Compensation_Cost Prevention_Score

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Lost_Work_Days	15	15.40000	16.68960	231.00000	1.00000	60.00000
Compensation_Cost	15	49533	58704	743000	3000	210000
Prevention_Score	15	64.26667	17.45798	964.00000	30.00000	90.00000

Pearson Correlation Coefficients, N = 15 Prob > |r| under H0: Rho=0
	Lost_Work_Days	Compensation_Cost	Prevention_Score
Lost_Work_Days	1.00000	0.99748 <.0001	-0.90254 <.0001
Compensation_Cost	0.99748 <.0001	1.00000	-0.89310 <.0001
Prevention_Score	-0.90254 <.0001	-0.89310 <.0001	1.00000

·  High correlation between lost days & compensation

·  Negative correlation with prevention score

10. PROC SGPLOT

proc sgplot data=safety_clean;

    vbar Industry / response=Compensation_Cost stat=mean;

    title "Average Compensation Cost by Industry";

run;

OUTPUT:

11. MACRO – INCIDENT SEVERITY CLASSIFICATION

%macro classify_severity;

data safety_classified;

    set safety_clean;

    length Risk_Level $10;

    if Severity_Level='Critical' or Compensation_Cost > 100000 then Risk_Level='Extreme';

    else if Severity_Level='High' then Risk_Level='High';

    else if Severity_Level='Medium' then Risk_Level='Moderate';

    else Risk_Level='Low';

run;

proc print data=safety_classified;

run;

%mend;

%classify_severity;

OUTPUT:

Obs	Incident_ID	Industry	Severity_Level	Root_Cause	Reported_By	Region	Incident_Date	Lost_Work_Days	Compensation_Cost	Prevention_Score	Days_Since_Incident	Review_Date	Industry_Upper	Industry_Lower	Industry_Proper	Root_Cause_Clean	Incident_Label	Prevention_Percentage	Cost_Adjusted	Risk_Level
1	INC001	Manufacturing	High	Machine Failure	Supervisor	South	15JAN2024	12	45000	60	755	23421	MANUFACTURING	manufacturing	Manufacturing	Machine Failure	INC001-Manufacturing	0.60	47250	High
2	INC002	Construction	Critical	Fall Hazard	Supervisor	West	10FEB2024	45	150000	40	729	23445	CONSTRUCTION	construction	Construction	Fall Hazard	INC002-Construction	0.40	157500	Extreme
3	INC003	Healthcare	Medium	Needle Injury	Worker	North	05MAR2024	6	18000	75	705	23471	HEALTHCARE	healthcare	Healthcare	Needle Injury	INC003-Healthcare	0.75	18900	Moderate
4	INC004	IT Services	Low	Ergonomic Issue	Worker	East	20MAR2024	1	3000	90	690	23486	IT SERVICES	it services	It Services	Ergonomic Issue	INC004-IT Services	0.90	3150	Low
5	INC005	Logistics	High	Vehicle Accident	Supervisor	South	02APR2024	18	52000	55	677	23498	LOGISTICS	logistics	Logistics	Vehicle Accident	INC005-Logistics	0.55	54600	High
6	INC006	Manufacturing	Medium	Chemical Exposure	Worker	West	15APR2024	8	22000	65	664	23511	MANUFACTURING	manufacturing	Manufacturing	Chemical Exposure	INC006-Manufacturing	0.65	23100	Moderate
7	INC007	Construction	High	Equipment Failure	Supervisor	North	10MAY2024	22	78000	50	639	23537	CONSTRUCTION	construction	Construction	Equipment Failure	INC007-Construction	0.50	81900	High
8	INC008	Healthcare	Low	Slip	Worker	East	22MAY2024	2	4000	85	627	23549	HEALTHCARE	healthcare	Healthcare	Slip	INC008-Healthcare	0.85	4200	Low
9	INC009	Logistics	Critical	Fire Accident	Supervisor	West	01JUN2024	60	210000	30	617	23558	LOGISTICS	logistics	Logistics	Fire Accident	INC009-Logistics	0.30	220500	Extreme
10	INC010	IT Services	Medium	Stress Injury	Worker	South	18JUN2024	4	9000	70	600	23575	IT SERVICES	it services	It Services	Stress Injury	INC010-IT Services	0.70	9450	Moderate
11	INC011	Manufacturing	High	Electrical Hazard	Supervisor	East	05JUL2024	15	48000	58	583	23593	MANUFACTURING	manufacturing	Manufacturing	Electrical Hazard	INC011-Manufacturing	0.58	50400	High
12	INC012	Construction	Medium	Tool Misuse	Worker	South	22JUL2024	10	25000	68	566	23610	CONSTRUCTION	construction	Construction	Tool Misuse	INC012-Construction	0.68	26250	Moderate
13	INC013	Healthcare	High	Patient Handling	Supervisor	North	08AUG2024	20	60000	52	549	23627	HEALTHCARE	healthcare	Healthcare	Patient Handling	INC013-Healthcare	0.52	63000	High
14	INC014	Logistics	Low	Manual Handling	Worker	East	20AUG2024	3	7000	88	537	23639	LOGISTICS	logistics	Logistics	Manual Handling	INC014-Logistics	0.88	7350	Low
15	INC015	IT Services	Medium	Eye Strain	Worker	West	02SEP2024	5	12000	78	524	23651	IT SERVICES	it services	It Services	Eye Strain	INC015-IT Services	0.78	12600	Moderate

·  Automation

·  Reusability

·  Interview favorite topic

12. FRAUD DETECTION LOGIC 

%macro fraud_check;

data fraud_flags;

    set safety_classified;

length Fraud_Flag $5.;

    Fraud_Flag='NO';

    if Lost_Work_Days > 40 and Compensation_Cost > 150000 then Fraud_Flag='YES';

run;

proc print data=fraud_flags;

run;

%mend;

%fraud_check;

OUTPUT:

Obs	Incident_ID	Industry	Severity_Level	Root_Cause	Reported_By	Region	Incident_Date	Lost_Work_Days	Compensation_Cost	Prevention_Score	Days_Since_Incident	Review_Date	Industry_Upper	Industry_Lower	Industry_Proper	Root_Cause_Clean	Incident_Label	Prevention_Percentage	Cost_Adjusted	Risk_Level	Fraud_Flag
1	INC001	Manufacturing	High	Machine Failure	Supervisor	South	15JAN2024	12	45000	60	755	23421	MANUFACTURING	manufacturing	Manufacturing	Machine Failure	INC001-Manufacturing	0.60	47250	High	NO
2	INC002	Construction	Critical	Fall Hazard	Supervisor	West	10FEB2024	45	150000	40	729	23445	CONSTRUCTION	construction	Construction	Fall Hazard	INC002-Construction	0.40	157500	Extreme	NO
3	INC003	Healthcare	Medium	Needle Injury	Worker	North	05MAR2024	6	18000	75	705	23471	HEALTHCARE	healthcare	Healthcare	Needle Injury	INC003-Healthcare	0.75	18900	Moderate	NO
4	INC004	IT Services	Low	Ergonomic Issue	Worker	East	20MAR2024	1	3000	90	690	23486	IT SERVICES	it services	It Services	Ergonomic Issue	INC004-IT Services	0.90	3150	Low	NO
5	INC005	Logistics	High	Vehicle Accident	Supervisor	South	02APR2024	18	52000	55	677	23498	LOGISTICS	logistics	Logistics	Vehicle Accident	INC005-Logistics	0.55	54600	High	NO
6	INC006	Manufacturing	Medium	Chemical Exposure	Worker	West	15APR2024	8	22000	65	664	23511	MANUFACTURING	manufacturing	Manufacturing	Chemical Exposure	INC006-Manufacturing	0.65	23100	Moderate	NO
7	INC007	Construction	High	Equipment Failure	Supervisor	North	10MAY2024	22	78000	50	639	23537	CONSTRUCTION	construction	Construction	Equipment Failure	INC007-Construction	0.50	81900	High	NO
8	INC008	Healthcare	Low	Slip	Worker	East	22MAY2024	2	4000	85	627	23549	HEALTHCARE	healthcare	Healthcare	Slip	INC008-Healthcare	0.85	4200	Low	NO
9	INC009	Logistics	Critical	Fire Accident	Supervisor	West	01JUN2024	60	210000	30	617	23558	LOGISTICS	logistics	Logistics	Fire Accident	INC009-Logistics	0.30	220500	Extreme	YES
10	INC010	IT Services	Medium	Stress Injury	Worker	South	18JUN2024	4	9000	70	600	23575	IT SERVICES	it services	It Services	Stress Injury	INC010-IT Services	0.70	9450	Moderate	NO
11	INC011	Manufacturing	High	Electrical Hazard	Supervisor	East	05JUL2024	15	48000	58	583	23593	MANUFACTURING	manufacturing	Manufacturing	Electrical Hazard	INC011-Manufacturing	0.58	50400	High	NO
12	INC012	Construction	Medium	Tool Misuse	Worker	South	22JUL2024	10	25000	68	566	23610	CONSTRUCTION	construction	Construction	Tool Misuse	INC012-Construction	0.68	26250	Moderate	NO
13	INC013	Healthcare	High	Patient Handling	Supervisor	North	08AUG2024	20	60000	52	549	23627	HEALTHCARE	healthcare	Healthcare	Patient Handling	INC013-Healthcare	0.52	63000	High	NO
14	INC014	Logistics	Low	Manual Handling	Worker	East	20AUG2024	3	7000	88	537	23639	LOGISTICS	logistics	Logistics	Manual Handling	INC014-Logistics	0.88	7350	Low	NO
15	INC015	IT Services	Medium	Eye Strain	Worker	West	02SEP2024	5	12000	78	524	23651	IT SERVICES	it services	It Services	Eye Strain	INC015-IT Services	0.78	12600	Moderate	NO

13. TRANSPOSE, APPEND, SORT,MERGE

proc transpose data=industry_summary out=industry_t;

    id Industry;

    var Incident_Count;

run;

proc print data=industry_t;

run;

OUTPUT:

Obs	_NAME_	Construction	Healthcare	IT Services	Logistics	Manufacturing
1	Incident_Count	3	3	3	3	3

proc sort data=safety_classified;by Industry;run;

proc print data=safety_classified;

run;

OUTPUT:

Obs	Incident_ID	Industry	Severity_Level	Root_Cause	Reported_By	Region	Incident_Date	Lost_Work_Days	Compensation_Cost	Prevention_Score	Days_Since_Incident	Review_Date	Industry_Upper	Industry_Lower	Industry_Proper	Root_Cause_Clean	Incident_Label	Prevention_Percentage	Cost_Adjusted	Risk_Level
1	INC002	Construction	Critical	Fall Hazard	Supervisor	West	10FEB2024	45	150000	40	729	23445	CONSTRUCTION	construction	Construction	Fall Hazard	INC002-Construction	0.40	157500	Extreme
2	INC007	Construction	High	Equipment Failure	Supervisor	North	10MAY2024	22	78000	50	639	23537	CONSTRUCTION	construction	Construction	Equipment Failure	INC007-Construction	0.50	81900	High
3	INC012	Construction	Medium	Tool Misuse	Worker	South	22JUL2024	10	25000	68	566	23610	CONSTRUCTION	construction	Construction	Tool Misuse	INC012-Construction	0.68	26250	Moderate
4	INC003	Healthcare	Medium	Needle Injury	Worker	North	05MAR2024	6	18000	75	705	23471	HEALTHCARE	healthcare	Healthcare	Needle Injury	INC003-Healthcare	0.75	18900	Moderate
5	INC008	Healthcare	Low	Slip	Worker	East	22MAY2024	2	4000	85	627	23549	HEALTHCARE	healthcare	Healthcare	Slip	INC008-Healthcare	0.85	4200	Low
6	INC013	Healthcare	High	Patient Handling	Supervisor	North	08AUG2024	20	60000	52	549	23627	HEALTHCARE	healthcare	Healthcare	Patient Handling	INC013-Healthcare	0.52	63000	High
7	INC004	IT Services	Low	Ergonomic Issue	Worker	East	20MAR2024	1	3000	90	690	23486	IT SERVICES	it services	It Services	Ergonomic Issue	INC004-IT Services	0.90	3150	Low
8	INC010	IT Services	Medium	Stress Injury	Worker	South	18JUN2024	4	9000	70	600	23575	IT SERVICES	it services	It Services	Stress Injury	INC010-IT Services	0.70	9450	Moderate
9	INC015	IT Services	Medium	Eye Strain	Worker	West	02SEP2024	5	12000	78	524	23651	IT SERVICES	it services	It Services	Eye Strain	INC015-IT Services	0.78	12600	Moderate
10	INC005	Logistics	High	Vehicle Accident	Supervisor	South	02APR2024	18	52000	55	677	23498	LOGISTICS	logistics	Logistics	Vehicle Accident	INC005-Logistics	0.55	54600	High
11	INC009	Logistics	Critical	Fire Accident	Supervisor	West	01JUN2024	60	210000	30	617	23558	LOGISTICS	logistics	Logistics	Fire Accident	INC009-Logistics	0.30	220500	Extreme
12	INC014	Logistics	Low	Manual Handling	Worker	East	20AUG2024	3	7000	88	537	23639	LOGISTICS	logistics	Logistics	Manual Handling	INC014-Logistics	0.88	7350	Low
13	INC001	Manufacturing	High	Machine Failure	Supervisor	South	15JAN2024	12	45000	60	755	23421	MANUFACTURING	manufacturing	Manufacturing	Machine Failure	INC001-Manufacturing	0.60	47250	High
14	INC006	Manufacturing	Medium	Chemical Exposure	Worker	West	15APR2024	8	22000	65	664	23511	MANUFACTURING	manufacturing	Manufacturing	Chemical Exposure	INC006-Manufacturing	0.65	23100	Moderate
15	INC011	Manufacturing	High	Electrical Hazard	Supervisor	East	05JUL2024	15	48000	58	583	23593	MANUFACTURING	manufacturing	Manufacturing	Electrical Hazard	INC011-Manufacturing	0.58	50400	High

proc sort data=industry_summary;by Industry;run;

proc print data=industry_summary;

run;

OUTPUT:

Obs	Industry	Incident_Count	Total_Lost_Days	Avg_Compensation	Avg_Prevention
1	Construction	3	77	$84,333	52.6667
2	Healthcare	3	28	$27,333	70.6667
3	IT Services	3	10	$8,000	79.3333
4	Logistics	3	81	$89,667	57.6667
5	Manufacturing	3	35	$38,333	61.0000

data combined;

    merge safety_classified 

          industry_summary;

    by Industry;

run;

proc print data=combined;

run;

OUTPUT:

Obs	Incident_ID	Industry	Severity_Level	Root_Cause	Reported_By	Region	Incident_Date	Lost_Work_Days	Compensation_Cost	Prevention_Score	Days_Since_Incident	Review_Date	Industry_Upper	Industry_Lower	Industry_Proper	Root_Cause_Clean	Incident_Label	Prevention_Percentage	Cost_Adjusted	Risk_Level	Incident_Count	Total_Lost_Days	Avg_Compensation	Avg_Prevention
1	INC002	Construction	Critical	Fall Hazard	Supervisor	West	10FEB2024	45	150000	40	729	23445	CONSTRUCTION	construction	Construction	Fall Hazard	INC002-Construction	0.40	157500	Extreme	3	77	$84,333	52.6667
2	INC007	Construction	High	Equipment Failure	Supervisor	North	10MAY2024	22	78000	50	639	23537	CONSTRUCTION	construction	Construction	Equipment Failure	INC007-Construction	0.50	81900	High	3	77	$84,333	52.6667
3	INC012	Construction	Medium	Tool Misuse	Worker	South	22JUL2024	10	25000	68	566	23610	CONSTRUCTION	construction	Construction	Tool Misuse	INC012-Construction	0.68	26250	Moderate	3	77	$84,333	52.6667
4	INC003	Healthcare	Medium	Needle Injury	Worker	North	05MAR2024	6	18000	75	705	23471	HEALTHCARE	healthcare	Healthcare	Needle Injury	INC003-Healthcare	0.75	18900	Moderate	3	28	$27,333	70.6667
5	INC008	Healthcare	Low	Slip	Worker	East	22MAY2024	2	4000	85	627	23549	HEALTHCARE	healthcare	Healthcare	Slip	INC008-Healthcare	0.85	4200	Low	3	28	$27,333	70.6667
6	INC013	Healthcare	High	Patient Handling	Supervisor	North	08AUG2024	20	60000	52	549	23627	HEALTHCARE	healthcare	Healthcare	Patient Handling	INC013-Healthcare	0.52	63000	High	3	28	$27,333	70.6667
7	INC004	IT Services	Low	Ergonomic Issue	Worker	East	20MAR2024	1	3000	90	690	23486	IT SERVICES	it services	It Services	Ergonomic Issue	INC004-IT Services	0.90	3150	Low	3	10	$8,000	79.3333
8	INC010	IT Services	Medium	Stress Injury	Worker	South	18JUN2024	4	9000	70	600	23575	IT SERVICES	it services	It Services	Stress Injury	INC010-IT Services	0.70	9450	Moderate	3	10	$8,000	79.3333
9	INC015	IT Services	Medium	Eye Strain	Worker	West	02SEP2024	5	12000	78	524	23651	IT SERVICES	it services	It Services	Eye Strain	INC015-IT Services	0.78	12600	Moderate	3	10	$8,000	79.3333
10	INC005	Logistics	High	Vehicle Accident	Supervisor	South	02APR2024	18	52000	55	677	23498	LOGISTICS	logistics	Logistics	Vehicle Accident	INC005-Logistics	0.55	54600	High	3	81	$89,667	57.6667
11	INC009	Logistics	Critical	Fire Accident	Supervisor	West	01JUN2024	60	210000	30	617	23558	LOGISTICS	logistics	Logistics	Fire Accident	INC009-Logistics	0.30	220500	Extreme	3	81	$89,667	57.6667
12	INC014	Logistics	Low	Manual Handling	Worker	East	20AUG2024	3	7000	88	537	23639	LOGISTICS	logistics	Logistics	Manual Handling	INC014-Logistics	0.88	7350	Low	3	81	$89,667	57.6667
13	INC001	Manufacturing	High	Machine Failure	Supervisor	South	15JAN2024	12	45000	60	755	23421	MANUFACTURING	manufacturing	Manufacturing	Machine Failure	INC001-Manufacturing	0.60	47250	High	3	35	$38,333	61.0000
14	INC006	Manufacturing	Medium	Chemical Exposure	Worker	West	15APR2024	8	22000	65	664	23511	MANUFACTURING	manufacturing	Manufacturing	Chemical Exposure	INC006-Manufacturing	0.65	23100	Moderate	3	35	$38,333	61.0000
15	INC011	Manufacturing	High	Electrical Hazard	Supervisor	East	05JUL2024	15	48000	58	583	23593	MANUFACTURING	manufacturing	Manufacturing	Electrical Hazard	INC011-Manufacturing	0.58	50400	High	3	35	$38,333	61.0000

14. PROC DATASETS DELETE

proc datasets library=work nolist;

    delete industry_t;

quit;

LOG:

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

·  Memory cleanup

·  Production best practice

Conclusion

This Workplace Safety Incidents Analytics project shows how raw safety data can be transformed into meaningful business insights using SAS. By applying PROC SQL, statistical procedures, macros, and date logic, we identified high-risk incidents, analyzed compensation impact, monitored SLA compliance, and detected potential fraud patterns.

The project demonstrates real-world problem solving, where data is not just analyzed but used to prevent future incidents, reduce costs, and improve employee safety. Overall, it reflects how SAS supports decision-making, compliance, and risk management in modern organizations.

INTERVIEW QUESTIONS FOR YOU

·  What is a correlated subquery?

·  How do you use CASE WHEN in SAS SQL?

·  What is the difference between UNION and UNION ALL?

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 SAFETY INCIDENTS 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 

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