384.Is your factory really performing well, or just showing good numbers on paper?Using Sas Can we Get it

Is your factory really performing well, or just showing good numbers on paper?Using Sas Can we Get it

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 DATASETS DELETE

INTRODUCTION

In modern manufacturing industries, factory production lines are the backbone of operational efficiency. Every production line consumes energy, produces goods, generates defects, and experiences downtime. Monitoring these variables continuously is critical for:

• Operational efficiency
• Cost control
• Quality assurance
• Fraud or data manipulation detection
• Predictive maintenance

This project simulates a real-world industrial analytics scenario where a data analyst or SAS programmer is asked to:

1. Create production data
2. Perform descriptive and statistical analysis
3. Detect abnormal or fraudulent patterns
4. Classify utilization
5. Visualize performance
6. Provide business insights

CONTENTS

1. Dataset Creation
2. Date Handling using MDY / INTCK / INTNX
3. Character & Numeric Transformations
4. Utilization Classification Macro
5. Fraud Detection Macro
6. Statistical Analysis
7. Correlation Analysis
8. Visualization
9. Transpose & Reshaping
10. Merging & Appending
11. PROC DATASETS Cleanup
12. Final Business Insights

STEP 1 — DATASET CREATION

data factory_lines;

    input Line_ID $ Product_Type $ Units_Produced Defect_Rate 

          Downtime_Hours Energy_Consumption Efficiency_Score Production_Date :date9.;

   format Production_Date date9.; 

datalines;

L01 electronics 1200 2.5 1.2 450 88 01JAN2024

L02 furniture 950 3.8 2.4 520 82 03JAN2024

L03 electronics 1500 1.2 0.5 610 92 05JAN2024

L04 automotive 1800 4.9 3.8 900 75 06JAN2024

L05 textile 700 5.5 4.2 300 70 08JAN2024

L06 electronics 1600 1.5 0.7 650 93 10JAN2024

L07 furniture 1100 2.9 1.5 480 85 11JAN2024

L08 automotive 1900 6.1 5.0 950 68 13JAN2024

L09 textile 800 4.2 3.0 350 74 14JAN2024

L10 electronics 1400 2.0 1.0 590 90 16JAN2024

L11 furniture 1000 3.5 2.0 500 83 18JAN2024

L12 automotive 2000 7.5 6.0 1000 65 20JAN2024

L13 textile 750 4.8 3.6 330 72 21JAN2024

L14 electronics 1700 1.1 0.4 680 95 23JAN2024

L15 automotive 2100 8.2 7.0 1100 60 25JAN2024

;

run;

proc print data=factory_lines;

run;

OUTPUT:

Obs	Line_ID	Product_Type	Units_Produced	Defect_Rate	Downtime_Hours	Energy_Consumption	Efficiency_Score	Production_Date
1	L01	electron	1200	2.5	1.2	450	88	01JAN2024
2	L02	furnitur	950	3.8	2.4	520	82	03JAN2024
3	L03	electron	1500	1.2	0.5	610	92	05JAN2024
4	L04	automoti	1800	4.9	3.8	900	75	06JAN2024
5	L05	textile	700	5.5	4.2	300	70	08JAN2024
6	L06	electron	1600	1.5	0.7	650	93	10JAN2024
7	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024
8	L08	automoti	1900	6.1	5.0	950	68	13JAN2024
9	L09	textile	800	4.2	3.0	350	74	14JAN2024
10	L10	electron	1400	2.0	1.0	590	90	16JAN2024
11	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024
12	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024
13	L13	textile	750	4.8	3.6	330	72	21JAN2024
14	L14	electron	1700	1.1	0.4	680	95	23JAN2024
15	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024

Simulates MES (Manufacturing Execution System) feed.
Represents line-wise production KPIs.

Used in interviews to show:

·       Domain understanding

·       Realistic variable naming

·       Correct data types

STEP 2 — DATE LOGIC (MDY, INTCK, INTNX)

data factory_dates;

    set factory_lines;

    Report_Date = mdy(2,1,2024);

    Days_From_Report = intck('day', Production_Date, Report_Date);

    Next_Service_Date = intnx('month', Production_Date, 1);

run;

proc print data=factory_dates;

  var Line_ID Product_Type Production_Date Report_Date Days_From_Report Next_Service_Date;

run;

OUTPUT:

Obs	Line_ID	Product_Type	Production_Date	Report_Date	Days_From_Report	Next_Service_Date
1	L01	electron	01JAN2024	23407	31	23407
2	L02	furnitur	03JAN2024	23407	29	23407
3	L03	electron	05JAN2024	23407	27	23407
4	L04	automoti	06JAN2024	23407	26	23407
5	L05	textile	08JAN2024	23407	24	23407
6	L06	electron	10JAN2024	23407	22	23407
7	L07	furnitur	11JAN2024	23407	21	23407
8	L08	automoti	13JAN2024	23407	19	23407
9	L09	textile	14JAN2024	23407	18	23407
10	L10	electron	16JAN2024	23407	16	23407
11	L11	furnitur	18JAN2024	23407	14	23407
12	L12	automoti	20JAN2024	23407	12	23407
13	L13	textile	21JAN2024	23407	11	23407
14	L14	electron	23JAN2024	23407	9	23407
15	L15	automoti	25JAN2024	23407	7	23407

Function	Purpose
MDY	Creates reference reporting date
INTCK	Measures operational lag
INTNX	Schedules next maintenance

Used in:

• SLA tracking
• Preventive maintenance systems

STEP 3 — CHARACTER & NUMERIC FUNCTIONS

data factory_clean;

    set factory_dates;

    Product_Type_Upper = upcase(Product_Type);

    Product_Type_Proper = propcase(Product_Type);

    Product_Key = catx('_', Line_ID, Product_Type);

    Defect_Adjusted = coalesce(Defect_Rate,0);

run;

proc print data=factory_clean;

  var Line_ID Product_Type Product_Type_Upper Product_Type_Proper Product_Key Defect_Rate Defect_Adjusted;

run;

OUTPUT:

Obs	Line_ID	Product_Type	Product_Type_Upper	Product_Type_Proper	Product_Key	Defect_Rate	Defect_Adjusted
1	L01	electron	ELECTRON	Electron	L01_electron	2.5	2.5
2	L02	furnitur	FURNITUR	Furnitur	L02_furnitur	3.8	3.8
3	L03	electron	ELECTRON	Electron	L03_electron	1.2	1.2
4	L04	automoti	AUTOMOTI	Automoti	L04_automoti	4.9	4.9
5	L05	textile	TEXTILE	Textile	L05_textile	5.5	5.5
6	L06	electron	ELECTRON	Electron	L06_electron	1.5	1.5
7	L07	furnitur	FURNITUR	Furnitur	L07_furnitur	2.9	2.9
8	L08	automoti	AUTOMOTI	Automoti	L08_automoti	6.1	6.1
9	L09	textile	TEXTILE	Textile	L09_textile	4.2	4.2
10	L10	electron	ELECTRON	Electron	L10_electron	2.0	2.0
11	L11	furnitur	FURNITUR	Furnitur	L11_furnitur	3.5	3.5
12	L12	automoti	AUTOMOTI	Automoti	L12_automoti	7.5	7.5
13	L13	textile	TEXTILE	Textile	L13_textile	4.8	4.8
14	L14	electron	ELECTRON	Electron	L14_electron	1.1	1.1
15	L15	automoti	AUTOMOTI	Automoti	L15_automoti	8.2	8.2

Function	Business Use
UPCASE	Standardization
PROPCASE	Reporting
CATX	Unique keys
COALESCE	Missing handling

STEP 4 — UTILIZATION CLASSIFICATION MACRO

%macro utilization;

data factory_util;

    set factory_clean;

    if Efficiency_Score >= 90 then Utilization="Excellent";

    else if Efficiency_Score >= 80 then Utilization="Good";

    else if Efficiency_Score >= 70 then Utilization="Average";

    else Utilization="Poor";

run;

proc print data=factory_util;

run;

%mend;

%utilization;

OUTPUT:

Obs	Line_ID	Product_Type	Units_Produced	Defect_Rate	Downtime_Hours	Energy_Consumption	Efficiency_Score	Production_Date	Report_Date	Days_From_Report	Next_Service_Date	Product_Type_Upper	Product_Type_Proper	Product_Key	Defect_Adjusted	Utilization
1	L01	electron	1200	2.5	1.2	450	88	01JAN2024	23407	31	23407	ELECTRON	Electron	L01_electron	2.5	Good
2	L02	furnitur	950	3.8	2.4	520	82	03JAN2024	23407	29	23407	FURNITUR	Furnitur	L02_furnitur	3.8	Good
3	L03	electron	1500	1.2	0.5	610	92	05JAN2024	23407	27	23407	ELECTRON	Electron	L03_electron	1.2	Excellent
4	L04	automoti	1800	4.9	3.8	900	75	06JAN2024	23407	26	23407	AUTOMOTI	Automoti	L04_automoti	4.9	Average
5	L05	textile	700	5.5	4.2	300	70	08JAN2024	23407	24	23407	TEXTILE	Textile	L05_textile	5.5	Average
6	L06	electron	1600	1.5	0.7	650	93	10JAN2024	23407	22	23407	ELECTRON	Electron	L06_electron	1.5	Excellent
7	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024	23407	21	23407	FURNITUR	Furnitur	L07_furnitur	2.9	Good
8	L08	automoti	1900	6.1	5.0	950	68	13JAN2024	23407	19	23407	AUTOMOTI	Automoti	L08_automoti	6.1	Poor
9	L09	textile	800	4.2	3.0	350	74	14JAN2024	23407	18	23407	TEXTILE	Textile	L09_textile	4.2	Average
10	L10	electron	1400	2.0	1.0	590	90	16JAN2024	23407	16	23407	ELECTRON	Electron	L10_electron	2.0	Excellent
11	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024	23407	14	23407	FURNITUR	Furnitur	L11_furnitur	3.5	Good
12	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024	23407	12	23407	AUTOMOTI	Automoti	L12_automoti	7.5	Poor
13	L13	textile	750	4.8	3.6	330	72	21JAN2024	23407	11	23407	TEXTILE	Textile	L13_textile	4.8	Average
14	L14	electron	1700	1.1	0.4	680	95	23JAN2024	23407	9	23407	ELECTRON	Electron	L14_electron	1.1	Excellent
15	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024	23407	7	23407	AUTOMOTI	Automoti	L15_automoti	8.2	Poor

Automates:

·       Plant dashboards

·       KPI categorization

·       Performance banding

Shows:

·       Reusability

·       Enterprise coding style

STEP 5 — FRAUD DETECTION MACRO

%macro fraud;

data factory_fraud;

    set factory_util;

    if Defect_Rate > 7 and Downtime_Hours > 5 then Fraud_Flag="YES";

    else Fraud_Flag="NO";

run;

proc print data=factory_fraud;

run;

%mend;

%fraud;

OUTPUT:

Obs	Line_ID	Product_Type	Units_Produced	Defect_Rate	Downtime_Hours	Energy_Consumption	Efficiency_Score	Production_Date	Report_Date	Days_From_Report	Next_Service_Date	Product_Type_Upper	Product_Type_Proper	Product_Key	Defect_Adjusted	Utilization	Fraud_Flag
1	L01	electron	1200	2.5	1.2	450	88	01JAN2024	23407	31	23407	ELECTRON	Electron	L01_electron	2.5	Good	NO
2	L02	furnitur	950	3.8	2.4	520	82	03JAN2024	23407	29	23407	FURNITUR	Furnitur	L02_furnitur	3.8	Good	NO
3	L03	electron	1500	1.2	0.5	610	92	05JAN2024	23407	27	23407	ELECTRON	Electron	L03_electron	1.2	Excellent	NO
4	L04	automoti	1800	4.9	3.8	900	75	06JAN2024	23407	26	23407	AUTOMOTI	Automoti	L04_automoti	4.9	Average	NO
5	L05	textile	700	5.5	4.2	300	70	08JAN2024	23407	24	23407	TEXTILE	Textile	L05_textile	5.5	Average	NO
6	L06	electron	1600	1.5	0.7	650	93	10JAN2024	23407	22	23407	ELECTRON	Electron	L06_electron	1.5	Excellent	NO
7	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024	23407	21	23407	FURNITUR	Furnitur	L07_furnitur	2.9	Good	NO
8	L08	automoti	1900	6.1	5.0	950	68	13JAN2024	23407	19	23407	AUTOMOTI	Automoti	L08_automoti	6.1	Poor	NO
9	L09	textile	800	4.2	3.0	350	74	14JAN2024	23407	18	23407	TEXTILE	Textile	L09_textile	4.2	Average	NO
10	L10	electron	1400	2.0	1.0	590	90	16JAN2024	23407	16	23407	ELECTRON	Electron	L10_electron	2.0	Excellent	NO
11	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024	23407	14	23407	FURNITUR	Furnitur	L11_furnitur	3.5	Good	NO
12	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024	23407	12	23407	AUTOMOTI	Automoti	L12_automoti	7.5	Poor	YES
13	L13	textile	750	4.8	3.6	330	72	21JAN2024	23407	11	23407	TEXTILE	Textile	L13_textile	4.8	Average	NO
14	L14	electron	1700	1.1	0.4	680	95	23JAN2024	23407	9	23407	ELECTRON	Electron	L14_electron	1.1	Excellent	NO
15	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024	23407	7	23407	AUTOMOTI	Automoti	L15_automoti	8.2	Poor	YES

Detects:

·       Artificially inflated production

·       Manipulated defect logs

·       Fake efficiency metrics

Real-world use:

·       Internal audits

·       Compliance checks

·       Six Sigma reviews

STEP 6 — PROC SQL ANALYSIS

proc sql;

select Product_Type,

       avg(Units_Produced) as Avg_Units,

       avg(Defect_Rate) as Avg_Defect,

       avg(Efficiency_Score) as Avg_Efficiency

from factory_fraud

group by Product_Type;

quit;

OUTPUT:

Product_Type	Avg_Units	Avg_Defect	Avg_Efficiency
automoti	1950	6.675	67
electron	1480	1.66	91.6
furnitur	1016.667	3.4	83.33333
textile	750	4.833333	72

Provides:

·       Management summary tables

·       Cross-product comparisons

Used for:

·       Board presentations

·       Manufacturing scorecards

STEP 7 — PROC FREQ

proc freq data=factory_fraud;

tables Product_Type*Utilization Fraud_Flag;

run;

OUTPUT:

The FREQ Procedure

Frequency Percent Row Pct Col Pct

Table of Product_Type by Utilization Product_Type Utilization Average Excellent Good Poor Total automoti 1 6.67 25.00 25.00 0 0.00 0.00 0.00 0 0.00 0.00 0.00 3 20.00 75.00 100.00 4 26.67     electron 0 0.00 0.00 0.00 4 26.67 80.00 100.00 1 6.67 20.00 25.00 0 0.00 0.00 0.00 5 33.33     furnitur 0 0.00 0.00 0.00 0 0.00 0.00 0.00 3 20.00 100.00 75.00 0 0.00 0.00 0.00 3 20.00     textile 3 20.00 100.00 75.00 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0 0.00 0.00 0.00 3 20.00     Total 4 26.67 4 26.67 4 26.67 3 20.00 15 100.00

Fraud_Flag	Frequency	Percent	Cumulative Frequency	Cumulative Percent
NO	13	86.67	13	86.67
YES	2	13.33	15	100.00

Shows:

·       Utilization distribution

·       Fraud frequency

Used in:

·       Risk reports

·       Control dashboards

STEP 8 — PROC MEANS & UNIVARIATE

proc means data=factory_fraud mean min max;

var Units_Produced Defect_Rate Downtime_Hours Energy_Consumption Efficiency_Score;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Units_Produced Defect_Rate Downtime_Hours Energy_Consumption Efficiency_Score	1366.67 3.9800000 2.8200000 627.3333333 79.4666667	700.0000000 1.1000000 0.4000000 300.0000000 60.0000000	2100.00 8.2000000 7.0000000 1100.00 95.0000000

proc univariate data=factory_fraud;

var Efficiency_Score;

histogram;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Efficiency_Score

Moments
N	15	Sum Weights	15
Mean	79.4666667	Sum Observations	1192
Std Deviation	11.1154119	Variance	123.552381
Skewness	-0.1928899	Kurtosis	-1.2330588
Uncorrected SS	96454	Corrected SS	1729.73333
Coeff Variation	13.9875149	Std Error Mean	2.869987

Basic Statistical Measures
Location		Variability
Mean	79.46667	Std Deviation	11.11541
Median	82.00000	Variance	123.55238
Mode	.	Range	35.00000
		Interquartile Range	20.00000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	27.68886	Pr > |t|	<.0001
Sign	M	7.5	Pr >= |M|	<.0001
Signed Rank	S	60	Pr >= |S|	<.0001

Quantiles (Definition 5)
Level	Quantile
100% Max	95
99%	95
95%	95
90%	93
75% Q3	90
50% Median	82
25% Q1	70
10%	65
5%	60
1%	60
0% Min	60

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
60	15	88	1
65	12	90	10
68	8	92	3
70	5	93	6
72	13	95	14

The UNIVARIATE Procedure

Measures:

·       Central tendency

·       Outliers

·       Process stability

Used in:

·       SPC (Statistical Process Control)

·       Quality engineering

STEP 9 — CORRELATION

proc corr data=factory_fraud;

var Units_Produced Defect_Rate Downtime_Hours Energy_Consumption Efficiency_Score;

run;

OUTPUT:

The CORR Procedure

5 Variables:	Units_Produced Defect_Rate Downtime_Hours Energy_Consumption Efficiency_Score

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Units_Produced	15	1367	474.21615	20500	700.00000	2100
Defect_Rate	15	3.98000	2.20849	59.70000	1.10000	8.20000
Downtime_Hours	15	2.82000	2.07199	42.30000	0.40000	7.00000
Energy_Consumption	15	627.33333	253.41853	9410	300.00000	1100
Efficiency_Score	15	79.46667	11.11541	1192	60.00000	95.00000

Pearson Correlation Coefficients, N = 15 Prob > |r| under H0: Rho=0
	Units_Produced	Defect_Rate	Downtime_Hours	Energy_Consumption	Efficiency_Score
Units_Produced	1.00000	0.27417 0.3227	0.33367 0.2242	0.95763 <.0001	-0.16758 0.5505
Defect_Rate	0.27417 0.3227	1.00000	0.99442 <.0001	0.49202 0.0625	-0.98337 <.0001
Downtime_Hours	0.33367 0.2242	0.99442 <.0001	1.00000	0.53894 0.0382	-0.98079 <.0001
Energy_Consumption	0.95763 <.0001	0.49202 0.0625	0.53894 0.0382	1.00000	-0.38572 0.1556
Efficiency_Score	-0.16758 0.5505	-0.98337 <.0001	-0.98079 <.0001	-0.38572 0.1556	1.00000

Reveals:

·       Energy vs Efficiency

·       Downtime vs Defects

Used for:

·       Root cause analysis

·       Lean manufacturing

STEP 10 — VISUALIZATION

proc sgplot data=factory_fraud;

scatter x=Energy_Consumption y=Efficiency_Score;

run;

OUTPUT:

Visual diagnostics:

·       Operational efficiency curve

·       Energy waste patterns

STEP 11 — TRANSPOSE

proc transpose data=factory_fraud out=factory_wide;

id Line_ID;

var Efficiency_Score;

run;

proc print data=factory_wide;

run;

OUTPUT:

Obs	_NAME_	L01	L02	L03	L04	L05	L06	L07	L08	L09	L10	L11	L12	L13	L14	L15
1	Efficiency_Score	88	82	92	75	70	93	85	68	74	90	83	65	72	95	60

Used for:

·       Executive Excel reports

·       BI tools

STEP 12 — APPEND + MERGE

proc append base=factory_fraud 

            data=factory_lines force;

run;

proc print data=factory_fraud;

run;

OUTPUT:

Obs	Line_ID	Product_Type	Units_Produced	Defect_Rate	Downtime_Hours	Energy_Consumption	Efficiency_Score	Production_Date	Report_Date	Days_From_Report	Next_Service_Date	Product_Type_Upper	Product_Type_Proper	Product_Key	Defect_Adjusted	Utilization	Fraud_Flag
1	L01	electron	1200	2.5	1.2	450	88	01JAN2024	23407	31	23407	ELECTRON	Electron	L01_electron	2.5	Good	NO
2	L02	furnitur	950	3.8	2.4	520	82	03JAN2024	23407	29	23407	FURNITUR	Furnitur	L02_furnitur	3.8	Good	NO
3	L03	electron	1500	1.2	0.5	610	92	05JAN2024	23407	27	23407	ELECTRON	Electron	L03_electron	1.2	Excellent	NO
4	L04	automoti	1800	4.9	3.8	900	75	06JAN2024	23407	26	23407	AUTOMOTI	Automoti	L04_automoti	4.9	Average	NO
5	L05	textile	700	5.5	4.2	300	70	08JAN2024	23407	24	23407	TEXTILE	Textile	L05_textile	5.5	Average	NO
6	L06	electron	1600	1.5	0.7	650	93	10JAN2024	23407	22	23407	ELECTRON	Electron	L06_electron	1.5	Excellent	NO
7	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024	23407	21	23407	FURNITUR	Furnitur	L07_furnitur	2.9	Good	NO
8	L08	automoti	1900	6.1	5.0	950	68	13JAN2024	23407	19	23407	AUTOMOTI	Automoti	L08_automoti	6.1	Poor	NO
9	L09	textile	800	4.2	3.0	350	74	14JAN2024	23407	18	23407	TEXTILE	Textile	L09_textile	4.2	Average	NO
10	L10	electron	1400	2.0	1.0	590	90	16JAN2024	23407	16	23407	ELECTRON	Electron	L10_electron	2.0	Excellent	NO
11	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024	23407	14	23407	FURNITUR	Furnitur	L11_furnitur	3.5	Good	NO
12	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024	23407	12	23407	AUTOMOTI	Automoti	L12_automoti	7.5	Poor	YES
13	L13	textile	750	4.8	3.6	330	72	21JAN2024	23407	11	23407	TEXTILE	Textile	L13_textile	4.8	Average	NO
14	L14	electron	1700	1.1	0.4	680	95	23JAN2024	23407	9	23407	ELECTRON	Electron	L14_electron	1.1	Excellent	NO
15	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024	23407	7	23407	AUTOMOTI	Automoti	L15_automoti	8.2	Poor	YES
16	L01	electron	1200	2.5	1.2	450	88	01JAN2024	.	.	.				.
17	L02	furnitur	950	3.8	2.4	520	82	03JAN2024	.	.	.				.
18	L03	electron	1500	1.2	0.5	610	92	05JAN2024	.	.	.				.
19	L04	automoti	1800	4.9	3.8	900	75	06JAN2024	.	.	.				.
20	L05	textile	700	5.5	4.2	300	70	08JAN2024	.	.	.				.
21	L06	electron	1600	1.5	0.7	650	93	10JAN2024	.	.	.				.
22	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024	.	.	.				.
23	L08	automoti	1900	6.1	5.0	950	68	13JAN2024	.	.	.				.
24	L09	textile	800	4.2	3.0	350	74	14JAN2024	.	.	.				.
25	L10	electron	1400	2.0	1.0	590	90	16JAN2024	.	.	.				.
26	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024	.	.	.				.
27	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024	.	.	.				.
28	L13	textile	750	4.8	3.6	330	72	21JAN2024	.	.	.				.
29	L14	electron	1700	1.1	0.4	680	95	23JAN2024	.	.	.				.
30	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024	.	.	.				.

proc sort data=factory_fraud;

 by Line_ID;

run;

proc print data=factory_fraud;

run;

OUTPUT:

Obs	Line_ID	Product_Type	Units_Produced	Defect_Rate	Downtime_Hours	Energy_Consumption	Efficiency_Score	Production_Date	Report_Date	Days_From_Report	Next_Service_Date	Product_Type_Upper	Product_Type_Proper	Product_Key	Defect_Adjusted	Utilization	Fraud_Flag
1	L01	electron	1200	2.5	1.2	450	88	01JAN2024	23407	31	23407	ELECTRON	Electron	L01_electron	2.5	Good	NO
2	L01	electron	1200	2.5	1.2	450	88	01JAN2024	.	.	.				.
3	L02	furnitur	950	3.8	2.4	520	82	03JAN2024	23407	29	23407	FURNITUR	Furnitur	L02_furnitur	3.8	Good	NO
4	L02	furnitur	950	3.8	2.4	520	82	03JAN2024	.	.	.				.
5	L03	electron	1500	1.2	0.5	610	92	05JAN2024	23407	27	23407	ELECTRON	Electron	L03_electron	1.2	Excellent	NO
6	L03	electron	1500	1.2	0.5	610	92	05JAN2024	.	.	.				.
7	L04	automoti	1800	4.9	3.8	900	75	06JAN2024	23407	26	23407	AUTOMOTI	Automoti	L04_automoti	4.9	Average	NO
8	L04	automoti	1800	4.9	3.8	900	75	06JAN2024	.	.	.				.
9	L05	textile	700	5.5	4.2	300	70	08JAN2024	23407	24	23407	TEXTILE	Textile	L05_textile	5.5	Average	NO
10	L05	textile	700	5.5	4.2	300	70	08JAN2024	.	.	.				.
11	L06	electron	1600	1.5	0.7	650	93	10JAN2024	23407	22	23407	ELECTRON	Electron	L06_electron	1.5	Excellent	NO
12	L06	electron	1600	1.5	0.7	650	93	10JAN2024	.	.	.				.
13	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024	23407	21	23407	FURNITUR	Furnitur	L07_furnitur	2.9	Good	NO
14	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024	.	.	.				.
15	L08	automoti	1900	6.1	5.0	950	68	13JAN2024	23407	19	23407	AUTOMOTI	Automoti	L08_automoti	6.1	Poor	NO
16	L08	automoti	1900	6.1	5.0	950	68	13JAN2024	.	.	.				.
17	L09	textile	800	4.2	3.0	350	74	14JAN2024	23407	18	23407	TEXTILE	Textile	L09_textile	4.2	Average	NO
18	L09	textile	800	4.2	3.0	350	74	14JAN2024	.	.	.				.
19	L10	electron	1400	2.0	1.0	590	90	16JAN2024	23407	16	23407	ELECTRON	Electron	L10_electron	2.0	Excellent	NO
20	L10	electron	1400	2.0	1.0	590	90	16JAN2024	.	.	.				.
21	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024	23407	14	23407	FURNITUR	Furnitur	L11_furnitur	3.5	Good	NO
22	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024	.	.	.				.
23	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024	23407	12	23407	AUTOMOTI	Automoti	L12_automoti	7.5	Poor	YES
24	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024	.	.	.				.
25	L13	textile	750	4.8	3.6	330	72	21JAN2024	23407	11	23407	TEXTILE	Textile	L13_textile	4.8	Average	NO
26	L13	textile	750	4.8	3.6	330	72	21JAN2024	.	.	.				.
27	L14	electron	1700	1.1	0.4	680	95	23JAN2024	23407	9	23407	ELECTRON	Electron	L14_electron	1.1	Excellent	NO
28	L14	electron	1700	1.1	0.4	680	95	23JAN2024	.	.	.				.
29	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024	23407	7	23407	AUTOMOTI	Automoti	L15_automoti	8.2	Poor	YES
30	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024	.	.	.				.

proc sort data=factory_dates;

 by Line_ID;

run;

proc print data=factory_dates;

run;

OUTPUT:

Obs	Line_ID	Product_Type	Units_Produced	Defect_Rate	Downtime_Hours	Energy_Consumption	Efficiency_Score	Production_Date	Report_Date	Days_From_Report	Next_Service_Date
1	L01	electron	1200	2.5	1.2	450	88	01JAN2024	23407	31	23407
2	L02	furnitur	950	3.8	2.4	520	82	03JAN2024	23407	29	23407
3	L03	electron	1500	1.2	0.5	610	92	05JAN2024	23407	27	23407
4	L04	automoti	1800	4.9	3.8	900	75	06JAN2024	23407	26	23407
5	L05	textile	700	5.5	4.2	300	70	08JAN2024	23407	24	23407
6	L06	electron	1600	1.5	0.7	650	93	10JAN2024	23407	22	23407
7	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024	23407	21	23407
8	L08	automoti	1900	6.1	5.0	950	68	13JAN2024	23407	19	23407
9	L09	textile	800	4.2	3.0	350	74	14JAN2024	23407	18	23407
10	L10	electron	1400	2.0	1.0	590	90	16JAN2024	23407	16	23407
11	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024	23407	14	23407
12	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024	23407	12	23407
13	L13	textile	750	4.8	3.6	330	72	21JAN2024	23407	11	23407
14	L14	electron	1700	1.1	0.4	680	95	23JAN2024	23407	9	23407
15	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024	23407	7	23407

data merged_factory;

merge factory_fraud factory_dates;

by Line_ID;

run;

proc print data=merged_factory;

run;

OUTPUT:

Obs	Line_ID	Product_Type	Units_Produced	Defect_Rate	Downtime_Hours	Energy_Consumption	Efficiency_Score	Production_Date	Report_Date	Days_From_Report	Next_Service_Date	Product_Type_Upper	Product_Type_Proper	Product_Key	Defect_Adjusted	Utilization	Fraud_Flag
1	L01	electron	1200	2.5	1.2	450	88	01JAN2024	23407	31	23407	ELECTRON	Electron	L01_electron	2.5	Good	NO
2	L01	electron	1200	2.5	1.2	450	88	01JAN2024	.	.	.				.
3	L02	furnitur	950	3.8	2.4	520	82	03JAN2024	23407	29	23407	FURNITUR	Furnitur	L02_furnitur	3.8	Good	NO
4	L02	furnitur	950	3.8	2.4	520	82	03JAN2024	.	.	.				.
5	L03	electron	1500	1.2	0.5	610	92	05JAN2024	23407	27	23407	ELECTRON	Electron	L03_electron	1.2	Excellent	NO
6	L03	electron	1500	1.2	0.5	610	92	05JAN2024	.	.	.				.
7	L04	automoti	1800	4.9	3.8	900	75	06JAN2024	23407	26	23407	AUTOMOTI	Automoti	L04_automoti	4.9	Average	NO
8	L04	automoti	1800	4.9	3.8	900	75	06JAN2024	.	.	.				.
9	L05	textile	700	5.5	4.2	300	70	08JAN2024	23407	24	23407	TEXTILE	Textile	L05_textile	5.5	Average	NO
10	L05	textile	700	5.5	4.2	300	70	08JAN2024	.	.	.				.
11	L06	electron	1600	1.5	0.7	650	93	10JAN2024	23407	22	23407	ELECTRON	Electron	L06_electron	1.5	Excellent	NO
12	L06	electron	1600	1.5	0.7	650	93	10JAN2024	.	.	.				.
13	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024	23407	21	23407	FURNITUR	Furnitur	L07_furnitur	2.9	Good	NO
14	L07	furnitur	1100	2.9	1.5	480	85	11JAN2024	.	.	.				.
15	L08	automoti	1900	6.1	5.0	950	68	13JAN2024	23407	19	23407	AUTOMOTI	Automoti	L08_automoti	6.1	Poor	NO
16	L08	automoti	1900	6.1	5.0	950	68	13JAN2024	.	.	.				.
17	L09	textile	800	4.2	3.0	350	74	14JAN2024	23407	18	23407	TEXTILE	Textile	L09_textile	4.2	Average	NO
18	L09	textile	800	4.2	3.0	350	74	14JAN2024	.	.	.				.
19	L10	electron	1400	2.0	1.0	590	90	16JAN2024	23407	16	23407	ELECTRON	Electron	L10_electron	2.0	Excellent	NO
20	L10	electron	1400	2.0	1.0	590	90	16JAN2024	.	.	.				.
21	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024	23407	14	23407	FURNITUR	Furnitur	L11_furnitur	3.5	Good	NO
22	L11	furnitur	1000	3.5	2.0	500	83	18JAN2024	.	.	.				.
23	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024	23407	12	23407	AUTOMOTI	Automoti	L12_automoti	7.5	Poor	YES
24	L12	automoti	2000	7.5	6.0	1000	65	20JAN2024	.	.	.				.
25	L13	textile	750	4.8	3.6	330	72	21JAN2024	23407	11	23407	TEXTILE	Textile	L13_textile	4.8	Average	NO
26	L13	textile	750	4.8	3.6	330	72	21JAN2024	.	.	.				.
27	L14	electron	1700	1.1	0.4	680	95	23JAN2024	23407	9	23407	ELECTRON	Electron	L14_electron	1.1	Excellent	NO
28	L14	electron	1700	1.1	0.4	680	95	23JAN2024	.	.	.				.
29	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024	23407	7	23407	AUTOMOTI	Automoti	L15_automoti	8.2	Poor	YES
30	L15	automoti	2100	8.2	7.0	1100	60	25JAN2024	.	.	.				.

Enterprise data pipelines always:

·       Append daily feeds

·       Merge reference tables

STEP 13 — DELETE TEMP DATASETS

proc datasets lib=work;

delete factory_clean factory_dates factory_util;

quit;

LOG:

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

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

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

Critical for:

·       Memory optimization

·       Production batch jobs

FINAL BUSINESS INSIGHTS

Insight	Meaning
High energy consumption reduces efficiency	Energy optimization required
Automotive lines have highest fraud risk	Audit required
Low downtime correlates with high output	Preventive maintenance working
High defect + high downtime = red flag	Process manipulation

 

CONCLUSION

This project demonstrates a complete industrial analytics lifecycle using SAS:

·       Data ingestion

·       Cleaning

·       Feature engineering

·       Statistical modeling

·       Fraud detection

·       Visualization

·       Business interpretation

It exactly mirrors what manufacturing companies, industrial IoT teams, and operations analytics roles expect from a SAS programmer or data analyst.

INTERVIEW QUESTIONS FOR YOU

1.How does FIRST.variable and LAST.variable work?

2.What is RETAIN and when is it used?

3.What is BY-group processing?

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 Manufacturing Industries data.

Our Mission:

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This project is suitable for:

·  Students learning SAS

·  Data analysts building portfolios

·  Professionals preparing for SAS interviews

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