Advanced Field Artillery System Trade-off Analysis: · PDF fileAdvanced Field Artillery System Trade-off Analysis: ... ADVANCED FIELD ARTILLERY SYSTEM TRADE-OFF ANALYSIS: TOT vs. ROF

United States Military Academy West Point, New York 10996

Advanced Field Artillery System Trade-off Analysis:

TOT vs. ROF

MAJ George F. Stone III MAJ James Moughon

SFC Paul West

OPERATIONS RESEARCH CENTER TECHNICAL REPORT NO. 93/93-8

June 1993

The Operations Research Center is supported by the Assistant Secretary of the Army for Financial Management.

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ADVANCED FIELD ARTILLERY SYSTEM TRADE-OFF ANALYSIS: TOT vs. ROF

6. AUTHOR(S) MAJ GEORGE F. STONE, III MAJ JAMES MOUGHON SFC PAUL WEST

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USMA OPERATIONS RESEARCH CENTER WEST POINT, NEW YORK 10996-1779

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13. ABSTRACT (Maximum 200 words)

THIS REPORT FOCUSES ON THE RESULTS OF RESEARCH EFFORTS ON METHODOLOGIES AND THE ADVANCED FIELD ARTILLERY SYSTEM (AFAS). OUR GOAL WAS TO CONCEPTUALIZE AND DESIGN ALTERNATIVE AFAS CAPABILITIES IN THE JANUS COMBAT SIMULATION TO EVALUATE THEIR EFFECTIVENESS ON TOMORROWS BATTLEFIELD UNDER SEVERAL DIFFERENT SCENARIOS AND MISSIONS. ADDITIONALLY, THE TEAM USED THE RESULTS TO VERIFY A METHODOLOGY FOR PREDICTING COMBAT SIMULATION RESULTS FOR ALTERNATIVE SENSITIVITY ANALYSES. THE RESULTS OF THIS FIRST PHASE INDICATE AS NEED FOR A FASTER DELIVERY SYSTEM TO MASS FIRES AT THE RIGHT TIME IN THE RIGHT PLACE ON THE FUTURE BATTLEFIELD.

14. SUBJECT TERMS

ADVANCED FIELD ARTILLERY SYSTEM TRADE-OFF ANALYSIS: TOT vs. ROF

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NSN 7540-01-280-5500 Standard Form 298 (Rev. 2-89) Prescribed by ANSI Std. Z39-18 298-102

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Advanced Field Artillery System Trade-off Analysis:

ROF vs. TOT

MAJ George F. Stone m MAJ James Moughon

SFC Paul West

A TECHNICAL REPORT OF THE

OPERATIONS RESEARCH CENTER UNITED STATES MILITARY ACADEMY

Directed by Lieutenant Colonel James E. Armstrong, Jr. Ph.D.

Director, Operations Research Center

Approved by Colonel James L. Kays, Ph.D.

Professor and Head Department of Systems Engineering

25 June 1993

The Operations Research Center is supported by the Assistant Secretary of the Army for Financial Management. The sponsor for this project was the Armament Research, Development and Engineering Center (ARDEC),

Picatinny Arsenal, NJ. Department of Systems Engineering, West Point, NY

Vitae

MAJ George F. Stone in

Born in November 1955, MAJ Stone graduated in 1980 from the US Military Academy, and has served in field artillery units in both CONUS and Europe. While assigned to the 3 Infantry Division, MAJ Stone commanded a nuclear-capable, 6-gun, 155mm battery for 18 months and a headquarters battery for 13 months. In 1989 he was awarded a Masters of Science Degree in Industrial Engineering (OR) from Texas A&M University. He published and presented his research project, Military Simulations for Noncombat Operations in Military Review and military operations research conferences. As an Assistant Professor of Systems Engineering, MAJ Stone organized and integrated the U.S. Military Academy's Combat Simulation Laboratory as part of the instruction and development of three separate courses in combat modeling and combat system design. MAJ Stone directed cadet and faculty research in the following areas: The Enhanced Integrated Soldier System, Smart Mortars, The Janus Enhanced Data Analyzer, Advanced Field Artillery System Study, C2 MOE for the Eagle Combat Simulation and Synchronized Intelligent Mine Systems. He graduated from the resident Command and General Staff College in 1993 and is enroute to earn a doctoral degree at the University of Central Florida. His military awards include the Meritorious Service Medal (1 OLC), the Army Commendation Medal and the Army Achievement Medal (3 OLCs).

MAJ James Moughon

MAJ James Moughon graduated from the University of Georgia in 1979 and received his masters in Operations Research from the Naval Postgraduate School in 1989. MAJ Moughon has served in command and staff positions in various field artillery units. He also attended the resident Command and General Staff College in 1990 prior to his assignment to the USMA faculty as an assistant professor for Systems Engineering. MAJ Moughon is currently in the 18th Airborne Corps Fire Support Element at Fort Bragg, North Carolina.

SFC Paul West

SFC Paul West has earned Masters of Business Administration from Long Island University. He is an armor noncommissioned officer and an assistant professor who teaches and develops courses in the Department of Military Instruction field of study at the U.S. Military Academy. He was responsible for integrating the Janus (Army) combat simulation into the DMI curricula under the auspices of the Department of History. SFC West's relationship and tactical knowledge made him an invaluable asset to the completion of the AFAS project.

Acknowledgments

We thank MAJ Mike Clark and LTC William Sole, USAFAS DCD, for their interest and assistance in providing a branch-related topic for us to research in the USMA Combat Simulation Lab. The project created an opportunity for all of us to refresh artillery experiences and prepare ourselves for future assignments.

We also appreciated the support and guidance LTC James Armstrong gave concerning modeling of AFAS.

CPT Mark Tillman, CPT Sue Romans and 2LT Clemens Kruse also devoted their time to the study. MAJ James Watson was crucial for continuity and will hopefully carry on this important effort.

Table of Contents

Section Page Executive Summary 1

Appendices

A AFAS Study: Phase I Results B Factorial Design Analysis: Time-on-Target C Factorial Design Analysis: Rate-of-Fire D Response Surface Methodology Calculations: Time-on-Target E Response Surface Methodology Calculations: Rate-of-Fire F 1000-Target Methodology Calculations G After-action Memo by MAJ James Watson on the AFAS Briefing

Executive Summary

This report focuses on the results of research efforts on methodologies and the Advanced Field Artillery System (AFAS). Our goal was to conceptualize and design alternative AFAS capabilities in the Janus combat simulation to evaluate their effectiveness on tomorrow's battlefield under several different scenarios and missions. Additionally, the team used the results to verify a methodology for predicting combat simulation results for alternative sensitivity analyses. The results of this first phase indicate a need for a faster delivery system to mass fires at the right time in the right place on the future battlefield.

The team developed several scenarios to fully exploit the effects of fire support in the defense. The scenarios involved a moving enemy force against static defensive friendly units. We evaluated several AFAS alternatives in terms of rate-of-fire and time-on-target capabilities on Janus (A). The AFAS either had an enhanced rate-of-fire or improved capability for providing more rounds in time-on-target missions. We conducted replications of each design matrix to test for significance of different factors or alternatives as part of the analysis. Once all runs were conducted, data were collected using the Janus Enhanced Data Analyzer. Next, the information was organized on a spreadsheet to conduct the factorial design analysis and response surface methodology. The measures of effectiveness and graphical analyses showed significant results. The results indicate: 1) Increasing the rate-of-fire significantly reduces the required amount of rounds for a kill, as well as, a greater number of kills for slow enemy forces; 2) Response surface methodology is a viable method for predicting parametric variations between factors such as ROF and TOT. SFC West also conducted a 1000-target analysis to determine whether cumulative effects exceeded individual target effects under time- on-target and rate-of-fire. The results indicated that more rounds per TOT and more rounds per minute for ROF increased the total number of kills, but with an expected decrease in efficiency in terms of kills per rounds.

As indicated in appendix A, the project was briefed from both an analytical methodology standpoint at the Military Operations Research Symposium and from an combat developer's view to AFAS key personnel at Fort Sill. The Department of Systems Engineering has incorporated response surface methodologies as a predictive method into research work on other projects. The project was well-received at both locations, but transitions to new assignments by almost all of the analysts and MAJ Clark precluded further research. Ft. Sill's AFAS project leaders are currently considering using USMA analysts in the latest AFAS Study Plan.

Appendix A AFAS Study Results: Phase I

\ Wargaming Lab t Operations Research- Cento'

..United States Military Academy ! West Point, New York 10996

Designing & Predicting Effects in Combat Simulations

Presented to \

/ MÖRSS60\ :^"""""""":"Monterey> CÄ

June 23,1992

Analysts: MAJ George Stone I MAJ James Moughon / SFC Paul WestDMI

—i Departments of |i MilJtaryLlnstriictioii. &

Systems Engineering United 'State's'i MilitaryAcademy

WestPoinU NewJ York I1099&!

Advanced FA System Trade-Off Analysis:

TOT vs. ROF SS5^^^5^5B!355E35?852*s

Presented to\

DCD,USÄp&

Analysts: MAJ George Stone MAJ James Moughon SFC Paul West \

AFAS TOT/ROF Trade-Off Analysis

Purpose: Investigate the Delivery Capabilities of the Advanced Field Artillery System (AFAS) for enhanced combat effectiveness

Scenario: Southwest Asia (U.S. Mech Inf Defending vs. T-72/BMP force)

• Rate of Fire (ROF)

• Time-On-Target (TOT)

- Varying Percentages of Soft and Hard Targets

AFAS TOT/ROF Considerations

Time-on-Taraet

How many volleys appear to be sufficient for combat effectiveness?

Rate of Fire

How fast a rate of fire is needed for the AFAS?

Modeling AFAS in Janus

■ On-Board Technical & Tactical Fire Control Automatic Gun System

• Advanced POS/NAV Advanced Munitions & Propellants

AFAS: Study Methodology

Set up the SWA Scenario as Desired By FT Sill

Conduct 10 Runs for each Design Point

Review Battles for Consistency in Using Indirect Fires

Graph Measures of Effectiveness for Trends

Analyze MOE for Main/Interaction Effects

Predict Middle-value Variations using Response Surface Methodology (RSM)

Verify RSM by Running Scenario w/Middle Values

Fire Plan

Time Mission 1:00 • Fire Target AB001 to coincide with execution of cratering

charge at 164610.

3:00 - Fire Group Bravo (AB002, AB003, AB004) to bottle the enemy north of the 60 gridline and destroy him as he begins to deploy north of the 61 gridline.

5:00 • Fire Target AC001, stationary trucks and troops in the open.

7:00 • (Scenario 1) Fire Targets A DO 03 and AD005 to destroy enemy armor and block withdrawal from direct fire engagement area.

- (Scenario 2) Fire Target AD001, moving trucks in the open.

9:00 • (Scenario 1) Fire Targets AD003 and AD004.

- (Scenario 2) Fire Target AD002, moving trucks in the open.

Classification of Enemy Soft and Hard Targets

Hard Tgts

T-80 MBT T-72 MBT T-62 MBT BMP-2 IFV BMP-1 IFV BMP CP Veh BRDM-AT

Soft Targets I

Riflemen RPG-7 LtMG AGS-17 ADATm 120mm Mtr 152mm How 122mm Mortar Truck Utility Truck POL Motorcycle

High-Resolution Force Structure SWA Mech Infantry Scenario #1

BLUE SYSTEMS

28 M1A1Tank 30 M2 Bradley Fighting Veh 12 M901TOWVeh 72 Infantry Riflemen 18 MedATWpn

6 FISTTm 6 FIST-V 1 OH-58D Helicopter

Indirect Fir? 8 Adv FA System 6 M106(4.2")Mbrtar

RED SYSTEMS ?

Moving 31 T-72 Tank

BMP-2 BMP-1 Riflemen RPG-7 LtMG AGS-17 ADATm 120mm Mtr BRDM-AT

111 19

419 81

135 18

9 24 24

-UMCP (Stationary Tats) 3 ea T-62, T-80, BMP-2

15 Riflemen 5 Truck Util 2 Truck POL 1 BMP Cmd Post 1 Motorcycle

Indirect Fire 72 152mm How 18 122mm Mortar

20% Soft Targets/ 80% Hard Targets

High-Resolution Force Structure SWA Mech Infantry Scenario #2

BLUE SYSTEMS

28 M1A1 Tank 30 M2 Bradley Fighting Veh 12 M901TOWVeh 72 Infantry Riflemen 18 MedATWpn 6 RSTTm 6 RST-V 1 OH-58D Helicopter

Indirect Fire 8 Adv FA System 6 M106 (4.2") Mortar

RED SYSTEMS Moving 31 T-72Tank

111 BMP-2 19 BMP-1

419 Riflemen 81 RPG-7

135 Lt MQ 18 AGS-17 9 ADA Tm

24 120mm Mtr 24 BRDM-AT

Indirect Fire 72 152mm How 18 122mm Mortar

UMCP/Stationary Tats) 3 ea T-62, T-80, BMP-2

15 Riflemen 5 Truck Util 2 Truck POL 1 BMP Cmd Post 1 Motorcycle

Ätäitforttl Umte

ll:

F2

Trucks Riflemen

SA-13

122mm MRU Motorcycles

€>Q%i> Soft Targets/ 40% Hard Targets

AFAS Variation Levels

Rate of Fire

1. 8 rds/minute

2. 10 rds/minute

3. 12 rds/minute

Time on Target

1. 4-rd volleys

2. 6-rd volleys

3. 8-rd volleys

- Levels 1 and 3 are the low and high levels for the Factorial Design

- Level 2 (middle-level values) will be predicted by Response Surface Methodology and then compared with the output from the actual runs.

AFAS: Full Factorial Design Conduct 10 Runs of each point to Assess Factors

| FACTOR (-) (♦)

1. ROForTOT 2. Target Type

SrprrV 4-rd Volleys 20%Sott/ 80% Hard

12rpnV 8-rd Volleys 60%SofV 40% Hard

• AFAS used DPICM in all missions

Factor I

Response"

R1 R2 R3 ft R4 £

Graph other Measures: - Det/Fire/Kill Rgs over time -#Red Systems Killed by Type

'* Six MOE (Responses) used: ■AFASRds/Kill - Det Ratio •DF Kills of Red • DF Engage Rg ■IF Kills of Red -AFAS Percent Contrib

Number of Hard Tgt Kills

25

24

23

22

21

20

19

18

17

Total Kills by AFAS on Hard Targets

Significant Change in Cl itrom 8 to 12 rpm

8 rpm 12 rpm 4 TOT 8 TOT

20% SofV 80% Hard Tgts

^

70% Confidence Interval

8 rpm 12 rpm 4 TOT 8 TOT

60% Soft/ 40% Hard Tgts I

Number of Soft Tgt Kills

130

120

110

100 ■

90

80

70

60

Total Kills by AFAS on Soft Targets

8 rpm

12 rpm

4 8 TOT TOT

20% Soft/ 80% Hard Tgts |

I I

70% Confidence Interval

8 rpm

12 rpm

4 8 TOT TOT

60% Soft/ 40% Hard Tgts j

Since all CIs overlap, there are no significant changes

Rounds per Kill Time-on-Tgt vs. Rate of Fire

Number of Rounds 5

-»-TOT -»ROF

Brpm/4TOT 12rpm/8T0T 8rpm/4T0T 12rpm/8T0T

20% Soft/ 80% Hard Tgts | 60% Soft/ 40% Hard Tgts I

Main Factor Effects Time-on-Tgt vs. Rate of Fire

MOE **: »Indirect Rounds Expended per Kitt öf Red Systems |

TOT Effects ROF Effects

0.374 0.4 r

0.2 Effects caused 0

-0.2

-0.4

by Changing from Low io hiah Level

•0.6

•0.8

•1

0.17

gpi? 1 -0.034

-0.94

D Upper E Mean B Lower

Only ROF Missions are Significant within a 70% Confidence Interval

Effects caused by Changing from

to High Leyej

Main Factor Effects 20% vs. 60%Soft Targets

jflOEai: »Indirect Rounds Expended pet KiWoi Red Systems |

TOT Effects ROF Effects o

-0.5

•1

-1.5 r

•2 -1.66

•1.83 -1.78 •1.9B

□ Upper E3 Mean ■ Lower

Both TOT & ROF Missions are Significant within a 70% Confidence Interval

Direct Fire Kills Time-on-Tgt vs. Rate of Fire

♦TOT ■"- ROF

8rpm/4TOT 12rpm/8TOT 8rpm/4TOT 12rpm/8TOT

20% Soft/ 80% Hard Tgts | 60% Soft/ 40% Hard Tgts


Effects caused by Changing from Low to High Level

WOE*2: ^Direct Fire Kills of Red Systems

TOT Effects ROF Effects 6.79

KmSSlJl 3-15

: 4

15.05

•0.49

•19-73 E3 Upper E Mean H Lower

Only TOT Missions are Significant within a 70% Confidence Interval

Effects caused by Changing from Low to

Hah Leyel

5

0

•5

•10

•15

Main Factor Effects 20% vs. 60% Soft Targets

|-MÖE#2: #DSfECl Fire JGIts of Fled Systems jj

TOT Effects ROF Effects 3.64

0.66

•10.54

•13.76

□ UpperE Mean BLower

Neither TOT nor ROF Missions are Significant within a 70% Confidence Interval

Indirect Fire Kills Time-on-Tgt vs. Rate of Fire

Number of Kills

150

140

130

120

110

100

90

60

♦TOT ■» ROF


20% Soft/ 80% Hard Tgts | 160% Soft/ 40% Hard Tgts~|


WPS #3: »Indirect Fire Ki»s of Red Systems I


Effects 20

caused 15 by Changing 10 from Low 5

to 0 High Level -5

19.16

6.17

1.4 'MV/MW,'..',

»:««<« "^•v' •3.37

□ Upper E3 Mean B Lower

Only ROF Missions are Significant within a 70% Confidence Interval



WOE #3: »Indirect Fire Kills of Red Systems $


60 r

50 ■

40 •

30 -

20

10 r

50.04

IIS

45.5

w\ 40.96 42.7

«lip

Ufa

□ Upper E3 Mean EB Lower

j Both TOT & ROF Missions are Significant within a 70% Confidence Interval

Engagement Ranges (Direct Fire) Time-on-Tgt vs. Rate of Fire

3500

3400

Range 3300 (meters) V ; 3200

3100 iK

3000

2900 8rpm/4TOT

TOT » ROF

12rpm/8TOT 8rpm/4TOT 12rpm/8TOT

20% Soft/80% Hard Tots ji 160% Soft/40% Hard Tots E


High Level


^°^ ffi.,8!*** Piriect &™ Engagement-Ranges: |


250 200 150 100

50 0

-50 •100 -150 •200 L

215 189

p| Valu

152 •166 •179

CD Upper E Mean ■ Lower

Both TOT& ROF Missions are Significant within a 70% Confidence Interval


MQE4&1 Blue Direct.if-ira Engagement Ranges |


Effects 250 r

caused 200 • by Changing 150 ■ from Low 100 ■

to 50 ■

High Level

203

Wmwt

182 j?} 161

Hr:

E3 Upper E Mean HLower

! Both TOTSL ROF Missions are Significant within a 70% Confidence Interval j

Detection Ratio Time-on-Tgt vs. Rate of Fire

27

26

25

#Blue Detects/ 24 #Red Detects

♦TOT ■» ROF


20% Soft/ 80% Hard Tgts~| 60% Soft/ 40% Hard Tgts [f



MQE:#5; #BIueDetects of Red/ #ffed Delects of Blue


2

1.5

1

0.5

0

•0.5

-1

•1.5

1.64

PP 0.861 0.569

0.08

•1.236

□ Upper E3 Mean B Lower

Only rOTMissions are Significant within a 70% Confidence Interval


tliah Level


MQE#5: #BfuE Detects of fled,' #Red Delects of Blue


0

-1

■2 •

•3

-4

•5

•2.41

•0.86

-2.26

•4.65

n Upper E3 Mean IB Lower

Both TOT & ROF Missions are Significant within a 70% Confidence Interval

Total Kills Time-on-Tgt vs. Rate of Fire

Number of Kills

420

400

380

360

340

320 8rpm/4TOT

♦TOT -»ROF

12rpm/8TOT 8rpm/4TOT 12rpm/8TOT

20% Soft/ 80% Hard Tgts 60% Soft/40% Hard Tgts j RSSS\N\NS«SSä«?5S«<S^%5555^\S<\\%

Percent Contribution ofAFAS Time-on-Tgt vs. Rate of Fire

#AFAS Kills/ Total Kills

(Values are in Percentages)

■*■ TOT * ROF

35

33 r

31

29

27

25 8rpm/4TOT 12rpm/8TOT 8rpm/4TOT 12rpm/8TOT

20% Soft/ 80% Hard Tgts| j 60% Soft/ 40% Hard Tgt



MOE #6: AFAS Percent Contribution of Red Kills AFAS grc-uped with Ml At iJBTs, M2 BFVs & M?01 "TOW*


3.5

3

2.5

2

1.5

1

0.5

0

3.38

2.8E

1.62

Values are M. in Percentages 0.39

2.11

n Upper E Mean B Lower




MOE#6: AFAS Percent Contribution of Red Kills AFAS grouped wKh MlA1 hBTs, M2 BFVs & M301 TOWs


12

10

e

6

4

2

0

10.33

:§j;:i; Values are JxS> in Percentages

8.45

$5S

$SS

£.68

□ Upper E Mean H Lower


Look at these MOE for RSM

Summary of DOE Results

Significant Main Effects &M«<««ÄVVvwvV\>»>i&:««^

MOE

Artv Rounds/Kill Direct Fire Kills Indirect Fire Kills DF Engagement Rg Detection Ratio Percent Contribution

Mission Factor

ROF TOT ROF TOT & ROF TOT TOT & ROF

Response Surface Method

Premise: RSM can be used to Predict the Effects of a Simulation

Proved bv: Comparing the RSM Values for Mid-Levels in ROF & TOT with the actual averages obtained from the runs in Janus

MOE to Compare:

1. indirect Fire Rounds per Red System Killed

2. AFAS Percent Contribution vice M1, M2 and M901 systems

6.1

5.9

5.7

5.5

5.3

5.1

4.9

4.7

4.5

RSM for AFAS Rds/Kill 20% Soft Tgts/80% Hard Tgts

TOT of 6 Rds & ROF = 10 Rds/Min

!

•

:-*— Predicted 5.5 rpk for TOT of 6 !

Predicted 5 rpk \ w/ROFof10 f

< ►

TOT ROF

Upper ■ Predict A Lower

RSM for AFAS % Contrib

Values in%

28

27

26

25

24

23

20% Soft Tgts/80% Hard Tgts TOT of 6 Rds & ROF = 10 Rds/Min

Predicted 26.7% w/TOT of 6 «MJMMWHWMWWMWMMMtWt

TOT ROF

♦ Upper ■ Predict * Lower

Predicted 27.1% | for ROF of 10 rpm I ».v.■■■■..■.■■■.■■■■■. '*..'■' ..:■■■■ --'S ■X»V«*.VvVvV<^O.VV».VV.VV\W

AFAS Study Conclusions

Comparison of Factorial Design of Experiments Results §

1. Increasing the rate of fire showed a significant decrease in artillery rounds per kill (-0.64 rpk).

2. For Indirect Fire Kills, there was a significant increase (13.1 kills) when changing from 8 rds/min ROF to 12 rds/ min.

3. AFAS percent contribution significantly increased in both methods of employment (1.62% for TOT and 2.11% on ROF).


Response Surface Methodology Results \

1. For both scenarios, predictions of artillery rounds per kill for a TOT of 6 rds and a ROF of 10 rds/min were within the actual runs' confidence intervals.

2. AFAS percent contribution predictions were also within the bounds of confidence intervals for the same cases as in '1'.

3. RSM does show potential for reducing the amount of runs required for a study of parameters, such as TOT or ROF.


Comparison of Graphs & Confidence Intervals about Means"~|

1. In Killing Hard Targets, the 12 rds/min ROF is most effective in a 20% Soft'80% Hard target distribution

2. For S»ft Targets, there appears to be no significant difference among any of the methods, although a higher ROF or TOT shows a larger mean.

3. The Method of Employment shows no sigificant effect on Direct Fire Engagement Ranges even though the Factorial Design Analysis suggested minor changes in effects due to changing factors.

1000 Target Methodology

1. Based on Soviet-style tank company in wedge formation.

2. 100 target groupings; 10 vehicles per group.

3. Two platoons with 3 vehicles; 1 with 4 (includes HO. tank).

4. One event per method of engagement:

- One 8-round volley per group under 8 TOT •- Twelve rounds, 5 seconds apart, pergroup under 12 ROF

5. ROF rounds are spread over a 1-minute attack.

6. Five runs for each method of engagement

7. Trucks substituted for tanks at the same locations.

Appendix B Factorial Design Analysis: TOT

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4 TOT 43 51 42 43 37 43.2 1.08 6 TOT 62 61 69 58 64 62.8 1.05 8 TOT 69 65 74 69 87 72.8 . 0.91

8RPM 92 85 89 70 91 85.4 1.07 10RPM 118 87 101 92 105 100.6 1.01 12RPM 139 122 133 115

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8RPM 204 151 172 173 168 173.6 2.17 10RPM 221 211 232 187 211 212.4 2.12 12RPM 212 231 223 199 231 219.2 1.83

Appendix G After-Action Memo by MAJ Watson on AFAS Briefing

OPERATIONS RESEARCH CENTER UNITED STATES MILITARY ACADEMY

WEST POINT, NEW YORK 10996

MADN-OR 22 July 1992

MEMORANDUM FOR LTC James E. Armstrong, Director ORCEN, USMA

SUBJECT: TDY Trip to Fort Sill, Oklahoma concerning the Advanced Field Artillery System (AFAS).

1. On 19-21 July 92, CPT Jim Watson went to Fort Sill, Oklahoma to attend a briefing by Majors George Stone and Jay Moughon, and SFC West of DMI. Their briefing, which occurred on 20 1300 July 92, covered their work on the Janus(A) combat modeling system regarding the AFAS. They briefed numerous (about 20) civilian and military personnel of the Directorate of Combat Developments (DCD), U.S. Army Field Artillery School. Please see the attached listing of the key personnel who attended.

2. The briefing went quite well with, at times, very lively interaction with the audience concerning the assumptions and set-up of the Janus scenarios which MAJ Stone and MAJ Moughon developed. They seemed very interested in comparing the effects of the AFAS between the Time-on-Target and Rate-of Fire target engagement methods. The briefing concluded with high interest on the part of all key personnel in the DCD to continue with the analysis with some refinements on the scenario set-up, as well as the modeling of key AFAS attributes.

3. I would highly recommend continued work on this project. The resulting analysis would be well received by not only DCD personnel, but also high level Field Artillery and Procurement Action Officers. The refinement of the scenarios could be done nicely by cadets in the Janus Combat'Modeling Lab as a SE489 project. In addition, SFC West informed me that COL Karr, the Director of Military Instruction, was very interested in pursuing this project.

4. I would be happy to continue this analysis. As a Field Artilleryman and an ORCEN Analyst, this project would exercise my education and training most appropriately.

JamesX. Watson, Jr. CPT, FA ORCEN Analyst

AFAS KEY PERSONNEL

LTC Sole Chief, Studies Division

f MAJ Mike Clark AFAS Project officer

1 Mr. Johnson Deputy, DCD Studies Division GM-14

I Mr. Bennett Cannons and Support Systems GM-14

I Mr. Loveless Special Projects GM-14

Documents

Advanced Field Artillery System Trade-off Analysis: · PDF fileAdvanced Field Artillery System Trade-off Analysis: ... ADVANCED FIELD ARTILLERY SYSTEM TRADE-OFF ANALYSIS: TOT vs. ROF