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Technical Report 721

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Evaluating the Benefits and Costs of the

Enlisted Personnel Allocation System

(ERAS)

Edward J. Schmitz

Army Research Institute

Peter B. McWhite

General Research Corporation

Manpower and Personnel Policy Research Group

Manpower and Personnel Research Laboratory

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U. S. Army

Research Institute for the Behavioral and Social Sciences

July 1986

Approved for public release; distribution unlimited.

87 2 25 160
U. S. ARMY RESEARCH INSTITUTE

FOR THE BEHAVIORAL AND SOCIAL SCIENCES

A Field Operating Agency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

EDGAR M. JOHNSON

Technical Director

WM. DARRYL HENDERSON

COL, IN

Commanding

Research accomplished under contract

for the Department of the Army

General Research Corporation

Technical review by

Charles J. Dale

COL Carolyn A. Post, AG USAR

Acresnion For

Mr:; G^A*!

DTIC TAB

ü'iarnoir.iced

Jus t if I-at i on

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Availnt1 lily Codes

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Diet Special

NOTICES

\M

DISTRIBUTION: Primary distribution of this report has been made by AR I. Please address correspondence concerning distribution of reports to: U.S. Army Research Institute for the Behavioral

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NOTE: The findings in this report are not to be construed as an official Department of the Army

position, unless so designated by other authorized documents.
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ARI Technical Report 721

READ INSTRUCTIONS

BEFORE COMPLETING FORM

2. COVX ACCESSION NO. *1. WCCIPICNT'S N CATALOG NUMBER

4. TITLE (m%4 SuMU»)

EVALUATING THE BENEFITS AND COSTS OF THE ENLISTED

PERSONNEL ALLOCATION SYSTEM (EPAS)

1. TYPE OF REPORT 4 PERIOD COVERED

October 1985-June 1986

** PERFORMING ORG. REPORT NUMBER

7. AUTHORf«;

Edward J. Schmlf. and Peter B. McUhite

• CONTRACT OR GRANT NUMBER^*)

^tDA903-82-C-0552

t

9. PERFORMING ORCANUATION NAME AND ADDRESS

General Research Corporation

7655 Old Springhouse Road

McLean, VA 22102

10. PROGRAM ELEMENT. PROJECT/ TASK

AREA 4 WORK UNIT NUMBERr

2Q263731A792

213C

II. CONTROLLING OFFICE NAME AND ADDRESS

U.S. Army Research Institute for the Behavioral

and Social Sciences

5001 Eisenhower Avenue, Alexandria, VA 22333-5600

14. MONITORING ACCNCY NAME 4 AOORCSifM Uttomnt tnm CmnlnlUn» Ollle»)

It. REPORT DATE

July 1986

II- NUMBER OF PAGES

50

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U* classified

ISa. DECLASSIFICATION/OOWNGRADING

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14. DISTRIBUTION STATEMENT fa/» <• Rap«*;

Approved for public release; distribution unlimited.

IT. DISTRIBUTION STATEMENT (•! Ma a6a<rac( an(araW In Block 30, II dlllarmtl horn Rwport)

'CtjAAiO*4$:

It. SUPPLEMENTARY MOTES

Contracting Officer's Representative, Edward J. Schmitz.

S:It. KEY WORDS (C—llntM an ravaraa »14» II nacaaaarr «« la—Illy by block numbor)

Army accessions^

Classification^

Man-j ob-match/

Optimization^ _

»Network optimization

Benefit-cost analysis^

REQUEST ^pro y C+j

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\

1ft. AWTWAQTfniBtBBii »mow ott H imfwm, mdMomtllr *r bl**nm,bm)

>The Army Research Institute, with the assistance of the General Research

Corporation, is undertaking a project to modernize and improve the way the Army

determines the Military Occupational Specialty (MOS) for which an individual

should be trained. This project is called the Development of the Enlisted

Personnel Allocation System (EPAS).

A key task in the EPAS development is the performance of a benefit-cost >

analysis of the prototype system that will provide Important (Continued)

DO.:;JAM 7S 1473 comoM or t MOV ct IS ETC UNCLASSIFIED

i SECURITY CLASSIFICATION OF THIS PACE (Whon Dot* Bnifd)
UNCLASSIFIED

SECURITY CLASSIFICATION OF THIS PACCfWkan Oa« B«il*r*0

ARI Technical Report 721

20. (Continued)

information on the potential benefits of Improving accession management and

training seat allocation.

To support the benefit-cost analysis, a series of simulations were run

which demonstrated the capability of EPAS to operate feasibly under realistic

scenarios. Further, EPAS is likely to produce substantial improvements over

present soldier allocation procedures.

/0

^/

r

UNCLASSIFIKD

*^ SECURITY CLASSIFICATION OF THIS PACErlFh»n DM« Rnlarad)
Technical Report 721

Evaluating the Benefits and Costs of the

Enlisted Personnel Allocation System

(ERAS)

Edward J. Schmitz

Army Research Institute

Peter B. McWhite

General Research Corporation

Manpower and Personnel Policy Research Group

Curtis L. Gilroy, Chief

Manpower and Personnel Research Laboratory

Newell K. Eaton, Director

U.S. ARMY RESEARCH INSTITUTE FOR THE BEHAVIORAL AND SOCIAL SCIENCES

5001 Eisenhower Avenue, Alexandria, Virginia 22333-5600

Office, Deputy Chief of Staff tor Personnel

Department of the Army

July 1986

Army Project Number Manpower and Personnel

20263731A782

Approved lor public release,- distribution unlimited.

Ill
ARI hewarch Reports and Technical Reports are intended for sponsors of

R&O tasks and for other research and military agencies. Any findings ready

for implementation at the time of publication are presented in the last part

of the Brief. Upon completion of a major phase of the task, formal recommendations for offie al action normally are conveyed to appropriate military

agencies by briefing c r Disposition Form.

XV
FOREWORD

The Army Research Institute for the Behavioral and Social Sciences (ARI)

is undertaking a comprehensive research program to Improve the selection,

classification, and allocation of Army personnel. A key part of this program

is the Enlisted Personnel Allocation System (EPAS), which will improve personnel performance by achieving a better match between Army requirements and the

capabilities of the people applying for service. This report presents the

benefit-cost analysis supporting the development and implementation of EPAS.

j^f^/f/nL*^-

EDGAR M. JOHNSON

Technical Director
EVALUATING THE BENEFITS AND COSTS OF THE ENLISTED PERSONNEL ALLOCATION

SYSTEM (EPAS)

EXECUTIVE SUMMARY

Requirement:

The Army's need to Improve the way new accessions are assigned to Military

Occupational Specialties (MOS) led to the development of the prototype Enlisted

Personnel Allocatioh System (EPAS). This report presents the benefit-cost data

supporting an EPAS.

Procedure:

A set of five simulations of the allocations EPAS would recommend were

run. The scenarios represented

1. conditions similar to those in FY 84

2. fewer quality applicants

3. a three-fold increase in summer training requirements over those in

the three previous quarters

4. simulated applicants not accepting the first recommended assignment

5. not using the look-ahead functic of the EPAS optimization

Using predicted attrition as a comparison of EPAS assignments to the actual assignments in FY 84, we computed the cost savings that could have resulted had EPAS been used.

Findings:

The simulations show that EPAS could save over $23M each year in attrition costs alone. Further, EPAS's performance in the test scenarios demonstrated that it was sufficiently robust to support policy analyses.

Utilization of Findings:

The EPAS design was been demonstrated to be a cost-effective system.

These findings indicate that a field test of EPAS should proceed.

vii
EVALUATING THE BENEFITS AND COSTS OF THE ENLISTED PERSONNEL ALLOCATION

SYSTEM (EPAS)

CONTENTS

Page

I. IK ODUCTION 1

II. BACKGROUND 2

Army Accession Process 2

The EPAS Approaach 4

EPAS Capabilltle 7

Process Description 8

III. APPROACH 13

Concerns 14

Identifying the Benefits and Costs of EPAS 15

Simulation Scenarios 17

How Simulation Supports the Benefit-Cost Analysis 24

IV. RESULTS 28

Baseline Case 28

Low Quality Case 28

High Summer Training 30

Probabilistic Choice 30

No Look-Ahead 30

EPAS Costs 31

V. DISCUSSION 32

REFERENCES 33

APPENDIX A. ATTRITION COSTS FROM THE US ARMY OMA & MPA COST FACTORS

(DCA-H-1) A-I

B. OPTIMIZATION PROBLEM FOR THE PLANNING MODE B-l

C. ALGORITHM FOR THE APPLICANT CLASSIFICATION MODULE C-l

LIST OF TABLES

Table 1. Simulation scenarios for benefit-cost analysis 18

2. Ordered list choices for soldier choice scenario 23

ix
CONTENTS (Continued)

Page

Table 3. EPAS simulation results 29

4. EPAS life-cycle costs 31

LIST OF FIGURES

Figure 1. Army personnel accession flow 3

2. EPAS applicant classification 9

3. Accessions - training schedule 21

4. Priority of MOS selected 22
I. INTBODUCTION

The Amy Research Institute, with the assistance of the General

Research Corporation, is undertaking a project to odernize and isprove

the way the Aray deterainea for whi :h Military Occupational Specialty

(MOS) an individual ahould be trained. This project is called the

Developaent of the Enlisted Personnel Allocation Systea (EPAS).

A key task in the EPAS developaent is the perforaance of a

benefit-cost analysis of the prototype systea. This analysis will

provide iaportant inforaation on the potential benefits of iaproving

accession sanageaent and training seat allocation.

A series of siaulations were run to support the benefit-cost

analysis. These siaulations deaonstrate the capability of EPAS to

operate feasibly under realistic scenarioa. Analysis of the results of

this task will identify whether EPAS warrants iapleaentation as

presently envisioned, what kinds of edifications aay be necessary, and

how final testing and iapleaentation should proceed.

Following this introduction. Section 2 describes the accession

process and the role of EPAS in aanaging that process. Section 3 then

discusses the benefit-cost criteria and the EPAS coaputer siaulations

that will be used to test the robustness of the results. Section 4

presents the results of the siaulations and Section 5 gives our

estiaates of EPAS costs.
II.

AMT AOCBSIOM PBOCKSS

The U.S. Any recruits and trains »ore people each year than any

other organization in this country. Over 300,000 people apply for

130,000 entry-level positions in over 250 different silitary

occupational specialities (M0S). Figure 1 Illustrates the aajor steps

an applicant goes through in the enlistaent process. The applicant

first takes the Araed Services Vocational Aptitude Battery (ASVAB) to

detemine if he is aentally qualified to enter the Aray. The ASVAB

includes subtests foniing the Araed Forces Qualification Test (AFQT),

which deteraines enlistaent eligibility, and other tests for qualifying

in nine groups of Jobs.

ASVAB scores play a aajor role in deteraining who is eligible to

enlist and what kind of skill training one can receive. The Aray

particularly desires high school graduates whose AFQT scores place thea

in the top half of the general population. These are called quality

applicants. The Aray is prohibited by Congress froa accepting

applicants froa the bottoa 10 percent of the population, and has

adsinistratively decided against accepting those in the lowest

quartile.

The aptitude testing is followed by a physical exaaination. After

satisfying the aental, physical, and aoral standards, the applicant is

offered a job assignaent by an Aray guidance counselor and signs an

enliataent contract. He then returns hoae until it is tiae to report

for active duty (up to 12 aonths in the future). This delay, between

contract signing and reporting for active duty, is peraitted by the

Delayed Entry Prograa (DEP).
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Signing the enliataent contract is a key decision point in an

applicant's Aray career. When they sign their contract, all applicants

are guaranteed the kind of training they will receive, even though it

ay be over a year before they actually receive their training.

While the guarantee of specific Job training ia a useful recruiting

incentive for the Aray, the claaaifi cation process nust be aanaged

carefully to meet the following requireaenta:

a Fill yearly Job requiresents.

s Ensure applicants neet NOS qualifications.

a Fill nesr-teni and critical training seats,

s Distribute quality personnel into vital Jobs.

The present Aray person-Job aatch ayatea has been successfully

filling all open Job requireaents. While it peraita a satisfactory

aatch of applicants to Jobs, its planning capabilities are very

liaited:

a It cannot "look ahead" and aatch the projected applicant

supply fco the reaaining Job openings,

a It cannot take corrective action to avoid probleaa or estiate how policy changes will affect the future supply and

distribution of personnel,

a It cannot sake trade-offa between other iaportant objectives

such as ainiaizing attrition and aaxiaizing Job perforaance.

THB BPAS APPBOACH

EPAS uses an optiaization approach to allocate applicants to NOS

training aeata. The general structure of the problea ia to:

a MAXIMIZE TOTAL PERFORMANCE

a SUBJECT TO:

Manpower requireaents

Peraonnel supply

Organizational constrainta
Maximzing total perforaance requires two iaportant aaauaptions.

First of all, it is aasuaed individuals differ in their perforaance

levels. If all "qualified" individuals cannot be distinguiahed as to

their perforaance or coats, then there would be nothing to optiaize.

Clearly, there is substantial evidence that individuals do differ in

teras of their perforaance. (See Soldier Quality Taak Force 1986, for

exaaple.)

Second, it is aasuaed that total Aray perforaance can be aaxiaized

by suaaing up individual perforaance. What the Aray ia ultiaately

after are units that perfom well. However, aodeling and evaluating

the interactions of individuals within units is beyond the scope of

this project, cannot be supported by current knowledge about group

perforaance, and is not addressed by current training aanageaent

procedures. Therefore, it appeara reaaonable to aaauae that an Aray

coaprised of individuals who perfom better (in the aggregate) ia

preferred over one that doesn't perfona as well.

Total perforaance ia preaently coaprised of two aajor parts:

personnel costs and individual Job perforaance. No doubt other

conbinations of perforaance factora will be developed in the near

future; this is entirely consistent with the design and flexibility of

KPAS. The structure of BPAS is largely deterained by the Aray'a

peraonnel aanageaent systen.

BPAS uses a two-stage optinization systea. The first stage aolvea

the aggregate planning prob lea of bringing aanpower requireaenta and

peraonnel supply into balance over tiae. The second atage recoaaenda

individual training seata to applicants baaed upon overall guidance and

specific individual attributes.
The optiaization approach guaranteea that the "beat" aolution to

the Banaceaent problea will be found. This approach not only provides

a aolution, but provides inforaation on the value of the beat solution,

and benefits and costs of resources and policies that affect the

problea. Alao, the optiaization fraaework facilitates chanting the

problea and resolving it. These are all very desirable features for

such an isportant deciaion aa acceasion aanafeaent and training aeat

assignaent. Nearly $2 billion in resourcea are involved in this

decision. Even aore iaportantly, the readiness and perforaance of the

Aray could be substantially iaproved by better personnel allocation.

Optiaization is widely used in industrial and ailitary applications to allocate scarce resources to attain a least coat or highest

vslue course of action. Applicationa in Aray aanpower support include

the NILPBRCBN systea which assigns recruits to their first unit end

ODCSPER's BLIM-COMPLIP which was honored by s society of aanageaent

professionals for its use of optiaization in aanpower planning (Holz

and Wroth, 1980). In these applications the aany thousands of

alternatives, conflicting requiresents, and process interactions have

aandated using large-scale optiaization.

Optiaization techniques will significantly help the Aray aanage

recruit classifications and school sssignaents. Here, the scarce

resources are quality applicants. They, along with AFQT Category

IIIB-IV applicants, aust be assigned an MOS so that the resulting

person-job natch provides the best MOS-specific perforaance attainable

froa the liaited supply of quality applicants.
KPAS CAPABILITIES

BPAS uses optiaization to iaprove applicant classification. It

encoapasses a aeries of integrated sodules that perfom forecasting,

optiaization, and decision analysis for personnel allocation. It will

let Aray personnel planners evaluate recruiting plena and policies, as

well aa recoaaend specific training assignaents for applicants. Over a

year period, it plans an optiaua aatch of groups of applicants to MOS

training seata. This allocation of applicants incorporates:

e A tine-sensitive training plan

e MOS quality requiresents

e Gender restrictions

e Applicant availability and propensity to accept different DEP

perioda

e MOS priorities.

The optiaization will be updated frequently with current data on

school openings and applicant availability.

KPAS Applicsnt ClMsificatioo Support

Bach week the BPAS optiaization will create an ordered liat of

reconaended MOS school seat assignaents for each supply group. These

supply groups are differentiated by AFQT scores, aptitude area acores,

education, and gender. The ordered liats will be input to REQUEST so

that day-to-day sequential classifications can incorporate the "look

ahead" guidance of the optiaizations. This link to REQUEST will

overcoae the liaitationa of sequential classification and:

e Make applicant classifications and school assignaents

consistent with optiaal allocation of the scarce high quality

applicanta.

s Fence attractive MOS for quality applicants.

e Allocate other applicants to MOS based on forecasted aarket

conditions for quality applicanta.

e Allocate applicants to aake efficient use of their aptitudea.

e Recoaaend DEP lengths to best cover slack recruiting aonths.
KPAS Becmiting IlMiiWBiit SiMPort

Headquarters, US Aray Recruiting Co—and (USARBC) will use SPAS to

assess its options for neeting training classes and quality goals under

various recruiting scenarios. USARBC can evaluate DIP policies that

are specific to NOS and applicant quality, as well aa evaluate the

iapact on predicted perfonance of alternative person-Job Matches. It

is particularly useful for evaluating coaplex and often competing Job

requireaents for quality and high school graduate applicants.

Starting at any point in a fiacal year, BPAS can run a day-to-day

siaulstion of recruit classification. A past year's or a hypothetical

series of recruit records can be processed to seet a given training

plan; and, policies such as DBF lengths, NOS priorities, and class fill

rates can be varied throughout the siwulation.

PROCESS DBSCRIPTION

BPAS nay be operated in either the Clasaification Mode, which

supports the actual classification of applicants to Jobs; or the

Planning Mode, which supports recruiting snageaent. The inforaation

flow between the BPAS aodules that auppor4* these nodes is depicted in

Figure 2.

Cl—ification Mods

Ibis node provides real tine support to the guidance counselors'

applicant classification decisions. Because it uses optinization, it

can sake better recoanendations than the current classification systea,

the REQUEST NOS Match Nodule (N3). As does M

3, it also uses the

existing reservation and other aanageaent support of the REQUEST

ays tea. The following paragraphs describe the functional flow of the

BPAS Claaaification Node.
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Applicant Forecast«. EPAS can generate alternative forecasts by

educational level and AFQT score. The Classification Node will use

forecasts that correspond to the USAREC aission statcasnts.

MOS Bequiwents. The Claasification Node uses the training

requir—ante fro« the REQUEST systea. (In the long run, reduced

turnover due to EPAS could alter training requiresents.)

Qptiaiaation. This is the key to SPAS' capabilities. Its inputs

are the NOS training requiresents and the nuaber of forecasted

applicants. Using a structure that incorporates iaportant recruiting

policiea, the optiaization developa an ordered liat of recoaaended Job

aaaignaanta for the different types (gender, education, quality) of

applicants forecasted for the next week. Bectrase the optiaization can

"look ahead" at both the unfilled Job requiresnnta and the forecasted

tiae-phaaed applicant supply, its ordered lists iapart an "artificial

intelligence" to classification recoaaendations. This allows it to

recoaaend Job ssaignaents which aeet Any goala while conaidering

trade-offs aaong applicant quality, availability, and tiaing of

acceaaiona.

Ordered tists of Recoaaended Joba. The optiaization aolution

would repreaent only one of aany poaaible sets of individual Job

aaaignaanta. Factors auch aa individual qualifications, intereata, and

the ahort-tera availability of Jobs preclude aany applicants froa

accepting thia aolution. Therefore the optiaization createa ordered

iiata of recossiwided aaaignaanta for different applicant groupa. Thus

if the optiaal i ecoaaendation ia not choaen, alternative feasible

recoaaandationa are aade in order of their desirability.

10
Applicant Claasification. This aodule directly supports the

guidance counselors. It operates auch like the current M

3

in that it

recossends Job assignsent« and training start dates as each applicant

appears for classification. It differs significantly in that it incorporates the ordered lists froa the optiaization in its classification

recoaaendations. These ordered lists then provide "look ahead" intelligence for the guidance counselors' day-to-day classification recoaaendations.

PI—ing node

The Planning Mode ufes the basic structure of the Classification

Mode to support USAREC's and ODCSPBR's accession planning. However,

rather than providing classification for individuals, planners can

evaluate the allocation recoaaendations froa the optiaization or

conduct a detailed classification of a hypothetical group of

applicants. It peraits flexible data input so that alternative

policies and applicant availability scenarios can be evaluated.

Applicant Forecasts. In the Planning Node applicant forecasts can

be varied to explore the effects of alternative policies or econoaetric

scenarios. Several econoaetric forecasting techniques are available or

aonthly forecasts can be input directly.

MOS Requir^rtr-ts. In like Banner MOS requireaents can be varied

to evaluate different training plane.

Optiaization. The applicant allocation strategy froa the

optiaization'a aolution is one aethod to assess policy options in the

Planning Mode. Evaluating altemativea is easy, since the optiaization

only takes several sinutes to run. However, the allocation plan is

general, as it deala with groups of applicants and categories of jobs.

More specific inforsation can be provided by classification

siaulations.

11
Applicant Clarification. ERAS can aimilate assigning hypothetical applicants to jobs by processing a streaa of siaulated applicants.

The applicant streaa can be siailar to soae previous year or can be

given characteristics (those iaportant to Job classification) that are

based on estiaates of future applicant supply. The Applicant Classification Module processes this streaa auch the saae as it would actual

applicants. It can incorporate probabilistic applicant behavior auch

as incentives acceptance as well as balking at unpopular jobs or

electing not to join at all. As the Applicant Classification Nodule

has a nuaber of "policy switches" to set goals for job fill, quality,

education, feaale representation, predicted perforaance, etc., the

siaulation can assess the effects of changing these "switches" prior to

ispleaentation.

12
III.

A key taak in the SPAS developaent process is the benefit-cost

analysis. The objective of this analysis is to:

e Deteraine whether the iaprovenwnts generated by BPAS warrant

full developaent.

e Identify the design configuration that appears to be aost

efficient.

An assuaption of this analysis is that soae of the benefits to be

derived froa SPAS are quantifiable and will result in soae level of

cost savings or cost avoidance. An exaaple would be the potential to

reduce first tens attrition by iaproving person-Job Batches. Other

easures of effectiveness will be acre acre difficult to calculate in

dollar teras but any be quantifiable. Finally there will be benefits,

due to iaproved aanageaent, that cannot be readily quantified, but any

be identifiable by systea users.

This section of the report describes the approach proposed for the

benefit-coat analysis. The first part identifies the Aray functional

ares where BPAS could have an iapact. The second part establishes the

criteria for benefit and cost aeasures. The third part discusses the

specific scenarios that will be evaluated to explore different

configurations and test the sensitivity of BPAS to various operating

environaent assuaptions. The final part discusses how the benefit-cost

criteria will be operationally seasured.

13
The Any categorizes it« Mnpower anageaent process into the

followinf functional

• Acquire • Sustain

s Train a Develop

a Distribute a Separate

a Deploy

SPAS should have soae beneficial inpact in all these areas. However,

the aajor benefits of BPAS should occur in the acquire and train

functions.

A key concern is the Army's ability to classify applicants into

the jobs where they will best perfom. laproved perforaance neasures

is the subject of a aajor Any selection and classification aeasure

developaent effort. Baton (1963) described thia effort, while Schnitz

and Nelson (1982) ahoued that "better" Job aaaifnaents can increase

overall Any perforaance.

laproved anafeaent of recruiting prograaa waa addressed in GAO

reports 83-17 and 82-70. Any recruiting anageaent ia especially

challenging because of the broad spectrua of applicant characteristics.

Soae applicants have a high aptitude for all jobs, while others qualify

in only a few NOS. Another layer of ccaplexity is created because the

Any is required to take soae applicants who did not graduate froa high

school. Conaidering the tiae-phased availability of the different

types of applicants and the Job training classes, recruiting aanageaent

ia exceptionally challenging. Aa discussed earlier, aanageaent aust

necessarily focus on short ten, high priority Jobs. There ia

considerable concern about the effects this crisis aanageaent approach

has on overall classifications.

U
GAO 82-70 also expressed concern about the Aray's ability to

select individuals with a high probability of retention. It aust be

noted, however, that aany of the soldiers who tend to stay in the Aray

have deaographic characteristics (less education end lower aptitude

scores) siailar to those who are the least desirable accessions. This

creates a difficult trade off between perforasnce and retention.

GAO 82-70 also recoaaended that the Aray develop the sophisticated

inforaation systeas that could better profile people in teras of biographical, attitudinal, and aptitudinal data.

Train

Increased training efficiency is a continuing Aray concern. One

way to increase efficiency is to reduce training losses. Nelson and

Schaitz (1985) show that iaproved allocation of high school graduate

aales can reduce attrition during the first 2 years of enlistees*

service by two percent. Thus, there is a potential for a reduction in

training losses in excess of 2,000 soldiers per year.

IDBNTIFYINQ IB BBNBFITS AND COSTS OF SPAS

The identification of the benefits froa iaproved personnel

silocation is the key research task in perforaing the benefit-cost

analysis. Indeed, this activity is key to not only BPAS, but also ARI

Project A: Develop and Validate Iaproved Selection and Classification

Instruaents and Standards. Without objective inforaation that such

selection, classification, and allocation work can realistically expect

to iaprove personnel decisions it would be folly to continue developaent efforts. Conversely, quantitative evidence of substantial

benefits froa iaproved allocation would provide a strong iapetus

towards iapieaenting such iaproveaents.

15
Potential benefits, auch aa the following, could be conaidered to

aaaeaa personnel allocation effectiveneaa:

a laproved soldier performance

a Reduced personnel coata

a Reduced recruiting coata

a Reduced operating coata

a laproved personnel anageaent

Soldier perfonsance ia perhaps the aoat important aspect of personnel decision making. The Soldier Quality Task Force (1985) attempted

to determine the benefits of having better performing soldiers. Incentives such aa enlistment bonuaea, the Army College Fund, and two year

enliatments are justified on the baaia of the need for iaproving

aoldier performance, eapecially in critical MOS.

Reduced personnel coata are another important concern for allocation. About one-third of all accessions attrit before completing

their enlistment term, and moat of thia attrition occurs during the

first year of service. Attrition ia costly, since the Army typically

inveata at leaat $8,800 in training each soldier. (See Appendix A.)

Reduced recruiting coata could alao be a significant factor in

laproved peraonnel management. The average variable personnel

acquiaition coata tend to be about $3,750 (aee Appendix A), and the

marginal coata for a high quality accession may exceed $8,000 (Armor

1982). Recruita have often atated that the kind of training offered

waa a priaary reaaon for enliating. Thus SPAS could have the potential

for increasing the ability of the Amy to enlist high quality

candidates by assuring an adequate supply of desirable training slots.

The operating coat of the aaaignment ayatem ia alao a consideration in the evaluation of SPAS. Significant reductions in these costa

would provide a compelling argument for implementation of the systea;

however, substantial operating coat increaaea could eliminate BPAS from

conaideration. Therefore, coat eatiaatea for supporting the BPAS

cosputer system need to be included in the analysis.

16
laproving the Banageaent of soldier accessions is a key part of

BPAS. It is designed explicitly to assist ODCSPBR and USARBC in terse

of achieving recruiting aissions, MOS distribution, class size goals,

quality objectives, and DBF policies. Additionally, BPAS is set up to

evaluate the feasibility or cost of achieving alternative recruiting

issions, quality goals, training schedules, and other sajor accession

anageaent issues. BPAS should deaonstrate a capability to deal with

such kinds of aanageaent inforaation.

SIMDLATION SCBNARIOS

The benefits and costs of BPAS were estiaated through a series of

siaulations. The purpose of these siaulations is to:

s Deaonstrate the value of BPAS.

s Identify the aost efficient configuration of aodules.

s Evaluate the sensitivity and flexibility of the system to

operate under different scenarios.

The benefit-cost analysis task was perforaed on the prototype

systea. Evaluation of this report will deteraine whether the prototype

warrants full field testing with planned iapleaentation. This analysis

is not intended to estiaate precisely the net benefits of the

iapleaented BPAS. Rather it is to assess whether the prototype is

likely to generate substantial inproveaents, which aeasures are aost

likely to be affected by BPAS, and how sensitive the results are to

operating environaent assuaptions. Thus, the benefit-cost analysis

also provides inforaation on where refineaenta are needed, how the

field test should be designed, and the appropriate iapleaentation

strategy.

Five different scenarios were run to deaonstrate the potential of

BPAS. These scenarios are described in Table 1. They could be

identified as follows:

17
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18
1. Baaeline

2. "Poor" supply

3. Uneven training schedule

4. Probabilistic soldier choice

5. No planning

The baseline scenario uses the FY 84 contract population and

training seat schedule for siaulation. Bach candidate will select the

highest-rated training seat available to hia. The entire BPAS will be

siaulated, including the Planning Node and the Classification Mode with

its look-ahead capability. Bach of the other siaulations reaoves or

alters one of these conditions to assess the sensitivity of results.

For purposes of the benefit-cost analysis it is assuaed that BPAS

will not affect the supply of contracts. This aay not be the case for

the fully operational systea. Clearly, it is the intention of BPAS to

increase the probability of high quality applicants signing contracts.

However, to evaluate thia aspect of the allocation systea, it would be

necessary to evaluate detailed data on the relationship of applicant

preferences. Any MOS offerings, and contract signings. (Assuaing the

benefit-cost analysis indicates continued work in this area, the BPAS

field test would provide detailed resolution of this effect.)

The BPAS benefit-cost optiaization problea is structured as

follows. The aggregate planning problea solved in Stage 1 is:

e MINIMIZE PERSONNEL COSTS

a SUBJECT TO:

Total requireaents

Monthly requireaents

MOS requireaents

MOS class size

Gender restrictions

MOS quality requireaents

DBP policy

MOS priorities

19
The individual classification problea solved in Stage 2 is:

e MAXIMIZE INDIVIDUAL PBRFOHMANCB

WHERE PBRTOiMANCB IS A FUNCTION OF:

Aggregate planning goals (look ahead)

Predicted Job perforaance

Predicted attrition

Difficulty of fill

Time to fill

Quality distribution

The baseline scenario evaluates the perforaance of EPAS in

allocating the FY84 nonprior service enlistaent cohort. This represents a high quality recruiting environaent. Training seat supply will

be the ease as in FY84, and candidatos will select the first offered

MOS. The scenario uses the full planning capability of SPAS.

The second scenario evaluates the sensitivity of results to supply

quality by using an enlistaent quality aix roughly equivalent to FY81.

That year represented a supply of only 40 percent high quality

soldiers, versus 63 percent in the baseline. The training requireents, applicant choice, and planning environaent will reaain the saae

as before.

The third scenario uses the alternative training schedule

described in figure 3. Here the siaaer requireaents are three tiaes

the requireaents for the preceding aonths. This is intended to test

the robustness of EPAS and its capability to support a potential policy

analyses of alternative training schedules.

The fourth scenario evaluates the robustness of results to

individuals' preference. In other scenarios applicants were siaply

given the highest priority MOS. Clearly, this is unrealistic in an

operational environaent, where individuals are peraitted choice. As

shown by Figure 4, the proportion selecting high priority MOS increases

as individuals' AFQT score declines. This is because candidates with

high AFQT have the bargaining position and the qualifications for the

lower priority MOS.

20
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22
We att*apt to Bodel the candidates' NOS selection behavior by

randoaly selectinf NOS froa the Applicant Classification Nodule's

ordered liat. As shown in Table 2, candidates with high APQT will be

allowed a greater range on the ordered liat for their randoa choice.

Thus, the aiaulation will select NOS from the ordered liat in

proportion to AFQT category. This will exaaine how sensitive the

assigment results are to the choice of the applicant.

Table 2

Ordered List Choices for Soldier Choice Scenario

APOT Test Category Ordered List Position

I 30

II 30

IIIA 20

IIIB 10

IV 5

We do not claia that our aiaulation of candidates' choices is

totally tealiatic. We do not know how they would change their NOS if

presented with alternative choicea. However, the purpose of this

aiaulation ia to assess the sensitivity of the systea's iapact on this

factor. The field test will addreas the iapact of applicant choice on

BPAS effectiveness along with other issues. Also, analysis of the

iapact of choice will indicate whether policy changea aay also be

warranted. Clearly, the institution of quality goala on NOS has

already altered the degree to which Aray preference aay override the

individual's.

The final scenario uses the baseline inputs for contract aupply,

training seata, and NOS preference, but will not use the look aheadfeature. This peraits an eatiaate to be aade of the iapact of the BPAS

planning function on the value of Job aaaignaanta. Presuaably the

look-ahead capability will enhance BPAS'a ability to aeet personnel

aanageaent goala, reduce attrition, and iaprove job perforaance.

However, the Applicant Claasification Nodule should be able to perfora

aaaignaanta without the look-ahead capability in the faahion of the

Navy'a CLASP ayatea (Kroeker and Rafacz 1984).

23
BON SDflJLATIOff llgPI— TB BBHBFIT-OOST ANALYSIS

Here we discuss how information froa the •isulations described

above is used to perfon the benefit-cost analysis of SPAS. The kinds

of indicators that were generated and how they can be interpreted are

described b«low.

Attrition and Personnel Allocation

Person-Job allocation has been shown to affect attrition in two

ways:

e The characteristics of the person allocated to the specific

NOS.

a The tiae he or she spends in the DBP for a particular NOS.

Research by Buddin (1982) and Nanganaris and Schnitz (1984) has

shown key deaographic factors such as education and gender to affect

attrition rates differentially. For exaaple, enlistees in NOS 51K

(Plunber) who do not have a high school diploaa have an attrition rate

8 percent higher than those who do; for NOS 3IN (Coaaunication Expert)

the difference is 40 percent. Other analyses by Baldwin and Daula

(1985), and Nanganaris and Schnitz (198S) provide siailar findings.

A second inpact of attrition that is directly related to BPAS

decision variables coaes froa tiae spent in the Delayed Entry Prograa

(DEP). Research by Baldwin and Daula (1985) and Buddin (1982) has

shown that individuals spending any tiae in the DBP experience substantially lower in-service attrition. An analysis by Nanganaris and

Phillips (1985) estiasted the loss probabilities and costs associated

with DEP length, applicant characteristics, and NOS. Costs related to

different DEP lengths were not included in the set of scenarios run for

this analysis. However, one preliainary run will be reported here,

while all the scenarios will soon be evaluated using differential DEP

costs.

24
Infomation Supplied by ihe Siaulationa.

In addition to providing predicted perforaance infomtion, the

aiwilation auat tell the benefit-coat analyaia how well it will aeet

the Aray'a goala for filling NOS training requireaenta and quality

repreaentation requiraaenta. Otherwiae, a high objective function

value could be generated by a aiaulation run which did a poor Job of

eeting Any requireaenta. Additionally the "fill" perforaance can

provide inforaation on the robuatneaa of a aiaulation. That ia, if

environaental conditions are substantially changed, and SPAS atill

its its "fill" requireaent, then the BPAS' perforaance ia robust.

ERAS provided the following indicator« for the benefit-coat

analyaia:

a Average attrition

a Attrition coat

a Average aptitude area score

a Percent fill of HOS requireaents

a Percent fill of quality requireaents

a Average DBP length.

All valuea are based on a aaaple of approxiaately 5,000

contracteea used by the siaulation. For conaiatency, the aaae aaaple

ia used for all runs (with the exception of the "Low Supply" Run, which

aust have a different coaposition of enlistees).

Attrition ratea were derived froa Nanganaria and Schaitz (1984)

for the MOS clusters in the Planning Mode. The coat of attrition it.

directly related to the coats of training. While it any be possible to

obtain aoae useful service froa attritees, Buddin shows that the

ajority of attrition occurs during initial training or in the firat

year of aervice. Thus, aost attriteea provide little productive

service tiae. Therefore, the average variable training and recruiting

coats are used aa an indicator of labor turnover coata. Preauaably, in

25
the future these coeta can be adjusted for iaproved predictors of

attrition, iaproved eatiaates of MOS specific coats froa the AHCOS

project (ARI 1985), and better eatiaatea of the econoaic coat of auch

turnover. However, while the specific reaults aay change draaatically,

the order of Magnitude of the results and their iaplication for the

benefit-coat analyaia results should not change.

Job perfonsance ia a key indicator of an iaproved aaaignaent

systea. Project A classification research will produce a new aet of

predictors for MOS deteraination. However, aeaaurea froa the current

ASVAB will be used at thia tiae. For thia analyaia we will uae the

aptitude area acorea aa indicators of expected perfonsance gains froa

iaproved Job aaaignaenta.

The value of iaproved Job perforaance has not been explicitly

aeaaured, although it ia obviously an iaportant policy concern. Nevertheless, Fernandez and Garfinkle (1985) and Schaitz and Nelson (1985)

have atteapted to eatiaate ita iaplied value through recruiting coats.

The Aray expends considerable resources through recruiter effort,

bonuses, educational benefits, and two year enliataent teraa to attract

high quality soldiers. One of the outputa of thia expenditure ia

increaaed perforaance on Job akill teata auch aa SQTa, which Schalte

and Nelson (1982) have shown to be a function of aptitude area acorea.

Thus if an iaproved allocation process can increase average aptitude

area acorea, the acceaaioned soldiers should show a net iaproveaent in

SOT perforaance. Thus it aay be possible to eatiaate the value of thia

iaproved perforaance by calculating the increaaed recruiting coata of

accessing the additional quality people needed to attain the aaae level

of perforaance.

26
One tray to value the increased perforaance of a given allocation

is to estiaate the nuaber of quality (AFQT Category I-IIIA) contracteea

that would be required (replacing an equivalent nuaber of AFQT Category

IVs) to give the saae increase in perforaance. This estiaate ia

included in the siaulation results.

One iaportant objective of BPAS is the achieveaent of aanageaent

policies with respect to MOS requiraaents, quality goals, gender

restrictions, DBF length, and class size. No atteapt will be aade to

foraally estiaate the value of achieving auch distributional requireaents. However, the perforaance of BPAS against these constraints will

be reported. Also, various potential uaeis of BPAS results, such aa

ODCSPBR, USAHBC, MILPBHCBN, and TRADOC will be asked to coaaent on the

iaportance of thia kind of inforaation for operational analysis, planning, and policy deteraination.

The final area of analysis is the cost of iapleaenting BPAS. An

estiaate is aade of the costs of operating such a systea through

coaparison to other Aray coaputer systeas. Such factors as the update

frequency, size and coaplexity of the coaputer prograas, and direct

ccaputer costs were assessed through coaparison to PORBCAST and RBQUBST

data processing experience. Based upon experience with such systeas a

projection of BPAS operational coaputer coats was aade.

The objective function used in the BPAS planning siaulations will

be s cost ainiaization function. Cost differentials are generated froa

the assignaent of different supply groups to MOS and the choice of DBP

length. Appendix B describes the BPAS optiaization planning problea.

Appendix C describes the BPAS applicant classification algoritha and

the weighting scheaes used in these siaulations.

27
IV. BBSULTS

This aection presents the results of the BPAS siaulations. In

general, the results deaonstrated that BPAS can he a coat-effective

enhanceaent to Aray recruiting sanageaent. The results froa each

aiaulation run are suaaarized in Table 3 and are discussed below.

The Baseline case showed an attrition reduction of 5.5 percent

which could aave about $30N. BPAS perforaed auch better than the

systea used in FT 84 as it aet virtually all the MOS requireaents and

nearly all the quality requireaents. As previously diacussed, aptitude

acorea are not part of the objective function of the optiaization but

are incorporated in the scoring for the classification aiaulation.

These iaproved by nearly 2 percent.

LOV QOALITT CASE

For thia scenario we used a contractee aaaple with 40 percent

quality as opposed to the FY 84 quality of about 63 percent. Therefore

we aust cjapare attrition reductions and aptitude area score iaproveaents to these valuea in the aaaple of contracteea that we used, not to

the baae case. The net attrition reductions and consequent dollar

savings were nearly the aaae as in the base case. However, the average

aptitude area score waa iaproved acre than in the baae caae. Both

theae aeasures indicate the robustness of the systea. As would be

expected, aany of the quality requireaents were not aet. It is significant that even though the population has lower aptitude area acorea

(and thus would be expected to quality for fewer MOS) BPAS atill aet 99

percent of the MOS requireaents.

28
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mm sofa TBAINING

This scenario tested how well SPAS could "look ahead" and bank

qualified applicants for (in this case) a very high axmmer training

load. Surprisingly, this scenario had a lower attrition than the base

case. This probably indicates that, even though there was a 3-fold

increase in requireaents froa Nay to June, the relative stability of

onth-to-aonth requireaents allowed the optiaisation to better allocate

aasignaents such that attrition was ainiaized. Nearly all of the

quality and NOS fill requireaents were aet and the average aptitude

area score iaproveaent was about the ssae as the baseline case.

PBOBABILISTIC CBOICB

This scenario showed that EPAS will be robust in the face of

applicants being allowed soae leeway in their choice of an NOS. The

attrition reduction was actually slightly greater than in the baseline

case but the average aptitude score did not iaprove quite as auch.

However the fill, both for quality and NOS requireaents, was lower than

desired, with aost of the discrepancy occurring in the last aonth of

the siaulation. With the saall saaple, EPAS could not allow flexibility in NOS choice, and still fill the NOS which had a saall nuaber

of total requireaents and a few openings in the later aonths. An

alternative would have been to not allow any "choices" in Septeab^r.

We will also "tune" the Classification Nodule to iaprove its perforaance under these conditions. However, we do not consider that this

would be a problea with a larger saaple size.

NO LOOK-AHKAD

This scenario helped assess the value of the look-ahead feature of

EPAS. The Classification Nodule did quite well in aeeting NOS and

quality fill requireaents. Based on this run, it would appear that the

look-ahead insights froa the optiaization account for about 40 percent

of the Baseline Scenario's attrition reduction. However, it should be

noted that we do not yet have a aeasure to evaluate the benefits of the

optiaization in creating a robust quality allocation strategy.

30
BPAS COSTS

Table 4 presents our estisate of BPAS life cycle costs. They will

support the transition of the working prototype systea into an

operational systea. Software developaent will be relatively ainor,

followed by an operations and aalntenance phase.

The values do not include "sunk" costs, such as the coot of the

BPAS Contract. Nor do they include coots to aaintain or operate the

current systeas, such as REQUEST, which are either incorporated or used

as an adjunct to BPAS.

Table 4

BPAS Life-Cycle Costs (Cost in $M)

Fiscal Year (19) 88 89 90 91 92 93

AIPE Costs .25 .2 .1 .1 .1 .1

Software Developaent 1.0 .5

Operations & Maintenance .25 .5 .5 .5 .5

Training .1 .1 .1 .05 .05 .05

Analysis .5 .5 .5 .5 .5

Total 1.35 1.55 1.2 1.15 1.15 1.15

The BPAS life cycle coats are quite low. Priaarily this is because

aost of the coaputer code developed during the BPAS contract for the

prototype systea can be used in the iapleaented systea.

31
V. DISCUSSION

The benefit-cost evaluation of the allocation process haa shown

that the prototype BPAS ia likely to produce:

e Subatantially reduced firat-tera attrition

e Increased soldier perforaance

e laproved accession aanageaent.

The tiaulationa indicate that these results can be produced under

a wide range of acenarioa. Furtheraore, the two-atage process used in

SPAS increaaea the value of the aolution aore than the one-stage

approach that ignores the aggregate deaand and supply inforaation.

Thus, the planning systea should iaprove observable coat and perforaance characteristics in addition to providing iaportant aanageaent

support.

While the analysis indicatea that NOS allocation can be perforaed,

additional work ia necesaary to deteraine how allocation policy should

be perforaed. Both scientific research and aanageaent analysis ia

needed to aaaure the Aray of a coat-effective systea. Reaearch needa

to be perforaed on the objective for the optiaization problea, the

eatiaation of trade off paraaetera froa exiating and future data

sources, the weights and atructure of the classification algoritha, and

the iapact of applicant preference in the negotiation proceaa. laproveaents in these areas could substantially isprove the value of the

systea.

A acre fundaaental issue for iapleaentation and utilization of

BPAS ia establishing ita coapatibility with the Aray'a operational

environaent. The Aray needs to be aaaured that the syaten can be

iapleaented without creating any significant negative iapact on the

present allocation process, auch as increaaed processing coats,

increaaed qualified-but-not-enliated ratea, or unintended diatributional aide effects. These probleaa can be avoided by working with the

systea users and acceaaion policy aakera to achieve a successful

iapleaentation.

32
REFERENCES

Baldwin, R. & Daula. The cost of high quality recruits. Armed Forces

and Society. 11., 1985, 96-114.

Buddin, R. The role of service experience in post-training attrition

in the Army and Air Force. Santa Monica, CA: Rand Corp.

(R-2682-MRAL), 1981.

Eaton, N. K., & M. H. Goer. laproving the selection, cla«sification,

and utilisation of Amy enlisted personnel; Technical appendix

to the annual report. ART Research Note 83-37, October 1983.

Fernandez, R. L., & J. B. Garfinkle. Setting enlistment standards and

matching recruits to jobs using job performance criteria. Santa

Monica, CA: Rand Corp., January 1985.

General Accounting Office. Perapectives on the effectiveness of

service enlisted bonus program. (FPCD-82-70), August 1982.

General Accounting Office. Military personnel issues; Managing and

ompensating the Armed Forces. (FPCD - 83-17), October 1982.

Holz, E., & J. Wroth. Improving strength forecasts: Support for

Army manpower management. Interfaces, December 1980.

Kroeker, L. P., & B. A. Rafacz. Classification and assignment within

PRIDE (CLASP): A recruit assignment model. Naval Personnel

Research and Development Center, September 1983.

Manganaris, A. G., & E. J. Schmitz. Projecting attrition by MGS. ARI

Technical Report, March 1984.

33
Manganaris, A. G.f & E. J. Schaitz. The impact of delayed entry

progr— participation on fir»t term attrition. ARI Technical

Report, February 1985.

Nangauari«, A. G. & C. E. Phillips. The delayed entry prograai

A policy analyais. ARI Technical Report, May 1985.

Nelson, A., & E. J. Schmitz. Multiobjective personnel allocation.

Revolution and Evolution; New directions in OR/MS.

Washington, DC: Washington Operations Research/Management

Science Council, October 1985.

Schmitz, E. J., & A. Nelson. The allocation of Army personnel to MOS.

ARI Technicsl Report, November 1982.

Schmitz, E. J., R. Nord, P. HcWhite, G. Brown, F. Konieczny, & D Smyre.

Development of the Army's enlisted personnel allocation system.

ARI Technical Report, August 1984.

Soldier Quality Task Force. The requirements for soldier quality.

Office of the Deputy Chief of Staff for Personnel, 1985.

34
APPENDIX A

Attrition Coat* Proa the US Army

OMA i MPA Cost Factor« (DCA-H-1)

Officer of the Coaptroller. Oecoaber 1964.

Variable Costs Associated with Attrition Turnover:

Training Costa (B-124) $8800

Acquisition Costs $3750

Recruiting (8-119) 2350

Clothing (B-104) 500

Accession Travel (8-110) 900

Total $12550

A-l
APPENDIX B

OptiaiMtioo Problea for the

Planning Node

Supply Groupe:

NOS Groupe:

Training Seate:

Supply Flow:

Attrition Ratee

Attrition Coete

DBP Constraints

Quality Goals:

MOS Requiresents:

Objective:

s.t.

where

Froa FY84 contract population. Defined by AFOT,

gender, and education.

Attrition Clusters.

Saw as FY84.

Same as FY84.

(ATTRIT) BstiMted by Nanganaris and Sdaits (1984).

(COST) Froa US Aray OMA «. MPA Cost Factors.

Graduatee fc nongraduates: 1-5 aonths.

Seniors: June-Septeaber after graduation.

Based on ODCSPBR goads for FY 84.

Based on ODCSPBR goals for FY 84.

MIN 53(ATTBIT^

C0ST)NlJ

Supply

NOS Requireaents

Quality Goals

Nu is the nunber of enlistees

froa Supply group i

assigned to NOS cluster J

B-l
APPENDIX C

Alforithn for the Applicant Classification Nodule

• PREDICTED JOB PKRFOiMANCB - user's choice froa:

- Aptitude area «core

Predicted SOT

Perforaance/Morth with predicted SOT for

perforaance.

e 1

st TBHM ATTRITION - ARI attrition equations.

e DIFFICULTY OF FILL - defined by priority:

1-3 are hard to fill

4-6 are aoderate

7-S are no difficulty

e TIME TO FILL

Annual deaand - reap function using ratio of class

rooa capacity to date/total capacity to contracts to

date/annual requiresent.

Specific class - 3 step function (1 for each

difficulty of fill category) generating score based

on nuaber of sonths left before class begins.

e CURRENT FILL

Annual - see tise to fill

Class - full value until noainal class size reached;

then half value; not eligible when sax sise reached.

e AFFIRMATIVE ACTION - rasp function varying score above/below

noainal value as current fill is behind or ahead of goal.

e QUALITY DISTRIBUTION - aiailar to affirsative action, but

uses AFQT CAT I-IIIA goals.

e ELIGIBILITY REQUIREMENTS

Quality goals

AFQT CAT IV cape

Aptitude area qualifying scores

Feaale exclusions

High school graduation

Others are possible such as citizenship, age, etc.

e WEIGHTS - see next page.

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