The Navy Enlisted Detailing Process: An Empirical Analysis

1
The Navy Enlisted Detailing Process An
Empirical Analysis

• Bill Gates
• Mark Nissen

2
Objective

• Analyze the technological and operational
  feasibility of establishing a web-based market,
  using intelligent agents, to match naval enlisted
  personnel to specific navy billets.
• Sponsor NPRST - PERS 1

3
Labor Markets
4
Web-Based Detailing Process
Commands State Preferences
Sailors State Preferences
Sailors Evaluate Commands
Commands Evaluate Sailors
Navy Articulates Policy
Sailors Assigned To Commands
5
Assignment Alternatives

• No-support (current process)
• DSS (multi-attribute decision analysis)
• Personnel mall
• Two-sided matching
• Optimization

6
Experimental Design

• Internal labor market
• 10 sailors, 12 billets
• Randomly drawn from pool of 1000 sailors/billets
• Compare performance-quality of match
• Quasi-prices
• Social welfare
• Stability

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Sailor/Billet Characteristics
Sailors Billets Pay grade (3) Pay grade
(3) NEC (4) NEC (4) Performance rating (4)
Promotion prospects (5) Preferred location (4)
Job location (4) Personal emphasis Employer
emphasis (promotion/location)
(performance/training)
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Sailor/Command Preferences
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Sailor Characteristics
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Billet Characteristics
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Assignment Performance

• US and UC vary from 1 to 5
• Quasi-Prices vary from 0.20 to 1.00
• Inverse utility functions
• Sailors Reservation Wage
• Billets cost of performing task
• Welfare 1.00 quasi-price

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No Support/DSS Alternatives

• No Support
• 80 Management students, detailing professionals
• Instructed on detailing process/objectives
• 10 sailors/12 billets arrive in one batch
• DSS (Logical Decisions for Windows)
• 22 Management students
• Instructed on detailing process/objectives
• Instructed on Logical Decisions for Windows
• 10 sailors/12 billets arrive in one batch

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Personnel Mall

• Multi-agent system-matching people jobs
• Adapted from supply chain domain
• Shopping mall metaphor
• Intelligent agents represents sailors/billets
• Assignments made first-come first-served
• Sailor or command bias

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Two-Sided Matching

• Game Theory
• Medical residency sororities
• Match based on sailor/command ranked preferences
• Sailor or command bias
• Ensures match stability

Proposed match Person A Person B Job X
Job Y
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Two-sided Matching ExampleSailor-Bias
6
6
2
6
6
3
3
8
4
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Optimization

• Maximize utility (minimize quasi-prices)
• Total sailor quasi-prices (2 10)
• Total command quasi-prices (2 10)
• Weighted average sailor plus command quasi-prices
• 0.5US 0.5UC (2 10)

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Results Quasi-Prices
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Results Social Welfare
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Findings

• Human subjects show significant command bias
• Automated improve efficiency
• Eliminate bias
• Pareto improvement in all but one case
• Potentially significant improvement in welfare
  (efficiency)
• Combined optimization best overall fit
• Unstable matches (6 15)
• Personnel Mall performance variable
• Two-sided matching has minimal data requirements

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Six Step Distribution Process
3) Sailors view scores and state preferences
through CCC
2) Commands screen sailors for eligibility
score for job-fit
1) Allocation
4) Assign sailors to billets using 2-sided
matching
5) Manage exceptions
6) Audit and write orders
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Future Research

• Develop AS community characteristics and
  preferences
• Further analysis/simulation
• Two-sided matching/optimization comparisons
• Performance, stability, data requirements
• Full-scale experiments/simulations
• Detailing window, preference lists, percent
  matched
• Examine gaming behaviors
• Multiple iterations, unmatched sailors/priority
  billets
• Integrate Assignment Incentive Pay
• Further mall/algorithm integration
• Chiefs Detailing Demo-October 02

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Completed Theses

• Melissa M. Short, Analysis of the Current Navy
  Enlisted Detailing Process, December 2000.
• Richard J. Schlegel, An Activity Based Costing
  Analysis of the Navys Enlisted Detailing
  Process, December 2000.
• Todd R. Wasmund, Analysis Of The U.S. Army
  Assignment ProcessImproving Effectiveness And
  Efficiency, June 2001.
• Kim D. Hill, An Organizational Analysis Of The
  United States Air Force Personnel Center (AFPC)
  Airman Assignment Management System (AMS), March
  2001.
• Ly T. Fecteau, 2002- Analysis Of The Marine Corps
  Enlisted Assignment Process, June 2002

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Completed Theses

• Paul A. Robards, Applying Two-Sided Matching
  Processes To The United States Navy Enlisted
  Assignment Process, March 2001.
• Suan Jow Tan and Che Meng Yeong, Designing
  Economics Experiments To Demonstrate The
  Advantages Of An Electronic Employment Market In
  A Large Military Organization, March 2001.
• Hock Sing Ng and Cheow Guan Soh, Agent-Based
  Simulation System A Demonstration Of The
  Advantages Of An Electronic Employment Market In
  A Large Military Organization, March 2001.
• Gerard Koh, A Redesign of the Navys Enlisted
  Personnel Distribution Process, March 2002.
• Virginia Butler and Valerie Molina, Command and
  Sailor Preferences in a Two-Sided Matching
  Distribution Process , March 2002.

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Results Sailors Rank
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Results Commands Rank
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Combined Sailor/Billet Rank
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DSS Results
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Command OptimizationInstability
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Command Optimization12 Unstable Matches
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Sailor Optimization15 Unstable Matches
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Combined Optimization6 Unstable Matches
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Personnel Mall Alternative Orders
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Satisfaction Vs Batch Size
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Matches Vs Batch Size
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Matches Vs Preference Lists
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Sailor/Command Preferences

• What are the top sailor and command preferences
  influencing the enlisted distribution process in
  the Aviation Support Equipment Technician (AS)
  community?
• Interview AS community manager and AS detailer
• Conduct focus groups with AS Sailors
• Conduct preference questionnaire with AS sailors
  and command manpower officers

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AS Sailor Preferences
80
80
64
60
60
43
39
40
31
30
24
20
20
10
0
Family Life
Location
Job
Training and
Incentive
Attributes
Attributes
Attributes
Education
Attributes
Attributes
Important-Chiefs
Important-E6 and Below
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AS Command Preferences
39
Family Life Attributes-Top 3 of 11
40
Location Attributes-Top 3 of 10
41
Job Attributes-Top 3 of 10
42
Training and Education Attributes-Top 1 of 3
43
Incentive Attributes-Top 1 of 3