Forecasting Navy issue and receipt workload at Defense Logistics Agency depots

78 Pages·2.7 MB·en_US

by  Warbrick, Perry A.

NAVAL POSTGRADUATE SCHOOL
MONTEREY,
CALIFORNIA
THESIS
FORECASTING NAVY ISSUE AND
RECEIPT WORKLOAD AT DEFENSE
LOGISTICS AGENCY DEPOTS
by
PerryA. Warbrick
December 1996
Thesis Advisor: Kevin Gue
Thesis
W2272
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1. AGENCYUSEONLY (Leaveblank) REPORTDATE REPORTTYPEANDDATES COVERED
December 1996 Master's Thesis
4. TITLEANDSUBTITLE: FORECASTINGNAVY ISSUE AND RECEIPT 5. FUNDINGNUMBERS
WORKLOAD ATDEFENSE LOGISTICS AGENCYDEPOTS
6. AUTHOR(S) Warbrick, PerryA.
PERFORMINGORGANIZATIONNAME(S)ANDADDRESS(ES) PERFORMING
7.
Naval Postgraduate School ORGANIZATION
Monterey, CA 93943-5000 REPORTNUMBER
9. SPONSORING/MONITORINGAGENCYNAME(S)ANDADDRESS(ES) 10. SPONSORING/MONITORING
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11. SUPPLEMENTARYNOTES The views expressed inthis thesis arethose ofthe author and do not reflectthe
official policy orposition ofthe Department ofDefense orthe U.S. Government.
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13 ABSTRACT (maximum 200words)
Each year the Defense Logistics Agency (DLA) asks the military services to estimate their
future issue and receipt workload demands at DLA distribution depots. DLA uses these
estimates to determine expected costs and revenues at the distribution depots. Accurate
DLA
workload forecasting allows planners to establish appropriate surcharges for their
DLA
services. Inaccurate estimates can lead to higher costs to and, ultimately, to the Navy.
We evaluate current Navy forecasting methods and develop several causative factors that
We
influence issue and receipt workload. present single and multiple regression models to
predict future issue and receipt demands and compare these models with those currently used
by Naval Supply Systems Command. Our results suggest that causal-based modeling is a
feasible alternative to current models and may more accurately estimate future issue and
receipt workload for the Navy.
14. SUBJECTTERMS Forecasting, Distribution Depots, DLA, Workload 15. NUMBEROF
PAGES 75
16. PRICECODE
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TIONOFREPORT CATIONOFTHISPAGE TIONOFABSTRACT ABSTRACT
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Approved for public release; distribution is unlimited.
FORECASTING NAVY ISSUE AND RECEIPT WORKLOAD AT
DEFENSE LOGISTICS AGENCY DEPOTS
Perry A. Warbrick
Lieutenant, United States Navy
B. S., University ofUtah, 1987
Submitted in partial fulfillment ofthe
requirements for the degree of
MASTER OF SCIENCE IN MANAGEMENT
from the
NAVAL POSTGRADUATE SCHOOL
December 1996
77
DUDLEY KNOX LIBRARY
NAVAL POSTGRADUATE SCHOOI
MONTEREY CA
93943 5101
ABSTRACT
Each year the Defense Logistics Agency (DLA) asks the military services to
DLA
estimate their future issue and receipt workload demands at distribution depots.
DLA
uses these estimates to determine expected costs and revenues at the distribution
DLA
depots. Accurate workload forecasting allows planners to establish appropriate
DLA
surcharges for their services. Inaccurate estimates can lead to higher costs to and,
ultimately, to the Navy. We evaluate current Navy forecasting methods and develop
We
several causative factors that influence issue and receipt workload. present single and
multiple regression models to predict future issue and receipt demands and compare these
models with those currently used by Naval Supply Systems Command. Our results
suggest that causal-based modeling is a feasible alternative to current models and may
more accurately estimate future issue and receipt workload for the Navy.
VI
TABLE OF CONTENTS
INTRODUCTION
I. 1
PURPOSE
A. 1
THE PROBLEM
B. 1
C. RESEARCH OBJECTIVES 4
D. RESEARCH QUESTIONS 4
PREVIEW
E. 4
BACKGROUND
H. 7
AGENCIES INVOLVED
A. 7
B. POLICY ISSUES 8
m. CURRENT FORECASTING MODELS
11
DLA FORECASTING
A. 11
THE NAVSUP FORECASTING MODEL
B. 11
C. NAVSUP WHOLESALE SALES PROJECTIONS 14
METHODOLOGY
IV. 15
A. CAUSAL-BASED MODELING 15
B. ASSUMPTIONS INHERENT IN MULTIPLE REGRESSION ANALYSIS 15
FORMATION OF A CAUSAL-BASED MODEL
C. 16
FORMULATION OF THE PROBLEM
D. 17
CHOICE OF RELEVANT INDICATORS
E. 17
SUMMARY
F. 23
V. ANALYSIS 25
A. INITIAL TEST RUN OF REGRESSIONS 25
B. DECIDING AMONG INDIVIDUAL REGRESSIONS 28
C. CHECKING THE VALIDITY OF THE REGRESSION ASSUMPTIONS 29
D. PREPARING A FORECAST 32
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
VI. 35
SUMMARY
A. 35
CONCLUSIONS
B. 35
RECOMMENDATIONS
C. 36
APPENDIX A. ANNUAL DATA FOR VARIABLES 37
APPENDIX B. NAVY MANAGED REPAIR PART ISSUES 39
APPENDIX C. REGRESSION DATA AND RESIDUAL PLOTS 41
LIST OF REFERENCES 59
BIBLIOGRAPHY
61
INDEX OF TERMS
63
INITIAL DISTRIBUTION LIST 65
vii