|Stockage Determination Made Easy
|by Dr. Kenneth Girardini, Chief Warrant Officer
(W–5) Arthur W. Lackey, USA (Ret.), and Eric Peltz
Every brigade combat team (BCT) and support brigade
in the Army has a mobile minidistribution center that stocks
repair parts and perhaps also class II (clothing and individual
equipment), IIIP (packaged petroleum, oils, and lubricants),
and IV (construction and barrier materials) items with national
stock numbers (NSNs) in its authorized stockage list (ASL).
This minidistribution center, called the supply support activity
(SSA), is the key to high equipment readiness. When equipment
fails and becomes not mission capable (NMC), but the needed
parts are on hand in the SSA, that equipment can be returned
to action very quickly. However, when the parts are not available
in the supporting SSA, it can sometimes take awhile to get
them, which only delays returning equipment to a mission capable
In operations in Iraq, getting the part from the United States
by air (if the part is well positioned for quick shipment)
or from theater stocks in Kuwait takes an average of a little
more than 10 days, with some shipments taking longer. If the
item is in short supply at U.S. distribution centers, the wait
can be much longer. Not having parts in the ASL sometimes leads
units to take extraordinary actions, such as controlled exchanges,
to get equipment back on line rather than accept lengthy downtimes
on critical end items.
Studies by RAND Arroyo Center have confirmed that high-performing
ASLs have the greatest direct affect on equipment readiness
through their impact on supply chain processes and resources.
(Reliability, of course, is the other central factor affecting
equipment readiness.) For example, a RAND Arroyo study at the
National Training Center at Fort Irwin, California found that
a 10-point swing in the ASL fill rate changed the equipment
readiness rate by 4 percentage points. However, Army ASL fill
rates were often under 20 percent in the late 1990s because
of very limited breadths of parts and some issues concerning
how depths were computed. (“Breadth” refers to
the number of different parts stocked. “Depth” refers
to the number of each part stocked.)
Development of Dollar Cost Banding
To address the low ASL fill rates, RAND Arroyo developed Dollar
Cost Banding (DCB). Piloted in 1998, DCB introduced three things
to ASL management: tying the decision of what and how much
to stock to both the benefits produced and the resources required,
emphasizing the need to deal with highly variable demand, and
using automated exclusion criteria.
The hypothesis underlying DCB development was that, even if
the benefit of adding a part is relatively low, if the part’s
cost and size are minimal and its absence can affect readiness,
it may be worth stocking. The DCB concept was introduced using
heuristics that adjusted add-and-retain thresholds in terms
of the number of demands based on item cost (inventory investment)
and size (storage space). Basically, the DCB rules said that
the smaller and less expensive an item is, and if it is coded
essential or has had high-priority demands, the threshold for
stocking it should be lessened. The add-and-retain criteria
for big, expensive items that are often critical to readiness
were not changed. This change increased the breadth of ASLs
by adding more of the small, inexpensive items that are often
needed in conjunction with the more expensive items to correct
NMC or deadlining faults.
The second, and less well known, aspect of DCB was a complete
change in how inventory depth is computed. Inventory levels
with DCB are set by using iterative simulations of the demand
streams at the national item identification number (NIIN) level
to achieve customer-wait-time goals that vary based on the
investment and storage resources associated with the NIIN.
This contrasts greatly with the former “days of supply” method,
which used only the mean demand rate and thus did not compensate
for the variability or timing of demands. (Demands during peak
training periods were averaged or were smoothed out with zero
or low demands during periods in garrison.)
The third aspect of DCB was a set of parameters that automatically
exclude certain NIINs that are not desirable to stock in SSAs
in order to reduce the ASL review workload. Examples of the
criteria used are acquisition advice code, nomenclature, class
of supply, and Federal supply class. The parameters have been
continually updated based on feedback from the field. The central
idea behind exclusions was to keep items from being recommended
for ASL stockage that were not critical to warfighting or that
the unit could otherwise wait to obtain through an order-ship
Initially, DCB was implemented by having a central team at
RAND develop the ASL recommendations. The central team also
assisted in reconfiguring warehouse storage and participated
in the SSAs’ review of the recommendations. After successful
pilots, this led to the incorporation of DCB into Army policy
in 2000 and a rapid, successful rollout through about half
the SSAs in the Army. Fill rates increased by about 10 percent
in those SSAs that adopted DCB. In 2001, DCB became available
in the Integrated Logistics Analysis Program (ILAP), which
allowed SSAs to initiate and run their own ASL reviews. However,
without the role played by the central team, the results became
less consistent as inventory expertise varied among SSAs and
other demands on personnel time sometimes impeded effective
implementation of the recommendations.
Introduction of Enhanced DCB
At about this time, RAND Arroyo Center developed the Equipment
Downtime Analyzer (EDA), which also was added as a module within
ILAP. The EDA archives daily NMC equipment reports. This information
is very valuable because it identifies all of the parts ordered
to return a system to mission capable status. With EDA data,
it is now possible to develop a critical parts list of those
parts that consistently deadline Army equipment.
The EDA critical parts list was used to develop Enhanced DCB
(EDCB), which, initially, simply changed the criticality criteria.
In DCB, a part was considered “critical” based
on the essentiality code or the use of high-priority requisitions
for the part. The problem was that, with these rules, most
parts (85 percent) are deemed “critical.” Using
the EDA critical parts list, we have found that the list of
true readiness drivers is much narrower—now only 35 percent
of demanded items. Therefore, EDCB allows us to concentrate
the allocation of limited SSA storage capacity and inventory
investment on these more critical parts.
EDCB was piloted with two BCTs at Fort Riley, Kansas, in 2002.
This pilot targeted three key
systems—the M1A1 Abrams tank, the M88A1 recovery vehicle,
and the M9 armored combat earthmover—with great success.
The readiness-driver fill rates for targeted systems improved
awaiting parts time, and, thus, overall time for deadlining
repairs, fell dramatically, thereby increasing readiness for
Impact of Operation Iraqi Freedom
Before implementation of EDCB could be expanded, Operation
Iraqi Freedom (OIF) began. This put further rollout of EDCB
on hold as units that were to prototype the new algorithm turned
their attention to deployment preparations and then combat
operations. When units initially deployed to OIF, they generally
took the ASLs they had at home with little change (or they
fell in on ASLs from Army pre-positioned stocks). Units that
had been involved in the DCB rollout found that their home-station
ASLs were relatively effective, at least initially. For most
units, the breadth of parts demanded in OIF was similar to
what they experienced when training at home station, so accommodation
rates held up.
However, a lack of connectivity with Standard Army Management
Information Systems (STAMIS), combined with severe distribution
challenges in 2003, significantly hampered replenishment and
quickly depleted those deployed ASLs. Without reliable replenishment,
ASL satisfaction rates fell to less than 10 percent, making
the ASLs ineffective. Another factor hampering the recovery
of satisfaction rates was that depth in the home-station ASLs
that units deployed with had been calculated using the actual
replenishment times for each NIIN at home station (a minimum
of 10 days was enforced); but those replenishment times had
not yet been achieved in OIF.
Realizing the need to better match ASLs with growing demands,
theater logisticians began ASL reviews using DCB (in ILAP),
which did lead to improvements in depth by using actual replenishment
lead times and demand rates for OIF. Combined with improved
distribution, these enabled ASLs to recover to about 30-percent
fill rates. However, additional problems hampered the effectiveness
of ASL reviews: Deployed demand histories were limited in duration
to less than the 2 years used in DCB; as rotations began, many
units were task organized, and so deployed SSAs were used to
support different types of units; and equipment changes sometimes
occurred (such as the addition of up-armored high-mobility,
multipurpose, wheeled vehicles). All of these factors rendered
the use of deployed SSA demand histories only partially effective
for forecasting future demands and setting inventory levels.
To tackle these issues, RAND began to assist in building virtual
demand histories for units. These histories were based on moving
unit demand streams at the company level, making adjustments
to account for limited demand histories, and using the demand
histories of proxy units to model requirements for equipment
new to a unit. Using these demand streams, EDCB was applied,
with recommendations passed to SSAs in Iraq, Kuwait, and Afghanistan.
Between late 2004 and the middle of 2006, acceptance and implementation
of the recommendations was mixed. Those SSAs that implemented
EDCB experienced dramatic ASL improvements; those that did
not saw stagnant performance. This led to 20- to 30-point gaps
in fill rates among SSAs supporting similar units. ASL reviews
also often took a long time, which impeded performance improvement
during a significant portion of year-long rotations. However,
where the recommendations were implemented, readiness-driver
fill rates climbed significantly above those of non-readiness
drivers. Despite variations by SSAs in the adoption of EDCB
recommendations, the overall readiness-driver fill rate for
OIF had climbed to almost 50 percent by late 2006.
One issue that sometimes created delays was the sheer time
needed to review the thousands of recommendations that came
from DCB and EDCB. This problem was aggravated if the recommendations
did not fit within the existing storage configuration of an
SSA. Another challenge was that the large numbers of ASL changes
required to implement the recommended “adds,” “deletes,” and
requisition objective (RO) changes created a greater workload
than some SSAs could handle given their daily ongoing work.
stockage list (ASL)
performance metrics for
Enhancement of ASL Management With IROC
Based on these OIF issues, and other lessons learned from this body of ASL research,
RAND Arroyo Center has developed a new model for computing ASL recommendations
called the Inventory Readiness Optimizer with Constraints (IROC).
IROC is essentially a product improvement of the EDCB algorithm. It is based
on a mixed-integer
programming formulation that is intended to optimize the readiness contribution
of the ASL, subject to constraints on inventory investment, transition workload,
and the number and volume of storage locations by type (such as small bin, medium
bin, shelf, rack, and bulk). A weighting derived from the EDA database indicates
the relative criticality of parts, rather than simply considering parts as critical
or not. The recommendations from this process were then input into a simulation
to determine the resulting readiness (down days) and to establish curves of ASL
performance and readiness impacts versus resources.
IROC was prototyped among units undergoing modularity transformation and led
to many insights on how to overcome the OIF ASL issues (particularly storage
feasibility and transition workload issues) affecting the implementation of ASL
RAND Arroyo Center uses IROC results to fine-tune EDCB and improve the recommendations
provided to deployed units by—
- Incorporating tighter storage constraints that are generally feasible for storage locations.
- Reducing the recommended changes in ways that produce the most potential benefit while limiting the transition workload. This is done by limiting “adds” to faster moving
readiness-drivers; “increases” to fast movers that exhibit poor satisfaction rates; “deletes” to items with no demand or that are no longer applicable; “decreases” to items that can be decreased
if there is a change in bin size and performance remains high; and “no change” to calculated recommendations that would not significantly
affect inventory investment and storage and would produce only a marginal change in performance. (Most of the recommendations for
high-performing ASLs are now “no change.”)
Institution of a New ASL Policy
Observing the variation of ASL performance among SSAs of similar type, and recognizing
that EDCB produced effective solutions that could be readily implemented, the
Army’s Deputy Chief of Staff, G–4, released a pilot ASL policy for
Southwest Asia in November 2006 after coordination with the Army Materiel Command,
the Army Combined Arms Support Command, and the Coalition Forces Land Component
Command of U.S. Central Command. Previously, Army supply policy had dealt with
the percentage of lines not recommended for the ASL that commanders could add
in the ASL review process. However, Army policy did not mandate the percentage
of recommendations for demand-supported lines that had to be accepted.
To address this gap, the fundamental change introduced by the new ASL policy
is to provide only a summary (such as number of changes and new storage requirements
by storage category) of the majority of recommendations that involve only small
changes to the overall cost and volume, or cube, of the ASL. If the summary is
acceptable, the bulk of the recommendations can be implemented without the need
for a line-by-line review. SSAs only review lines that satisfy one of the following
- Increases (which could be the result of adding a new ASL line) or decreases
(which could be the result of deleting an existing ASL line) in cube greater than 8 cubic feet.
- Increases or decreases of RO value of more than $10,000.
- All items—even if no change is recommended—that have an RO value of more than $100,000 or a
cube greater than 100 cubic feet.
- Items that have an RO greater than 500 and all operation and maintenance Army-funded NIINs.
Thus, beyond targeting improved performance, this new pilot ASL greatly reduces the
work associated with making ASL review decisions while still allowing units to do detailed reviews of
the lines that account for 85 to 90 percent of the ASL cube and dollar value. This new ASL policy also
sets forth a 2-week review time limit on this subset of items. Finally, it calls for ASL updates every
3 to 4 months rather than on an annual basis. The intent of the updates is to implement a small number
of adjustments to the ASL that could make a significant performance difference. In a dynamic environment
such as OIF and Operation Enduring Freedom, this is particularly important.
This policy was first implemented for SSAs in Iraq in December 2006. All 24 SSAs were changed in a 40-day
time period, with an average of 17 days per SSA—a much improved performance over the weeks and even months that
the process was taking previously. The numbers of lines that had to be reviewed by SSAs were
- 150 to 200 for an infantry BCT.
- 300 for a heavy BCT.
- 400 for a combat aviation brigade.
The first quarterly ASL update of these SSAs
was made over justa few days in late March 2007 and typically
involved 30 to 60 recommendations for each SSA that had to
be reviewed under the new Army policy.
Best of all, performance has risen to an all-time high. As
shown in the chart at left, the readiness-driver accommodation
rate for SSAs in Iraq has jumped from the high 50-percent range
to about 73 percent. This is about the limit achievable without
adding very low demand items that are very difficult to accurately
forecast and often do not recur from year to year at the SSA
level. Satisfaction is at about 77 percent, but that should
climb as stocks continue to arrive to fill the new inventory
Some recent distribution problems have caused replenishment
times to exceed the 20-day replenishment wait time (RWT) planning
factor; these problems are in the process of being corrected.
readiness-driver fill rate has reached 56 percent. Replaying
demands with the 20-day RWT indicates that the fill rate would
have increased to 63 percent, getting close to the feasible
limit given current storage constraints and the large number
of very low demand parts.
The G–4 staff is also in the process of implementing
a companion change. In this article, we have been referring
to the readiness-driver fill rate and associated diagnostic
accommodation and satisfaction
metrics. Currently, these are not Army metrics in the Logistics
Integrated Warehouse (LIW), which only provides these metrics
by supply class. However, the purpose of ASLs is to stock readiness
drivers as well as other small parts that are very fast moving
in order to reduce receipt workload. Thus, the Army’s
metrics should be aligned to focus on readiness drivers in
order to measure whether or not ASL policies are having the
intended effect. For example, the overall repair parts fill
rate in Iraq has only increased to 40 percent because non-readiness
drivers are in the low 30-percent range. In this light, the
G–4 staff is pursuing the implementation of ASL metrics
in LIW that are limited to readiness drivers in order to align
the metrics and the ASL review process.
This successful experience in Southwest Asia provides impetus
for changing the ASL requirements determination process across
the Army. Accordingly, the G–4 policy for Southwest Asia
will potentially be expanded to all ASL reviews as the Army
continues to build on the central-expert ASL review team concept
implemented by the Army Materiel Command. All this effort should
result in significantly better performing ASLs across the Army,
which will, in turn, result in improved Army readiness rates
and help get repair parts into the hands of maintenance personnel
Dr. Kenneth Girardini is a senior analyst working in RAND’s
Arroyo Center. He has led numerous projects in Army logistics,
focusing on distribution and inventory levels for sustainment
materiel. He holds M.S. and Ph.D. degrees in operations research
from the University of California at Los Angeles and a B.S.
degree from General Motors Institute.
Chief Warrant Officer (W–5) Arthur W. Lackey, USA (Ret.),
is a senior project manager in RAND’s Arroyo Center.
His military service covered 35 years in a variety of logistics
positions. He holds a B.S. degree from the University of Maryland
and is a graduate of the Master Warrant Officer Training Course,
the Supply Management Officers Course, and the Noncommissioned
Officers Logistics Program Course.
Eric Peltz directs the logistics program at the RAND Arroyo
Center, the Army’s Federally funded research and development
center for studies and policy analyses. He also manages logistics
studies conducted by RAND for the Defense Logistics Agency
and the U.S. Transportation Command. He has a B.S. degree from
the U.S. Military Academy and M.B.A. and M.S.E. degrees from
the University of Michigan.