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Solved by verified expert:write a one-page assessment of what you believe to be the benefits to DLA of efficient and accurate management of its inventory.  Benefits to DLA with regard to the efficient and accurate management of inventory
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Contact name: Matt Daigle
Contact title: Senior Public Affairs Specialist
Contact organization/company name: LMI
Contact mailing address: 2000 Corporate Ridge, McLean, VA 22102
Contact email address: mdaigle@lmi.org
Contact phone number: 703-677-3621
Case study title: Helping DLA Manage Infrequent and Highly Variable Demand
Organizations/Companies covered in case study: LMI, Defense Logistics Agency
(DLA)
Team member presenter names: Dr. Pamela Williams, LMI; Dr. Brad Silver, LMI;
Robert Carroll, DLA; Dr. Tovey Bachman, LMI (presenter).
50-word description of case study: Employing proprietary tools that account for need
and risk, LMI helped DLA develop a more efficient system for managing items with
infrequent demands, without sacrificing mission-readiness. Projected benefits include
reducing unfilled orders by 30 percent while concurrently reducing procurement
workload by 50 percent and driving down inventory by $180 million.
Helping DLA Manage Infrequent and Highly Variable Demand
MARCH 2013
Organization:
LMI (consultant)
Defense Logistics Agency (client)
Authors:
Dr. Pamela Williams, Senior Consultant, LMI
Dr. Brad Silver, Senior Consultant, LMI
Robert Carroll, DLA Logistics Operations J331 (non-endorsing verifier of work)
Speaker/Presenter:
Dr. Tovey Bachman, Senior Consultant, LMI
Summary of the initiative: LMI, a longtime supporter of the Defense Logistics Agency
(DLA), was tasked with helping DLA develop a more efficient system for managing
items with infrequent demands through an innovative system that accounts for need and
risk, without sacrificing mission-readiness. DLA, which manages nine supply chains and
5 million items, has been directed to reduce procurement requests by 25 percent—while
maintaining or increasing material availability—along with reducing its inventory by $10
billion within the next five years.
Innovation statement: The Next Generation Inventory Model (NextGen) does not require “forcastable” demand, basing its decisions about when to buy and how much to
buy directly on the times between customer demands and the demand quantities.
NextGen uses revolutionary algorithms make full use of the information in the demand
stream, an approach that is remains fast enough to be practical. Peak Policy determines
when to buy and how much to using a simulation-based hedging strategy that balances
the risks of either being out of stock or over investing. Peak Policy can reduce customer
wait time by 20–50 percent without increasing long-term inventory investment, or alternatively, can reduce inventory investment by as much as 10 percent without affecting
customer service.
When used in tandem, the implementation of PNG levels at DLA will reduce inventory
investment and procurement requests as well as demand planner and buyer workload.
In working with LMI, DLA decision makers selected points on the tradeoff curves with
projected benefits reducing unfilled orders by 30 percent on the subpopulation of items.
This would concurrently reduce procurement workload by 50 percent and driving down
inventory by $180 million; this is the specific inventory strategy that DLA chose to pursue. PNG is powerful enough to enables DLA consider what its needs dictate, allowing
the agency to significantly reduce inventory investment while maintaining or improving
customer service and procurement workload; DLA also has the option to emphasize improvements in customer service or workload over inventory investment, all using the
same system.
1
Impact statement: In January 2013, LMI deployed PNG to the DLA project for approximately 500,000 wholesale demand items, which represents $3.6 billion in sales for
DLA. As we expand to 800,000 items, LMI could drive down DLA inventory by $1 billion
over three years, while simultaneously improving customer service and decreasing procurement workload. For a research and development investment of approximately $6.5
million, the return on in-vestment is 27:1 for the initial DLA implementation.
Applicability: By bringing PNG to DLA’s inventory challenges, LMI has shown that
when organizations are demand planning, one size does not fit all. An organization
should manage its inventory based on demand type and variability not by the options
available in its demand planning software, and these PNG methods are applicable to
companies with similar demand patterns.
Frequent Demand
Low Variability
Frequent Demand
High Variability
Figure 1. DLA’s Demand Types
Figure 2. Tradeoff Curve
2
Infrequent
Demand
Starts with histograms built from demand data
size Requisition time series
time
freq
Inter-arrival time
histogram
Requisition size
histogram
freq
time
size
Figure 3. NextGen Removes Assumptions
Probability density
50
40
30
20
10
0
2. How much demand is
normal approximation
received
in a lead time?
actual
0.015
0.010
0.005
0.000
2006
2007
2008
0
2009
100
200
300
400
500
Lead time demand
Year
Stock on hand
Demand size
histogram
1. How often is inventory
at a given level?
Inventory leve
Inter-arrival
time
histogram
4. Optimal min and max
levels
3. Predicted
performance metrics
40
20
0
-20
-40
2006
2007
Figure 4. NextGen and the Inventory Level Cycle
3
2008
2009
Helping DLA Manage Infrequent and Highly Variable Demand
MARCH 2013
An organization with a diverse and robust supply chain, a wide variety within its multimillionitem inventory, and enough sales and revenue to qualify near the top 50 of the Fortune 500 is
able to achieve sweeping efficiency goals to reflect a changing fiscal landscape, while continuing
to meet its strategic goals—that’s a headline that any shareholder would be pleased to hear.
When the shareholder is the American taxpayer, and the organization is a critical component of
our nation’s ability to maintain and support a ready military, the story of how those efficiencies
were achieved is especially noteworthy.
***
LMI has a long history of providing contracted support to the Defense Logistics Agency (DLA),
whose mission is to provide logistics support to the U.S. military, civilian agencies, and foreign
countries. In this case, LMI helped DLA achieve its stated goals to develop a more efficient system for managing items with infrequent or highly variable demand through an innovative system
that accounts for need and risk, without sacrificing mission readiness.
Traditional methods for managing items with infrequent or highly variable demand often suffer
from extreme forecast error, and, as a result, generate excess inventory and poor customer service. LMI bypasses the intermediate step of attempting to forecast demand for these inherently
“unforecastable” items and employs risk-based hedging strategies to set levels directly from the
demand history and item characteristics. To help DLA achieve its goals for a more efficient inventory, LMI deployed two of our independently developed tools—Peak Policy and Next Gener-
1
ation Inventory Model (NextGen), known collectively as PNG. These tools provide users with
the ability to make three-way tradeoffs among inventory investment, procurement requests, and
customer wait time, depending upon their organization’s specific supply chain performance objectives. The result is a significant reduction in procurement requests and modest improvements
in inventory investment, both in line with DLA’s stated organizational priorities.
In January 2013, LMI implemented PNG levels for approximately 500,000 wholesale demand
items, which represent $3.6 billion in sales for DLA. If expanded to 800,000 items and with emphasis placed on inventory reduction, LMI could drive down DLA inventory by $1 billion over 3
years, while simultaneously improving customer service and decreasing procurement workload.
BACKGROUND
The Defense Logistics Agency is the Department of Defense’s largest logistics combat support
agency, providing worldwide logistics support in both peacetime and wartime to the military services as well as several civilian agencies and foreign countries. DLA manages nine supply chains
and 5 million items, including clothing, subsistence, medical, and spare parts. In FY2012, DLA
had $53 billion in sales and revenue (http://ww.dla.mil/Pages/ataglance.aspx), which would rank
them 53rd on the Fortune 500 list.
Each fiscal year, DLA’s director provides guidance for determining where to focus resources and
efforts in the upcoming year. For the past 2 years, these directives have been to reduce procurement requests by 25 percent—while maintaining or increasing material availability—in order to
reduce costs through disposals and right-sized buys, thereby improving support for the warfight-
2
er. To reflect declining defense budgets, DLA is also seeking to reduce its inventory by $10 billion within the next 5 years.
DLA manages more than 1.4 million consumable Class IX items (repair parts), with $14 billion
in annual sales. The demand pattern for DLA items can be characterized as either frequently or
infrequently demanded. Many DLA-managed items have highly variable demand, making managing them especially challenging. Figure 1 depicts demand streams that represent the three demand types.
Frequent Demand
Low Variability
Frequent Demand
High Variability
Infrequent
Demand
Figure 1. DLA’s Demand Types
DLA’s Enterprise Business System (EBS) uses demand planning software that is widely available on a commercial basis. This suite of tools has a number of direct impacts, affecting the
magnitude of DLA’s inventory investment, number of procurement requests, and number of
unfilled orders. It is not unusual for the variance of a DLA replenishment item’s monthly demand to be 10 to 100 times its average demand. (In commercial inventory systems, a variance
that is twice as large as the mean is considered very large.) Consequently, DLA’s commercial
supply chain software that determines the timing and size of buys is faced with variability well
beyond for what it was designed. The assumptions underlying the buy logic in that software do
not fit the data, leading to buying the wrong items at the wrong time. This in turn leads to ex-
3
cessive inventory for some items, backorders for others, excessive procurement workload, and
depleted working capital.
For items with frequent demand, EBS uses a suite of forecasting methods, ranging from simple
(naïve, mean, exponential smoothing) to complex (adaptive smoothing, regression-based
weighted average of methods). Unfortunately, these current forecasting models often generate
large forecasting errors and multiple procurement requests, which increase the procurement
workload and inventory investment value. From 2004 to 2010, key DLA inventory metrics (inventory investment, procurement workload, and wait time) trended in the wrong direction.
The process by which maintenance activities generate demands for Class IX items is inherently
highly variable. Furthermore, these items have replenishment lead-times that range from months
to years and represent low-volume business for manufacturers.
When lead-times last anywhere from months to years, a common factor is the significant variability in customer demand, stemming from two major sources of uncertainty in the maintenance
world:

Uncertainty of which parts will fail and need to be replaced

Uncertainty because of changing programmatic factors, such as operating tempo, force
structure, maintenance programs, and maintenance doctrine.
Neither of these factors is easily managed or controlled. Any system that seeks to manage the
Class IX supply chain must cope with this high degree of uncertainty and expect a troubling result: large errors in demand forecasts.
4
Further compounding the challenge is the fact that DLA is under extreme pressure to make the
most efficient use of its inventory investment and provide a high level of support to its maintenance customers, and ultimately to the maintenance of weapon systems. Current level-setting
policies for managing Class IX supply chains do not support these items well and do not give
DLA’s decision makers the flexibility to implement alternative inventory strategies that offer
tradeoffs among inventory investment, procurement requests, and wait time.
IMPLEMENTATION
To facilitate the implementation of PNG into DLA’s processes, LMI participated in weekly teleconference calls with subject matter experts who represented DLA’s supply chains and information operations; the goal was to uncover key business decisions and understand DLA’s
complex planning business from current practices to the potential impact of the PNG levels load.
DLA Information Operations played a pivotal role in the implementation process, as LMI
worked with them to identify and de-conflict with demand month start, followed by demand
month end (DME) processes, and nightly fulfillment. During the initial start process, EBS segmented qualifying items into a quarterly extract for LMI. Items that did not qualify for PNG followed the current level-setting process. For qualifying items, LMI computed the PNG levels
offline, passing these levels back to DLA for upload and publication at DME.
During DME, the planning system publishes new monthly levels. Then, during the nightly fulfillment process, EBS compares the inventory position with the minimum level (reorder point) to
see if a procurement request is warranted.
5
In the fall of 2012, LMI visited each supply chain to brief managers on tradeoff curves; it was
also an opportunity to facilitate the process of having decision makers select inventory policies
that were capable of tradeoffs that allowed organizational objectives to remain intact. Initially,
the supply chains targeted 90 percent material availability at wholesale, in accordance with the
DLA Director’s guidance; there was also a secondary emphasis on reducing the number of procurement requests.
Based on the operating position the supply chains selected, LMI simulated impacts for Day 1,
Year 1, and Year 2 for the number of procurement requests and inventory investment. Because
PNG levels yield a different mix of items on the shelf, DLA would observe an initial increase in
procurement activity and inventory investment. This additional information on initial transient
conditions allowed the supply chains to adapt their selected inventory policies, while trading off
between long-term benefits and near-term pain.
ESTIMATED BENEFITS
The implementation of PNG levels will reduce inventory investment and procurement requests
as well as demand planner and buyer workload. DLA decision makers selected points on the
tradeoff curves with projected benefits reducing unfilled orders by 30 percent on the subpopulation of items. This would concurrently reduce procurement workload by 50 percent and drive
down inventory by $180 million; this is the specific inventory strategy that DLA chose to pursue.
(For a research and development investment of approximately $6.5 million, the return on investment is 27:1 for the initial DLA implementation.) PNG is powerful enough to enable DLA to
consider what its needs dictate, allowing the agency to significantly reduce inventory investment
while maintaining or improving customer service and procurement workload; DLA also has the
6
option to emphasize improvements in customer service or workload over inventory investment,
all using the same system.
Figure 2 is a sample tradeoff curve that shows the three-way tradeoffs among wait time, inventory investments, and annual procurement requests generated.
Figure 2. Tradeoff Curve
The tradeoff curves enable decision makers to see the effect of emphasizing one metric over another. Each point on the curves represents a different set parameter for either Peak Policy or
NextGen. The black square represents DLA’s current inventory policies. All points to the left of
the black square reduce both inventory investment and procurement requests.
One of the DLA demand planner’s responsibilities is to manage the list of demand forecast unit
(DFU) exceptions—recent demand too low or demand too high—generated by a forecasting
model. Because PNG minimizes demand variability, it reduces the number of exceptions reported. The recent rollout of the PNG levels reduced the total number of DFU exceptions by 60 per7
cent (30 percent reduction in demand too low exceptions and 59 percent reduction in demand too
high exceptions). The implementation of PNG levels will also reduce the number of procurement
requests that are cancelled, ultimately increasing buyer productivity.
Implementing PNG levels may result in an initial surge in procurement requests (and buyer
workload), but these will subside, and DLA can reap the benefits of several billion dollars in savings from better spares investments.
ALTERNATE LEVEL-SETTING METHODS
For Class IX consumable items managed by DLA, infrequent demand and/or high variability
present significant challenges. So why forecast? LMI proposes risk-management level-setting
strategies based on optimization for items with these demand characteristics. Using LMI’s riskmanagement strategies, inventory metrics will move in the right direction.
NextGen
The key components of modern inventory control systems include forecasting models (to compute lead-time demand), safety level calculations (to protect against variance in demand), and
economic order quantity—or coverage duration—calculations (to minimize investment). The ordering policies for stocked items typically rely on two levels: first, a reorder point (ROP), and
second, a requisitioning objective (RO). For items with frequent demand, the ROP is a function
of lead-item demand and safety level. For items with infrequent demand, the ROP is solely a
function of lead-time demand. (The RO = ROP + the economic order quantity.)
8
Typically, lead-time demand is modeled using a theoretical probability distribution with the
forecast as the mean and a forecast error-based variance. LMI research has shown that theoretical
probability distributions, such as Laplace, do not work well on DLA demands because the underlying assumptions are rarely met in practice. Statistical tests show that theoretical distributions
do not fit the empirical data well.
Once a lead-time demand distribution has been chosen, it is necessary to estimate its parameters—typically mean and variance. However, studies of forecasting error, whether using sophisticated commercial forecasting packages or simple methods, such as moving averages and
exponential smoothing, show errors of as much as 100–200 percent for many items. Variance
estimates computed from squared deviations from the mean (which often has the large errors referred to above), must therefore also have large errors. Each step in the current stock level computation introduces distortions into the model, and their cumulative effect on buy decisions is
significant. Because of nonlinear interactions, a forecast error can quickly propagate through the
system and lead to either backorders or excess inventory.
LMI developed NextGen to address these forecasting challenges for items with frequent demand
but high variability. With NextGen, buy decisions are based on empirical distributions of demand sizes and interarrival times rather than a theoretical probability distribution. The empirical
distributions are inputs to an optimization problem that includes minimization of expected
backorders. NextGen avoids theoretical probabilities, which are the cornerstone of most of the
other commercially available tools. This fundamental difference in how NextGen treats uncertainty in the demand process is the key to why it is able to outperform existing models.
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Figure 3. NextGen Removes Assumptions
Unlike traditional level-setting methods, NextGen does not use forecast, safety stock, or coverage duration to set an ROP and RO. Rather, NextGen computes a minimum and maximum level.
When the inve …
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