Modeling and Analysis of Commercial Finished Goods Inventory
By
Oliver Stiles Schrang
B.S. Operations Research, United States Military Academy, 2005
SUBMITTED TO THE MIT SLOAN SCHOOL OF MANAGEMENT AND THE ENGINEERING SYSTEMS
DIVISION IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREES OF
Master of Business Administration
and
Master of Science in Engineering Systems
In conjunction with the Leaders for Global Operations Program at the Massachusetts Institute of
Technology
JUNE 2014
@ 2014 Oliver S. Schrang. All Rights Reserved.
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and
electronic copies of this thesis document in whole or in part in any medium now known or
hereafter.
Signature redacted
S
Signature of Author
Certified by
MIT Sloan School of INbagement, MIT Engineering Systems Division
May 9, 2014
Signature redacted
Leigh Hafrey, Thesis Supervisor
Senior e cturer, MIT Sloan School of Management
Certified by
Signature redacted
p
Signature redacted
Accepted by
A
Accepted by
David Simchi-Levi, Thesis Supervisor
fessor okCivil and Environmental Engineering and Engineering Systems
Richard C. Larson, Mitsui Professor of Engineering Systems
Chair, Engineering Systems Division Education Committee
Signature redacted
Maura Herson, Director of MIT Sloan MBA Program
MIT Sloan School of Management
MASSACHUSETTS INMftE
OF TECHNOLOGY
JUN 13 201
LIBRARIES
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2
Modeling and Analysis of Commercial Finished Goods Inventory
by
Oliver S. Schrang
Submitted to the MIT Sloan School of Management and the MIT Engineering Systems
Division on May 9, 2014 in Partial Fulfillment of the Requirements for the Degrees of
Master of Business Administration and Master of Science in Engineering Systems Division
Abstract
As the commoditization of the PC market erodes product margins, increasing emphasis is
placed on cost optimization within the supply chain. One critical component of this is the
financial impact of inventory policies and the transportation choices affecting these policies.
Overseas manufacturing and ocean transportation are the most cost-effective solutions, but
this requires building products to a forecast. The uncertainty induced by forecasts affects the
inventory volumes necessary to achieve specified service levels. Inventory volume and its
associated holding cost can be reduced through air transport, but this must be balanced
against the increased expense of this particular shipping option. This thesis seeks to develop
a framework informing inventory levels, transportation policies, and replenishment
decisions.
Holding inventory to a target level that does not vary across product type or replenishment
method has the advantages of ease of management and low inventory variability within
merge centers, but is sub-optimal from a customer satisfaction and cost perspective. The
model presented introduces a flexible approach that considers variations in product
characteristics to determine optimal inventory and transportation strategies.
Differences between generalized target inventory levels and the levels achievable through a
non-uniform approach are demonstrated. The implications of these inventory levels on
required forecast accuracy levels are also considered. From these differences are
extrapolated cost savings under current commercial finished goods volumes for the North
American region as well as target volumes for the same. Current and target ocean volumes
are discussed, with an analysis of their effect on inventory levels and costs.
Thesis Advisors
Thesis Supervisor: Leigh Hafrey
Title: Senior Lecturer, MIT Sloan School of Management
Thesis Supervisor: David Simchi-Levi
Title: Professor of Civil and Environmental Engineering and Engineering Systems
3
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4
Acknowledgments
First I would like to thank Dell for their longstanding support of the LGO program and for
sponsoring this internship. I was provided with opportunities to work with and learn from a
tremendously talented group of individuals and this made the experience all the more
rewarding.
I would like to thank my supervisor, Jen Felch, and my project champion, Cathy Arledge, for
their mentorship and support during my time with Dell. Josh Freeman and Jerry Becker were
instrumental in outlining and explaining relevant processes and ensuring I connected with
the right individuals in order to access any and all relevant data. Many thanks to Juan Correa,
LFM '07, for his feedback and advice throughout the duration of this internship.
I would like to thank my two advisors, Leigh Hafrey and David Simchi-Levi, for the time and
effort they invested in my project. Their guidance and input is greatly appreciated.
Thank you to the LGO staff and our Director, Don Rosenfield, for supporting not only this
thesis, but also my educational growth throughout the program.
Additionally I would like to thank my classmates. The past two years have been a blast, and
I'm proud to know you and call you friends.
Finally I would like to thank my family. Without their love and support none of this would
have been possible.
5
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6
Note on Proprietary Information
In order to protect proprietary Dell information, the data presented throughout this thesis
has been altered and does not represent actual values used by Dell, Inc. Any dollar values,
product names or logistic network data has been disguised, altered, or converted to
percentages in order to protect competitive information.
7
Table of Contents
A bstract ......................................................................................................................................
3
A cknow ledgm ents ............................................................................................................
5
N ote on Proprietary Inform ation ..................................................................................
7
Table of Contents ....................................................................................................................
8
List of Figures...........................................................................................................................
9
List of Equations ......................................................................................................................
9
List of Tables ..........................................................................................................................
10
1
Introduction: Background and Findings ..........................................................
12
1.1
Introduction.....................................................................................................................12
1.2
Background......................................................................................................................
1.2.1
Dell's History as a PC M anufacturer ................................................................................
1.2.2
Historical Fulfillm ent M ethod ..............................................................................................
1.2.3
New Business M odel ......................................................................................................................
1.3
Project M otivation .....................................................................................................
1.4
Approach...........................................................................................................................
13
13
14
15
16
17
1.5
19
2
Literature Review ...................................................................................................
2.1
2.2
2.3
2.4
3
Introduction.....................................................................................................................
Data Collection/Analysis ........................................................................................
Process fulfillm ent characteristics ......................................................................
Current state process.....................................................................................................................27
Proposed future state ................................................................................................
Service Level ......................................................................................................................................
Forecast Accuracy and Standard Deviation of Forecast Error ..............................
Mixed Replenishm ent M odes...............................................................................................
Costs associated w ith inventory ............................................................................
Purchase costs...................................................................................................................................
Transportation Costs .....................................................................................................................
Holding Costs.....................................................................................................................................37
M odeling Techniques................................................................................................
Results/Recom m endations ..................................................................................
4.1
4.2
5
Introduction.....................................................................................................................
Inventory M odels ........................................................................................................
Methods of Forecasting/measuring forecast accuracy....................................
Effects of Under-stocking cost .................................................................................
M ethodology ..................................................................................................................
3.1
3.2
3.3
3.3.1
3.4
3.4.1
3.4.2
3.4.3
3.5
3.5.1
3.5.2
3.5.3
3.6
4
Findings.............................................................................................................................
Forecast accuracy im pact on inventory ..............................................................
Comparison between DSI and base stock methodology.................................
Conclusion ......................................................................................................................
5.1
Recom m endations .....................................................................................................
8
20
20
20
21
22
23
23
24
26
28
30
32
34
35
36
36
39
40
40
45
50
50
5.2
5 .3
6
51
51
Appendices.....................................................................................................................53
6.1
6.2
7
Opportunities for further work ...........................................................................
C o da ....................................................................................................................................
Appendix A: Model Dashboard..............................................................................
Appendix B: W eekly Screenshot............................................................................
53
54
References......................................................................................................................55
List of Figures
14
Figure 1: Dell revenue by business segm ent............................................................................
Figure 2: Global supply chain rankings from 2007-2013....................
17
Figure 3: China - US supply channels..........................................................................................
24
Figure 4: North American commercial product dem and patterns ................................
25
Figure 5: Dem and m anagem ent review cycle. .............................................................................
26
Figure 6: China - US shipping tim es...............................................................................................
27
Figure 7: Inventory profile.....................................................................................................................
30
Figure 8: Norm al demand distribution.......................................................................................
33
Figure 9: Monte Carlo output. ..............................................................................................................
40
Figure 10: DSI levels by forecast accuracy given 100% ocean replenishment............ 41
Figure 11: DSI levels by forecast accuracy given 10% ocean replenishment..... 42
Figure 12: Forecast accuracy required to hit a target DSI for various transportation
m ix es . .....................................................................................................................................................
43
Figure 13: DSI by ocean transportation for a given forecast accuracy......................... 44
Figure 14: Shipping and holding costs per box across North American smart
selectio n po rtfo lio ............................................................................................................................
47
Figure 15: Sales volume and smart selection percentage for analyzed product
p o rtfo lio ................................................................................................................................................
48
Figure 16: Global smart selection sales as a percent of volum e ......................................
49
List of Equations
Equation
Equation
Equation
Equation
Equation
Equation
E q ua tio n
Equation
Equation
Equation
Equation
1: Coefficient of Variation. ...........................................................................................
2: DSI target m odel ................................................................................................................
3: Base stock for air replenishm ent ........................................................................
4: Base stock for ocean replenishm ent...................................................................
5: Type II fill rate....................................................................................................................
6: Expected units short. .................................................................................................
7 ......................................................................................................................................................
8: Follows from eq. 6............................................................................................................
9: Follows from eq. 8 ............................................................................................................
10: Partial loss function ..................................................................................................
11: Forecast accuracy. ....................................................................................................
9
25
28
29
29
31
31
31
31
32
32
33
Equation 12: Root mean square error. ........................................................................................
Equation 13: Mixed replenishment base stock model. ......................................................
34
35
List of Tables
T ab le 1: Discou nt sch edule....................................................................................................................
Table 2: Inventory mismatch under current conditions....................................................
Table 3: DSI difference between current and proposed model across North
American smart selection portfolio....................................................................................
Table 4: Predicted savings under proposed base stock model........................................
10
38
45
46
49
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11
1
1.1
Introduction: Background and Findings
Introduction
This thesis represents the culmination of six months of collaborative work between the
author, Dell employees, and MIT academic advisors. It was born of both commercial
necessity and academic interest, and as such seeks to strike a balance between these realms.
Dell has a long history of operational excellence and also a longstanding partnership with
the MIT Leaders for Global Operations program, to include a seat on the Governing Board.
This partnership is reflected in the numerous internships conducted by LGO Fellows and the
extensive collaborative work between Dell and members of the MIT staff and faculty.
Strong operational performance, once achieved, can be short lived if continual efforts
at improvement are not made. The business environment is nothing if not fluid, and a static
approach can quickly become outdated. In the larger scheme, then, this thesis can be seen as
a continuation of previous Dell/MIT partnerships, even if the specific content matter is not
identical.
As a company with an international manufacturing footprint and a global distribution
network, Dell's supply chains are correspondingly long and networked. Globally optimal
solutions can be difficult to derive, let alone implement, so local solutions are sought which
can be integrated into the larger whole. One such local component is the role of inventory
within the supply, manufacturing, and distribution networks. Specifically: where to hold it
and how much to hold. These questions can be further specialized to accommodate raw
material, component, work in progress, and finished goods inventory. This thesis primarily
focuses on the ultimate category, and further concerns itself with the question of how much
inventory to hold.
Finished goods inventory, while to a certain degree inescapable in any manufacturing
and distribution process, has a varied history within Dell. Many of its most impactful
operations successes came through the near elimination of finished goods inventory in its
factories. It is useful, then, to examine more closely the role inventory has played throughout
the company's evolution.
12
1.2
1.2.1
Background
Dell's History as a PC Manufacturer
In 1984, three years after the introduction of the IBM PC, Michael Dell founded his
namesake company in a dorm room at the University of Texas, Austin (Holzner, 2006).
Originally the company modified existing IBM hardware, but by 1985 it introduced its first
purebred PC. A voracious market coupled with IBM's inability to fulfill all orders meant Dell
grew at a phenomenal rate. The company moved into new, larger facilities in the first year,
and by 1987 opened its first international subsidiary in the UK. In 1988 the decision was
made to go public, in no small part due to the company's 80% annual growth rate. Soon after
followed the construction of Dell's first overseas manufacturing facility, in Limerick, Ireland,
to meet rising customer demand from Europe, the Middle East, and Africa.
Much of this success was built on Dell's direct to customer model. In the classic
business practice of eliminating the middle man, Dell eschewed retailers and instead sold
their computers directly to customers over the phone. Holzner (2006) suggests that Michael
Dell himself estimated that going direct saved 25% to 45% of mark-up costs on each
machine, an advantage that would prove to be decisive in the early PC market. As technology
evolved so too did Dell's direct business model: dell.com launched in 1996 providing an
online order platform. Within six months the site was generating over $1 million in sales
each day; by 2000 that amount had risen to $40 million per day.
Not content to remain solely a manufacturer of PCs, Dell developed a line of blade
servers in 2001 and continued to expand into new segments, either through internal
development or acquisition of external firms. By 2012 the company was organized and
focused on four key areas: end user computing, enterprise solutions, software, and services.
As of the end of fiscal year 2013 (the fiscal year ending in February, 2013) over 100,000
13
employees supported these four areas and the
Revenue mix by segment
(Dollars in billions)
company posted revenues in excess of $56
$70
billion.' Figure 1 depicts Dell's revenue across
$60
these four areas.
$50
$611
$611
FY'08
FY09
$615
$621
FY'1
FY'12*
$40
1.2.2
Historical Fulfillment Method
$30
In addition to introducing a direct to
$20
customer sales model, Dell also championed a
$*
just-in-time manufacturing philosophy. "Dell's
model
direct
customer's
has
way
given
culture,"
it a
that
do-it-the-
the way
manufactured
in which
(Holzner,
UPublic
Large Enterprise
FY'10
Consumer
SMB
extended
beyond how computers were sold and
shaped
$0
Figure 1: Dell revenue by business segment.
they were
At
2006).
manufacturing sites in Ireland and the United
Taken from Dell 2012 Year in Review,
available at
http://www.dell.com/learn/us/en/uscorp1
/about-dell-investor?s=corp
States, Dell routinely built PCs directly to
each individual customer's specifications. The manufacturing process was refined and
streamlined in an iterative process with a goal of reducing as many unnecessary process
steps as possible. Internals were redesigned to eliminate the need for screws. Components
instead snapped together, reducing the time it took to assemble a computer. According to a
senior design engineer, "every screw you design out of a product reduces assembly time by
approximately eight seconds" (Holzner, 2006). This devotion to efficiency resulted in PC
assembly times as low as three minutes. The entire process, from order to entry to a PC
leaving the factory was four to eight hours. As impressive as these statistics are from a
manufacturing perspective, combined with an innovative supplier relationship they had an
even bigger impact on inventory.
Because process lead times were so short, Dell could afford to operate solely based on
realized demand. No finished goods were ever built to forecast or stock, because the
1 More information regarding Dell's history and financial information can be accessed at
http://www.dell.com/learn/us/en/uscorpl/about-dell-company-timeline, and
http://www.dell.com/learn/us/en/uscorp1/about-dell-investor?s=corp, respectively.
14
manufacturing process didn't begin until after a customer entered an order. Combined with
in-region manufacturing, a customer who ordered a pre-built PC from another manufacturer
could expect to receive it at the same time as a custom built PC from Dell. Additionally,
because products were built to order, there was no need to hold finished goods inventory.
As soon as a PC was completed it could be shipped to the customer. Such inventory
reductions occurred upstream in the supply chain as well. Suppliers maintained warehouses
near Dell manufacturing facilities and assumed responsibility for much of the component
inventory. The close proximity between supplier and manufacturer enabled re-supply
shipments to occur on an hourly instead of daily basis. From 2000 to 2006, Dell saw
inventories in its Austin factory shrink from six days to five to seven hours (Holzman, 2006).
While such component gains are impressive, this thesis is primarily concerned with Dell's
current finished goods inventory model, so the focus will be restricted as such for the
remainder of the text.
1.2.3
New Business Model
Despite the efficiency of their in-region manufacturing processes, the changing nature
of the PC industry required cost-cutting moves by the late 2000's. As consumer demand
surged, Dell began to sell through large retail chains like Wal-Mart and Best Buy. Such
contracts often consisted of large bulk orders purchased ahead of time and held by the
retailer. In the case of a 50,000+ order for laptops, outsourcing production to a contract
manufacturer began to look attractive from a cost and efficiency perspective. Dell's network
of regional factories, while flexible and responsive, were not optimized for such large scale
production. In 2008, the New York Times reported that Dell had announced they would close
their manufacturing facility in Austin by the end of the year (Lohr, 2008). In time, Dell
continued to shift production to original design manufacturers (ODMs) located in China.
Today, the bulk of laptops sold in the US are manufactured in facilities in Shanghai,
Yantian/Zhongshan, and Chengdu.
Outsourced manufacturing has significant impacts on the way in which Dell serves its
customers' needs. The most efficient process involves building larger volumes of units to a
retailers order or forecast, and shipping them to the US via ocean. This works well with the
retail model, but doesn't allow for the same degree of customization as had been previously
15
built into Dell's manufacturing operations. In order to meet customer expectations of
acceptable delivery times, built to order products must be shipped via air to avoid the
lengthy travel time required by ocean freight. In Dell's direct commercial model, this also
means the company is required to hold build to stock finished goods inventory in
warehouses in the US for eventual distribution to consumers.
To address these issues, Dell has developed a new model that tiers products according
to their level of customization: build to stock products are defined models that the consumer
can select "off the shelf', catalog models are customizable within a range of pre-defined
offerings, and custom models are fully customizable platforms tailor built to a consumer's
specifications. Within this model Dell continues to push the built to stock offerings as they
offer the highest product margin to the company. These offerings also have the most impact
on Dell's finished goods inventory holdings, so the analysis of this thesis will be focused on
this particular portfolio of products.
1.3
Project Motivation
The commoditization of the PC market has existed as a phenomenon for at least the
past decade. Eric Bangeman, writing in Ars Technica, an online technology news and
information journal, described, "the utter commoditization of the PC market," in an article
published in January of 2005. As products become increasingly advanced, even basic models
offer every feature a typical consumer needs and often everything they want as well.
Attributes which were formerly unique to a brand become generic and thus relatively
indistinguishable. Customers no longer pay a premium for these attributes because they are
available from all manufacturers. As this commoditization trend continues it becomes
increasingly important to streamline operations and achieve the most efficient cost structure
possible. The overall consumer demand for customized, built to specification systems is no
longer as prevalent, meaning manufacturers can no longer expect to command as much of a
price premium for these features. With industry leaders competing to offer products at the
lowest possible price, cost reduction becomes an increasingly important component in
maintaining product margins. While operational gains can be made throughout the entire
supply chain, one key area is inventory management. This thesis examines and analyzes
Dell's finished goods inventory model in an effort to achieve greater operational efficiency.
16
When Bangeman addressed the commoditization of the industry, he also stressed
Dell's successes in the previous year, stating that they, "held on to the top spot with 17.9
percent of the worldwide PC market," in 2004. Much of that market share was built upon
operation excellence. According to AMR Research (now Gartner), Dell ran the 3rd most
efficient global supply chain in 2008. Unfortunately, by 2013 they ranked 11th (Gartner,
2013). While these rankings are based on a variety of inputs, one key component is inventory
turns. Clearly there is not only a need to streamline operations for the sake of cost, but also
for competitiveness. A depiction of Dell's supply chain ranking compared to two industry
competitors is given in Figure 2.
Gartner Supply Chain Rankings 2007-2013
2006
2008
Axis Title
2010
2012
2014
0
2
4
--
Dell
-Competitor
6
A
Competitor B
8
10
12
14
Figure 2: Global supply chain rankings from 2007-2013.
1.4
Approach
Analyzing the finished goods inventory models for every business line, product type
which Dell offers, and region in which Dell operates would be a prohibitive undertaking due
to the intensity of the time and labor required. Therefore, a more restricted approach to the
problem was taken. The scope of the analysis was narrowed to focus on the finished goods
inventory model of Dell's commercial business for the North American region. This portfolio
of products includes both laptop and desktop offerings manufactured in both China and
Mexico. Under this more defined scope, the following generalized approach was utilized:
17
1. Define current model
Before any analysis can be conducted it is important to first capture and understand
the existing business process. This was primarily achieved through interviews with
relevant members of Dell's business transformation, demand planning, logistics, and
inventory management teams. Once the current state was captured and defined, it
was used as a benchmark against proposed future models.
2. Collect and analyze relevant data
Dell tracks millions of data sets to enable informed business decision making. This
provides a wealth of information, but the scale of data quickly becomes unwieldy. To
combat this, the entire Dell database is broken into smaller datamarts which contain
only that data relevant to a specific organization. This requires the collection and
aggregation of data from a variety of locations in order to make cross-functional
decisions. Analysis of this data provides insight into potential process improvements.
More detail into the data collection and analysis is given in Chapter 3.
3. Formulate alternative model
There is a wealth of literature available regarding inventory models and their effects
on the supply chain. When developing alternatives to the current process, traditional
models were consulted and applied. When necessary, these models were adapted to
fit Dell's operational process as much as possible. The intent was not to wildly revamp
their existing processes, but introduce a more efficient model which works within the
current framework. The development of this model is explained in greater depth in
Chapter 3.
4. Compare alternative model with current model
The two models are compared from a cost perspective to determine if any benefits
are derived through the introduction of new techniques. The comparison is based on
current data, but the results are then extrapolated to include other product lines and
projected volume growth. The model comparison and associated recommendations
are covered in Chapters 4 and 5.
18
1.5
Findings
As discussed in the previous section, a subset of Dell's finished goods business was
analyzed. A variety of factors contributed to determining which section to more closely
scrutinize, but chief among them were relevance to Dell's newest business model and access
to and availability of data. Dell's retail channel has operated a finished goods model through
third party retailers (Best Buy, Walmart, etc.) for several years, but tiered commercial
offerings and their associated inventory impacts are more recent developments. North
American merge centers were closely linked to Dell's headquarters in Texas both
geographically and by personnel, through the frequent travel of employees between the two.
This provided an ease of data access which fostered the development of this project.
During the course of this project it was determined that existing inventory holding
models provided certain key benefits to the company: they were relatively easy to
implement and provided a uniformity across product lines. With these advantages, however,
came certain concessions. The uniform process does not fully exploit unique product
characteristics which can aid in the construction of a more optimized model. The
implementation of a model taking into account such unique characteristics yielded
interesting implications to current processes. Of prime importance were inventory volumes
and service levels associated with various products. Certain models were carried at higher
inventory volumes than required for targeted service levels, while the opposite (low
inventory volumes at a corresponding reduction of service levels) was true for others. By
applying a new model to the existing product portfolio, service levels were smoothed out
and the corresponding inventory volumes were altered as a result.
Of note is that while some products were indicated to be carried at lower levels and some
products at higher levels under the new model, overall inventory volumes were shown to be
reduced, and costs associated with that inventory were reduced as well. The remainder of
this thesis will be an exploration into how this model was developed, how it was applied, and
what results were indicated through the process.
19
2
2.1
Literature Review
Introduction
There is a wealth of literature available covering many inventory models, starting with
Ford Harris' groundbreaking paper How Many Partsto Make at Once, published in 1913. His
economic order quantity (EOQ) model formed the basis for many inventory systems over the
previous century, and has provided a rich landscape for further study and expansion. Though
the volume of materials published is large, the remainder of this section will examine why
companies hold inventory, how product demand can be forecasted and measured, and how
this affects commodity supply chain design.
2.2
Inventory Models
In 1915, Harris noted there were limits to the optimal quantity of parts that could be
produced at one time.
Interest tied up in wages, material and overhead sets a maximum limit to the
quantity of parts which can be profitably manufactured at one time; "set-up"
costs on the job fix the minimum. Experience has shown one manager a way
to determine the economic size of lots. (Harris, 1915, pg. 1)
This manufacturing model can be directly translated into an inventory model by considering
the opposing forces of the fixed costs of ordering inventory with the costs of holding said
inventory (Simchi-Levi, Chin, & Bramel, 1997). Simple models deal with single warehouse,
single retailer scenarios and deterministic demand, but expanded derivations include
multiple warehouses and multiple retailers, stochastic demand, and various time horizons.
The EOQ model, and its multiple derivations, are based on a policy of continuous review. You
establish a minimum inventory threshold (s) that triggers a replenishment order a
predetermined number of items (Q). However, as will be discussed more thoroughly in
Chapter 3, the practice of continuously monitoring inventory levels required to implement
an (s,Q) model is not always feasible in the context of a large firm's institutional rhythm.
Many times decisions are made on a very consistent schedule, so a classification of inventory
models based on periodic review is often a better complement.
20
A generalized periodic review model, which will be expanded upon further in Chapter
3, is the base stock model. For this policy, a firm determines an optimal order up to level (S),
and orders enough inventory to replenish their current supply back up to that level every
review period (R). Unlike the EOQ model, where a company orders the same amount of
inventory at varying intervals, in a base policy the company orders varying amounts of
inventory at regular, specified intervals. Extensive reviews have been conducted on (R,S)
models and their reaction to lead-time uncertainty (Song, 1994), the necessity to rush parts
of an order (Groenevelt &Rudi, 2003), and their implications for multi-echelon supply chains
(Graves, 1996). In 2000, Roundy and Muckstadt developed a heuristic for computing base
stock policies. While their findings that their heuristic is not accurate for products in which
the coefficient of variation is greater than two is and interesting result, many of the products
analyzed for this thesis demonstrated far more predictable demand behavior. Consequently,
the methodology section of this thesis focuses on a single echelon, multi-product model and
does not rely on Roundy and Muckstadt's heuristic.
2.3
Methods of Forecasting/measuring forecast accuracy
While basic inventory models can be formulated assuming deterministic demand, the
models we discuss in this thesis rely upon an assumption of stochastic demand. Often this
demand is expressed according to the parameters which define the underlying distribution
from which it is drawn. One key assumption we will make in the formulation of our model is
that demand is independent and identically distributed and is drawn from the normal
distribution. Such an assumption means our demand parameters take the form of a mean
demand, y, and a standard deviation of demand, a-.
However, while companies must shifts in demand, they attempt to reduce the impact
such shifts have on their operations through forecasting techniques. If companies can predict
demand variations, they can adjust production and inventory levels accordingly and smooth
out the effects of these variations. The extreme example of such a system would be perfect
forecasting: knowing with perfect clarity how demand would behave in the future. Under
such conditions demand no longer behaves stochastically, it essentially becomes
deterministic from the company's point of view. In such a scenario no safety stock or buffer
inventory would need to be held, because there are no unpredictable ebbs and flows in
21
demand to protect against. Clearly this is an unrealistic scenario, but it helps highlight the
importance of forecasting techniques and methods.
In this thesis we are less concerned with how to forecast, and more concerned with
using the results of that forecast to our advantage. Stevenson (2012) stresses that, "accurate
forecasts are very important for the supply chain. Inaccurate forecasts can lead to shortages
and excesses throughout the supply chain." The question then arises of how to best measure
the accuracy of a forecast. Hyndman and Koehler (2006) provide a comprehensive survey of
various forecast accuracy measurements, and assess the positive and negative attributes of
each. Of the various measures they assess (root mean square error (RMSE), mean absolute
error (MAE), mean absolute percent error (MAPE), etc.), Kahn (1999) highlights that MAPE
is by far the most popular within business and has become almost standard practice. Mean
absolute percent error compares the error between the actual value and the forecast value
to the actual value itself. This measure is scale independent, which allows for comparing
accuracies across various data sets. Dell slightly modifies MAPE by comparing the errors to
the forecast, not the actual values; this will be examined in greater depth in Chapter 3.4.2.
2.4
Effects of Under-stocking cost
Stevenson's comment regarding shortages and excesses in the supply chain has direct
cost implications. Optimistic forecasts which lead to excessive finished goods inventory
levels stress the supply chain in multiple ways. Money was invested in producing and
transporting an object that is not generating revenue, space is being occupied in a warehouse
that could be used to hold more revenue generating products, and the items themselves are
often sold at steep discounts, or written off entirely, in order to clear them out of the system.
Not only is this capital lost, but so is the opportunity to invest it in other, more profitable
ways.
Determining the effects of shortages is a more difficult endeavor. While it is tempting
to assume the cost of under-stocking is the lost revenue on the unavailable item, the true cost
is more elusive. The out of stock item will not be purchased, but it is possible the customer
chooses another item they might not have originally, thus offsetting or even overcoming (in
the case of a more expensive item) the lost revenue on the original item. Additionally, there
is some measure of goodwill which is lost when a desired item is not available. This might be
22
negligible for a small number of instances, but the accumulated effect can be such that
consumers might begin to look elsewhere when placing orders in the first place. Dhalla
(2008) cites several previous models that assess the effects of shortage costs (Oral et al.,
1972; Graves, 2002; and Aksen, 2007). Various techniques are applied, but both Graves and
Aksen discuss how difficult it is to measure losses of good will. Dhalla formulates a model
which considers a dependence on the number of days late an item is, and then assesses two
types of customers.
Type 1 customers: those informed their order is delayed after they purchase a system.
Type 2 customers: those who choose not to order because the quoted lead times are
too long (i.e. a system is unavailable and will not be restocked for some time).
This thesis, specifically the analysis devoted to ensuring service levels and fill rates are met,
is primarily concerned with avoiding Type 2 customers. Provided there is inventory in the
merge center, Type 1 customers are the result of factors outside the scope of this project
(incorrect order entries, inaccurate stocking information, issues with national carriers, etc.),
and will consequently not be considered.
3
3.1
Methodology
Introduction
When examining Dell's North American commercial fulfillment model, it is important to
understand its context within the rest of the company as a whole. Dell's four key focus areas
(EUCS, Enterprise Services, Software, and Support) are tasked with developing, marketing,
selling, fulfilling, and support products brought market. Further, the company is divided
globally into regions. AMER covers most of North and South America, EMEA has
responsibility for Europe, the Middle East, and Africa, and APJ is concerned with Asia, the
Pacific, and Japan. While the model described and analyzed in this thesis is primarily
concerned with one segment of a global operation, the results can ultimately be scaled and
applied to other regions by changing the associated input variables. Figure 3 depicts the
generalized supply chain channels for North American products manufactured in China.
23
Ocean replenishment
Ar replenishment
Figure 3: China - US supply channels.
3.2
Data Collection/Analysis
In order to understand the characteristics of products sold through the North American
commercial network, two years of data for over 300 products was analyzed. Due to the
nature of Dell's data management systems, data was unavailable for products at the finished
goods level. Instead, relevant forecast and demand data was captured one level higher. For
instance, a stocked finished goods item consists of a specific part list: hard drive type and
size, graphics card, operating system, etc. The next higher level in the product hierarchy
specifies that the product is a 15" performance laptop, but doesn't include data regarding the
specific components mentioned above. Regardless, it was assumed that finished goods
products followed similar demand fluctuations as their associated parent.
Unsurprisingly, these products demonstrated a wide variety of demand volumes and
fluctuations. Each product's forecast distribution parameters were also widely disparate.
These forecasts were captured at multiple intervals (one, four, and eight weeks)
corresponding to the lead times associated with various transportation methods. These
forecasts will be covered in more detail later in this chapter. Actual demand data was
assumed to be normally distributed, and each item was graphed according to its coefficient
of variation (CoV), which is expressed as the demand standard deviation (0) divided by the
demand mean (p) for the period of time measured.
24
CoV =Equation 1: Coefficient of Variation.
When displayed graphically (shown in Figure 4), the product CoV's display the classic long
tail associated with retail goods.
Product Demand Pattern
4.#
CoefOKdntOf Vatason
Figure 4: North American commercial product demand patterns.
The
area
bounded
in green
represents
products with
favorable
demand
characteristics: the volumes sold are high and variation is low. Products bounded in red are
unfavorable to a forecasting and inventory model. Volume is relatively low, but variation is
extremely high. The fluctuations in these products demand make them very difficult to
forecast accurately and stock at appropriate volumes. This introduces the concept of product
differentiation and supply chain segmentation. Replenishment and stocking policies might
not be uniform across a product portfolio due to the difference in demand characteristics.
This idea will be discussed in more detail in Chapter 3.4.
25
3.3
Process fulfillment characteristics
As introduced in Section 2, many companies do not monitor inventory levels and make
replenishment decisions on a continuous basis. Dell is no different in this regard. The
demand plan is generated monthly, while the item level plan and demand management occur
on a weekly basis as highlighted in Figure 5. Effectively this means that decisions regarding
order volumes with regards to finished goods inventory are made once a week. This fixes the
review period (R) which will be incorporated into the base stock model.
Item Le
Plan
Demand
Mgmt.
Item Level
Plan
Demand
Mgmt.
Item Level
Plan
Demand
Mgmt.
Item Level
Plan
Demand
Mgmt.
Week 1
Week 2
Week 3
Week 4
Figure 5: Demand management review cycle.
In addition to this review period, the various transportation characteristics must be
considered. When transporting products from China to the United States, Dell has two
primary options: ocean or air. (A third version, delayed air, exists but is not elaborated upon
in this thesis.) Manufacturing time is equivalent regardless of the shipping method, but
transportation times differ drastically. In the case of an air shipment, goods can be expected
at the warehouse three weeks after order entry (two weeks of manufacturing time and one
week of transit time). For ocean shipments that interval more than doubles to seven weeks
(two weeks of manufacturing time and five weeks of transit time). These variations in lead
time drastically affect the amount of inventory needed in the warehouse. Approximate
shipping times are highlighted in Figure 6.
26
China Lane to
us
via air
nus
netto
China Lane
V
irAi
ODM to
Port
tj
-t-
via ocean
ODM to
Sea Pon
jAr~
Shutte
Port
Ocean
Sailing
Figure 6: China - US shipping times.
Adapted from Dell logistics.
As with any system of transporting and storing inventory there are various costs
associated with each process step. Because Dell does not own the actual manufacturing
facilities they essentially buy each product from the ODM. This product cost varies
depending on the PC being manufactured and the order quantity. Once the PC is ordered it
is either transported via air or ocean to the warehouse in Nashville. Air freight costs exceed
ocean costs by a factor of four, which incentivizes high rates of ocean shipping. Dell
technically does not own inventory until it reaches the US, so there are vendor managed
inventory (VMI) fees associated with transportation as well. Receiving costs at the
warehouse are on a per pallet basis, with holding costs assessed on a per unit basis.
Depreciation and cost of capital are also factored into the holding cost of a unit. Segrera
(2011) cites a Harvard Business Review study by Callioni et al. which indicates that fully
assembled PCs depreciate at a rate of 1% per week due to technological advances and
component cost declines. Additionally there is the mark down schedule applied to aged
inventory. As inventory sits on the warehouse shelf beyond 30, 60, and 90 day intervals its
sale price is reduced to reflect waning consumer demand.
3.3.1
Current state process
Dell currently manages finished goods inventory according to a periodic review, days
of sale inventory (DSI) target model. This model sets a target for the number of DSI on hand
at the warehouse at any given time. This target is applied to all products, irrespective of
demand characteristics or transportation lead times. The target is calculated by summing all
27
on hand inventory, dividing it by the average forecast for the next two weeks of sales, and
then converting the units from weeks to days.
DSI target
Z on hand inventory
[forecastt+1+ forecastt+2 ]
day
week
Equation 2: DSI target model.
This ensures uniformity of inventory levels (in terms of DSI, not absolute volumes) and
provides ease of management, but cannot be decomposed into various types of inventory.
Specifically, it fails to account for product differentiation and variation in transportation
times in determining appropriate safety stock levels. This has implications not only on
inventory levels, but also fulfillment rates. (A more detailed investigation of this is given in
Chapter 3.4.)
Each week, system and warehouse inventories are monitored and an order up to level
is determined based on the DSI target. This follows the pattern described in Chapter 2.2 of
ordering variable amounts of inventory on a fixed cycle. Based on these process
characteristics it becomes easy to adopt a base stock model which follows the same cycle.
3.4
Proposed future state
Given the current model, an opportunity exists to optimize ordering quantities and
holding levels based on various inputs and parameters. The goal is to carry the minimum
inventory such that demand is filled and demand fluctuations are buffered against. A
generalized base stock model is introduced to determine inventory levels based on demand
characteristics and transportation lead times. The following variables are defined:
S = order up to level
IPt = inventory position in week t
R = review period in weeks
Lm
manufacturing lead time
La = lead time of air replenishment
LO= lead time of ocean replenishment
28
yi = mean demand f or product i
-i = standard deviation of forecast errorfor product i
k = safety stock factor
Additionally, the following assumptions are made:
Demandfor each item follows a normal distribution.
Demandfor each item is i.i.d. with respect to time and every other item.
Note that manufacturing lead times are the same across the entire product portfolio and lead
times only vary according to the mode of transportation chosen. The generalized base stock
model then takes the following forms for each transportation mode:
Sa = pii[(Lm + La) + R] + ka-iV[(Lm + La) + R]
Equation 3: Base stock for air replenishment.
So = ij[(Lm + LO) + R] + k-i[(Lm + LO) + R]
Equation 4: Base stock for ocean replenishment.
The inventory profile of a system of this form is depicted in Figure 7. At t = 0 an
amount of inventory
Q is ordered
equal to S - IPt. After L weeks the inventory arrives and
after R weeks the system is reviewed and another amount equal to S - IPt is ordered.
29
S
-
.-.--..-..
L
t
0
R
tr
t2r
Time
Figure 7: Inventory profile.
Adapted from Graves, 2013.
In reality, multiple R periods exist within each L, so you can consider the true profile to be
multiple overlapping periods such that nR periods occur before the arrival of inventor
ordered at t = 0.
The base stock equation is decomposed into three primary components: demand
during lead time and review period, safety stock factor, and standard deviation during lead
time and review period. The safety stock inventory carried is what protects the system
against demand fluctuations, so the concepts of fill rate and forecast error are now
introduced.
3.4.1
Service Level
A stocked item's fill service level describes, in probabilistic terms, its availability at any given
time. This can be divided into two categories: type I service level, which describe the
probability that a given item is stocked out, and type II fill rate, which describe the percent
of orders that can be immediately fulfilled. Dell's contract with its customers is, in part, based
30
on the expectation of a 95% type II fill rate. That is, if 100 orders are placed for a particular
PC, there will be at least 95 PCs in stock at that time. The following variables are defined:
f
=
item fill rate
Q= order quantity for product i = S - IPt
E [US] = expected units short
The general expression is given in Equation 5.
E[US]
Q
Equation 5: Type II fill rate.
Calculating a type II service level is somewhat non-intuitive and relies on determining a
partial loss expression for the item, G(k).
E[US] = a-VpfG(k)
Equation 6: Expected units short.
From here we need to choose some k such that
E[US] = Qj(1 - fl)
Equation 7
oTiG (k)
Qi
Equation 8: Follows from eq. 6.
31
G~)=Qi(1 - fl)
Equation 9: Follows from eq. 8
G(k) =
fX=k(x
- z)cP(x)dx = P(k) - kx(1 - c1(k))
Equation 10: Partial loss function.
Equations 6 - 10 demonstrate how the steps required to find k. From this point k is
determined by searching across all values in a standard normal distribution table. This k
factor is multiplied by the standard deviation of forecast error to determine the optimal
safety stock level for a given product J: This standard deviation of forecast error is described
more fully in the next section.
3.4.2
Forecast Accuracy and Standard Deviation of Forecast Error
As stated in Chapter 3.4, the demand distributions for each product are assumed to be
normal and i.i.d. This implies that each product has some constant demand p and some
standard deviation o. Figure 8 depicts such a demand profile.
32
0
I
Demand
Figure 8: Normal demand distribution.
However, no company reacts blindly to customer demand. Forecasts for future demand
cycles are made in order to smooth the effects of consumer demand on the system. In the
ideal case, future demand would be forecast with perfect accuracy. No safety stock would be
kept because it is known exactly how many units of each product to carry at any given time.
This is obviously an unrealistic scenario, but the closer a forecast is to the actual demand the
smaller the effects of variations in that demand are to the system. As discussed in Chapter
2.3, there are a variety of methods for calculating the accuracy of a forecast. Dell slightly
modifies the MAPE formula to compare the difference between error and forecast, as shown
in Equation 11.
forecast accuracy
=
1
-
| actual - forecast|
frcs
forecast
Equation 11: Forecast accuracy.
33
However, this doesn't capture the required distribution information necessary to calculate
safety stock levels. For this, the raw data is manipulated to determine the root mean square
error (RMSE), given in Equation 12. The following variables are defined:
fAt
=
forecast for product i in week t
di,t= demand for product i in week t
R MS=
RMSE-
E (di,t - fi,t
t
-
Equation 12: Root mean square error.
This RMSE is used for the value of a-when calculating inventory levels according to the base
stock policy shown in Equations 3 and 4.
3.4.3
Mixed Replenishment Modes
When we first introduced the base stock policy we considered the case where all
inventory was shipped either via ocean or air. Within these extremes exist all possible
combinations of mixing air and ocean replenishment. For instance, Dell might choose to ship
60% of their inventory via ocean to take advantage of the low shipping costs, and 40% of
their inventory via air in order to build responsiveness into their supply chain. Simchi-Levi,
Clayton, and Raven discuss the concept of balancing efficiency and responsiveness and how
it can be applied to a differentiated product portfolio, and the same principles apply in this
case.
Both Do (2009) and Franken (2012) discuss mixed replenishment models and how
they can be optimized. Their solutions depend on a model in which the lead time associated
with air component is less than the review period. In our case, the total lead time associated
with air includes the manufacturing time, which exceeds our review period of only one week.
Additionally, we are interested in investigating a variety of solutions across multiple mixed
models, and for these reasons their techniques will not be applied.
34
For the sake of simplicity, the possible ocean and air combinations were defined
according to 11 choices, from a 100% ocean solution to a 100% air solution in increments of
10%. Furthermore, each combination was assessed against 5 fill rates: 99%, 98%, 95%, 90%,
and 85%. As discussed earlier, Dell's commitment to their customer involves a 95% fill rate,
but we explored a larger solution space to weigh the tradeoffs between fill rate and inventory
levels, and consequently cost. In order to assess the optimal inventory levels for mixed
replenishment modes, a weighted average was taken of Equations 3 and 4. This new model
takes the form
S
= Pa (/-ii((Lm + La) + R)) + kaO'i ((Lm + La) + R)]
+ Po (iii((Lm + LO) + R)) + k 0 0ai ((Lm + LO) + R)]
Equation 13: Mixed replenishment base stock model.
Where:
Pa = percentage of inventory shipped via air
P0 = percentage of inventory shipped via ocean
ka = safety stock factor for air replenishment
ko = safety stock factor for ocean replenishment
Our final inventory model, as expressed in Equation 13, captures the variation
between replenishment mode volumes and lead times as well as the particular demand
characteristics associated with each product. In Chapter 3.6 we will turn our attention to the
techniques used to solve this model.
3.5
Costs associated with inventory
Thus far in our model development we have been concerned with determining the
appropriate
inventory levels based on demand variability/forecast
accuracy and
manufacturing and transportation lead times. The goal has been to carry the minimum
35
inventory for a specific transportation mode that satisfies demand according to the desired
fill rate and buffers against uncertainty. In this section we turn our attention more closely to
the costs associated with transporting and holding this inventory. Broadly we can
characterize these as purchasing costs, transportation costs, and holding costs. All three
depend on the amount of inventory ordered, but they do not scale at the same rate. By
understanding these cost relationships, we can better understand how the various inputs
into our model affect the overall system efficiency.
3.5.1
Purchase costs
As described in Section 3.2, Dell manages their item level plan and the demand
associated with it on a weekly basis. Each week they are making the decision to order (or
not) more inventory based on the expected demand in future weeks. Because Dell has
entered into partnerships with ODMs, and doesn't actually manufacture the systems
themselves, they are essentially buy laptops from the ODMs which are manufactured to their
specifications. This purchase cost is the cost associated with ordering one system, and varies
based on the particular type of system being ordered. (Obviously a high end business laptop
with a large screen, touch capabilities, an extremely fast processor, and extensive
networking features will cost more than a simpler machine aimed at companies requiring
less computing power.) Because the order sizes are (relatively) stable from week to week,
we can assume the purchase cost to vary linearly with order size. Unlike components, which
can be bought in bulk in advance to take advantage of economies of scale, the cost declines
and high turnover of PCs require a weekly replenishment cycle which does not favor large,
bulk orders.
3.5.2
Transportation Costs
Transporting inventory via air can have significant cost implications, but this is
balanced by the flexibility it provides the supply chain. Ocean shipping is extremely cost
efficient, but introduces extended lead times and inventory levels. This natural tension
provides the context in which transportation costs are described. The cost to ship PCs from
China to North America are expressed in a per unit basis. Each PC shipped via ocean incurs
a cost of $5, and each PC shipped via air costs $20. These costs varies directly with quantity,
and so assume a linear relationship. It is also important to note that while air transportation
36
is four times as expensive as ocean transportation, it also has a three week lead time as
opposed to a seven week lead time. Thus, while transportation costs for air are much higher,
the associated
inventory costs are lower. This tradeoff between efficiency and
responsiveness will be discussed further in Chapter 4.
3.5.3
Holding Costs
Once PCs have arrived at the Nashville merge center Dell's 3PL provider charges two
associated inventory costs. One is to receive inventory, charged on a per PC basis, and one is
to hold inventory, charged on a per pallet per week basis. The receiving cost of $1 varies
linearly with the number of systems delivered to the merge center in a given week. The
holding cost of $4.00 is also linear, but varies with the number of pallets of inventory being
held. Laptops are shipped in pallets of 60, and desktops are shipped in pallets of 12. From
this we can see that desktops are five times more expensive to hold over the long run than
laptops.
Two additional holding costs not explicitly associated with the merge center are Dell's
cost of capital and depreciation rate. Every time Dell chooses to purchase, transport, and
hold inventory they are tying up capital that could be otherwise invested in different
opportunities. Dell assigns this cost of capital a value of 10%, essentially determining that if
funds were not tied up in inventory they could expect a return of 10% through other means.
The depreciation rate is simply the rate at which Dell expects their products to lose value,
which they have determined to be 3%.
One final cost associated with holding inventory is the markdown cost. Due to both the
extremely short lifecycle of many PCs and the volume of space they occupy in the merge
center, aged inventory loses value at a rate greater than depreciation alone. Inventory which
sits in the merge center for extended periods of time due to forecasts and demand being out
of sync, must eventually be disposed of to make room for additional inventory of new
products. In order to combat flagging demand (often exacerbated by the introduction of new
products), Dell assigns a markdown schedule to each product (shown in Table 1).
37
Discount Rate
>60 Days
>90 Days
>120 Days
>150 Days
Table 1: Discount schedule.
If this continues for too long Dell must eventually turn to a bulk discount retailer in order to
eliminate their excess inventory holdings. Such "fire sales" can be an effective way to
eliminate inventory from the system, but they represent an almost complete loss to Dell.
Based on the above it becomes clear that while transportation costs vary strictly with
the amount of inventory ordered, holding costs vary not only with the amount of inventory,
but also with its value as well. Ocean and air freight carriers are not concerned with the value
of each laptop they are transporting. Their costs are based primarily on size and weight, so
the cost to ship a $600 laptop vs. a $2100 laptop is equivalent. However, the $2100 laptop
has a far great effect on holding costs than does its less expensive counterpart. While this
thesis is not explicitly concerned with determining the optimal product portfolio, it is
beneficial to bear in mind the adverse effects incurred by holding high value inventory.
To summarize, we define the following costs associated with Dell's finished goods
inventory model:
Ki = purchase cost of one system i
Ta = cost associatedwith shipping one system via air
To = cost associatedwith shipping one system via ocean
R = cost to receive one system in the merge center
H = cost to hold one pallet of systems per week
C=
cost of capital
D = depreciationcost
d (t) = discount rate as a function of time
38
The model introduced in Chapter 3.3 and expanded upon in Chapter 3.3.3 determines our
optimal order quantities. The above costs are then used to determine those order quantities'
financial impact on the system.
3.6
Modeling Techniques
A large number of software programs exist to model inventory systems. For the sake
of continuity within Dell two simulations were created in Excel. The first considered the long
run averages associated with the system under the conditions defined above. This simulation
decomposed inventory by pipeline, cycle, and safety stock, but did not offer insight into the
behavior of the system on a weekly basis. An event based simulation was also constructed
which analyzed the behavior of the system on a week by week basis. The simulation
dashboard is shown in Appendix A, and the weekly snapshot is shown in Appendix B. The
long run averages calculated by the first simulation was useful in understanding the
expected behavior of the system. For the event model, Monte Carlo simulation was employed
to explore the possible inventory positions which might exist under a given demand
distribution. Five thousand trial runs were conducted in order to assess not only the mean
inventory positions, but also the standard deviation around the mean. Because the
underlying demand distributions were assumed to be normal, the resulting inventory
positions also assumed a normal distribution. While the mean gave an indication as to the
expected behavior, the standard deviation provided some insight into the range of inventory
positions that could be expected for a given demand distribution. An example of a potential
inventory position distribution is given in Figure 9.
39
Average Inventory Position (Merge Center)
g0
so
70
s0
2
40
30
20
10
600
900
1200
1500
1600
2100
2400
2700
3000
3300
3600
3900
4200
4500
Figure 9: Monte Carlo output.
Though we are primarily concerned with a specific portfolio of products, each with a distinct
set of characteristics (replenishment mode, demand distribution, forecast accuracy, price,
etc.), solutions were created for all possible conditions. This allowed for a complete
exploration of the solution space and fully demonstrated the effects of replenishment mode
and forecast accuracy on inventory levels within the system. These interactions as well as
the costs associated with them are presented in more detail in Chapter 4.
4
4.1
Results/Recommendations
Forecast accuracy impact on inventory
As discussed in Chapter3.2.1, the current model relies on a set DSI target when
determining how much inventory to order. The proposed base stock model determines
inventory order quantities and positions based on system and product characteristics, and
allows DSI to be an output of the model. The distinction may be subtle, but it is important:
allowing DSI to be an output, as opposed to an input, results in ordering policies which
optimize inventory levels for a given fill rate. In essence the fill rate serves as the target, not
the DSI level, which ensures Dell's customer commitments can be met.
40
If we assume DSI to be the model's output, then there are three other dimensions
which affect this result: forecast accuracy, replenishment mode, and fill rate. By fixing the fill
rate it is possible to demonstrate the range of inventory levels associated with varying the
replenishment mode for a given forecast accuracy. Figure 10 demonstrates the relationship
between DSI and forecast accuracy assuming all inventory is shipped via ocean.
100% Ocean - DSI by FA
Fill Rate
99%
------
Target DS
98%
-9%
-90%
Forecast Accuracy
Figure 10: DSI levels by forecast accuracy given 100% ocean replenishment.
Unsurprisingly, for a given fill rate and replenishment mix, the amount of inventory carried
decreases as forecast accuracy increases. By depicting several different fill rates for a
particular replenishment mix the tradeoffs between inventory levels and fill rate become
more explicit. The target DSI indicated represents the current process target, and can be used
to predict what forecast accuracy is necessary to achieve that target under a 95% fill rate.
When compared to the existing forecast accuracy for a given product it indicates whether
that target is appropriate. If the required forecast accuracy for a given target is lower than
the actual forecast accuracy, too much inventory of that particular product is being carried.
If the required forecast accuracy for a given target is higher than the actual forecast accuracy,
the desired fill rate will not be met. Four choices (either individually, or combined) can be
made if this is the case: more inventory can be ordered, more inventory can be shipped via
41
air, a lower fill rate can be accepted, or efforts can be made to increase the accuracy of the
forecasting model. All four require cost tradeoffs, and the goal is to determine which decision
minimizes the cost impacts. The first two are relatively straight forward. If more inventory
is ordered the firm will incur additional order costs, additional transportation costs, and
additional holding costs. Furthermore, additional capital will be tied up in the overall value
of the inventory which could be put to use elsewhere. Should the decision be made to ship
more inventory via the more responsive replenishment mode, the primary cost incurred is
the increased transportation cost. While this might seem to be the better option, it greatly
hinges on how much more expensive it is to ship via air then ocean. These costs and the
tradeoff they present will be examined in more detail in Chapter 4.2.
10% Ocean - DSI by FA
Target DS1
Fill Rate
95%
Forecast Accuracy
Figure 11: DSI levels by forecast accuracy given 10% ocean replenishment.
Figure 11 demonstrates the effects on forecast accuracy for a given DSI as more
inventory is shipped via air. It may not be immediately apparent, but there is a significant
reduction in the required forecast accuracy when only 10% of the inventory is shipped via
ocean. This is of particular importance to Dell, because it allows them to set targets for their
forecasting models. If a certain forecast accuracy is needed to achieve maximum cost savings
by shipping everything via ocean (assuming, of course, they maintain their current DSI
target), that provides the benchmark against which their forecasting tools can be measured.
42
The decision to assess transportation mixes from pure ocean to pure air solutions was
made in response to the fact that Dell ships the majority of its commercial laptops to North
America via air. They have set target goals for higher ocean shipments, so it is instructive to
investigate the effects of these goals in the context of their current operations. Assuming Dell
maintains their current DSI target policy, the relationship between forecast accuracy and
transportation mix can be described.
Forecast Accuracy required to achieve target DSI
70%
60%
5,0%
340%
W
4
------
S30%
20%
10%
0%
Current ocean mix
Target ocean mix
Ocan Attainmnt
Figure 12: Forecast accuracy required to hit a target DSI for various transportationmixes.
Figure 12 demonstrates the increase in forecast accuracy required to maintain their target
DSI should they achieve their goal ocean mix. This is not a surprising result: for a given
forecast accuracy, safety stock and DSI levels increase as ocean attainment increases due to
extended lead times. In order to maintain a consistent DSI level the associated forecast must
get better. However, this chart highlights a more intriguing result. We have effectively
established an upper bound for the accuracy of our forecasts at a specified DSI target across
all replenishment methods. Any product with a forecast accuracy of greater than 58% can be
carried at less than the target DSI level, regardless of the replenishment mode. This extends
43
to forecasts for entire portfolios of products, and can be used to identify potential
mismatches in inventory volumes.
DSI by Ocean %
Target
DSI
------------------------
Forecast
Accuracy
60%
Actual Ocean
Goal
Ocean %
Ocean %
Figure 13: DSI by ocean transportation for a given forecast accuracy.
Figure 13 highlights this mismatch. Dell's aggregate forecast accuracy for their North
American build to stock commercial portfolio is 60%. Their target DSI, the volume of
inventory carried week to week, can be achieved at ocean attainment levels not only greater
than their current transportation mix, but greater than the goal transportation mix as well.
Given inventory levels in the Nashville merge center as of October 2013 (the last available
data), inventory levels can be reduced by four days under the current transportation mix,
and two days under the goal mix. Table 2 outlines these results.
44
FA
Ocean %
DSI
Fill Rate
60%
27%
13
95%
FA
Ocean %
DSI
Fill Rate
60%
27%
9 (-4)
95%
60%
55%
7 (-2)
95%
Table 2: Inventory mismatch under current conditions.
Given the parameters of Dell's current inventory holdings (namely the ocean
attainment percentage and forecast accuracy), it is possible to reduce inventory levels in the
merge center by four DSI under current conditions, and two DSI under the goal ocean
transportation mix. It might seem counterintuitive that Dell's target transportation mix
would actually increase inventory levels. However, this increase in holding costs is more
than compensated by the decrease in shipping costs. More on this is discussed in the next
section.
4.2
Comparison between DSI and base stock methodology
The North American smart selection portfolio consists of 22 products, 12 of which are
laptop configurations. They primarily consist of offerings from the Latitude line, with several
Precision workstations included as well. This portfolio of products provided the test bed
against which our recommended inventory model could be tested. Each of the 12 products
was analyzed under current forecast accuracy levels and ocean attainment rates. The initial
analysis demonstrated that certain products required safety stock volumes which resulted
in DSI levels below Dell's target, and that certain products required safety stock volumes
which resulted in DSI levels above Dell's current target. Table 3 demonstrates these results
(desktop offerings were not included in this analysis). Of the 12 products analyzed, five
45
required safety stock levels which resulted in DSI
volumes below Dell's current target. Seven products
required DSI's greater than Dell's target (due to
unfavorable
forecast
accuracies),
but
due
to
substantial gains made by several of the products the
average across all platforms resulted in a decrease of
one day of inventory. (This result differs slightly from
the analysis conducted of the merge center as a whole
due to the fact that the merge center analysis includes
products not listed in Dell's smart selection portfolio.)
The results at right demonstrate the reduction
in inventory which can be achieved under the current
transportation mix. On a per product level, some
items will cost Dell more than they do under the
current system because of the increase in their DSI
(remember that for these products targeting a lower
DSI the tradeoff is a lower service level and the
potential impacts
associated with not meeting
customer expectations),
while the rest of the
products result in a cost decrease. A lower DSI results
in purchasing few systems (order cost decrease),
transporting
fewer systems
(transportation
Product
Latitude 3330
Optiplex 3011 AIO (DT)
Optiplex 9020 (DT)
Optiplex XE2 (DT)
Precision R7610 (DT)
Precision T1700 (DT)
Latitude E3440
Latitude E3540
Latitude E5440
Latitude E5540
Latitude E6440
Latitude E6540
Latitude E7240
Latitude E7440
Optiplex 3020 (DT)
Optiplex 7010 (DT)
Precision M3800
Precision M4800
Precision M6800
Precision T3610 (DT)
Precision T5610 (DT)
Precision T7610 (DT)
Average
DSI+13
-7
-71
4
7
-5
-8
3
3
-9j
8
-1
Table 3: DSI difference between
current and proposed model across
North American smart selection
cost portfolio.
decrease), and storing fewer systems (holding cost
decrease). Additionally, storing fewer systems reduces the danger of aged inventory and
their associated markdowns. As with the DSI levels, these costs can be directly computed and
compared to the existing inventory model. In Figure 14 we see the cost per box associated
with the current inventory model depicted in red, and the cost per box under the proposed
inventory model depicted in blue. Again, this analysis is conducted for the current forecast
accuracies, current transportation mix, and current smart selection volumes.
46
Shipping + Holding Cost per Box
MBase Stock - 95% FR
E Target DSI
Latitude
3330
Latitude
E3440
Latitude
E3540
Latitude
E5440
Latitude
£5540
Latitude
£6440
Latitude
£ 6540
Latitude
£7240
Latitude
£7440
Precision Precision
M3800 IM4800
Precision
M6800
Product portfolio
Figure 14: Shipping and holding costs per box across North American smart selection portfolio.
The products outlined in green demonstrate a direct cost savings under adopting a base
stock inventory model. Other products incur a cost penalty, but this is only to ensure that
Dell meets its committed 95% fill rate. When comparing the current target DSI model and
the proposed base stock model, the weighted (by portfolio percentage) cost per box under
the base stock model is $.30 cheaper.
When applied to current and target smart selection volumes, the $.30 savings per box
is not insignificant. Figure 15 shows that smart selection products currently represent about
25% of the total volume sold of the analyzed portfolio. For the first four weeks of quarter
four in FY'14, Dell sold 89,250 smart selection units out of this portfolio in North America.
This represents a savings of $26,775 under the proposed model. When current sales volumes
and smart selection percentages are extended to a 52 week horizon, the yearly savings
becomes $278,460. However, Dell expects growth not only in total volume for this portfolio,
but also in the percentage of these products that are sold as smart selection offerings. This is
in line with global trends, as shown in Figure 16.
47
NA weekly unit volume
100%
NORTH AMERICA
357000
80%
60%
40%
20%
0%
Q47D
Week 40
Week 41
Week 42
Week 43
Week 44
Figure 15: Sales volume and smart selection percentage for analyzed product portfolio.
Adapted from Dell internal weekly report.
Over the 10 week interval displayed, global smart selection sales as a percent of total volume
grew by over 120%. This was during the initial offerings of this portfolio, and growth rates
are expected to increase over the life of these products. Dell has set internal targets for each
of its regions, with the expectations that these growth rates and volumes are achieved by
FY'15. The current smart selection percentage varies by product across the portfolio, but the
target percentage is set at 70% across the entire portfolio. Given the current sales volumes
and applying the target smart selection percentages, potential savings can be extrapolated
under the proposed model. On a 52 week horizon these savings amount to $779,688. Table
4 shows the cost savings under the proposed model for both current and expected growth
rates.
48
Sales Mix %
Custom BTO -Catalog
BTO -Smart
Selection
729%
4a.X 61,1%
77 r 31 1%
238%
Q2Wk6
Q2Wk7
Q2Wk8
Q2Wk9
Q2Wk1O
Q2Wk11
Q2Wk12
Q2Wk13
Q3Wk1
Q3Wk2
Figure 16: Global smart selection sales as a percent of volume.
Adapted from Dell global productions.
Time Horizon Smart Selection %
52 weeks
52 weeks
25% (current)
70% (target)
Savings
$278,460.00
$779,688.00
Table 4: Predicted savings under proposed base stock model.
These savings represent only a single portfolio of products in one geographical region under
current sales volumes and current and predicted smart selection growth. Though the model
specifics would necessarily change (lead times and forecast accuracies), and some specifics
might change (fill rate), it is possible to apply the generalized model to any finished goods
inventory system throughout Dell's global network. On an aggregate level it is feasible to
assume that the savings would then grow into several millions of dollars, and a more
meaningful conversation about the benefits could be pursued.
49
5
5.1
Conclusion
Recommendations
Though the DSI target model has certain advantages, namely uniformity and ease of
management, Dell should adopt a policy which better differentiates between product
characteristics and the impact of transportation decisions. Much of Dell's historical DNA is
coded to provide rapid response capabilities to existing demand. However, the consumer
side of the business has largely adopted new business models, and the commercial side
continues to undergo this transformation. Finished goods inventory becomes a critical
component when running a build to forecast, direct to customer model. Care must be taken
to efficiently manage this inventory and the costs associated with it.
Under this new model Dell can immediately impact their supply chain by holding less
inventory for the current conditions. This streamlines the system and reduces cost at all
points along the supply chain: ordering, transportation, and storage. No changes need to be
made to ocean attainment rates and no investments need to be made in forecast capabilities.
(Both these can be advantageous, but impacts can be felt without implementing them.)
Further, this policy provides a direct link between inventory levels and fill rates. Dell can
confidently make service commitments to its customers knowing that adequate safety stock
levels exist for all products. While this policy does introduce a certain level of variability
between products, the overall effect is a contraction of inventory and a savings to the
company.
For products that are currently held at DSI levels in excess of what is required under a
95% fill rate, Dell can take a different approach and immediately begin shipping more via
ocean. This will actually result in an increased costs savings, because the difference in
shipping costs is so pronounced. The temptation will exist to revert to air shipments when
forecasts aren't aligned with sales, but this quick fix should be avoided when possible. Not
only are the costs high, but the potential to inject unnecessary turbulence into the system
exists.
50
5.2
Opportunities for further work
This project is concerned with one aspect of Dell's commercial finished goods
replenishment model, but opportunities for follow on work exist in several domains. As
fewer and fewer commercial platforms are built to order, forecasts and their accuracy will
be increasingly important. Forecasts are made at the component level all the way down to
finished goods, and exist for multiple time horizons. A comprehensive study of Dell's
forecasting techniques and the interactions between forecasts should be undertaken. So
many forecast metrics are created that it can be difficult to communicate in any sort of lingua
franca. An understanding of these dependencies can help assist in the creation of such a
bridge. From a more mechanical perspective, the forecasting process itself can be analyzed.
Forecasting is a rich field with extensive literature and new insights on a regular basis, and
this body of knowledge should be exploited.
Both forecasting and finished goods replenishment form subsections of Dell's larger
supply chain and logistics network. Dell currently operates without a comprehensive
enterprise resource planning (ERP) tool, and manages many of its operation decisions
through individual modules. Work is underway to determine the feasibility of implementing
a large scale ERP, and the impacts this would have on the business as a whole. Many aspects
of such an implementation need to be analyzed, from cost to scalability to the interactions
what would exist with current systems. A project of this scope could certainly benefit from
the additional manpower and resources an LGO internship would bring. Dell faces an ever
changing future as a PC manufacturer, and these changes will continue to provide
opportunities for further collaboration and study.
5.3
Coda
As stated in Chapter 1, this thesis represents merely one link in a chain of collaborative
efforts between MIT and Dell. The preceding section enumerates additional projects the
author believes would be of benefit to both Dell and the LGO Fellows who might one day
work on them. The thread linking one project to the next (aside from the obvious and
aforementioned MIT and Dell partnership, and the LGO and LFM alumni at Dell who have
mentored multiple interns over the years) is a desire to not only improve the operational
practices within the company, but also to engender a culture in which seeking out new ideas
51
and new methods of implementing those ideas is not merely commonplace, but praised and
elevated as an act the benefits not only the company but also the individual. In short, it is a
declaration that one can always improve, and should always strive to do so. For the intern
this takes the form of increased technical knowledge and a greater awareness of the
challenges inherent to the competitive business landscape. For the company it is not only the
embracement of academic research and the greater implications this research has for its
success, but also an acknowledgment that the pursuit of such knowledge is of primal
importance to the cultural and organizational health of the company.
Each project in this tradition co-mingles academic models and contemporary business
practices in an effort to benefit the company, its employees, and its customers; as well as the
student and the institution. Such collaborations are of the highest benefit, and serve only to
reinforce the partnership between MIT and Dell. This thesis stands as a testament to both
those benefits, and the partnership.
52
0.
0.
Ocean
Lead Time
Air
Lead Time
Review Period
[Forecast Variance
1M Forecast Mean
Forecast StdDev
o Forecast
CoV
In
Forecast Error StdDev
Forecast Error CoV
Forecast Error Variance
0-u
o Cum Forecast Accuracy
Cost
Hedge upper
Hedge lower
-Item
Transportation Cost (Ocean)
Cost (Air)
0L Transportation
0L Receiving Charge
<
Pallet Charge / day
- Pallet Charge / wk
- Pallet Size (Ocean)
Size (Air)
- Pallet
-Tnr=
0.1
2500
0.526
69169
62%
1.15
0.85
Transportation Mix
Fill Rate (Type I Service)
Average Total Inventory
Average Inventory Position (Merge Center)
Average DSI (Total Inventory)
Average DSI (Merge Center)
DS1 variance (Total Innventory)
DSI variance (Merge Center)
Total Demand
Total On Time Fill
Average Fill Rate
Stockout Events
Aged Inventory < 30 Days
Aged Inventory > 30, <= 60 Days
Aged Inventory > 60, <= 90 Days
Aged Inventory > 90, <= 120 Days
Aged Inventory > 120 Days
-
-
-
$
$
$
98%
4826
1254
66
17
56
82
12792
12792
100%
0
20455
107
0
0
0
Ocean 100%
99%
5006
1434
69
20
57
82
12792
12792
100%
0
21272
107
0
0
0
-
$
-
-
-
-
-
$
$
$
$
-
-
$
$
$
$
-
Transportation Cost (Ocean)
Transportation Cost (Air)
Receiving Cost
Pallet Cost
Aged Inventory Discount
Cost of Capital
Deflation Cost
-
-
$
$
$
$
Total Cost
Cost per box
95%
4526
954
62
13
54
77
12792
12528
98%
1
19114
107
0
0
0
Air 0%
-
90%
4286
714
59
10
52
73
12792
12240
96%
2
16130
48
0
0
0
-
85%
4120
564
57
8
52
67
12792
11796
92%
4
13397
0
0
0
0
-
-
-
-
-
-
-
-
-
$
$
$
$
$
$
$
$
-
-
-
$
$
$
$
-
-
$
$
$
$
-
-
-
$
$
-
$
$
-
-
$
$
$
$
$
$
$
$
-
-
80%
4000
444
55
7
51
61
12792
11166
87%
7
11409
0
0
0
0
$
$
-
-
-
-
-
$
$
0
.4-0
L.a)
U
CL
.
0.00672
0.00951
2.0900
1.9600
99% Fill Rate
100% Ocean
0% Air
PLF (z) - Ocean
PLF (z) - Air
Z value - Ocean
Z value - Air
Week
Base Stock
Safetv Stock
Order (Ocean)
Order (Air)
Build
Ship (Ocean)
Ship (Air)
Pipeline Inventory
Raw Forecast
Actual (hedged) Forecast
Start Inventory
Arriving Inventory
Demand
Total Inventory
Ending Inventory (Merge Center)
DSI (Total Inventory)
DSI (Merge Center)
Fill Rate
Aged Inventory 0 - 1 Week
Aged Inventory 1 - 2 Weeks
Aged Inventory 2 - 3 Weeks
Aged Inventory 3 - 4 Weeks
Aged Inventory 4 - 5 Weeks
Aged Inventory 5 - 6 Weeks
Aged Inventory 6 - 7 Weeks
Aged Inventory 7 - 8 Weeks
Aged Inventory 8 - 9 Weeks
Aged Inventory 9 - 10 Weeks
Aged Inventory 10 - 11 Weeks
1560
-8
5694
1560
1560
600
-7
5931
2160
2160
600
-6
6173
2760
1200
1560
540
-5
5662
3300
0
1140
2160
600
-4
5993
3900
0
1140
2760
540
-3
5522
4440
480
0
1140
3300
-2
5306
-1
5543
540
0
1020
3900
0
4920
1
5513
1555
720
0
1020
2880
0
3900
517
517
0
1560
647
4813
913
60
11
100%
913
2
5606
1555
S40
0
1260
2760
0
4020
547
547
913
600
422
5111
1091
66
14
100%
600
491
3
5245
1555
0
0
1260
2700
0
3960
577
577
1091
600
394
5257
1297
69
17
100%
600
206
97
540
165
0
0
4
5798
1555
1020
0
540
2880
0
3420
513
513
1297
540
435
4823
1403
64
19
100%
600
152
0
0
0
5
6166
1555
720
0
1020
2820
0
3840
555
555
1403
600
388
5455
1615
79
23
100%
Lrn
7
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