Dell Case study
Open the file (Undergrad Reqt_Individual In-Depth Case Study) for instruction I already highlighted the section that you need to answer which is SECTION 1. I uploaded 2 articles that you need to read to answer the questions and Pay attention to (Individual In-Depth Case Study Rubric).
This will be 4 full pages
MGT 54
2
Individual In-Depth Case Study
Undergraduate Requirement
50 Points per Weekly Submission
1)
Read the two identified Dell articles and answer all the questions listed below. You can find the articles in the content tab in Blackboard titled “Course Reserves”.
2) Each question should be answered with a minimum of one paragraph. A paragraph is a minimum of 3 complete sentences.
3) A minimum of 4 pages per submission of written content is required. Some sections will require more pages to fulfill the requirements of the rubric and the in-depth case study instructions listed here.
4) APA format is required.
5) A minimum of 2 additional sources per submission are required to be cited.
6) A bibliography with all citations, including the given articles, is required.
7) There will be four in-depth case study submissions. Please reference the course schedule in the syllabus for the required dates of each submission.
SECTION 1 – 50 Points
Kapuscinski, R., Zhang, R.Q., Carbonneau, P., Moore, R., & Reeves, B. (2004). Inventory Decisions in Dell’s Supply Chain, Interfaces 34(3), 191-205.
1. Describe the Supply Chain Business Model used by Dell.
2. How does Dell Supply Chain benefit from low inventory levels?
3. What does Dell do to help their suppliers make optimum ordering decisions?
4. Describe Dell’s relationship with their suppliers.
5. Describe the revolver inventory model.
6. Describe the purpose of the golf analogy as it relates to inventory concepts. Include the analogy for the handicap (Dell’s and the Supplier’s).
7. What is the purpose of safety stock and how is it controlled?
SECTION 2 – 50 Points
Kapuscinski, R., Zhang, R.Q., Carbonneau, P., Moore, R., & Reeves, B. (2004). Inventory Decisions in Dell’s Supply Chain, Interfaces 34(3), 191-205.
8. Describe the impact of the pull variance if a commodity is multi-sourced.
a. Explain how an uneven pull from different suppliers of the same commodity effect the suppliers’ inventory levels, planning, and forecasting.
9. The balancing act: Discuss the cost of underage and overage as it relates to inventory
10. The article references “Z scores” as the system inventory levels just before orders are placed to understand Dell’s method of making ordering decisions. Explain the logic of why there was significant volatility found of the “Z scores”.
11. Forecast errors are an input in determining the optimal inventory levels. In Dell’s case, it was found the forecast errors at the commodity level were not independent from time period to time period. Why was this the case and what could be done differently to minimize forecast error?
12. Explain the impact of the actual system inventory level fluctuations. What are the benefits and risks. Should high fluctuations be avoided?
13. Discuss describing inventory levels in terms of “days of supply” vs. “units of inventory”. What would you recommend and why?
14. Is 100% service level an ideal recommendation? Why or why not? How does this impact inventory levels?
Section 3 – 50 Points
Fugate, B.S., Mentzer, J.T. (2004). Dell’s Supply Chain DNA, Supply Chain Management Review; October 20-24.
1. What are the four qualities of Dell’s supply chain competency?
2. Describe the concept of each one individually.
3. Discuss the Business Process Improvement that shifted Dell from brand-specific assembly plants to generic factories. Why was this successful?
Section 4 – 50 Points
Final Questions
1. What advice would you have for Dell’s Supply Chain business model? What would you do differently? Why?
2. What are the main challenges in front of Dell in reducing inventory while maintaining their Direct Sales model?
3. What inventory reduction improvement initiatives would you recommend?
4. What would you recommend that Dell try differently to further optimize their supply chain?
2
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Dell’s Supply Chain DNA
Fugate, Brian S;Mentzer, John T
Supply Chain Management Review; Oct 2004; 8, 7; Business Premium Collection
pg. 20
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Inventory Decisions in Dell’s Supply Chain
Author(s): Roman Kapuscinski, Rachel Q. Zhang, Paul Carbonneau, Robert Moore and Bill
Reeves
Source: Interfaces, Vol. 34, No. 3 (May – Jun., 2004), pp. 191-205
Published by: INFORMS
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Interfaces infjIML
Vol. 34, No. 3, May-June 2004, pp. 191-205 DOI i0.1287/inte.l030.0068
ISSN 0092-21021 eissn 1526-551X1041340310191 @ 2004 INFORMS
Inventory Decisions in Dell’s Supply Chain
Roman Kapuscinski
University of Michigan Business School, Ann Arbor, Michigan 48109, roman.kapuscinski@umich.edu
Rachel Q. Zhang
Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853, rqz2@cornell.edu
Paul Carbonneau
McKinsey & Company, 3 Landmark Square, Stamford, Connecticut 06901, paul_carbonneau@mckinsey.com
Robert Moore, Bill Reeves
Dell Inc., Mail Stop 6363, Austin, Texas 78682 {robert_a_moore@dell.com, bill_reeves@dell.com}
The Tauber Manufacturing Institute (TMI) is a partnership between the engineering and business schools at
the University of Michigan. In the summer of 1999, a TMI team spent 14 weeks at Dell Inc. in Austin, Texas,
and developed an inventory model to identify inventory drivers and quantify target levels for inventory in the
final stage of Dell’s supply chain, the revolvers or supplier logistics centers (SLC). With the information and
analysis provided by this model, Dell’s regional materials organizations could tactically manage revolver inven
tory while Dell’s worldwide commodity management could partner with suppliers in improvement projects to
identify inventory drivers and to reduce inventory. Dell also initiated a pilot program for procurement of XDX
(a disguised name for one of the major components of personal computers (PCs)) in the United States to insti
tutionalize the model and promote partnership with suppliers. Based on the model predictions, Dell launched
e-commerce and manufacturing initiatives with its suppliers to lower supply-chain-inventory costs by reducing
revolver inventory by 40 percent. This reduction would raise the corresponding inventory turns by 67 percent.
Net Present Value (NPV) calculations for XDX alone suggest $43 million in potential savings. To ensure project
longevity, Dell formed the supply-chain-optimization team and charged it with incorporating the model into a
strategic redesign of Dell’s business practices and supervising improvement projects the model identified.
Key words: inventory, production: applications; industries: computer, electronic.
History: This paper was refereed.
Dell and Our Project
Dell is the largest computer-systems company based
on estimates of global market share. It is also the
fastest growing of the major computer-systems com
panies competing in the business, education, gov
ernment, and consumer markets. Dell’s product line
includes desktop computers, notebook computers,
network servers, workstations, and storage products.
Michael Dell founded the company based on the con
cept of bypassing retailers and selling personal com
puter systems directly to customers, thereby avoiding
the delays and costs of an additional stage in the
supply chain. Much of Dell’s superior financial per
formance can be attributed to its successful imple
mentation of this direct-sales model.
While the computer industry has grown tremen
dously over the past decade, firms in this industry
face their own challenges. First, rapid changes in tech
nology make holding inventory a huge liability. Many
components lose 0.5 to 2.0 percent of their value per
week, and a supply chain packed with yesterday’s
technology is nearly worthless. With its direct sales,
however, Dell carries very little inventory: the whole
organization concentrates on speeding components
and products through its supply chain. Dell delivers
new products to market faster than its competitors
and does not have to sell old products at a discount,
because it has none. Second, the traditional model of
vertical integration that the original equipment man
ufacturers (OEMs) follow to manufacture all of the
major components of their products has almost disap
peared in the computer industry. Research and devel
opment (R and D) costs are too high and technology
changes too rapid for one company to sustain lead
ership in every component of its product lines. Dell
has close-relationship agreements with its suppliers
that allow them to focus on their specific compo
nents. At the same time, Dell concentrates its research
on customer-focused, collaborative efforts to lever
age the collective R and D of its partners (Dell and
Magretta 1998), which includes cultivating its supply
chain competence.
Dell management was concerned that, although the
firm carried almost no inventory, its suppliers might
191
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Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain
192
Interfaces 34(3), pp. 191-205, ?2004 INFORMS
be holding much more inventory than was needed to
provide desired customer service. For this reason, Dell
asked a team from the Tauber Manufacturing Institute
(TMI), a partnership between the engineering and
business schools at the University of Michigan, to
study this issue. Dell sought recommendations for
a sustainable process and decision-support tools for
determining optimal levels of component inventory
to support the final assembly process.
Dell bases its business model on integrating five
key strategies: rapid time to volume, products built
to order, elimination of reseller markups, superior
service and support, and low inventory and capital
investment. We designed our project and the resulting
tools to support Dell’s low-inventory and low-capital
investment strategy and to extend its impact beyond
the plant floor into the preceding stage of its supply
chain. Tom Meredith, at the time chief financial offi
cer, said in the May 18,1999 earnings conference call:
“Customers see no advantage in a manufacturer low
ering inventory to six days if there are still 90 days in
the supply line.” Our project was the first step taken
to combat the “90 days in the supply line.”
Dell’s Supply Chain
Dell’s supply chain works as follows. After a cus
tomer places an order, either by phone or through the
Internet on www.dell.com, Dell processes the order
through financial evaluation (credit checking) and
configuration evaluations (checking the feasibility of
a specific technical configuration), which takes two
to three days, after which it sends the order to one
of its manufacturing plants in Austin, Texas. These
plants can build, test, and package the product in
about eight hours. The general rule for production
is first in, first out, and Dell typically plans to ship
all orders no later than five days after receipt. There
are, however, some exceptions. For example, Dell may
manipulate the schedule when there is a need to
replace defective units or when facing large customers
with specific service-level agreements (who have non
standard quoted manufacturing lead times) for their
orders.
In most cases, Dell has significantly less time to
respond to customers than it takes to transport com
ponents from its suppliers to its assembly plants.
Many of the suppliers are located in Southeast Asia
and their transportation times to Austin range from
seven days for air shipments to upwards of 30 days
by water and ground. To compensate for long lead
times and buffer against demand variability, Dell
requires its suppliers to keep inventory on hand
in the Austin revolvers (for “revolving” inventory).
Revolvers or supplier logistics centers (SLCs) are
small warehouses located within a few miles of Dell’s
assembly plants. Each of the revolvers is shared
by several suppliers who pay rents for using their
revolver.
Dell does not own the inventory in its revolvers;
this inventory is owned by suppliers and charged to
Dell indirectly through component pricing. The cost
of maintaining inventory in the supply chain is, how
ever, eventually included in the final prices of the
computers. Therefore, any reduction in inventory ben
efits Dell’s customers directly by reducing product
prices. Low inventories also lead to higher product
quality, because Dell detects any quality problems
more quickly than it would with high inventories.
Dell wishes to stay ahead of competitors who
adopt a direct-sales approach, and it must be able to
reduce supplier inventory to gain significant lever
age. Although arguably supply-chain costs include all
costs incurred from raw parts to final assembly, Dell
concentrates on Dell-specific inventory (that is, parts
designed to Dell’s specifications or stored in Dell
specific locations, such as its revolvers and assembly
plants). Because assembly plants hold inventories for
only a few hours, Dell’s primary target, and ours in
this project, was the inventory in revolvers.
Dell has a special vendor-managed-inventory
(VMI) arrangement with its suppliers: suppliers
decide how much inventory to order and when to
order while Dell sets target inventory levels and
records suppliers’ deviations from the targets. Dell
heuristically chose an inventory target of 10 days
supply, and it uses a quarterly supplier scorecard to
evaluate how well each supplier does in maintaining
this target inventory in the revolver. Dell withdraws
inventory from the revolvers as needed, on average
every two hours. If the commodity is multisourced
(that is, parts from different suppliers are completely
interchangeable), Dell can withdraw (pull) those com
ponents from any subset of the suppliers. Dell often
withdraws components from one supplier for a few
days before switching to another. Suppliers decide
when to send their goods to their revolvers. In prac
tice, most suppliers deliver to their revolvers on aver
age three times a week.
To help suppliers make good ordering decisions,
Dell shares its forecasts with them once per month.
These forecasts are generated by Dell’s line of busi
ness (LOB) marketing department. In addition to
product-specific trends, they obviously reflect the
seasonality in sales. For home systems, Christmas
is the top time of the year. Other high-demand
periods include the back-to-school season, the end
of the year when the government makes big pur
chases, and country-specific high seasons for foreign
purchases (foreign language keyboards are especially
influenced). Dell sales also increase at the ends of
quarters (referred to as the hockey stick).
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Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain
Interfaces 34(3), pp. 191-205, ?2004 INFORMS 193
After the center of competence (COC) checks a fore
cast for predicted availability of components, the fore
cast goes to Dell’s commodity teams and becomes the
basis for a six-month rolling forecast that they update
weekly. The commodity teams make generic forecasts
for systems and components and break those forecasts
down to a level of the specific parts that need to be
ordered. If the forecast is not feasible, the LOB mar
keting department revises it, although such revisions
are very rare. The buyer-planner for each commodity
receives an updated rolling forecast weekly; suppliers
receive forecasts monthly.
The objectives of our project were to recommend
target inventories for the revolvers to minimize
inventory-related costs subject to a service-level con
straint and to develop a process and tools for iden
tifying and updating target levels for inventories of
the items in the revolvers. (Suppliers who make the
replenishment decisions attempt to follow Dell’s tar
gets and guidelines.) Dell had been setting inventory
targets based on empirical data and judgment with
no clear reference to any desired service levels. Dell
hypothesized that it could reduce revolver inventory
markedly by using a more rigorous approach and
gaining better visibility of the inventory throughout
the supply chain. Once it determined an optimized
inventory level, Dell could collaborate with its sup
pliers to eliminate excess inventory.
Dell emphasized that it wanted to sustain any
changes over the long term, which would require
integrating them into its informational infrastruc
ture. ValueChain is a program intended to extend
Dell’s successful direct-sales approach back into the
supply chain with the goal of increasing the speed
and quality of the information flow between Dell
and its supply base. The corresponding Web site
valuechain.dell.com is an extranet for sharing such
information as points of contact, inventory in the sup
ply chain, supply and demand data, component qual
ity metrics, and new part transitions. Dell envisions
using this site to exchange with suppliers current
data, forecasted data, new product ideas, and other
dynamic information that might help it to optimize
the flow of information and materials in the supply
chain.
By integrating the process and associated tools
that we developed with valuechain.dell.com, we want
to make the tools part of Dell’s and its suppliers’
procurement-business processes. Dell and its suppli
ers through ValueChain can share such information
as target inventory levels to support collaboration on
future improvements.
Our Approach
We concentrated on the last stage of Dell’s supply
chain, the revolvers, where the inventories of all com
ponents are Dell specific. Dell believes that inventory
savings at this level will produce comparable supply
chain savings. Also, analysis of this stage is a neces
sary first step to further improve the whole supply
chain. We looked at the inventories of an important
component, XDX, in the revolvers themselves and in
transit to revolvers. XDX is one of the major compo
nents of PCs, supplied by a few suppliers, that is fully
interchangeable (customers do not choose the man
ufacturer of this component when ordering the final
product).
In our analysis, we made several simplifying
assumptions. Because the supply chain provides a
fairly high service level, simultaneous shortages of
multiple components are very infrequent, and thus,
we could ignore them without significantly altering
the results. Although Dell regularly updates its fore
casts of demand and hence its desired inventory lev
els, we assumed stationary demand and inventory
targets during a rolling forecast horizon of 10 busi
ness days. We realized that the behaviors of demand
and inventories at the beginning and the end of a
product’s life cycle differ from those mid life cycle;
however, we handled them separately and do not
describe them here. (Kurawarwala and Matsuo 1996
describe ramp-on using the Bass 1969 model.) We
call the inventory in the revolvers the revolver inven
tory and the revolver inventory plus any inventory
ordered but not yet delivered the system inventory.
Although revolver inventory determines the availabil
ity of parts, Dell can control it only by placing new
orders (increasing the system inventory).
Currently, Dell’s suppliers order in batches (to off
set fixed ordering costs incurred every time an order
is placed) when inventory levels drop. However, the
actual order (batch) sizes and points (levels) at which
the suppliers reorder are quite inconsistent. Our first
task was to develop a tool that would bring con
sistency to suppliers’ ordering decisions. (Our esti
mates of the benefits do not include benefits from
eliminating existing inconsistencies.) We considered
using continuous-time, discrete-time (time buckets),
or fixed-time-period approaches. Because suppliers
and buyer-planners react to changes in conditions
as they occur, we used a continuous-time approach
and the corresponding optimal policy, a (Q, R) pol
icy. Also, we found that (Q,R) policies are far eas
ier for the managers at Dell to understand and use
than the (s, S) policies used in periodic settings. In
a (Q, R) policy, R denotes the reorder point and Q is
the size of the order (that is, we order a batch of
size Q whenever system inventory drops to R). Con
sequently, R + Q denotes the order-up-to level. We
also used the newsvendor ratio to heuristically find
the best reorder point, R. We focused on identifying
the optimal reorder point R for XDX and assumed
that the order size Q would remain at its current level.
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Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain
194 Interfaces 34(3), pp. 191-205, ?2004 INFORMS
That is, based on long-term data, we estimated the
order size Q suppliers were using and assumed it
would not change.
The reorder point is closely related to safety stock,
which is the extra inventory held to buffer against
multiple sources of variability. Specifically, safety stock
is the difference between the reorder point and the
average requirements during replenishment lead time
(the latter is also called pipeline inventory). Assum
ing normal distributions of forecast error and of
replenishment lead time and independence of fore
cast errors, the safety stock level for (Q, R) policy is
given by Z * Jfi2a2-\-?m^j, and the reorder point is
the sum of the pipeline inventory and safety stock.
That is,
R = lil?if + zJfjL2f a2 + iLi [if is the average of the daily forecasted demand ay is the standard deviation of daily forecast error ?jl1 and Z is a score that links safety stock with required probability that demand is met from stock, Z = >-1 normal distribution.
Safety stock protects against variability of demand After specifying the model, we decided how to esti We developed a user-friendly spreadsheet tem buyer-planner to set target inventory levels and track such parameters as the costs of underage and over fied by the model and performed our original analysis The Golf Analogy at Dell This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain level of inventory for several points in the supply total system par * (replenishment time for the supplier The first part of the total system par is some Dell’s Handicap mance relative to an expected baseline, the par. As a Safety stock insures against potential variations on Important elements of Dell’s handicap are forecast pulls out of the revolver daily and what the supplier and not a major contributor to the safety stock, the the expected demand for a component and the actual Safety stock is driven by variances (in the square Our objective is to examine the total variance and to If the commodity in question is multisourced, Dell vidual suppliers. Dell tends to pull material from pull variance (Figure 1). Dell pulled different numbers This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC 196 Interfaces 34(3), pp. 191-205, ?2004 INFORMS 12/1 12/2 12/3 12/4 12/5 12/7 12/8 12/9 12/10 12/1112/12 12/14 12/15 12/16 12/17 12/18 12/19 12/21
I Supplier A M Supplier B Date
Figure 1: Dell pulled different numbers of units of XDX from two suppliers’ revolvers. Light bars and dark bars pulling in batches of different sizes results in a larger underlying logic as the other two variances except none from Supplier B, it would cause high levels of results in three different inventory requirements. Even Safety stock = Z * ?\l?)o] + ?i
Figure 2: In this breakdown of Dell’s handicap, the left side shows the levels of inventory required due to the This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain Supplier’s Dell’s Replenishment time
Figure 3: We developed a total inventory breakdown assuming the current driven by aggregate variance, attach variance, and Data Collection and Analysis manage their inventory at a desired service level, and Data Collection We used two groups of data in our analysis: data The Cost of Underage ?the lost profit from a canceled order, nent when needed (the product is express shipped to waived or both), lost profit, and costs.
Several costs (for example, for substitution and for requested part, price of the system for each day above the standard because of noncompliance with stated lead times, tomers, and tomers to explain delinquent orders instead of calling The Cost of Overage capital was slightly higher than Dell’s. data on price decreases for XDXs. deliveries was two days based on conversations with This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain Lead Time Demand Variance tion but not other pulls (for replacement parts or for ?daily receipts of suppliers’ components at the the suppliers and the revolver, quantity of each supplier’s XDX that Dell expects to from the suppliers’ representatives in Austin (includ Dell-specific), transport times, and the amount of supplier lead times with the data collected; we esti Other Parameters the system (the system inventory) by adding inven ing lead time by directly collecting data on the quan (a measure common at Dell) by dividing a given day’s ?To assess Dell’s method of making ordering deci Z-score into the corresponding service level, using the els, in transit and system inventory levels, for the average in-transit inventory = ^??x^, and ~^~Zo”DfLT, JfjAaf +/??aj is the standard deviation of demand XDX that Dell should have held by dividing each Data Analysis Although our model can handle all the factors listed wanted to eliminate the current operational disadvan This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain Part number System Z-score System service level (%)
Aggregate 2.26 98.80 XA2(A) 1.51 93.49 Table 1: In this table of historical average system service levels (as of Given these simplifications and Dell’s require Service Level ad hoc techniques, and we were not able to capture Our estimates of the average historical service level Forecast Error The strong error trends could arise from several 7 –
6
5 (fi 8 4 CO 2
1
0
%
20 40 60 80 100 120 140
Figure 4: In this graph of Z-scores for system inventory associated with historical service levels over the course This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC 200 Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain Interfaces 34(3), pp. 191-205, ?2004 INFORMS
O 2.50 ? 0.50 c CO 0.00 / v.* \~?
-“V
r
11/21 12/11 12/31 1/20 2/9 3/1 3/21 4/10 4/30 5/20 6/9
Figure 5: The forecast error for XDX over the course of study showed strong linear trends.
optimization team has targeted these forecasts for mise the results from the model, even though the lack In graphing forecast error for XA2, we observed day (the first day of the two weeks). As a result, the Several other factors (most related to human errors) mally expected levels. Ultimately, these forecast errors Results Analysis mation on each component, we created a series of tory in units, days of inventory (the above output translated into / * * # 11/21 12/11 12/31 1/20 2/9 3/1 3/21 4/10 4/30 5/20 6/9
Figure 6: The forecast error for XA2 over the course of study showed a difference between actual orders and This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain ?current service level compared to the recom ?inventory drivers, that is, inventory units broken tory units broken down into causes and listed as Data at this moment must be manually (copy and Service Levels Optimal vs. Actual Inventory Levels Average historical Average level Part number Units Days Units Days
Aggregate 81,606 11.0 77,643 10.7 Table 2: The historical average inventory levels were fairly close to aver system inventory levels fluctuated widely (Figure 7). make Dell aware of the remaining ones.
10 t
Actual Inventory
Order-up-to Level
– Average Recommended Inventory
Reorder Point
12/01/98 01/20/99 03/11/99 04/30/99
Figure 7: Although on average actual XC inventory was close to the average recommended by the model, the indi This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain These two examples illustrate the potential bene model that can quantify the inventory needed in the Inventory Percentage Drivers modity level leave room for further refinement (Fig procurement team’s experience with supplier delay, and because no data were available. (Should the data ated with par. If there were no variance in the entire Supplier Pull (or Supplier Attach (or Replenishment Figure 8: This breakdown of total inventory shows the inventory room for inventory reductions if it can implement and Days of Inventory vs. Units of Inventory more stable measure than units of inventory. As long mended days of inventory decrease, the units recom ing and improved decisions, which should translate we believe that days of inventory will remain the pri down scenarios. XA2 was in its ramp-down phase Our results highlight the degree of error in the fore Furthermore, the high inventory level should This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain Order-up-to Level -Actual Inventory
12/01/98 01/20/99 03/11/99 04/30/99 Figure 9: The XA2 component has a recommended inventory level close to 60 days, but the actual reorder points 120,000
100,000 4
80,000 4
60,000
40,000 4
20,000
Order-up-to Level Figure 10: The recommended units of XA2 inventory increase in January due to increased demand.
This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain for a supplier. Dell specified that it wanted to main Trial Implementation and Impact The goal of the tutorials varied depending on the We also assessed the improvement projects we Finally, we discussed our project with teams for The vocabulary we introduced in our project per the financial impact of various improvement projects. scheduling methods would all help to decrease mately four days of safety-stock inventory, Dell could its PowerConnect line of business from 20 days to These benefits are likely lower than the true savings Throughout the project, we concentrated on a basic most likely it would also benefit other companies with and prepared the buyer-planners to use it before we References durables. Management Sei. 15(5) 215-227.
Dell, M., J. Magretta. 1998. The power of virtual integration: An Kurawarwala, A. A., H. Matsuo. 1996. Forecasting and inven Nahmias, S. 1997. Production and Operations Analysis, 3rd ed. Irwin, Dick Hunter, Vice President, Americas Manufac This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain opportunity to describe the benefits that Dell Com model with us in 1999. This model and corresponding method to reduce inventory and improve velocity. this model, we have taken action to change how we “Supplier logistics center inventory levels have not “A systematic reduction of inventory using this This content downloaded from 141.217.20.120 on Wed, 13 Feb 2019 19:24:25 UTC 191 Interfaces, Vol. 34, No. 3 (May – Jun., 2004), pp. 171-244 Dell In-Depth Case Study Rubric Level of Achievement Criteria Poor 0-20% Fair 20-45% Good 45-80% Excellent 80-100% Content 25 points Most questions NOT addressed. Some questions are addressed. Most questions addressed. All questions are addressed. Grammar 10 points Several spelling, grammar or sentence structure errors. The structure of the paper has many discrepancies to the requirements of: clear paragraphs for each question, double spacing, 12-point font, and 1-inch margins. Paper is significantly short of four full pages. Moderate number of spelling or grammar errors. The requirements for the structure of the paper are lacking: clear paragraphs for each question, double spacing, 12-point font, and 1-inch margins. Paper is somewhat short of four full pages. A couple of spelling, grammar or sentence structure errors. The structure of the paper mostly meets the requirements of: clear paragraphs for each question, double spacing, 12-point font, and 1-inch margins. Paper is marginally short of four full pages. No spelling, grammar or sentence structure errors. The structure of the paper should have clear paragraphs for each question. Four-page minimum, double spaced, 12-point font, 1-inch margins. Organization 10 points Few or no answers are organized, easy to follow and logical. Answers do not include supporting details and/or examples. Some answers are organized, easy to follow and logical. Answers do not include supporting details and/or examples. Most answers are organized, easy to follow and logical. Answers include some supporting details and/or examples. All answers are organized, easy to follow and logical. Answers include several supporting details and/or examples. Bibliography Citation 5 points No required citations referenced on last page. Few required citations referenced on last page. Most required citations clearly referenced on last page. All required citations clearly referenced on last page.
during replenishment lead time,
during replenishment lead time,
service level. For a desired service level defined as the
(service level), where ( ) is the cumulative standard
during lead time. Here, we control safety stock
through the total system inventory (the inventory in
the revolvers and the inventory on order), as these
two differ only by a constant. We can approximate
the optimal safety stock using the newsvendor logic
(Nahmias 1997). According to this approximation,
the probability of satisfying all demand (or the ser
vice level) needs to be equal to the critical fractile
cu/(cu + c0), where cu is the cost of not being able
to satisfy one unit of demand (underage cost), and
c0 is the cost of maintaining an unused unit of inven
tory between consecutive ordering decisions (overage
cost). The critical fractile is thus the same as the ser
vice level that balances the costs of carrying one too
many items versus one too few items in inventory.
With 4>() being the cumulative standard normal dis
tribution, optimal Z = ^>~1(cu/(cu + c0)). To calculate
the optimal service level, we needed data to estimate
cu and c0.
mate the input parameters (for example, demand data
and cost coefficients) and focused on the supply chain
for XDX. For all of the input parameters, we used his
torical data.
plate, the revolver inventory model, which allows a
actual inventory levels daily. The spreadsheet uses
historical data to estimate demand. It also estimates
age, calculates the suggested policy, illustrates the his
torical behavior, and permits numerical and graphical
analyses of scenarios. The revolver inventory model
also serves as a strategic or diagnostic tool by relating
components of the total-required-inventory levels to
the various underlying causes. For example, we could
determine what portion of the required inventory is
due to variation in the forecast error. We collected his
torical data for XDX for each of the variables identi
for the period from December 1,1998 through May 27,
1999 (we included a methodology for daily updates).
In the final step in this portion of the project, we used
the analysis of the data and the model to identify
areas for improvement.
While many people at Dell understand inventory
replenishment concepts, we found that they had no
knowledge of formal operations-management theo
ries. To explain these ideas, we used an analogy from
golf, employing terms from the sport to describe the
expressions and conditions commonly found in basic
inventory equations. A golf course provides many
physical obstacles, such as sand traps, water hazards,
and the long distances to the hole. All of these ele
ments are completely out of the control of the golfer,
and they must accommodate these perils through
out the game. Like water hazards and sand traps,
several factors can hinder a smooth delivery of com
ponents in a supply chain, such as road construc
tion or congested highways. The distance between
the tee and the hole is analogous to the distance
from suppliers to the OEM. In golf, each hole has a
standard, or par that the course designer sets based
on a variety of factors. This par will not change
unless the designers of the course return and make
fundamental alterations to the layout. The interest
ing thing about par is that it gives golfers a target
to strive for. They have constant feedback on how
they are doing, compared to a set standard. With no
variability in the system, the inventory necessary to
maintain steady production depends on only three
factors: demand, replenishment time, and shipping
frequency (or the size of shipments). We dubbed this
level of inventory as par, based on the notion that a
problem-free manufacturing environment should run
efficiently with a certain natural level of inventory
just as an average, problem-free golfer should achieve
an expected score on a course. We calculated the par
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Interfaces 34(3), pp. 191-205, ?2004 INFORMS 195
chain:
= forecasted demand for time period
+ time between shipments/2).
times called the pipeline inventory, that is, the aver
age inventory ordered but not yet delivered, while
the second part is the cycle inventory, inventory
due to batching. By Little’s law, the average time
between orders is given by Q/fif (Nahmias 1997) for
given order quantity Q and forecasted average daily
demand during replenishment lead time fij.
In golf, a handicap is a measure of a golfer’s perfor
golfer improves over time, his or her handicap score
decreases. We used this term and found it very effec
tive to describe safety stock and the reasons why the
organization might carry extra material.
both Dell’s side and the supplier’s side of the sup
ply chain. To gauge improvements and to determine
causes of variation, we separated the sources of vari
ance. We referred to the safety stock required to cover
problems and variance within Dell’s portion of the
supply chain as Dell’s handicap, and we referred to
the remaining inventory needed to guard against the
supplier’s failures to deliver quality goods consis
tently on time as the supplier’s handicap. For the
project, Dell and its suppliers agreed to outline the
elements that would constitute the supplier’s handi
cap. We planned to make the supplier’s handicap part
of the supplier scorecard.
error and pull variance. We use variance in the sta
tistical sense as the expected value of the square of
deviations from target values. Dell forecasts its need
for individual components in two stages. In the first
stage, it forecasts an aggregate number of final prod
ucts; in the second, it estimates the mix of compo
nents (corresponding to different features). An error
is associated with each of the two stages. The absolute
difference between the number of units actually sold
on the aggregate level and the number forecasted is
called the aggregate deviation. The difference between
the actual units and the forecasted units that incorpo
rate a specific component (feature) is another forecast
error, labeled the attach deviation. Both are absolute
differences, not fractions. Outside of the forecasting
process, a deviation also exists between what Dell
expected Dell would pull based on the production
schedule. This is the pull deviation.
While aggregate demand is generally predictable
attach rate varies noticeably. Customer preferences
change every day, sometimes drastically, despite
Dell’s amplifying or reducing demand through sales
and price reductions or increases for various compo
nents. The attach deviation is the difference between
number of components Dell uses, assuming that Dell
pulls at the predetermined percentage and has made
no aggregate-level error. For example, suppose that
Dell forecasts an aggregate demand for 4,000 units,
2,000 of which would have XDXs attached, represent
ing a 50 percent attach rate. If the actual demand
turns out to be 3,200 units, then the forecasted
demand at the aggregate level deviates from the
actual by 800 units. However, suppose that demand
perfectly matches the forecast of 4,000 (resulting in
an aggregate deviation of 0), but only 1,500 com
puters need an XDX. In this case, the actual attach
rate is only 37.5 percent and the attach deviation is
2,000 – 1,500 = 500. This attach deviation can be cal
culated for each supplier. If Suppliers A and B each
have 50 percent of Dell’s business for this device, Dell
would plan to consume 1,000 of A’s XDXs and 1,000
of B’s XDXs. If the pulled quantities are proportional
to the allocated ratios (50 percent for each supplier),
then each would provide 750 units. In this case, the
attach deviation equals 1,000 ? 750 = 250 for each sup
plier. (The actual pull is 250 units less than the fore
casted pull of 1,000 units.)
root formula for safety stock), and therefore, we trans
late all of the deviations into corresponding variances.
attribute it to specific causes. Because it is more dif
ficult to forecast on the component level than on the
aggregate level, we expect that the attach variance will
be greater than or equal to the aggregate-level vari
ance. By subtracting the aggregate-level variance from
the attach variance, we can estimate the incremen
tal contribution of attach rates to the variance, even
though these effects are obviously not independent.
must further allocate the attach rate forecast to indi
competing suppliers in batches, switching from one to
the other, rather than taking a fixed percentage from
each one. Thus, even without any aggregate-level
variance or attach variance, the suppliers’ inventory
is different than forecasted. This is referred to as the
of units of XDX from two suppliers’ revolvers. Dell’s
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Kapuscinski et al.: Inventory Decisions in Dell’s Supply Chain
correspond to quantities pulled by two different suppliers. The arrows above the graph denote the range of days
during which Dell pulled predominately one supplier’s goods. Dell’s pulling in batches of different sizes results
in a larger forecast error than would be the case if it used all components in a fixed ratio (level schedule or
level pull).
forecast error than would be the case if it used
all components in a fixed ratio (level schedule or
level pull). Even though the suppliers do not order
every day, they continuously monitor the inventory
levels and, therefore, the pull variance influences their
decisions.
We compute the pull variance using the same
that it derives from the build-pull error instead of the
forecast-build error. So, if Dell expected to use 2,000
XDXs per day and it actually used 2,000, the aggre
gate and attach deviations would be zero. If each of
the two suppliers has 50 percent of the business, then
each would expect to supply 1,000 units during the
day. However, if Dell ignored the percentage of busi
ness and pulled all 2,000 units from Supplier A and
forecast error (and confusion) for both suppliers. If,
on the other hand, Dell pulled according to the fore
casted business percentage for each supplier, it would
eliminate the pull deviations (and consequently vari
ance). The data clearly indicate that the variance from
Dell’s pulls is greater than the variance in the attach
rate, which is greater than the aggregate variance,
thus leading to inflated inventories.
Using these three variances as inputs to the model
though interdependencies exist among these vari
ances, by calculating the differences between these
inventory requirements, Dell can estimate the cost of
error propagation from the aggregate level, through
the detailed component level, to the actual variance
of pulls from the revolvers (Figure 2). The inventory
accumulation of variance. The right side illustrates how we associate the inventory with each of the underlying
causes. We interpret the incremental inventory as a result of the three causes.
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Interfaces 34(3), pp. 191-205, ?2004 INFORMS 197
handicap
handicap
sources of inefficiency, and we call the inventory driven by aggregate vari
ance, attach variance, and pull variance Dell’s handicap.
pull variance is called Dell’s handicap (Figure 3). Our
next logical step was to attack this level of inventory
through various improvement initiatives.
The model provides Dell with a scientific tool to
determine the revolver inventory levels and hence to
manage its supply chain in a systematic way. The
model can also create a reference point for future
improvements and estimate the savings Dell can
obtain by changing its current manufacturing sys
tem. Specifically, buyer-planners can use the model to
commodity managers can use it to measure improve
ments in the supply chain. To implement the model,
we needed to gather historical data and create a self
contained, user-friendly spreadsheet template buyer
planners can use daily.
We collected data for the period from December 1,
1998 through May 27, 1999. Three companies (A, B,
and C) supplied XDXs to Dell during this period.
Supplier A supplied two different XDXs, Supplier B
and Supplier C each provided one part number but
many versions of that part. The parts supplied by A,
B, and C are labeled as XA1, XA2, XB, and XC.
related to the economics of safety stock (mainly the
costs of underage and of overage) and data related
to historical estimates of the variance of lead-time
demand.
We calculated the cost of underage by evaluating the
impact of not having an XDX in inventory. We did
not consider the costs of switching among suppliers
but instead concentrated on the cost of running out
of XDXs altogether. Consequently, we based lost mar
gins on cancelled orders and compensation for the
expedited shipping costs for delayed orders. The data
included estimates of
?increased shipping cost for not having a compo
the customer or the shipping charges are reduced or
?the portion of customer orders that results in a
?the fraction of orders that incur higher shipping
air freight) are caused by part shortages, and they are
not easy to quantify; many have a greater impact on
profit than lost sales. We advised Dell to try to quan
tify all of these costs in the future. They include
?providing a better part at the same price as the
?paying a penalty, for example, a percentage of the
lead time (as specified in some of Dell’s contracts),
?making price concessions on future orders
?losing sales opportunities with disappointed cus
?losing sales when sales representatives call cus
other customers to make new sales.
In calculating the cost of overage, we recognize that
the effect of current ordering decisions is limited to
the time between consecutive deliveries of the compo
nent. During that time, capital is tied up, price erosion
occurs, and the revolver assesses a storage charge. We
considered the following data in determining the cost
of overage:
?We assumed that the supplier’s annual cost of
?We estimated price erosion based on empirical
?We estimated that the average time between
sales representatives from all suppliers. Most said that
they order from their factories approximately three
times per week. Dell’s need for the parts and suppli
ers’ presumptions about the size of the order, and not
preset schedules, drive their ordering.
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198 Interfaces 34(3), pp. 191-205, ?2004 INFORMS
We collected the following data for use in our histor
ical study of variance of lead-time demand for XDXs:
?Dell’s daily pulls from the revolver for produc
quality issues), although they were still used to deter
mine the inventory level in the revolver,
revolver,
?daily quantities of in-transit inventory between
?daily sales or builds of systems needing XDXs,
?Dell’s forecasts to its suppliers specifying the
use during the next month, and
?the suppliers lead times for XDXs obtained
ing day stamps indicating when parts became
Dell-specific finished-goods inventory suppliers held
at their manufacturing and configuration sites.
We could not calculate the standard deviation of
mated it based on anecdotal evidence. Also, in many
cases, suppliers made deliveries of more than one
batch, which meant that average lead time could be
an inappropriate variable. Because the purpose of
safety stock is to reduce the risk of running out of
inventory, we used the lead time for the first batch as
an estimate of the relevant variable.
We collected the data to derive values for other
needed parameters.
?We calculated the daily quantity of inventory in
tory in the revolver to that in transit for each part
number on each production day.
?We calculated the forecast error for demand dur
tity analyzed. That is, we considered buckets of time
equal to the lead time and calculated the forecast error
for each bucket by summing the forecasted demand
for all days in the bucket and subtracting Dell’ actual
pulls over the same period. Then, we calculated a
standard deviation of these errors. To express the
error as a portion of average demand, we divided
the result by the average of the forecasts for the next
10 days, a number we believed was appropriate after
observing some real data.
?We calculated the days of inventory Dell held
inventory level by the average of the forecasts for the
next 10 days. In addition to expressing a given day’s
inventory in terms of inventory days, we also calcu
late the average inventory in units.
sion, we translated the system inventory levels just
before orders into corresponding Z-scores. That is, we
used the system inventory to solve for the Z-score
for each lead-time bucket and then converted the
standard normal distribution. The resulting quantity
described the imputed service level.
?We determined the optimal XDX inventory lev
aggregate and for individual suppliers that should
have been carried. These values were calculated using
the following formulas:
average system inventory = R + Q/2 = (/x? + L/2)fif
where Q is the order size, L is the average time
between shipments received at the revolver, (rDfLT =
during the lead time, and R = ?n^f + ZaD LT is the
reorder point.
?We calculated the optimal number of days of
day’s optimal inventory by the average of the fore
casts for the next 10 days. We calculated these figures
for each part number for each date included in the
study. (We wanted to compare the optimal number of
days with the actual number of days of inventory in
the system.)
Although interested in the inventory savings, Dell’s
management wanted to be very cautious and to over
estimate, rather than underestimate, inventory needs
to avoid damaging customer service due to unavail
ability of components.
below, we initially ignored some factors that would
have reduced inventory needs even further. First, Dell
assembles most of its products two or three days after
receiving an order. To be conservative, we ignored
this extra lead time. We can easily incorporate its
effects by reducing suppliers’ delivery times to the
revolvers. Second, Dell wanted to run the model at
its current average service levels for each component
before making any further changes. Third, pooling
might provide benefits because three firms were sup
plying interchangeable parts. For all three suppliers
combined, to achieve a 98 percent service level, each
would need to achieve a much lower service level
(about 70 to 75 percent if they were of comparable
sizes). Before exploiting the benefits of pooling, Dell
tages caused by clumpy pulls. Also, we ignored ramp
up and ramp-down effects.
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Interfaces 34(3), pp. 191-205, ?2004 INFORMS 199
XA1(A) 1.50 93.27
XB(B) 1.25 89.35
XC(C) 0.93 82.42
May 27,1999), the parts supplied by A, B, and C are labeled as XA1, XA2,
XB, and XC.
ments, we started by investigating the historical ser
vice levels and then the forecast error.
The aggregated imputed historical service level of
98.80 percent was surprisingly close to the optimal
service level of 98.66 percent provided by the model
(Table 1). Individual products, however, had signif
icantly different average service levels, and actual
service levels (and the corresponding Z-scores) var
ied significantly over the course of the study (Fig
ure 4). Inventory theory would suggest a fairly stable
level of service. If demand were stationary, both the
reorder point and the inventory order-up-to levels
would be constant. Even when demand forecasts vary
from period to period, because the costs of underage
and overage are very stable, the expected service level
and the corresponding Z-score, where Z = 4>_1(cM/
(cu + c0)), should remain constant (unless there are
substantial nonstationarities). We therefore expressed
the inventory levels just before orders were placed
as Z-scores. Our results show major swings of the
Z-scores, which indicates that Dell’s suppliers do not
follow a (Q, R) policy. We understood that they used
them formally. We suspect that these swings were
caused by suppliers overreacting to current situations
and possibly by long-term trends. (We performed the
same analysis for inventory order-up-to levels, which
should also remain stable, yet they were equally
volatile.) While individual orders varied widely dur
ing the study period, the average service level for
all three suppliers combined was very close to the
optimal.
are very close to the estimates of optimal service lev
els, and thus the average service level should remain
at the current level. However, because in the past
inventories shifted erratically and Dell had no consis
tent policy, it should be able to reduce average inven
tory by following a consistent policy.
We gathered information on historical forecasts for
XDX and the actual Dell pulls and compared the two
for each of the replenishment periods. Forecast error
is a critical input in determining the optimal inven
tory level. In our model, we assumed the errors to
be random. However, it was apparent (and confirmed
by formal analysis) that at the commodity level the
errors are not independent from period to period. The
error showed strong linear trends (Figure 5).
factors, including forecasting. Like many companies,
Dell passes forecasts through various internal organi
zations before disseminating them to suppliers. While
innocently acting with the company’s best interest
in mind, each group used its own judgment and
biases to modify the forecasts of demand. Such iter
ative hedges and adjustments can erode the qual
ity of the original information. Dell’s supply-chain
(D
*3
Time
of the study, each point corresponds to a placed order.
All use subject to https://about.jstor.org/terms
c
2.00
1.50
1.00
O
CO
?
-0.50
-1.00
-1.50
-2.00
-2.50
process improvement. Despite this concern, we used
the forecast data to estimate inventory levels. We exer
cised extra caution, however, and to reduce potential
inaccuracy, we dealt with the lack of independence
heuristically (as described in the data analysis sec
tion). In this way, we believe that we did not compro
of independence reduced the accuracy of the model’s
recommendations.
strong cyclic behavior (Figure 6). This can be
attributed to the frequency and format of the data
Dell provides to the suppliers. The information is pro
vided weekly in monthly buckets. Suppliers generally
divide these aggregate monthly figures evenly into
four weekly buckets but do not divide it further by
days of the week. Sometimes they may even consider
the forecast for two weeks as demand needed in one
forecast for cumulative demand becomes a step func
tion, while it actually grows linearly.
can contribute to forecast inaccuracies beyond nor
could prompt Dell to investigate its forecasting pro
cedures and possibly adopt a more quantitative
approach.
After analyzing the data required for the model, we
created an Excel-based tool for the buyer-planners.
This spreadsheet-based tool was self-contained and
included an overview of an input area, actual cal
culations, suggested decisions, and explanations of
the logic for each step. To provide detailed infor
charts dynamically linked to source data. These charts
depicted
?current inventory versus recommended inven
?current days of inventory versus recommended
days),
*
forecasted values before Christmas in December of 1998.
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Interfaces 34(3), pp. 191-205, ?2004 INFORMS 201
mended level,
down into causes (such as aggregate error, attach rate
error, and pull error), and
?inventory percentage drivers, that is, inven
percentages.
paste) entered, but Dell is independently proceeding
with automating this link.
After developing a format for the data and determin
ing the historical service levels, we examined what
the model’s recommendations would have been for
the past six months. Because the levels of service
for individual products were significantly different
from those the model would have recommended, Dell
and the TMI team agreed to implement the model.
The implementation, however, would be split into
two stages. In stage 1, historical service levels would
be imposed and used as an input to the model; in
stage 2, service levels for individual products would
be adjusted in accordance with the model’s recom
mendations. Not surprisingly, in stage 1, the model’s
outputs were close to the average historical levels
(Table 2).
While the order-up-to levels that we calculated using
the model remained fairly stable over time, the actual
level suggested by the model
XA1(A) 32,351 11.2 24,625 8.4
XA2(A) 26,625 22.1 27,147 23.7
XB(B) 21,111 9.8 18,186 8.2
XC(C) 44,109 9.6 43,414 9.3
age inventory levels suggested by the model as of May 27,1999.
The average system inventory levels were close to
those suggested by the model, but the actual num
ber of units in the system at any time differed drasti
cally from the number required. For example, around
February 1, 1999, the model showed that the sys
tem should have held nine to 11 days of inventory
to handle expected fluctuations in demand and sup
ply. However, the actual number in the system was
close to 18 days of inventory. The extra seven days of
inventory in the revolver were costly for the supplier.
Likewise, in the beginning of March, Dell was carry
ing five to seven days worth of inventory, while the
model showed that eight to 10 days were required.
This means Dell was at a much higher risk of stock
ing out during that period. Clearly Dell should avoid
such huge fluctuations. The systematic ordering pol
icy we proposed will eliminate most of them and
vidual observations indicate that most of the time the actuals differed from the recommended levels significantly.
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202 Interfaces 34(3), pp. 191-205, ?2004 INFORMS
fits of a management tool like the proposed inventory
system at any time. Dell’s buyer-planners who mon
itor the inventory levels should communicate with
suppliers and require inventory levels based on the
model’s recommendations to prevent Dell from risk
ing a stock-out or paying for unnecessary inventory.
One of the important features of the model is its
ability to categorize causes of required inventory. We
analyzed the historical data for XDX and found that
attach variance and pull variance each contributed
20 to 25 percent to the total inventory. After hearing
several suppliers complain about the clumpy pull, we
expected the pull variance to be nonnegligible. The
attach variance shows that Dell’s forecasts at the com
ure 8). We broke down the drivers for inventory based
on the following assumptions:
?The supplier handicap was one day based on the
?the replenishment time variance was (one day)2,
?the aggregate variance we assumed to be zero
become available, they would not change the aggre
gate level of inventory recommended but would alter
the breakdown percentages.)
Around 15 percent of the total inventory is associ
manufacturing system, Dell would need only 15 per
cent of the inventory it needed at the time of our study.
Granted, problems and fluctuations will always exist,
but this lower bound shows that Dell has tremendous
Handicap (Days)
Part) Variance
(Days)
Component)
Variance (Days)
Time Variance
(Days)
Par (Days)
associated with the drivers of inventory (based on historical data).
sustain the recommended improvements.
Dell and its suppliers describe inventory levels in
terms of days of supply. Days of supply is a much
as target service levels remain unchanged over a
given period and demand does not change dramat
ically, days of inventory will not vary significantly.
(When the coefficient of variation is constant, days
of inventory should remain constant.) However, the
actual units of inventory may change over time as
demand changes (Figures 9 and 10). The compo
nent has a recommended inventory level of close to
60 days for nearly the entire product life span, yet
the actual reorder points fluctuate widely. In particu
lar, there is a dramatic drop in recommended days of
inventory in early January 1999. Although the recom
mended do not because the average demand per day
is rapidly increasing (Figure 10). The day calculation
is an average of 10 future days’ forecasts; in this case,
the forecasts were increasing rapidly. Furthermore,
two more factors amplify the level of fluctuation: the
historical service level of 99 percent and the tremen
dous supplier pull variance. This example shows why
it is important for the buyer-planners to manage
inventory in terms of both units and days. The combi
nation of the two charts will allow better understand
into decreased costs for Dell and its suppliers. While
mary tool for deciding how much to order, buyer
planners can use graphs of units of inventory to
see whether the requirements for a given part are
stable or changing. They can interpret any difference
between the targeted number of days and the actual
number of days of inventory into monetary exposure
from holding too much or too little inventory.
Our model does not capture ramp-up and ramp
between late March and early April of 1999, after
which Dell ceased using the product. Looking at both
graphs of units of inventory and days of inventory,
however, one can easily recognize such ramp-down
and ramp-up periods.
casts suppliers were using and should encourage Dell
to investigate why the error was higher than expected
(for example, Dell may not have informed the sup
pliers promptly of changes or Dell may have delayed
a product launch because of a quality problem). This
should spark Dell’s immediate action to correct data
frequently and improve its business process.
encourage Dell to reconsider the optimal service level
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Interfaces 34(3), pp. 191-205, ?2004 INFORMS 203
Average Recommended Inventory
Reorder Point
fluctuate widely. The recommended days of inventory decreased at the end of December and beginning of January
because the forecasts were growing rapidly.
Average Recommended Inventory
Reorder Point
Actual Inventory
12/01/98 01/20/99 03/11/99 04/30/99
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204 Interfaces 34(3), pp. 191-205, ?2004 INFORMS
tain the existing service levels initially. A service level
of 99 percent is extremely high and most likely exces
sive given the data we collected. For this compo
nent, Dell could operate with a lower level of supplier
service.
After we analyzed the data and developed the tools,
we moved to initial implementation. We conducted
several tutorials, during which we discussed the the
ory behind the model and the specific instructions for
using the tools (in Excel format).
group targeted. For instance, in training the buyer
planners of XDXs, we focused on using the tactical
tool (to set inventory levels and track them); whereas
in training the supply-chain-optimization team, we
stressed the theory behind the model and the drivers
of inventory levels (so that they could conduct
improvement projects and quantify their impact). In a
tutorial for all of the XDX suppliers, we concentrated
on Dell’s efforts to reduce the inventories it required
them to hold and asked them to help Dell to collect
the data needed to run the model.
identified earlier. Other supply-chain-optimization
team members were identified as leaders for these
projects, and we roughly quantified the effects some
of the improvement projects will have on Dell and its
suppliers.
commodities other than XDX with the idea of find
ing commodities most appropriate for analysis with
our model and validating the model’s assumptions
for other commodities.
sists at Dell, particularly par and handicap. Dell
has started other projects to eliminate elements of
its handicap. It has defined sources of variability
and linked various variance-increasing and variance
decreasing actions to costs and benefits. On a tactical
level, our project led to Dell’s implementing tools to
identify optimal inventory levels.
We analyzed a number of scenarios to estimate
Using the model to extrapolate savings across all
XDXs, we calculated the cost savings Dell could
achieve by reducing inventory. We used inventory
drivers and the corresponding breakdown of inven
tory costs to estimate the reductions in inventory that
could be achieved through various improvements.
Eliminating the clumpy pull, reducing the replen
ishment time by 25 percent, doubling the delivery
frequency, and reducing the attach-rate error through
inventory. Using specific estimates (for price erosion,
storage charges, and costs of parts), we estimated
that Dell could reduce the current 10.5 days average
inventory level by 38 percent. By removing approxi
achieve perpetual savings for all XDXs that will pass
through the revolver in the future. The resulting NPV
of savings is $43 million, at an eight percent cost of
capital. While the actual implementation for XDX is
still under way, Dell has used the model to set tar
get inventory levels for networking accessories at the
revolvers. So far Dell has decreased inventories for
between 10 and 15 days, which is close to the target
of 10 days recommended by the model, and it expects
estimated annual savings of $2.7 million.
possible as they ignore the facts that Dell does not
implement the current inventory policy consistently
(the Z-scores are not stable) and does not set service
levels correctly, basing them on historical service lev
els. Consequently, we expect actual savings to be even
higher than our estimates.
solution and showed its use as not only a tactical, but
also a diagnostic tool. If such a tool yields savings
for a fairly sophisticated manufacturer like Dell Inc.,
similar supply-chain issues.
We presented the model to all the stakeholders
departed. Also, the project was awarded the second
prize at the Spotlight (a formal festive presentation of
results from all TMI projects) held at the University
of Michigan in September 1999. A second TMI team
continued the project, focusing on decreasing variance
on the suppliers’ side, and a third team focused on
an efficient process for collecting and maintaining the
inputs to the model.
Bass, F. M. 1969. A new product growth for model consumer
interview with Dell Computer’s Michael Dell. Harvard Bus. Rev.
76(2) 72-84.
tory management of short life-cycle products. Oper. Res. 44(1)
131-150.
Boston, MA.
turing Operations, Dell Inc., One Dell Way, Round
Rock, Texas 78682-2244, writes: “Thank you for the
All use subject to https://about.jstor.org/terms
Interfaces 34(3), pp. 191-205, ?2004 INFORMS 205
puter Corporation has experienced by implementing
the Inventory Analysis model in our supply chain. A
team of University of Michigan Tauber Manufactur
ing Institute students and professors developed this
logic changed our thinking by directing us to focus on
the drivers of variation in our supply chain as a key
“Understanding the variation drivers quantified in
pull inventory from supplier logistics centers into our
factories. As a result, our suppliers see a more linear,
predictable pull of product. For Dell, this means that
supply continuity becomes more robust as suppliers
are better able to handle unforecasted upsides.
been reduced so far; rather we have increased our
service level to our factories and customers. Dell’s
ability to deliver quality systems to our customers has
increased since inventory previously in place to han
dle pull-variation is now able to extend our imple
mentation time for quality improvements. Similarly,
exogenous factors such as overseas natural disasters
are less likely to impact the velocity with which we
deliver our systems.
model requires an integrated solution with a signif
icant I/T investment. Dell is working towards that
direction based on the model algorithms to minimize
channel inventory and reap the benefits of increased
service levels through reduced variation.”
All use subject to https://about.jstor.org/terms
192
193
194
195
196
197
198
199
200
201
202
203
204
205
Front Matter
Metrics for Managing Online Procurement Auctions [pp. 171-179]
Allocating Vendor Risks in the Hanford Waste Cleanup [pp. 180-190]
Inventory Decisions in Dell’s Supply Chain [pp. 191-205]
Practice Abstracts [pp. 206-207]
Schlumberger Optimizes Receiver Location for Automated Meter Reading [pp. 208-214]
The Slab-Design Problem in the Steel Industry [pp. 215-225]
Decision Rules for the Academy Awards versus Those for Elections [pp. 226-234]
Book Reviews
Review: untitled [pp. 235-236]
Review: untitled [pp. 236-237]
Review: untitled [pp. 237-238]
Review: untitled [pp. 238-239]
Review: untitled [pp. 239-240]
Books Received for Review [pp. 240-241]
Back Matter
Elaboration and detail are not nominally evident. Information is not clear and/or developed. Answers reflect little complexity and critical thinking. Discussion is difficult to understand with little clarity. Lacks meaning, applicability.
Some information relative to the topic but lacking elaboration and detail. Answers reflect a basic understanding of the material but no critical thinking or complexity is evident. Discussion is understood but lacks some clarity. Lacks relevancy, meaning, applicability and is somewhat difficult to understand.
Clear information relative to the topic but lacking elaboration and detail. Answers reflect some complexity and critical thinking is not fully developed. Discussion is understood but lacks some clarity. Mostly relevant, meaningful, applicable and can be understood.
Elaborate and detailed information relative to the topic. Critical thinking and insight evident in the complexity of answers. Discussion is articulated in a clear manner.
Answers are relevant, meaningful, applicable and easily understood.
.
Some sources referenced are appropriate and applicable to the topic.
Most sources referenced are appropriate and applicable to the topic.
All sources referenced are appropriate and applicable to the topic.