Practical Example of Base Source Optimization - Footwear Profiling at Nike, Inc.
by
David G. Jacobs, P.E.
B.S. Civil Engineering
United States Military Academy - West Point, 2008
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 2015
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Practical Example of Base Source Optimization - Footwear Profiling at Nike, Inc.
by
David G. Jacobs, P.E.
Submitted to the MIT Sloan School of Management and the Engineering Systems Division on
May 8, 2015 in partial fulfillment of the requirements for the degrees of Master of Business
Administration and Master of Science in Engineering Systems.
ABSTRACT
The long term sourcing for footwear development, or "profiling," at Nike, Inc. has grown with
the company and become significantly complex. It is no longer possible for a single person, no
matter the level of experience, to optimize the company's profiling plan without computational
assistance. Optimization methods, specifically mixed-integer linear programing, present an
opportunity to save between 6.7 and 9.7% of combined labor and duty costs to the company. The
model proposed by this research is responsible for justifying that potential but is merely a
starting point for Nike, Inc. Further application and research into the company's manufacturing
processes including transportation costs, technology groupings, and the Manufacturing Index
(MI) could wield results that far surpass the levels obtained by this research. Implementation of
an algorithmic approach is challenging for an organization that values "storytelling,"
collaboration, and narrative. However, in time I believe that this model, or something similar,
will find a place, and deliver results, for Nike, Inc.
Thesis Supervisor: Stephen Graves
Title: Abraham J. Siegel Professor of Management Science,
Professor of Mechanical Engineering and Engineering Systems
Thesis Supervisor: David Simchi-Levi
Title: Professor of Civil and Environmental Engineering and Engineering Systems,
LGO Co-Director
3
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4
ACKNOWLEDGEMENTS
I would like to thank Nike's Sourcing and Manufacturing division for sponsoring this project and
for their candid support for the Leaders for Global Operations program. I would also like to
thank the Global Footwear Planning team for supporting me with open minds, enthusiasm, and
wisdom.
Thank you to Jon McCracken (whose profile adorns the model's user interface), to Roger Sklar
(who now has the esteemed privilege of using the model), to Scott Powell for his leadership of
the Supply Chain Innovation Team, and to Carlo Quinonez, who provided his perspective,
friendship, and wisdom.
Most importantly, I am indebted to Ryan Warr, who designed the project and mentored me
throughout. I could not have come to any meaningful result, much less one this successful,
without his collaboration.
Finally, I'd like to thank my fellow interns from MIT: Yalu Wu, John Kang, Ryan Jacobs,
Jonathan Dobberstein, and Alessandra Mak. I probably would have made it without you all, but
it wouldn't have been nearly as fun.
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6
TABLE OF CONTENTS
T able of Contents ..................................................................................................................................................................... 7
1.
2.
Introduction ...................................................................................................................................................................10
1.1.
Purp ose of Project ..............................................................................................................................................10
1.2.
Profiling Description .........................................................................................................................................10
1.3.
Prob lem Statem ent ............................................................................................................................................10
1.4.
H ypothesis .............................................................................................................................................................10
1.5.
Project G oals .........................................................................................................................................................11
1.6.
Project A pproach ................................................................................................................................................11
1.7.
Confi dentiality .....................................................................................................................................................11
Background of Profiling in Global Footw ear Planning ................................................................................. 11
2.1.
B ackground of Com pany ................................................................................................................................. 11
2.2.
Footwear Planning at N ike ............................................................................................................................. 12
2.2.1.
G lobal Footw ear Planning Team ........................................................................................................ 12
2.2.2.
Footwear Categories ............................................................................................................................... 12
2.2.3.
Footw ear Planning Process .................................................................................................................. 12
2.3.
3.
4.
Literature Review ........................................................................................................................................................14
3.1.
Prior Research in N ike Global Footw ear Planning ............................................................................... 14
3.2.
Research in Retail M anufacturing ............................................................................................................... 14
3.3.
Research in Im plem enting Softw are Solutions ......................................................................................is
M ethodology ..................................................................................................................................................................15
4.1.
Current State A nalysis ......................................................................................................................................15
4.1.1.
Interview s ....................................................................................................................................................16
4.1.2.
Process M apping Across Stakeholders ............................................................................................ 16
4.2.
5.
Current Profiling Process ................................................................................................................................ 13
Prototyping ...........................................................................................................................................................17
4.2.1.
In itial Prototype ........................................................................................................................................17
4.2.2.
Second Prototype ......................................................................................................................................18
4.2.3.
Final Prototype ..........................................................................................................................................19
4.3.
V alidation of the M odel ....................................................................................................................................21
4.4.
R obustness of M odel .........................................................................................................................................22
M odel Form ulation ......................................................................................................................................................22
7
5.1.
Form ulation and D efinitions ......................................................................................................................... 22
5.2.
O bjective Function ............................................................................................................................................. 22
5.2.1.
6.
7.
Grouping Constraint ................................................................................................................................ 23
5.3.
Constraints ............................................................................................................................................................23
5.4.
U ser Interface D evelopm ent .......................................................................................................................... 26
S.S.
O utput Pages ........................................................................................................................................................26
R esults ..............................................................................................................................................................................27
6.1.
Q uantitative A nalysis ........................................................................................................................................27
6.2.
Com parison versus current state ................................................................................................................27
6.3.
R ecom mendations and K ey Findings .........................................................................................................28
6.4.
Business Problem and Change Im plem entation ................................................................................... 28
A reas for Future Research ........................................................................................................................................29
7.1.
T echnology Groupings .....................................................................................................................................29
7.2.
T ransportation Costs ........................................................................................................................................29
7.3.
M anufacturing Index Im pact .........................................................................................................................29
8.
Conclusions .....................................................................................................................................................................30
9.
References .......................................................................................................................................................................31
8
TABLE OF FIGURES
Figure 1. Footw ear Planning Timeline.............................................................................................................................12
Figure 2. Profiling Stakeholders.........................................................................................................................................16
Figure 3. Solver Progression................................................................................................................................................17
Figure 4. Open Solver Interface..........................................................................................................................................18
Figure 5. Solver studio user interface ...........................................................................................................................
19
Figure 6. A IMM S Profiling Optimization Tool.......................................................................................................
20
Figure 7. Changes in M odel Performance with Minimum Fill Level.....................................................................28
9
1. INTRODUCTION
1.1.
Purpose of Project
The purpose of this project is to evaluate footwear "profiling" at Nike and to suggest
improvements, specifically to apply mixed-integer linear programming optimization techniques
as a means of cost reduction. The project was designed by leadership within Nike Inc.'s Global
Footwear Planning team in concert with the Leaders for Global Operations (LGO) program at
MIT.
1.2.
Profiling Description
Profiling is a method of advanced sourcing used by Nike, Inc. The goal of profiling is to
incorporate demand forecasts and production capability into the product development cycle.
Approximately two years before product delivery to retailers, footwear products go into the
development cycle. The development cycle occurs as a product evolves from design concept, to
prototype, to a model offering. The footwear concept created by a Nike design team is developed
under the guidance of a Footwear Development Director (FDD) who arranges a partnership with
a factory group. Once in partnership with a factory the FDD directs the coordination of the
designers with production engineers in the factory. Steps taken to design for manufacturing and
technological limitations of the factory are taken into account. During the process of creating a
prototype specific machinery and tooling is developed to accommodate the footwear model line
production. By the end of the process (approximately 9 months from product delivery) the model
is ready to be released for full production supported by an updated design, performance
requirements, and an "engineering tech kit" that specifies production standard work.
Consequently, it is preferable that products be developed with factories (or at least factory
groups) by whom they will eventually be produced. Moving a product or product line requires
transferring intellectual property, machinery (called tooling), and personal skills between
contracted manufacturers. Profiling is done to minimize the need for these transfers but still
allocate future factory capacity and enable footwear development directors to form partnerships
with factories.
1.3.
Problem Statement
Describe, assess, and improve (if possible) the footwear profiling process at Nike, Inc. using
algorithmic methods.
1.4.
Hypothesis
The Nike, Inc. profiling procedure can be quantitatively improved (assessed using net dollar
expenditure) using mixed integer linear programming techniques.
10
1.5.
Project Goals
At the outset of the project I had three main goals:
1. Build a decision support tool capable of being implemented by Nike, Inc.
2. Conduct a proof-of-concept to validate that the tool is valuable to the problem.
3. Build-out the tool and lead the effort to begin implementation for Nike, Inc.
1.6.
Project Approach
This research is broken into phases: Diagnosis, which includes interviewing employees and
diagramming the current process flow; Initial Prototyping, developing a simple prototype as a
proof-of-concept; Expansion of Prototype, iteratively improving and growing the prototype into a
usable model; and Implementation, the socialization of the model leading to adoption.
1.7.
Confidentiality
As part of this research only publicly available data has been used without modification. All
proprietary information has been avoided wherever possible, or at a minimum disguised, so as not
to violate terms of non-disclosure with Nike, Inc. The merit of this research is in the approach and
solution, not in the proprietary data from Nike, Inc.
2. BACKGROUND OF PROFILING IN GLOBAL FOOTWEAR PLANNING
This chapter provides the reader with necessary information about Nike, Global Footwear
Planning, and the profiling process.
2.1.
Background of Company
Nike, Inc. (NKE) is a "global marketer of athletic footwear, apparel, and equipment that is
unrivaled in the world" (Nike 2014 "Investor Relations). The company was founded as Blue
Ribbon Sports in 1964 and in 1968 by Bill Bowerman (deceased) and Phil Knight (chairman of
the board). In the last company annual report (May 2013 - May 2014) the company reported
$27.7B in revenue, of which, $16.2B is attributable to aggregated footwear product lines (Nike
2014 10-k).
Nike manufactures under the Nike (SwooshTM), Jordan, Nike Golf, Hurley, and Converse brand
names. These brands are proliferated across six "Geographies": North America, Western Europe,
Central and Eastern Europe, Greater China, Japan, and Emerging Markets. The North America
Geography accounts for $7.4B in footwear revenue or roughly 45.6% of company-wide footwear
revenues (Nike 2014 10k).
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Three countries compose the majority of the source base for footwear manufacturing for Nike
footwear products with Vietnam, China, and Indonesia comprising 43%, 28%, and 23% of all
footwear manufacturing. Within these countries there are approximately 40 factories that
primarily manufacture footwear (not including sandals).
Nike, Inc. is headquartered in Beaverton, Oregon and staffed by over 56,000 employees
worldwide (as of 31 May 2014). The President and Chief Executive Officer is Mark Parker who
has held that role since 2006.
2.2.
Footwear Planning at Nike
2.2.1. Global Footwear Planning Team
Strategic planning and high-level (above Geography) production planning at Nike, Inc. are
conducted by the Global Footwear Planning (GFP) team. Within GFP, there is demand
management, supply management, and a supply chain innovation section (the sponsor for this
research). Generally, demand management is responsible for generating reports that indicate the
anticipated demand in future seasons, supply management is responsible for allocating that
demand against factory capacity, and supply chain innovation is expected to continually
implement process improvement and analytics.
2.2.2. Footwear Categories
GFP works in concert with 16 Footwear Categories, which are the functional footwear product
lines (i.e. running, basketball, etc.). The footwear categories are concerned chiefly with the
quality of their product and the cost to produce. The categories hold significant power in the
organization as the product line managers and design/implementation shops. Sourcing plans are
the result of negotiated outcomes between GFP and footwear categories.
2.2.3. Footwear Planning Process
FIGURE 1. FOOTWEAR PLANNING TIMELINE
The footwear planning process at Nike starts with a 10-year horizon for capacity management.
Using growth projections, factory improvement projections, and demand forecasts as inputs,
Global Footwear Planning uses the Strategic Capacity Optimization Tool (SCOT) as a decision
support tool for expanding or acquiring additional capacity to support demand. The tool optimizes
12
around total cost under the constraint that all projected demand is able to be met at an internallydetermined level of service.
Meanwhile, product concepts and footwear technologies are constantly being innovated in Nike
"Innovation Kitchens" by footwear design teams, chemical engineers, performance scientists, and
the athletes themselves, The "Innovation Kitchens" are the life-blood of Nike's technological
advancements in footwear. New product concepts and technologies that are available two-years
from product delivery are eligible to be programmed into footwear profiling. It should be noted,
that not all shoes use "new" technology from the "Innovation Kitchens" in any given season.
Many products are fusions or new applications of existing technologies to the company or are
planned revisions of existing products. I merely mean to highly the cycle of innovation at the
company is a continual process that affects capacity planning.
Two years from product delivery, footwear profiles are developed that pair footwear categories
with factory capacity (expanded in the next section). The goal of footwear profiling at Nike, Inc.
is to assign the appropriate capacity and technological capability mix to footwear categories so
that they are able to translate products from design to production without incurring unnecessary
costs or variability in their source base.
Between profiling and 9 months from production, the company continually updates profiles in a
process called "relative sourcing" that iteratively updates the profile to include more details as
products become finalized. For example, a specific running shoe may finish ahead of others and
be locked into production at one or two factories 15 months from the production start date. The
company is still not able to guarantee the production plan for every model in the target season but
can begin to form a more complete picture.
The final step in footwear production planning at Nike, Inc. is called "product alignment."
Product alignment assigns specific models, styles, sizes, and color against capacity in a specific
factory. Guertin (2014) developed an optimization technique for this process for Nike sandals and
Quinonez (LGO 2013) is currently expanding the technique to incorporate all of Nike footwear
into a product alignment optimization. After product alignment, the footwear models in a specific
season are fully allocated and begin heavy production.
2.3.
Current Profiling Process
The profiling process as it stands is executed through compromise and heuristics. The Director of
Sourcing within the Supply Management division of GFP is responsible for producing a "factory
profile." The profile assigns factory space to footwear categories by percentages of factory
capacity in a given season (i.e. running has 33% of the capacity at factory XY in season mm).
The profile at any given time runs two years out, or 8 footwear seasons. Profiles are reviewed
seasonally and updated after meetings between Global Footwear Planning and Footwear Category
directors.
To create a new profile the Director of Sourcing would first look at the allocation for the season
prior (in, for "in" in seasons). Then incorporating demand forecasts for footwear categories in the
13
next (m+1) season look to see if the current profile could support the new demand without
exceeding capacity constraints. Changes would then be made iteratively until all business rules
were satisfied.
Some of the business rules are not obvious. For example, certain footwear categories'
development directors had strong relationships with certain factories but not others. Additionally,
not every factory could support every product category due to equipment, specialization, etc.
These business rules were not always codified - they were simply known by the Director of
Sourcing from their experience.
Finally, once the profile satisfies internal constraints, it must pass through a sourcing meeting for
approval. The sourcing meeting would be attended by GFP Directors, Footwear Development
Directors, and executives.
3. LITERATURE REVIEW
3.1.
Prior Research in Nike Global Footwear Planning
This project builds on work done by Guertin, 2014 that examined the use of optimization
techniques in sourcing sandal production. Guertin found that algorithmic optimization could
produce between 4-11% total landed cost savings for Nike, Inc. Sandal production. The
formulation for that optimization considered "total landed cost" in its objective function. "Total
Landed Cost" is comprised of manufacturing, transportation, tooling, holding, and duty costs. A
key difference between Guertin's research and this project is the timeline and level of information
on the product. Guertin was directly sourcing sandals where factory capabilities and design
specifications are fully known while this project is two years from delivery and is more uncertain.
Quinonez (2013) conducted significant research into "risk-adjusted total landed cost" (RATLC).
RATLC is a single measurement for both quantifiable costs as well as factory risks derived from
the Nike Manufacturing Index (MI). The goal was to derive a single cost metric to describe the
total impact of strategic sourcing decisions.
3.2.
Research in Retail Manufacturing
Mercier and Battle (2012) highlight the importance of collaboration between retailers and
suppliers in eliminating waste in a supply chain. Particular recommendations include: choose
partners carefully, commit for the long-term, build cross-functional teams, and turn pilots into
business as usual. This study highlights much of what Nike, Inc.'s operations team does very
well. The footwear development process, particularly, is an example of partnering for the long
term and graduating from pilot to 'business as usual.'
Diaz-Madronero, Mula, and Peidro (2014) identify trends in optimization-based sourcing
decisions. They find that mixed-integer/linear programs solved by commercial solvers are the
most common in the manufacturing industry. They find that constraints are prioritized by limited
14
resources which correspond to productive resources (i.e. factory capacity). This research indicates
a useful starting point in testing the hypothesis of this project.
Xia, Zu, and Shi (2015), discuss optimizing for profit in a socially responsible supply chain. They
experiment with closed-form optimal solutions for social responsibility and then expand to
include profit-driven sensitivity analysis for the supply chain. This work holds relevance to a
suggested expansion of the Manufacturing Index (MI) later in this research and is in line with
Nike, Inc.'s core manufacturing values.
Larson and Odoni (1981) primarily derive algorithmic and heuristic approaches to solving urban
transportation and optimization problems. However, their work is also seminal in queuing theory
and can be applied to sourcing. Of importance is the concept of wait times increasing
exponentially for queues above 90 percent utilization. This affects sourcing for Nike, Inc. as a
business rule. Factories should generally not be permitted to take on greater than 90 percent of
their absolute capacity if capacity is fixed and on-time-performance (OTP) is an objective.
3.3.
Research in Implementing Software Solutions
Sonenstein (2014) discusses the importance of management in implementing new corporate
strategies. In multi-year studies, the efficacy of outcomes in retailers attempting to implement
manufacturing changes was highly dependent on the collaboration and approach taken by
management.
From Guertin (2014), Giesler and Rubenstein (1987) discuss the importance of management in
implementing software solutions. They observe that the number of interactions between user and
developer as well as strong support from management before, during, and after implementation is
essential in the success of a software pilot. These principles are consistent with challenges
encountered during this research.
4. METHODOLOGY
The purpose of this chapter is to explain the current process for Profiling at Nike and to explain
the approach taken.
4.1.
Current State Analysis
Profiling at Nike is the process by which anticipated factory capacity is allocated to footwear
categories (i.e. running, basketball, etc.). The Global Footwear Planning division (organized
under the umbrella of Sourcing and Manufacturing) is responsible for allocating footwear factory
space. Within the Global Footwear Planning division, the Sourcing Director creates the factory
"profile" containing the percentage of each footwear factory's capacity dedicated to each
footwear category over the next eight retail seasons. The profile is a constantly iterated document
15
that is updated incrementally following meetings with Footwear Development Directors within
each footwear category.
The process for testing the hypothesis that an optimization based profiling strategy could be
effective for the Nike Global Footwear Planning Division includes the following steps: 1)
Identify Stakeholders 2) Interview Stakeholders 3) Process Map Activities Across Stakeholders
4) Begin Prototyping.
4.1.1. Interviews
Interviews were conducted to determine stakeholder amenability to an optimization based
profiling strategy and to understand the organizational priorities of each stakeholder. I
interviewed managers and directors of the Global Footwear Planning division and Footwear
Development Directors from 10 of the 16 footwear categories. These interviews took place during
the month of February as part of the "scoping" portion of the research project.
4.1.2. Process Mapping Across Stakeholders
The final result is very much a compromise of interests indicated in Figure 2 below.
Uw
Key Concems:
1) On-Time Perf.
2) Utilizatiorw
3) Cost
Key Concerns:
1) Quality
2) On-Time Perf.
3) Global Margin
I
Categories
Assigns
Sources
(16)
Key Concerns:
1) Quality/Performance
2) Margns*
3) On-Time Perf.
Production
A
DVs:
1) Cost
2) Demand
3) Capacity
4) Technology Matching
5) Factory Matching
Key Concerns:
1) Margins*
2) Capacity
3) InvestmentOpp.
FIGURE 2.PROFILING STAKEHOLDERS.
To understand the figure, the orange "Swoosh" represents the overall company, Nike Inc. GFP is
the Global Footwear Planning Division. "Categories" are the footwear categories and "Factory
Groups" are the contracted factory partners.
The company as a whole prioritizes the quality of the product (delivering on performance
specifications without defects), and the value of the Nike brand, above all other concerns. The
next most valued aspect is meeting the orders of retail partners and finally maintaining the highest
"global margin" possible. By this I mean aggregate margin combining the margins of all footwear
categories. This is worth noting because footwear categories must manage individual "margin
16
targets" which are heavily influenced by the factories to which they are assigned (remember that
each factory has different labor costs). Consequently, each footwear category is motivated to
optimize their individual margin (and therefore be assigned to capable, lower cost factories). The
Factory Groups also are concerned with margin but in this case, they receive higher fees per unit
by making higher value products. The Factory Groups are all interested in making the most
expensive products by estimated Free-On-Board costs in order to maximize their own revenues.
4.2.
Prototyping
This section will discuss the prototyping process for developing the Profiling Optimization Tool
for Global Footwear Planning at Nike, Inc.
.p.
Solver
Studio
for Excel
FIGURE 3. SOLVER PROGRESSION
4.2.1. Initial Prototype
The initial profiling prototype was a simple proof-of-concept done in Excel using an Add-In
called "Open Solver". This first version of the prototype considered only a single period (one
season) and matched supply with demand. The objective was a cost minimization but only
considering the Labor and Overhead (LOH) of each factory. It did not consider technology
matching, supplier-destination country connections, multiple seasons, or user controls to alter
factory-category connections.
The initial prototype was restricted to using the existing profile "gateways". Mathematically, this
means that only existing arcs could be used between categories (i) and factories (j) according to
the current profile. Even this simplified model showed the potential for significant cost savings
over the current method of conducting profiling (see methodology for a more thorough
explanation of arcs in the model). However, it relied on Excel cell objects and could not support
more complex problems than the one described above. It was primarily useful as a short, quick,
proof-of-concept that provided more power than the simple Solver provided with Excel. An
example of the "Open Solver" interface is shown below:
17
FIGURE 4.
OPEN
SOLVER INTERFACE
4.2.2. Second Prototype
With the first model producing simple, but reasonable results, the program was expanded in a
different suite called "Solver Studio" (also an Excel add-in). Solver Studio runs using named
ranges in Excel to identify data sets and runs a simplified version of Python (called PuLP) in its
execution prompt. This platform allowed for more constraints to be added to the formulation and
even decisions about what factory arcs to open or close. The two main shortcomings of "Solver
Studio" are processing time and the user interface (limited to Visual Basic macros or the PuLP
script). "Solver Studio" enabled the research to expand the model to incorporate taxes,
technology groupings, and mixed-integer decisions for factory category arcs (instead of relying
on the status quo). "Solver Studio" could not incorporate multiple seasons as the number of
decision variables (approximately 123k for one season) increased run times past one half hour.
Additionally, the "Solver Studio" interface was not user-friendly for the intended customer.
Coding PuLP and altering individual cells in Excel was required to make the model run
effectively.Nevertheless, it was a building block in the step to the final model and validated the
usefulness of the model for a single season
18
Levelling Controls & Factory Button
Grouping Percentages (User Input)
FIGURE 5. SOLVER STUDIO USER INTERFACE
4.2.3. Final Prototype
The final housing for the model was proprietary software called AIMMS. AIMMS is capable of
running multiple solver engines (i.e. CPLEX, GUROBI, XA, etc.) and supports a user interface.
Additionally, the programming in AIMMS allows for database query and automated data uploads
from master documents. This streamlines the data entry for the user (not necessarily an individual
familiar with writing queries) and allows the solver to run separate from EXCEL. The improved
engines combined with a more streamlined process allowed the prototype to be expanded into
multi-season and allowed for the development of a user interface. This prototype could process a
model that has over 600,000 linear variables and decisions (both linear and mixed-integer) in less
than 30 seconds on average. Additionally, the user has a simple way to control most of the
constraints in the model. They can formulate the optimization easily to their precise business
problem.
For the final user interface, an image of the Director of Sourcing (who was retiring) was added in
homage.
19
Embedded User Pages
Optimization
O
o Engine
The improved user interface linked the user to easily-understood input pages and had friendly
buttons that allowed for running the model and viewing/exporting results. In this final state it was
reasonable that a user untrained in optimization could fully source a profile using only demand
inputs.
The program also allows for easy changes to the factory landscape by adding new factories or
changing their capabilities (along with any constraints). However, some training in AIMMS
would be required to handle those structural changes in the code and I left with a recommendation
that the model be maintained by Global Footwear Planning's Supply Chain Innovation group for
changes to the base of the model.
4.2.4. Customization
The final model is a customized solution rather than an out-of-the-box optimization
package. There are tradeoffs in taking this approach. For customization, there is an
increased responsibility on the developer to maintain the package - there are no
consultants coming in to fix errors that occur. Additionally, the user typically must have a
higher level of understanding than they would for a supported software package. The
company also undertakes an adoption risk if the knowledgeable users of the system leave
the group and new users cannot easily transfer the skills.
On the positive side, this model is capable of greater flexibility than the other commercial
options the company explored. With the use of AIMMS, the problem can be modeled more
precisely and the run time is faster. The main benefit to run time is the lack of a
requirement for translating data or functions between mediums or languages. Everything
from database queries to solver runs to output is done in the same language. Run time is a
major advantage for a problem of this size and the output can be directed into any data
visualization package the developer or user desires. Additionally, the AIMMS software
20
allowed for a custom user interface that alleviated much of the training concerns associated
with a custom software solution. The developer still maintains more responsibility than
they would for a commercial solution but the user is relatively unaffected. In fact, the user is
advantaged by the quicker run time and flexibility to alter constraints easily. Finally, the
custom solution offers a major improvement for the user interface. The other software
packages available in the company simply did not compare with the ability to interact with a
non-expert user.
Overall, the custom solution was necessary for this highly unique business problem.
Traditional network design packages (the most common that were considered for
alternatives) simply did not provide the flexibility or user interface to justify the
investment. The custom solution was far more likely to develop a feasible and
implementable outcome and consequently became the most desirable option for this
project.
4.3.
Validation of the Model
To validate the applicability of the model it was run with the same constraints used by the current
Director of Sourcing on past seasons to see how it performed. Data existed for each profiled
season through FY 2013 including duties paid by the company and total labor spending. For past
seasons these numbers were used as the benchmark for comparison.
To validate the model, past forecasting data (from FY 2013) was used and two different "runs"
were done: 1) A run where the model could not alter the factory-category arcs that existed in the
season - it could only alter volumes; and 2) A run where the model could alter factory-category
arcs but subject to a deviation constraint. The "past data" allowed the researcher to compare the
model's logic to what actually happened as a benchmark and to determine if the constraints
worked to simulate the business problem. Fortunately, the company maintained data sets that
dated back over five years and included both "forecasted" as well as "actual" data for demand and
production.
The results of the validation runs were promising. The model generated "feasible" results given
the constraints programmed (i.e. no results were generated that violated the bounds set by the
user) and validated that the multi-season model was able to incorporate the seasonality of the
product array. This had been the largest concern of the stakeholders - that the model would view
each season individually and not be able to handle the complexity of deviations between seasons.
In the first run, where no new factories could be established for any category, the model was
highly constrained but generated savings from improved duty spend (data on the actual amount
spent on duties in FY 2013 was accessible) and from labor rate improvement (essentially moving
product whenever possible to lower labor-rate factories). During this scenario savings of 3%
were achievable.
In the second run, the constraints were relaxed to allow for 1 new factory to open for a category
between seasons and for a 20% deviation in product allocation for any category in any factory. In
this scenario the model outperformed (up to 9% improvement on cost savings with the relaxed
constraints).
21
These results both validated the multi-season model as a concept and generated benchmarks for
the savings that could reasonably be anticipated for applying to the next profiling season (which
was larger in volume due to company demand growth). The ability to control deviations proved
critical in generating adoption of the product as did the user having the option to use the current
set of factory arcs and only deviate with production volumes (this was due mostly to simple
apprehension of changing too much too fast as an organization, not due to the rejection of the
utility of the model).
4.4.
Robustness of Model
Forecasted demand can change significantly for individual products between seasons but is
more stable for aggregated categories. Rather than expand the scope of this project further
to include simulating and modeling demand forecasting error (a separate internship the
same year), the model was made to incorporate business rules that the user could adjust as
constraints. For example, both the contract manufacturers and the company carry risks if
too few product lines are sourced to a single factory. The user has the ability to dictate
minimum and maximum categories assigned to a factory and vice versa. These controls are
really just heuristics but work for the company. I advise using simulation to justify these
heuristics in future research.
5. MODEL FORMULATION
5.1.
Formulation and Definitions
The primary objective for the model is to minimize cost with respect to constraints for quality and
seasonal volume deviation. Other formulations are possible, including optimizations that seek to
minimize the number of arcs between categories and factories (something that may be desirable
from a work-flow perspective). However, the most directly applicable is the traditional cost
minimization described below because of its bottom-line impact.
5.2.
Objective Function
i
i
Min I I
i=1 j=1
k
I
m
III
Xi,j,k,,m * (LOHj,m + ti,j,k,m + Fj,k,m)
k=1 1=1 m=1
Decision Variables:
Xi,j,k,,m: The number of units of product in category, i, sourced to factory j, destined for country
k, in product grouping 1, for season m
i E I: for i in the set of available categories "I"(Called SSCategories in model)
22
j E J: forj in the set of available factories "J" (Called SSFactories in the model)
k E K: for k in the set of available destination countries "K" (Called SSCountries in the model)
1 E L: for 1in the set of available groupings "L" (Called SSConstruction in the model).
5.2.1. Grouping Constraint
A grouping is an aggregation of technology capabilities that creates distinct factory
technological capabilities. It indicates the ability of a factory to meet the production
requirements of a potential category in a season. This constraint was developed as the
result of a business problem facing the company where developers would say "we can't
make that product in that factory." I found that this was due to technological constraints
that were undocumented for a model-level of aggregation. For example, the
technologies at a factory were documented (i.e. the factory can produce Nike Free'
products). However, one product may have 15 different technologies designed for a
single model. Many factories would not be able to support that hypothetical product.
The "Groupings" constraint is an attempt to aggregate technology pairings into
meaningful sets that categories can plan against. See Section 7.1 for recommendation
about improving this concept.
m E M: for m in the set of seasons involved in the model "M" (Called SSSeason in the model)
Cost Drivers:
L OHj,m: Labor and overhead rate (per each pair of shoes, X) at factory j in season m
tij,k,m: variable duty rate for category i from factory j to country k in season m
Fjkm: flat duty rate from factory j to country k in season m. Some countries charge a set rate
regardless of the value of a product for imports.
Summary: This function minimizes cost for sourcing subject to the constraints
below.
Constraints
Demand Constraint
i
*
5.3.
~ Xii ~kim * FactoryCapabilityMatrixi,j,m)
Dji,k,m
1=1 1=1
23
Di,k,m: Demand for category i in country k in season m
FactoryCapabilityMatrixij,m:
Binary table (0,1) opening and closing category i to factory j.
Accounts for sourcing relationships and factory capabilities. Can change or remain the same
between seasons. Changing a grid square in the matrix would close a factory-category
combination. For example at the intersection of Factory "A" and "Running" a I indicates that
Factory "A" can produce running shoes where a 0 would prevent sourcing Running to Facotry A.
In the optimization logic, the model will never source product to a factory with a "0" because it
would increase costs without helping to meet constraints.
The demand constraint simply requires that the product allocation must be greater than the
anticipated demand for a category in a country. This does not fall into a "level of service," or
traditional newsvendor, inventory consideration because this decision does not necessitate
production - only capability to produce.
Loading Constraints
i
k
I
I
I
Z
Xi,j,k,l,m 5 Cj,m * MaxLoadingj;
i=1 k=1 1=1
Cm: capacity of a factory
j, in a season m
MaxLoadingj: factory maximum percentage loading s.t. 0
i
1
k
MaxLoading
1
1
YZ Y. Xi,j,k,l,m : Cpm * MinLoadingjm
i=1 k=1 1=1
MinLoadingj,: factory maximum percentage loading s.t.
0 MinLoading
1; MinLoading
MaxLoading
Loading constraints are quite simply the maximum and minimum levels of capacity that can be
allocated to any factory. It is a user-driven control with the default values set by company rules of
thumb or contractual obligation.
It is worth noting that removing the minimum loading constraint risks long term damage to the
supply base (i.e. if 0 loading is permitted the factory in question may either shut down or leave
Nike to stay in business).
The maximum loading constraint allows for building in buffers for the system without forcing
prioritization of product.
Grouping Constraint
1k
E
Xi,j,k,,m
* Techno1ogyCapabilityMatrixjj,
Dim * Pim,
j=1 k=1
24
TechnologyCapabilityMatrixj,,m:Binary matrix [0,1] associating construction groupings, 1, to
factories j, during season m. A factory with 1 in a grouping can manufacture products of that
technology set in a season, factories with 0 in a grouping matrix could not. For example, a
category that has demand for "Autoclave" could not be sourced to a factory with a "0" in its
matrix for "Autoclave."
Dim: Demand for category i (aggregate, not broken out by country) in season m
Pi,,m: Percentage of category i, that falls into grouping 1, in season m s.t.
=im
=
1
This is the most difficult constraint to understand intuitively. Essentially, it works in the same
way as the factory category capability matrix. It has a binary matrix of arcs available between
factories and groupings. Groupings are combinations of technological capabilities. . Although
mentioned briefly above, the grouping constraint serves to control for technology gaps between
factories. This is a key challenge because the attribute set for individual products is not known at
the time profiling occurs. That requires that these "groupings" be more category-specific.
Consequently, they are an aggregation of individual technologies into category specific
combinations. For example a set of unique technologies required on every basketball shoe would
comprise a "Basketball" grouping. A factory possessing that production capability would have a
"I" in the Technology Capability Matrix in its box for "Basketball." Any factory that could not
produce those technologies should not be sourced Basketball product and would have a "0." The
matrix works by multiplying the coefficient from the matrix against decision variables which are
constraint to meet demand. Improvement opportunities for this constraint are mentioned in
section 7.1.
Seasonality Constraint - General
|Xi,j,k,I,m, - Xi,j,kl,mn+
I<
sm
sm: volume deviation allowed for product combinations between seasons
The seasonality constraint was added to make implementation more suitable. This constraint
controls for the amount of any category (i) changing from a given factory (j) in a following
season. This makes the transition to a more optimal solution gradual and avoids "shocking" the
system. Nike, as a publicly traded company, would be uncomfortable makin g drastic changes to
the supply chain and incurring short-term revenue risk.
However, this absolute value function is not linear. To implement as a linear program mixed
integers were introduced.
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Ni,j,m: A new pathway between a category i and a factory j, in month m; Binary[0,1]
Seasonality Linear Conversion Part 1
Z
Ni,j,m
; AllowableFactoryDeviationsim
j=1
AllowableFactoryDeviationsim:The new arcs permitted to be formed for a category i, in
month m.
Seasonality Linear Conversion Part 2
Ni,j,m > PathwayDeviationi,m
PathwayDeviationim= FactoryPathwaysi,j,m+j- FactoryPathwaysijm
FactoryPathwaysij,m:The arcs (binary) available between categories i and factories j in month
m.
These constraints create two new binary variables (New Pathways and Factory Pathways) that
help account for the absolute value constraint needed for deviation control.
FactoryPathwaysi,j,mcan be user controlled or the engine can program the arcs for each month
(dramatically increases run time because of integrating a matrix of mixed integer variables).
Typically, the matrix for FactoryPathwaysij,mwill be pre-determined and the program is
deviating only for the FactoryPathwaysi,jm,+ month which includes all the arcs from
FactoryPathwaysi,j,mplus the Nij,m arcs that are added. In this way, the program is
dynamically updating over seasons.
The goal of writing this constraint is to control for deviations between seasons. The company is
very sensitive to moving large quantities of product too quickly from one factory to another and
also to opening new factories to categories for the first time. This constraint allows the user to
control the number of new factories and the rate at which changes are made. However, it
dramatically increases the run time due to the introduction of two binary variables.
5.4.
User Interface Development
The user interface was developed to allow the user to manually change settings for individual
factory-category arcs in each season, minimum/maximum fill percentages for factories in each
season, the allowable deviation for a category in a factory in a season, labor and duty rates for
each country and factory in each season, and could even cap the source base capacity coming
from individual factory groups or countries. These alterations are available from drop down
menus and tables. The "default" settings are either the current rate (for duties or labor) or the
"optimal" value in an unconstrained scenario. Each model run by the user can be saved as a
different scenario and run incrementally.
5.5.
Output Pages
26
The output pages from the model give both graphic and tabular outputs. Key outputs are the total
cost of production, cost per pair of footwear produced, loading at individual factories, duty costs,
labor costs, and margin percentages at the global and category level.
Shipping costs, or even better, Total Landed Cost (from Quinonez 2013) would add detail to the
model and are proposed in Section 7.2. The scope of this project did not include transportation
costs but much of that research exists and could be implemented quickly.
Eventually, it would be preferable to output each model run into a data visualization dashboard
suite in a program like Tableau or similar but AIMMS allows for relatively simple output
dashboards organically.
6. RESULTS
6.1.
Quantitative Analysis
The results of the optimization are very promising. With a model size comprised of 1,628,000
decision and mixed-integer variables, and a run time under 3 minutes on average, the model
generates a potential improvement of up to 4.9% savings on labor and overhead costs in the next
fiscal year if implemented immediately. Additionally, the potential tax savings produced by the
model may be as high as 3.6% annually over the current baseline.
6.2.
Comparison versus current state
To conduct this comparison, I used FY 13 data and ran the model and then compared its solution
versus the baseline of actual spend during that year. I then used the demand data for the next
profiling season and compared only labor and overhead (since future duty payments are much
more difficult for which to account) model projections against what current rates would imply. As
mentioned above, the combined effect of tax savings and labor ranged between 5.7-9.7%
depending on year and assumptions made.
A key driver of savings over baseline is the minimum fill level required of individual factories.
By that I mean Factory X must be filled above XY% of its stated capacity. Hence the key
decision made at this time is to decide at which factory or factories will a new product be planned
to be made. By maintaining every factory at differing minimum fill levels the savings over
baseline erodes gradually. It eventually becomes negative simply because more footwear is being
produced by the source base than is needed in the market (Figure 7). However, even at relatively
high minimum fill levels of 70%, and a constant maximum fill level of 90%, the model produces
savings over baseline. The graphic indicating this relationship is shown below. For the current
state comparison I used company business rules as the assumption for both fill levels.
The source of the savings is deviating from where product is currently source to realize lower
labor rates (5-7% savings) and optimizing for the lowest possible duty rate for source-destination
combinations (2-3% savings). The model adjusts the quantity of product sourced to factories with
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lower labor rates higher whenever it can based on the constraints, and takes product out of
factories with higher labor rates. The user has control over most of the constraints in the model
and can craft scenario plans. It was intentional to give the user significant control over the inputs
of the model to prevent a "black box" solution and create a product that would be viable for quick
implementation.
%Improvement Over Baseline (Generated Profile FYI5)
FIURE
7.CAGE4A24%
EC70
RFO
000
6.3.
ecom
nd
endaions
Ky
Fiding
FIGURE 7. CHANGES IN MODEL PERFORMANCE WITH MINIMUM FILL LEVEL
6.3.
Recommendations and Key Findings
Applying this model to design future footwear profiles presents a significant opportunity for
Nike, Inc. The company anticipates growing revenue to $37 billion by 2017 (Nike Press Release,
10/09/2013). The rapid annual growth of the company incentivizes implementing the model
quickly (which was the impetus behind sponsoring the project). The opportunity cost of delaying
implementation of the model grows every year.
6.4.
Business Problem and Change Implementation
Significantly more difficult than achieving results is getting those results implemented. Nike
employs a "matrix" structure, and as described in the "Current Profiling Process" section, the
profiling process involves many stakeholders and is not an algorithm. Many of the individuals
involved are experts in developing and manufacturing footwear but not in understanding
optimization scripts. Leading the adoption of the model proved to be the most difficult part of this
project.
One of the reasons for the difficulty lies in a lack of authority. The matrix structure incentivizes
collaboration and works best with consensus. The incentives for footwear categories are to
maximize their category margins and to design great footwear. Any category that suffers a
decrease in their margins as a result of the model is not likely to support consensus. This means
that unless incentives are changed to allow some categories to take lower-margins the optimal
solution will struggle to gain their support. Additionally, constraints could be included to preserve
minimum category margins, but that outcome would come at the cost of the company as a whole.
28
I believe top-down direction would greatly speed the rate at which optimization is adopted in
manufacturing decision making at Nike. Otherwise, incentives are not present to generate
consensus and the status quo could persist longer than is necessary or beneficial from a financial
perspective.
Another challenge is the familiarity of stakeholders with operations research and optimization
software. While the user interface is meant to be intuitive, maintaining and updating the model
logic is not. The Supply Chain Innovation team in GFP is meant to be the subject matter experts
but they only have the ability to affect footwear planning. If the approach is expanded to apparel,
equipment, or to the entire Nike operations practice a centralized center of excellence may be
required.
Nevertheless, the product allows for the user to manipulate the scenarios in ways that make only
minor structural deviations from the status quo (for example, preventing decisions that would
change any category into a new factory). Even in a resistant adoptive environment, the model can
product 1-2% savings by optimizing for labor and duty rates in the current matrix. This simplified
scenario can be implemented immediately (and had internal support).
7. AREAS FOR FUTURE RESEARCH
Despite the impact I believe the model can have immediately, there are opportunities to expand
the research and potentially improve on the results.
7.1.
Technology Groupings
The technology groupings used in the model are only a start. Nike, Inc. has not needed this level
of aggregation for footwear technology before and will be able to find a more robust set of
grouping parameters with more research. At present, I created the capability for the groupings to
be incorporated in the model, and gave a first shot at what I thought they could be. However,
what is really needed is the lowest level of aggregation for manufacturing technology packages
that do not have interactions. I am not confident that the groupings I developed are completely
homogenous from one another. The impact of this is that the outcome may be sub-optimal with
respect to flexibility. If there are more "true" groupings than I identified, the model should be
more constrained. If there are fewer, the model would be less constrained on how factories and
categories can be assigned.
7.2.
Transportation Costs
As mentioned in the Literature Review, Quinonez (2013) examined the "Total Landed Cost" for
manufacturing footwear for Nike, Inc. This model uses averages for footwear component costs
(the footwear products are still in development at the time of profiling) and does not use
transportation costs despite having country of origin and country of destination arcs. Adding this
to the model would be an easy input and help incorporate transportation in addition to duties.
7.3.
Manufacturing Index Impact
29
Nike, Inc. internally maintains a rating system of all contracted manufacturers. This rating system
combines both qualitative and quantitative evaluation for: lean manufacturing, labor and health,
safety and environment, country risk, leadership planning and development, and transparency.
Factories are then rated at red, yellow, bronze, silver, or gold status (Nike Corporate
Responsibility). Factories that consistently fail to achieve bronze status or above are gradually
culled from the sourcing pool but there are no hard-wired incentives in sourcing decisions for
manufacturing index ratings.
Evaluating the value for the company in having factories of each rating reflected in sourcing
decisions could give real financial incentives to Nike, Inc.'s sustainability and manufacturing
efforts. These incentives could be written into the model and produce an outcome that is
optimizing for more than simply objective cost. This essentially, would be an expansion into
footwear of the research done by Quinonez, 2013.
8. CONCLUSIONS
The hypothesis for this research, that an algorithmic approach to profiling could produce
improved results, is found to be correct. The model produced by this research is promising but
significant opportunities for improvement and expansion remain. Additionally, organizational
structures to support implementation are not ideal. The matrix structure that incentivizes
collaboration for design and marketing inhibits the ability of traditionally back-office functions,
like operations, to standardize and improve. In addition to researching the impact of
transportation costs, technology groupings, and the MI on the cost to the company an assessment
on the operations organizational structure should be examined (probably by the corporate strategy
team in Nike's headquarters).
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Tool. Massachusetts Institute of Technology, Sloan School of Management.
Quinonez, C. (2013). Development of a criteriabased strategicsourcing model.
Massachusetts Institute of Technology, Sloan School of Management.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2008). Designing and managing the supply
chain: concepts, strategies, and case studies. Mcgraw-Hill/irwinseries operationsand
decision sciences. 3rd.
Nike, Inc. (2013). "Investor Day Press Release." October 9, 2013.
Nike, Inc.(2014). Annual Report on Form 10-k.
Nike, Inc. (2014). "About Nike, Inc." from http://nikeinc.com/pages/about-nike-inc
Larson, Richard & Odoni, Amadeo (1981). Urban Operations Research. Prentice-Hall.NJ.
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Binns, Jessica. For UnderArmour, Supply Chain is Key to becoming a $10-Billion Brand. Supply
Chain Case Study, Apparel Magazine. June 2014.
YIldIrIm, I., Tan, B., & Karaesmen, F. (2006). A multiperiod stochasticproductionplanning and
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Xia, Yu, Shi (2014). "A Profit Driven Approach to Building a 'People:Responsible' Supply
Chain." European Journalof Operations Research. Volume 241. Issue 2.
Diaz-Madronero, Mula, Peidro (2014). "A review of discrete-time optimization models for
tactical production planning." InternationalJournalof ProductionResearch. Volume 52. Issue
17.
Shonenstein, S. (2014). "How Organizations Foster the Creative Use of Resources." Academy of
Management Journal. Volume 57. Issue 3.
Mercier, Peter and Battle, Stuart (2012). "Retailer-Supplier Collaboration in the Supply Chain."
From
products-retailer_supplier-c
https://www.bcgperspectives.com/content/articles/retailconsumer
ollaborationinthe-supply-chain/
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