Discussion Paper No. 71
Quantifying the emergence of modularity trap
Case of Hard Disc Drive industry.
Imane Bouamama
Tomoatsu Shibata

September , 2017

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research
Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANTOHOKU UNIVERSITYGLOBAL PROGRAM ECONOMICS AND MANAGEMENTIMANE BOUAMAMA
B6EM1001

Quantifying the emergence of modularity trap
Case of Hard Disc Drive industry.
Imane Bouamama1, Tomoatsu Shibata2
1,2Department of Economics and management, Innovation management,
Tohoku University, Japan
Abstract:
With increasingly competitive markets and technological advances driving theworld towards a “converging commonality”, adopting “modularity” seems to be
more and more appealing for companies and businesses wishing to adapt to various
market conditions and to benefit from decentralized innovations. However, thedecision to adopt a modular product architecture often comes with a need to deeply
analyze the current market state and to predict the future technological shifts. Thus,
minimizing the risk of falling into the “organizational traps” that comes in as areason to a “misalignment” between the product architecture and the organizational
structure of a firm. This paper proposes a way to quantify the modularity trap, using
the ‘coupling index’ as a measure to the interdependencies and inter-connections
between product’s components, and applying the hypothesis to the Hard Disc Drive
industry.
Keywords: Modularity, modularity trap, DSM, coupling index, Hard Disc Drive,
product architecture.

IIntroduction
The concept of modularity is gaining popularity among various fields ofindustries, since the year of 1964 where the first modular computer IBM
360 was born. The IBM 360 was the first product to allow consumers toswap out older modules for new ones without having to replace the entire
system, as the interfaces between the modules were well documented. This
been said, it reflects the definition of modularity itself. In fact, “modules
have been defined as units in a larger system that are structurally
independent of one another, but work together. The system as a whole must
therefore provide a framework (an architecture) that allows for both
independence of structure and integration of function” (Baldwin & Clarck2000).
Modularity in product architecture has a lot to do with interdependencies
between components, when these interdependencies are well defined andtranslate into a ‘design rule’ then we can start talking about ‘modules’.

Quantifying the emergence of modularity trap

In simple terms, it allows the components to work together while being
structurally independent. On the opposite side, an integral system or
product, is one where components are structurally and functionallyintegrated making the functioning of one components, dependent andhighly ‘sensitive’ to a change in another component.
Researchers argued that the “technology of the firm shapes the organizationof that firm” (Nonaka & Teece 2001). Thus, a firm adopting a modular(integral) product architecture, must adopt a decentralized (centralized)organizational structure, respectively. This been said, when a firm’sorganizational structure is not aligned with its technological structure thenthat firm might fall into what is referred to as “organizational traps”. The
motivation behind this research is to present a sufficient mathematicalcondition for the emergence of modularity trap. We base on the couplingindex which allows for a measure of how strongly linked the componentsof the products are, i.e. how much a change in a component can affect, andto the extreme case, will require a change in other components.
IILiterature review:
A. Technological shifts
Chesbrough and Prencipe (2008) argued that in most complex industries,
when the technology first emerges in the market, it is usually in its integral
state. This state is referred to as pre-modular integral state. Ernest (2005)
concludes that “many scholars share the assumption (at least implicitly)that every technology will proceed from a less modular, more integrated
state towards a more modular state”. When the interdependencies between
the product’s components begin to be clear and well understood by themarket players the technology might shift to a modular state. Chesbrough(2003).
Although there is a growing tendency 1 to assume that modularity can
become the “stable end state for technology evolution irrespective of thespecific characteristics of diverse industries and technologies” Ernest(2005), many scholars oppose to the assumption, and some studies of thetechnological evolution throughout industries, have shown that thetechnology can shift back to an integral state, following the emergence ofFor example, Sanchez (2000) emphasizes that, ‘‘once component interface
specifications are fully defined in a modular product architecture, there is little
or no need for the traditional development management function of
adjudicating interface issues among teams developing interdependent com’’.
12

modularity. Chesbrough and Prencipe (2008) define this state as the postmodular integral state.
B. Organizational traps
Sanchez and Mahoney (1996) attests that modularity can be a characteristic
to both product architecture and to the organizational structure. Ernest(2005) argued that the organizational structure of the firm should be in linewith its technological structure. i.e. a firm must adopt a decentralizedorganizational structure when the technology is in its modular phase, and acentralized organizational structure when the technology is in its integral
phase.
Sanchez (2000), explores ‘‘how the design rules an organization adopts forcreating product architectures fundamentally constrain the feasibleorganization designs an organization can adopt for creating, producing, andsupporting its products”. Within the same lines, Chesbrough and Kusunoki(2001) claim that “as the technology shifts from one phase to the other, theoptimal organizational configuration of the firm must also shift if it is tocontinue to capture value from its innovation activities”.
Further, Chesbrough and Prencipe (2008) assert that when a firm fails toalign its organizational structure with its technological structure, anorganizational trap occurs as a consequence to that misalignment. The
authors then specify two types of organizational traps: modularity trap andintegrality trap. The modularity trap is defined as a kind of organizational
misalignments that occurs when a decentralized firm fails to shift to acentralized one when the technology of its product shifts from a modular
to an integral phase. In other words, if a firm remains decentralized when
the technology shifts to an integral phase, and finds it difficult to see
through the interdependencies in the new integral product that has emergedin the market then we talk about ‘modularity trap’.
Similarly, a firm with a centralized organization might fall into theintegrality trap when the modularity emerges in the market. Nonaka andTeece (2001) explored the Hard Disc Drive industry and detected both
modularity and integrality traps along the evolution of the industry. Ernest(2005) studied the Chip design industry, and talked about the occurrenceof modularity trap, referred to as ‘modularity limits’.

3Quantifying the emergence of modularity trap

C. Product architecture and the coupling index:
Ulrich (1995) defines product architecture as "the scheme by which thefunction of a product is allocated to its physical components". The
definition suggests a double perspective, including the mapping fromfunctional elements to physical components and the specification of theinterfaces among physical components. Hence, the notion of change inproduct architecture represents any modification to one of the architecture'parameters, i.e. the mapping between functions and components, and/or theinteractions between the components. In their effort to develop a methodthat helps create designs taking into account eventual future design changes,
Martin and Ishii (2007) defined the coupling index as a way to capture theweight by which a change in a component would affect another component
coupled to it. The coupling index was first introduced by Ulrich (1995) tomeasure or weight up the interdependencies between components. “Twocomponents are considered coupled if a change made to one of thecomponents can require the other component to change.”
Modularity in product architecture is a fertile ground research for many
researchers wishing to understand the dynamics of modularity and its
benefits. And a large body of scholars has examined the advantages anddisadvantages of modularity in product architecture. However, fewer
researches examined the limits of modularity, or the emergence ofmodularity trap, and the ones who did have tended to focus on particularcases studies from specific industries. In this paper, we propose aquantifiable measure to the emergence of modularity trap. We base on the
coupling index to build a sufficient mathematical equation to theemergence of modularity trap, and we propose an illustration of the method
using a case from the Hard Disc Drive industry.

4III- Coupling index hypothesis:
A. Stating the hypothesis
The coupling index was introduced to measure or weight up theinterdependencies between components. “Two components are considered
coupled if a change made to one of the components can require the othercomponent to change.” (Robertson, K. Ulrich 1995).
Building upon this definition, comparing the coupling indexes of two
products can be an effective way to tell which one of the two has a higher
degree of component' interdependencies.

Figure 1: Modularity trap in the time line of technological shifts

Accordingly, and based on the concept of technological shifts amongindustries and the definition of Modularity trap (Figure 1), we built our
hypothesis concerning the emergence of modularity trap as follows:
If the coupling index of the newly introduced product, referred to as the
Post-Modular Integral product is greater than the coupling index of the PreModular Integral product, we can say that the new integral product has
some new interdependencies that didn’t exist in the integral product thetechnology has started with. This means that the now-decentralized2 firms
might not be able to clearly detect these new interdependencies, and thus
might fall into the modularity trap.

2 With the assumption that, when modularity emerged in the market, the firms has

adopted a decentralized organizational structure.
5

Quantifying the emergence of modularity trap

We introduce the Coupling index ratio as follows:1
CI*(p)= 𝑝 ∑CIni/CIi(1)
Where p is the number of components, CIni is the coupling index ofcomponent “i” in the Post-Modular integral product, and CIi is the couplingindex of the component “i” in the Pre-Modular Integral product.
Our hypothesis supposes that if CI*>1, in other words, CIin > CIi, the firms
might fall into the modularity trap. Since the emergence of modularity trap
depends in a great part on the organizational structure of the firm as wellas the technological structure of the market, we add two conditions to thecoupling index hypothesis, as follows:
𝐷𝑒𝑐𝑒𝑛𝑡𝑟𝑎𝑙𝑖𝑧𝑒𝑑 𝑜𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑠𝑡𝑟𝑢𝑐𝑡𝑟𝑢𝑟𝑒{𝐼𝑛𝑡𝑒𝑔𝑟𝑎𝑙 𝑡𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑛𝑒𝑤𝑙𝑦 𝑒𝑚𝑒𝑟𝑔𝑖𝑛𝑔 𝑖𝑛 𝑡ℎ𝑒 𝑚𝑎𝑟𝑘𝑒𝑡→
𝐶𝐼 ∗ > 1
Modularity trap (2)The coupling index of each component is calculated using the weighting
system in Table I, considering the inter-dependencies between thecomponents and the functions first, and then weighting up the “distance”
of each component to the specific function.
TABLE I
Component’s dependency to functions’ weighting system

6

B. Steps of calculating the coupling index:

Step I: Quality Function Deployment matrix
Before diving into the calculation of the coupling index, it is necessary to
first state the functions (specifications) of the product, and the way they aremapped to the physical components. These mappings are depicted in a
Quality Function Deployment matrix (Figure 2), where the components are
represented in the columns and the specifications in the rows. When acomponent j enables a function i, the element Dij is represented by 1. In thecase where the component i doesn't enable the function j, Dij takes the valueof 0.

Figure 2: Example of the Quality Function Deployment matrix

Step II: Graphical representation of specification flows
In order to illustrate the specification flow between the components, wewill use the concept of closeness centrality. This concept first appeared inSocial network theory and was adapted to product architecture, "using thenotion of distance between components, such that the more distant acomponent is from the other components, the further its design
dependencies have to propagate" (Sosa & Eppinger 2007). Thus, in order
to illustrate the closeness centrality in the Graphical representations, wedraw the components (control volumes) taking into account the concept of"distance" between the control volumes. In other words, two components
are directly linked in the Graphical representation if the functioning of one
is directly dependent on the specification of the other. In this case, the latteris said to be "supplying" the specification to the former. The Graphicalrepresentation is then the illustration of theses "supplies" (flows) of thespecifications from the components.

7

Quantifying the emergence of modularity trap

Step III: Design Structure Matrix
In order to capture the criticality or weight of each dependency we use a 5points scale represented in Table 1. Accordingly, a component that is
directly dependent for its functionality, on a specification of the component
will have a higher dependency' weight on that specification, than that of acomponent that is indirectly dependent on the same specification. The
design structure matrix (Figure 3) depicts these dependency' weights in two
directions, namely: supply and requirement of specifications. i.e. when thefunctioning of a component i depends on the specification (a metric) of
another component j, then the component j is said to be "supplying" thespecification to the component i while the component i is said to "require"that specification. The supply of the specifications is represented as thecolumns of the DSM matrix and the requirements as the rows.

Figure 3: Example of Design Structure Matrix

In the Figure 4, the example highlighted shows the dependency between
the function F2 and the component C4. In other terms, we can see from thematrix functions-components that there are four components that canenable the function F2: C1 C4 C7 and C8. Each dependency is given by thecoefficient Di2 with i referring to Ci and 2 referring to F2.
Moving on to the components-components matrix, we can now look at theinterdependencies between the components i and j, measured as a distanceof component i to the function enabled by the component j.

8Following the example highlighted in the Figure 4, we can spot thecoefficient C42 in each of the cases notifying the interdependency between
the components that enable3 the function 2.
Once the interdependencies between each component to every othercomponent are determined, we move on to weight up these interdependencies. Usually, it is the work of the system experts to determine
these weights, but for the sake of convenience, we base our weighting on
the analysis behind the functioning principle of the hard disc drives, for ourillustration in the following chapter.

Figure 4: Illustrating interdependencies using DSM and QFD

In the example of a hard disc drive, the ‘head’ is considered to be the main
component ‘delivering’ the specification ‘seek time’ which is the period between the
moment the head receives a read/write signal from the control-circuit to themoment it finds the track it is requested to read/write from. In the example show
above, we suppose that the component 4 is the main component that enables ordelivers the function 2.
3

9Quantifying the emergence of modularity trap

IVCase study: Toshiba’s modularity trap, disc drive industry
In order to illustrate the hypothesis proposed in this paper, we will use acase study from the Hard Disc Drive industry, since the industry had
noticed rapid and disruptive changes in a ‘relatively’ short period of time
(Figure 5).

Figure 5: Time line of technological shifts in the disc drive' head industry“Those who study genetics avoid studying humans, because newgenerations come along only every thirty years or so, it takes a long timeto understand the cause and effect of any changes. Instead, they study fruit
flies, because they are conceived, born, mature, and die all within a single
day. If you want to understand why something happens in business, study
the disk drive industry. Those companies are the closest things to fruit flies
that the business world will ever see.” (Christensen 1997)
In this paper, we will focus on the disk drive’s head technology, andparticularly the emergence of the magneto-resistive technology, in order toprove the coupling index hypothesis, testing it on Toshiba’s response to theemergence of this at-the- time-new modular technology.
A. Technological shifts in the disc drive’s technology:
Since their introduction by IBM in 1956, disc drives used ferrite headtechnology up until late 1970’s, when IBM introduced a new technology
using ‘Thin Film Inductive heads’ in 1971 (Figure 5). With this newtechnology came new interdependencies between the head and the otherelements of the drive, announcing a technological shift to an integral phase.
A few years later, specifically in 1992, and after the thin film headtechnology shifted towards a modular phase, as the interdependencies
10became well understood by the market’ players, developmental limits tothis technology started to be felt, which gave birth to a new technology
using Magneto-resistive (MR) heads.
During the modular phase of the technology, i.e. the thin film technology,
Toshiba had adopted a decentralized organizational structure. Once the MR
heads emerged in the market, causing the technology to shift back to an
integral phase, Toshiba was faced with a challenge to communicate its
requirements and specifications to its head suppliers, which suggests thatToshiba appears to have fallen into the modularity trap.
B. Hard disc drive’s operation principle:
Testing our Coupling Index hypothesis on Toshiba’s ‘modularity trap’ in
the early 90’s will require comparing the coupling index of the thin film
heads technology when it was in its integral phase to the coupling index of
the MR heads technology when it had first emerged in an integral state as
well.
• Disc drive operation principle:
The hard disc drive is an electronic device for which the main function isto write, store and read computer data from a rigid/hard rapidly rotating
disc. In addition to the turning disc (platter), the drive also contains amagnetic head flying over the disc to write data on the surface and readfrom it. (Christopher & Bajorek 2014)• Main differences between TFI technology and MR technology:
In the Thin film inductive technology, the read head consists of aferromagnetic material wrapped with wire. The operation principle follows
Faraday’s law: when the head passes over magnetized sections of the disc,the change of the magnetic flux induces an EMF, generating a current flowin the wire.
On the other hand, the magneto-resistive technology is based on the simple
fact that the electrons move easier when their magnetization is aligned withthe magnetization of the layer they are moving into. Thus, the MR head iscomposed of a tiny electrical resistor, when the head is reading data fromthe disc, the changes in the magnetic flux emanating from stored bits,
induce changes in the electrical resistance in the head.
This been said, we can already point out some interdependencies’
differences between the two technologies. The change of the resistance ofthe MR head doesn’t depend on the disc velocity (rotational speed), which
11Quantifying the emergence of modularity trapmeans that the read-back signal captured by the head is independent of the
speed. In other words, there is more weight to the specification “read-backsignal” in the interface head-disc in the TFI drive than that in the MR drive.
Other major differences can be derived from the challenges that the MR
heads presented when they first emerged in the market, namely: ESD:
Electrostatic discharge, and TA: Thermal Asperities4. Overcoming thesechallenges suggested redesigning the pre-amplifier, to be able to recoverfrom the distorted signal by TA. The pre-amplifier had to also ensure a near
0Volt potential for the MR sensor to avoid ESD.
Since these challenges didn’t exist in the era of thin film head, we can saythat the MR head drive came with new specifications, leading to newinterconnections between the drive’s components 5.
C. Coupling index for the hard disc drive•

Quality Function Deployment Matrix: QFD

The Quality Function Deployment matrix for the Thin film drive and theMR drive are displayed in (TABLE II) and (TABLE III), respectively.
TABLE IIQFD for the thin film head’ drive

4 TA is defined as physical defects caused when the head flies too close to the disc causing

physical defects and distortion of the signal in the part of the disc where the head
inadvertently touches.
5 We tried to use control volumes: components (or in some cases sets of components) at
approximately the same level of complexity as the “head”. For example, the DC motor
contains in itself many components (Stator, Rotor, Shaft, Logic Controller and a power
supply inverter), but since the objective of this paper is to prove the CI hypothesis and totest it on the disc drive’s heads industry, we ignored the interactions between
components inside our control volumes.
12For example: the arm actuator, the head, the platter, the DC motor and thecontrol circuit all have an impact of the specification “Seek time 6”. For the
MR drive, the head, the platter and the pre-amplifier can affect the
specification of “avoiding ESD”.
TABLE IIIQFD for the MR head’ drive•

Graphical representation of specification flows

We draw the graphical representation of specification flows for the thinfilm head HDD (Figure 6) and the MR head HDD in (Figure 7). The squares
indicate the components (control volumes), and the arrows indicates thedependency’ directionality. An arrow in the same color with control
volumes indicates a specification supplied from that component.

Figure 6: Graphical representation of specification flows of the TFI drive
6 The seek time measures the time the head needs to access the data on the drive.

13

Quantifying the emergence of modularity trap

For example, the cylinder switch time CST 7, is a specification proper tothe platter. Thus, the specification flow is represented by an arrow
emanating from the control volume “platter” and of the same color of thecontrol volume: orange. On the other hand, the functionality of the platteris directly dependent on the DC motor controlling the rotation of the platter.
If the DC motor was to stop supplying the specification “rotational speed
RS” to the platter, the platter will lose its functionality. This is represented
by a direct link from the DC motor to the platter, supplying the specification“rotational speed RS”.

Figure 7: Graphical representation of specification flows of the MR drive•

Design Structure matrix: DSM

The main differences between the two technologies reside in thespecification flows from the pre-amplifier. The interface head-platter has
also changed, and the weight of the head’s dependency on the media rate
(read-signal) has changed from 9 in the thin film drive (Table 4) to 6 in the
MR head (Table 5). This is due to the fact that in the MR head the change
in the resistance does not depend on the velocity of the platter, making thesignal independent from the platter’s rotational speed.

7 CST is the time it takes the platter to switch from one cylinder to another.

14

Figure 8: Coupling index of the Thin Film head drive

In the case of the ‘seek time’ specification, which is a specification properto the head, the weight of dependency of the arm actuator on thisspecification is 6, since the arm actuator is directly coupled with acomponent: the head, which depends for its functionality on the seek time
specification. And that the arm actuator doesn’t depend on the specification“seek time” for its functionality.
15

Quantifying the emergence of modularity trap

Figure 9: Coupling index of the MR head drive16

D. Result’s interpretation:
Following the notations adopted in this paper, we can write:
𝐶𝐼𝑛𝑖 = 295 (3) 𝐶𝐼𝑖 = 247 (4)→ 𝐶𝐼 ∗= 1.19 > 1 (5)This result shows the change in the coupling index, representing the weightof inter-dependencies between components, following the technology
evolution from a pre-modular integral state, to a modular state, and then toa post-modular integral state. When Toshiba had fallen into the modularity
trap, the company was adopting a decentralized organizational structure.
When the new technology (the MR head technology) emerged in themarket as an integral technology, Toshiba was not able to understand the
new inter-dependencies between the components, and thus it was not able
to clearly specify its needs to its suppliers, thus falling into the modularity
trap.
Calculating the coupling index of the newly emerging technology, andcomparing it with the coupling index of the integral technology thatpreceded the emergence of “modularity” in the HDD market, have shownthat in this particular case, the post-modular integral technology contained
new inter-dependencies that didn’t exist in the product that preceded theemergence of modularity, which have caused Toshiba to fall into themodularity trap.
VCONCLUSION
To conclude, it is necessary to mention that, the coupling index we
introduce is a "risk factor" translating the risk of falling into the modularity
trap, for firms adopting a decentralized organizational structure, when themarket shifts back to an integral state. In that case, and if the coupling index
hypothesis is satisfied, i.e. if the newly introduced product brings in new
interdependencies that didn't exist before, then those organizationallydecentralized firms might face a high risk of falling into the modularity trap,
should they not be able to quickly get an insight into the newinterdependencies.
For the particular case of Toshiba, illustrating the steps of calculating thecoupling index using the QFD and DSM matrixes, have helped us show theextent to which the inter-dependencies between the HDD components havechanged along the technological shifts that the industry has noticed in a
relatively short period of time. The fact that the MR head had a greater

17Quantifying the emergence of modularity trap

coupling index than that of the TFI head explains the reasons behindToshiba’s modularity trap, as the company was not able to clearly see
through the new inter-dependencies that emerged with the post-modular
integral product.
Having a quantifiable measure to the emergence of modularity trap will bebeneficial in a double dimension. First, relaying on the coupling index as ameasure to the inter-dependencies between the components, and on thecomplexity managing tools (such that the DSM and QFD) to depict thechange propagation within a product, might be of good help to firms andengineers wishing to keep track on the evolution of the technologicalstructure of their product in terms of its component’s interdependencies,
along with the firm’s organizational structure. On the other hand, this paper
can serve for a basis to a future study, on modelling the emergence of themodularity trap. Having a mathematical condition to the emergence ofmodularity trap, can help building a computational model that simulates,
and thus shows the conditions behind the emergence of the modularity trap.
The next step of this research will consist of using a generative approach,that is building an Agent Based Computational Model, to simulate theemergence of modularity trap and show the conditions under which a firm
can fall into the modularity trap.

18

REFERENCES
Y. C. Baldwin, K. B. Clarck, Design rule: the power of modularity. MIT press, pp.63-91, 2000.
H. Chesbrough, A. Prencipe, Networks of innovation and modularity: a dynamic perspective. 414Int.
J. Technology Management, Vol. 42, No. 4, 2008.
D. Ernst Limits to Modularity: Reflections on Recent Developments in Chip Design. Industry andInnovation, Vol. 12, No. 3, pp.303–335, 2005.
S. Russel, P. Norvig. Artificial Intelligence A modern approach. Prentice Hall, pp. 31-106, 2004.
R. Bellman The theory of dynamic programming. The Rand corporation, pp 550, 1954.
R. E. Mellor Investigating Kauffman's NK Model for Agent-Based Modelling. 2007.
C. J. Woodard, E. K. Clemons. The Evolution of Modular Product Architectures and the Emergenceof Platform Ecosystems. MIT press, 2013.
M. E. Sosa, S. D. Eppinger, and C. M. Rowles, A network approach to define the modularity ofcomponents in complex products. Journal of mechanical Design, Vol. 129, 2007.
M. V. Martin, K. Ishii. Design for variety: developing standardized and modularized productplatform architectures, 2007.I
D. Robertson, K. Ulrich. Planning for product platforms. Sloan Management Review 39:19, 1995.
Christopher H. Bajorek 2014. Magneto-resistive (MR) heads and the earliest MR head-based disk
drives: SAWMILL AND CORSAIR, 1995.
H. W. Chesbrough, K. Kusunoki. The Modularity Trap: Innovation, Technology Phase Shifts andthe Resulting Limits of Virtual Organizations, 2001, in I. Nonaka, D. Teece Managing industrial
knowledge. SAGE Publications, pp.202-227, 2001.
R. Sanchez. Modular architectures, knowledge assets, and organizational learning: new management
processes for product creation. International Journal of Technology Management, 19(6), pp. 610–
629, 2000.

19