Online ISSN 1347-4456 Print ISSN 1347-4464

ABAS: Annals ofBusiness Administrative
ScienceVol. 4, No. 2, April 2005

Kenichi KUWASHIMA
Nobuo TAKAHASHI
Masaki TAMADA

9An Analysis of Organizing Process of R&D Projects:
Multi-agent Simulation and Case Study

http://www.gbrc.jp© 2005 Global Business Research Center

ABAS: Annals of Business Administrative ScienceVol. 4, No. 2, April 2005

CHIEF EDITOR

Junjiro Shintaku

University of TokyoEDITORIAL BOARD

Makoto Abe
University of Tokyo
Takahiro Fujimoto University of Tokyo
Makoto Kasuya
University of Tokyo
Hotaka Katahira
University of Tokyo
Nobuo Takahashi University of Tokyo
Technical Editor: Maki Nishida, Global Business Research Center

ABAS: Annals of Business Administrative Science is published quarterly in January,
April, July, and October, by Global Business Research Center, Hongo, Bunkyo-ku, Tokyo,
JAPAN. http://www.gbrc.jp

Annals of Business Administrative Science Vol. 4, No. 2 (April 2005)

An Analysis of Organizing Process of R&D Projects:
Multi-agent Simulation and Case Study
Kenichi KUWASHIMA
Graduate School of Business Sciences, University of Tsukuba
E-mail: kuwa@gssm.otsuka.tsukuba.ac.jp

Nobuo TAKAHASHI
Graduate School of Economics, University of Tokyo
E-mail: nobuta@e.u-tokyo.ac.jp

Masaki TAMADA
Kozo Keikaku Engineering Inc.
E-mail: tamada@kke.co.jp

Abstract: The purpose of this paper is to analyze the organizing process of research
projects, which affect the performance of research and development (R&D) crucially,by using agent-based simulation and case study. We develop a multi-agent
simulation model that contains two types of R&D style: Japanese pharmaceuticalcompanies’ style and Merck’s style. Simulation result proves that the senior
managers observed in Merck possessing strong communication capabilities, whom
we call “HWCM (Heavy-Weight Communication Manager)” in this paper, enhance
initial start-up of projects. Furthermore, we study the case of Merck again and try toshow the effect of HWCM on high R&D performance of Merck.
Keywords: R&D, multi-agent simulation, HWCM

1. Introduction

process of research projects, which affect theThis paper employs multi-agent simulation and caseperformance of research and development (R&D)

study for the purpose of analyzing the organizing

crucially. Multi-agent simulation is a relatively new

9

Kuwashima-Takahashi-Tamada
method, which came to gather attention in the 1990s,(Heavy-Weight Communication Manager) at Merck

yet has already been applied in various fields of

who have strong communication capability enhances

studies (e.g., Epstein & Axtell, 1996; Thomas &start-up speed of projects. Based on simulation

Seibel, 1999, 2000). From existing studies up to thisresults, this paper compares again the cases of Merckdate, we may say that there are two types of opposite

and Japanese pharmaceutical companies to confirm

approaches in multi-agent simulation method.

the possibilities that HWCM practically contributes
to the high R&D performances at Merck.

(a) Build a relatively abstract model, such as
artificial society, and extract various findings
from there

2. Communication Competition Model
2.1.

(b) Build a model on the most specific individual
example and use the model literally forcalculation

Pharmaceutical

R&D

Process

andHeavy-Weight Communication Manager
Pharmaceutical industry in general is said to be
determined much of their corporate performances by

For instance, Epstein and Axtell (1996) is a

R&D achievements. Placing a new innovative drug

typical example of approach (a); they built anon the market will yield enormous profit, on theartificial society model called Sugarscape whereother hand pharmaceutical product development

food (sugar) is placed here and there for the agents

costs nearly 20 years of time and 10 to 20 billion(ants) to eat. They analyzed the model and extracted

yen; 30 to 50 billion yen including failed projects.

various implications as mating, culture, war, andTherefore, at pharmaceutical companies, release of aepidemic. On the other hand, as an example of

new drug has become of great managerial goal

approach (b), Thomas and Seibel (1999, 2000) have

(Kuwashima & Takahashi, 2001).

successfully improved cargo operations at Southwest

In typical cases at Japanese pharmaceutical

Airlines by putting the multi-agent simulationcompanies, first a research theme is set thenmethod into practice.

chemists

While the two approaches are thus quite

andbiologists

startworkingindevelopment teams of one to several personnel.

opposite, this paper will follow approach (a). We

Ideas are brought up within these small teams and if

build a multi-agent model from different R&D styles

a potential chemical compound is discovered,at Japanese pharmaceutical companies and one ofchemists and biologists are added to examine various

the world’s largest pharmaceutical company, Merck.

derivatives synthesized from the leading compoundWe further go through the different organizational

to attain stronger activity. Accordingly, research at

performances which the different R&D styles leadindividual level is launched as an official project.

to.

As an example, we would like to investigate a

In conclusion, a senior manager as HWCM
10

Organizing Process of R&D Projects
R&D case of a carbapenem antibiotic Carbenin;

companies as to rank in the top ten, generally various

nonproprietary name panipenem (Kuwashima, 1998).

research themes are studied at the laboratories. In

Carbenin has been released from Sankyo in 1993 asaddition, multiple research teams are organized for a

the first domestic carbapanem antibiotic product,

single theme according to different approaches.

which is one of the company’s core products selling

Typically, when a certain theme or approach turns

12.3 billion yen in 2000. Research which lead toout to be promising, researchers on other themes andCarbenin started in 1977 where two themes were set

approaches join the hopeful one, thus an official

simultaneously: search for carbapenem compound

project is launched. Relocation of researchers fromand synthesis of related penem compound. Initiallytheme or approach is basically agreed upon talks

though, according to differences in approaches, there

between researchers and locale supervisor of general

were in total four research teams, one carbapenem

manager rank.

team and three penem teams. Since 1978, various

In complete contrast, when we turn our eyes

penem compounds were synthesized yet none proved

from the typical project start-up process in Japan toeffective displaying acute toxicity or insufficient inthat of overseas companies as Merck in US, wevivo activity. On the other hand, carbapenem showed

would notice an aspect outstandingly dynamic; thatstronger antibacterial activity. At this, penem

is the presence of senior managers with strong

research teams shifted to carbapenem research andcommunication capability and their commitment toapplied penem compound synthesis methods tocorporate performance. At Merck, ideas fromcarbapenem. Various derivatives were synthesizedresearch centers worldwide are gathered andsuch as pyrrolidine derivative and other cyclic amine

launched as official projects. During the process,derivatives. In 1981, panipenem, the sought after

managers at the research centers play a vital role. In

substance, has been discovered. Thereafter, yet many

many cases, these managers are world authorities on

problems stood before them from pre-clinical test toa specific field of illness, who is a medical doctor at

actual market release, we would like to point out two

the same time a researcher of medicine. For example,

aspects in the R&D process hitherto.

at Banyu Pharmaceutical in Japan, which is affiliatedto Merck, the head of Tsukuba Research Institute is a(1) At the initial stage, four teams of different
approaches existed under two research themes.

world authority on cancer, who has been picked out
from a national laboratory. Merck lay emphasis that

(2) Since penem research team shifted tocarbapenem research, synthesis methods from
penem teams were brought to the joint research
team.
In

such

major

Japanese

these managers play a crucial role upon project
launch.
This paper aims to examine what benefit isbrought to R&D process, in particular, organizing

pharmaceutical
11

Kuwashima-Takahashi-Tamada
process of research projects, when a senior manager

themes and approaches. We shall depict the ways

with strong communication capability (HWCM:

that agents flow into promising clusters, thus

Heavy-Weight Communication Manager) is ineffecting research themes and approaches to merge.

presence. We intend to utilize multi-agent simulation

Our interest is to see what happens when
HWCM as found in Merck is introduced to above

method for our purpose.

Japanese company model. How are the start-up
2.2. Outline of Model

processes in Red and Blue research projects

As we can see from the Carbenin case in section 2.1,

influenced by this? In the simulation model, “ideas”“ideas” are significant at the organizing stage ofare in a sense treated as “bait” for which agents

pharmaceutical research projects. Therefore, we willcompete, thus, hereafter we shall call this modelbuild an multi-agent simulation model assuming that

Communication Competition Model, for short,the organizing process of a research project is a

ComCom Model. Below, we will describe the model“cluster formation process by agents (researchers)by specific rules presuming that we use KK-Multi

who possess ideas.”

Agent Simulator for multi-agent simulation.

First, we picture the R&D process in the
Carbenin case at a Japanese pharmaceutical company.

(1) Path Length: L

For simplification, we assume that there were two

Path length is defined as following 1 to 3 andthemes in presence, Red and Blue, and researchersdetermined as in the example of Figure 1.

are initially involved in either. Next, as it is hard to
give external criteria to the potential of themes orapproaches, we simply grant that the more the ideas

Figure 1. Determination of Path Length L

are in presence, the more promising the theme orapproach is. Then we hypothesize that

Path length

Cluster Formation Examples

L=1

A

B

A

A

B

(1) agents possess ideas and move towards
positions where they are more able tocommunicate with other ideas and agents
possessing ideas;

L=5

A

B

A
B
A
B

(2) when multiple clusters (research teams) exist,
agents choose a cluster where they are able tocommunicate with more ideas.

L=6

We thus describe the rules by which agents
form clusters seeking more promising research
12

A
B

A

B

A

B

A
B

B

Organizing Process of R&D Projects
1. A path is formed between agents A and B when
A and B are the same color and all agentsaligned in between A and B are also the samecolor. A and B are able to communicate.

As shown in Figure 2(A), agents belonging to asame cluster have different amounts of effective idea
according to where the agent stands within thecluster. In Figure 2(A), the amount of effective idea

2. Path length L is determined by the number of
agents between agents A and B. However, agent
B is counted as well, thus when A and B is next
to each other, it is L=1.

for A is larger than B, and that for C is larger than A.
In general, agents posted nearer the center of thecluster have larger amount of effective idea. We shall

3. If multiple choices of paths exist, the shortest
should be determined as path length L.

describe as follows the rules of ComCom Model by
the amount of effective idea.

(2) Amount of Effective Idea

1. An agent moves towards larger amount ofeffective idea.

We define the amount of an agent’s effective idea as
the sum of the total number of same colored agents

2. An agent moves the distance of either 0 or 1 in
one period.

whom the agent is able to communicate withweighted by 1/L. A typical calculation is shown in

3. An agent searches other agents within distance
1. In other words, an agent cannot perceive
agents beyond distance 2.

Figure 2(A). It is weighted by 1/L because we
assume that where path length L is greater, the4. An agent in contact with a cluster of othercolored agents will switch sides (i.e., change
color) when by doing so the amount of effective

impact of a idea weakens and communication of theidea takes time.

Figure 2. Calculation of the Amount of Effective Idea
(A) Determining Amount of Effective Idea
calculation of corresponding number of agents and amount of effective idea

A
L=1
L=2
L=3
total

C
A

B

B
3
5
3
11

3×1=3.05×(1/2)=2.5
3×(1/3)=1.0
6.5

C
5
6

5×1=5.0
6×(1/2)=3.08
38×1=8.0
3×(1/2)=1.5

11

8.0

11

9.5

(B) Change in Amount of Effective Idea after Big Agent Input
calculation of corresponding number of agents and amount of effective idea

A
C
A

DD
B

L=1
L=2
L=3
total

B
3
8

3×1=3.08×(1/2)=4.0

4
7

4×1=4.07×(1/2)=3.5

11

7.0

11

7.5

13

Kuwashima-Takahashi-Tamada
that move in each period. The activity rate for
period t+1 represents the amount of activity
which occurred between the state in the map oftime period t and t+1.

idea of the agent becomes larger.
Under above rule, an agent will preferbelonging to a cluster, and possibly a larger cluster,

2. Total amount of effective idea: This is the totalsum of the amount of effective idea of allagents belonging to the same cluster. From thelattice model in Figure 1, we treat agents joint
at a corner of a cluster as members of thecluster as well.

than staying alone.

(3) Big Agent
Big agent is a concept created with HWCM at Merckin mind, who is literally a “big” agent capable to3. Mean cluster scale: The number of agents
consisting a cluster is called cluster scale. Meancluster scale is attained by dividing the total
number of agents by the number of clusters.

contact and communicate directly with greaternumber of agents. In particular, for example, in
Figure 2(A), suppose agent C has become the size of
three cells. Given that the number of agentscomposing a cluster does not alter, it should become

2.3. Simulation Results

as in Figure 2(B). Accordingly, 1) a big agent has

2.3.1. Competition between Research Themes

more surfaces potentially in contact with otherfor Agentsagents, and 2) however big the agent is, path length

In the present model, we presumed that every agent

is still measured as 1. Therefore, as in Figure 2(B)is involved in either Red or Blue research theme.

the amount of effective idea changes.

Here, for the purpose of investigating the effect ofStill, casting in a big agent does not necessarily

big agents, we would input big agents to theme Red

mean that all other agents in the cluster will gain

and see if this will avail theme Red in acquiring

more amount of effective idea as well. In fact,

more agents.
In practice, we compare the following two

comparing Figure 2(A) and Figure 2(B), we can see

cases.

that the amount of effective idea of agent A has
increased from 6.5 to 7.0, on the other hand, theCase 1: ten L=1 Red agents; and ten L=1 Blueagents.

amount of effective idea of agent B has dropped
from 8.0 to 7.5.

Case 2: eight L=1 Red agents and two L=3 Red
agents, in total ten Red agents; and ten L=1
Blue agents.

(4) Index
In our model, the following three indexes are

Cluster formation is significantly influenced byindicated in numeric value and map.

the initial posting of agents, therefore prior to a
1. Activity rate: This shows the number of agentssimulation we specified a random number seed value
14

Organizing Process of R&D Projects
for KK-MAS execution configuration. Particularly,

result was approximately 10. Next we proceed toin each case, random number seed value takes 1 toCase 2 where big agents were input in theme Red

30, increased by 1 in 30 trial runs. “Mean cluster

alone, replacing two regular agents with two big

scale” and “total amount of effective idea” are kept

agents. In result, we could not see any advantage

record up to period 300 in each trial run.

gained by theme Red against Blue in gatheringFirst we compare the results at period 300 in theagents. Comparing results at period 300, the average

standard Case 1. The average number of agents innumber of Red theme agents is reduced to 7.70 fromtheme Red was 10.07 while average number of

10.07 in Case 1. Mean difference is not significant (tagents in theme Blue was 9.93, thus in both cases=1.633; p =0.108), nevertheless, theme Red proves

Figure 3. Average Mean Cluster Scale
Regular Agents Only

2 Big Agents Input

7

average mean cluster scale

6
5
4
3
2
1
0
0

50

100

150

200

250

300

periodFigure 4. Average Total Amount of Effective Idea
Regular Agents Only

2 Big Agents Input

average total amount of effective idea

120
100
80
60
40
20
0
0

50

100

150
period

15

200

250

300

Kuwashima-Takahashi-Tamada
narrowed.

weaker. From observation of simulation process, it
revealed that the reason for this was that the big

In sum, 1) input of big agents as a wholeagents have switched to theme Blue at an early stage.

enhance the formation of clusters and total amountThus, big agents choose between themes as well asof effective idea at an earlier stage. 2) Though speed

regular agents. In other words, the theme big agentsis enhanced, neither mean cluster scale nor total

are assigned to at an initial stage is not critical whenamount of effective idea take lager values in the end.

research themes compete to acquire more agents, inother words, when they compete for research

3. HWCM in Practice and R&D

resources.

Performances
3.1.

Case

Study:

Merck

andJapanese

2.3.2. Mean Cluster Scale and Total AmountCompanies

of Effective Idea

As have been described, the effect of big agent

Based on above finding, next we shall give the(HWCM)’s presence were proved in the simulation,

statistics on cluster scale and total amount ofthat is, initial rise in (1) number of cluster formation,effective idea on the whole disregarding themes Red

and (2) amount of effective idea. What kind ofand Blue. Figure 3 shows the average mean cluster

observation can we make from this simulation result

scale for 30 trial runs concerning Case 1 and Case 2.

in actual R&D process? Here, based on simulation

Accordingly, Case 2 where big agents are cast in,results, we shall compare again the case at Merckreveals a tendency to experience a growth of mean

and Japanese pharmaceutical companies.

cluster scale at an earlier stage. Nevertheless, atFirst, we will go over result (1). As we haveperiod 300, mean cluster scales are slightly more

briefed in 2.1, at Merck, HWCM, who is a medical

than 6 in either case, thus the gap is narrowed.

doctor knowledgeable of biological mechanism at

Therefore, big agents do not commit to the size ofthe same time engaging in pharmaceutical studies,clusters but to the earlier formation of clusters.

play a vital role in R&D process. As an example, we

Likewise, in Figure 4, total amount of effective

would like to investigate the case of an anti-cancer

idea for each period in 30 trial runs are averaged out.

drug which has been at initial clinical testing stage at

As the figure indicates, total amount of effective idea

West Point in 1999. When biologists and chemists atincreases at an earlier stage in Case 2 where big

West Point were discussing the improvement ofagents are cast in, similar to the analysis of mean

existing drugs, a researcher invited from National

cluster scale. However, at period 300, total amountCancer Institute reviewed the idea of an anti-cancerof effective idea in either case are slightly more thandrug among their discussions. Then a cancer expert

100; again the gap between Case 1 and Case 2 are

invited to Tsukuba Research Institute of Banyu
16

Organizing Process of R&D Projects
Pharmaceutical, one of Merck affiliates, at oncestage of R&D process, that is, there are moreparticipated in strategic councils and researchdifferent and varying ideas which become thereviews at Merck. This lead to collaborative cancer

sources of new drugs. Concerning this issue, there isstudies between Banyu Tsukuba Research Institutecollateral evidence. From the latter half of 1980’s toand West Point. Cluster formation was thus swift andthe 1990’s, a new technology called HTS (HighHWCM acted much alike big agents as in a

Throughput

simulation model. We can see that the presence ofreviewing various samples of new drugs at high

HWCM contributes significantly to such quick

velocities. HTS review large quantities of samples

cluster formation. In fact, a person in charge of

automatically at one time by processing a microplate

Worldwide Strategic & Capital Planning at Merckwith numerous (generally 96) holes. It is frequently

remarked that “the secret of Merck’s strength lie in

used together as a set with a massive synthesis

the fact that people who are both medical doctor andtechnology called CC (Combinatorial Chemistry).

pharmaceutical

held

Lately, by far powerful UTS (Ultra Throughput

responsible posts for the past 25 years.” Being

Screening) have appeared, which claims capable offamiliar with both human biological mechanism

reviewing 3000 samples at a time and 500 thousand(medicaldrugs

samples in a week. The most crucial key to success(pharmaceutical science) will allow, for example, toutilizing such high-velocity screening machine is toenhance multilingual communication (Clark &provide massive amounts of samples of various

Fujimoto, 1991) between two different groups ofstructures. Theoretically, given that as long as there

knowledge system or influence review competence

are ideas infinite numbers of samples can beof samples and projects. This presumably contributes

synthesized with CC, the crucial point is to haveto earlier formation of clusters. On the contrary,

distinct and various ideas sufficient to run thewhen compared to cases in Europe and US, Japanesemachine at capacity. In regard of this fact, at leading

pharmaceutical companies have much few numbers

Japanese pharmaceutical companies HTS operates

of medical doctors, and such senior manager as

far below their capacity, though almost 80% of them

HWCM in Merck practically do not seem to exist.

introduce HTS (Nihon Seiyaku Kogyo Kyokai,

researcher

science)and(HWCM)

pharmaceutical

Screening)

becamepopularfor

Next, we will consider Result (2). We would

1998). In contrast, UTS is open for 24 hours andlike to investigate whether in fact at Merck amountoperates most of the time at Merck West Point. Canof ideas build up as quickly as in the simulation

we not say from this example that quantity andresult. To prove this, we need to know that at Merck,

quality of ideas differ between Merck and Japaneseinfirms?

comparison

withJapanese

pharmaceuticalcompanies, there are more ideas in the upstream
17

Kuwashima-Takahashi-Tamada
3.2. Influence on R&D Performances

release.

From above discussion, it is confirmed by both

Actually, Merck with HWCM system shows

simulation and case analysis that presumably the

extraordinary performance in placing new productspresence of HWCM enhances the speed of initialon the market. Some of Japanese top ten

cluster formation and building up of ideas. Then, we

pharmaceutical companies seem to have none or few

would like to investigate how the actual R&D

self-developed product in the past ten years. In

performances at pharmaceutical companies arecontrast, Merck released Mevacor (lipid-lowering

influenced by the two “speeds” achieved by theagent, released 1983), Vasotec (antihypertensive

presence of HWCM. In general, R&D speed

drug, released 1986), Pepcid (anti-gastric ulcer drug,(development lead time) is considered to bereleased 1986), Zocor (lipid-lowering agent, released

extremely crucial to the competitive advantage of a1992), and Cozaar (antihypertensive drug, released

firm (Clark & Fujimoto, 1991). Speedier R&D result1998), naming merely the big hits selling over one

in shorter time required for a project, thus more

hundred million dollars a year. As a result, Merck

projects could be undertaken in a given period. In

kept a profit increase rate over 10% per year

pharmaceutical companies as well, assuming thatthroughout the 1990s, and in 2000, sales reachedsuccess rate is fixed, a firm which has gained R&D

approximately

speed should be able to develop more new drugs.

pharmaceutical industry where M&A mammoths

when

two

companiesofbilliondollars.

Inthekeep growing, Merck walks its unique path while

Moreover, in the case of pharmaceuticalindustry,39

maintaining world best standing.

different

R&D-product development speed compete, there
would be a wider gap in the number of new drug

4. Conclusion

release between the two than the number of newThis paper built multi-agent simulation model fromdrug development successes. This is because in

the different R&D styles at Japanese pharmaceuticalpharmaceutical industry, “first-mover advantage” iscompanies

particularly strong. There is said to be no market forpharmaceutical company, Merck. We reviewed how

latecomers who could not rank within the first few inthe differences influence the R&D processes anda particular area of medicinal properties. Therefore,

performances. Simulation result showed that a senior

release is canceled when a costly project turns out be

manager as HWCM in Merck, who has strong

almost a success in product development itself,

communication capabilities, shorten organizing timenevertheless, a good many number of competingof R&D projects. Furthermore, we compared again

products already exist in the market. Development

Merck and Japanese pharmaceutical companies by

speed decisively effects the number of new drugcase analysis to prove the possibility that HWCM
18

and

one

of

the

world

largestOrganizing Process of R&D Projects
does in fact contribute to strong R&D performancescase analysis; We aim to measure HWCM byof Merck.

manipulated profiles and behavioral patterns alongwith more systematic analysis of the relation

The reason that our study conducted casebetween HWCM and R&D performances.

analysis in addition to simulation analysis was toprovide complementary investigation to the result
drawn from the “rather abstract simulation model

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ni

okeru

chitekiseisanryoku

kojoKuwashima-Takahashi-Tamada
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