**Simulation in Japan: State-of-the-Art Update **

**Shigeki Umeda\***

**and**

**Albert Jones**

**Abstract**

Computer hardware continues to decrease in cost and increase in power.
Simulation vendors have hypothesized that this situation, together with
new requirements for system integration, has produced a new business
environment for discrete-event simulation all over the world. This
paper, which provides an update to an earlier report, describes current
trends in Japanese industry regarding the use of discrete-event
simulation. The information contained in the paper was derived from
interviews with many representatives of Japanese industry. It shows
that, in general, this hypothesis is true in Japan.

· Guest Researcher from Musashi University, Tokyo, Japan**1.
Introduction**

Recent advances in a number of technologies have provided industrial
users with high performance computer hardware and powerful Graphical
User Interfaces (GUI). This, in turn, has led to significant advances in
computer performance and visualization. The result is a collection of
very powerful software tools for solving problems across the
manufacturing enterprise \[1-4\]. Part designers, manufacturing
engineers, and production managers can now compute [and]{.underline}
visualize the impact of their individual decisions right at their
desktop. They can assess, to a limited degree, the impact of their
decisions on both product and process performance and company
profitability. To compute the real impact of those decisions, however,
they must work together. That is, their decisions must be made in a
coordinated way. This requires communication between the people, and
integration of the software tools that they use to make those decisions
\[5-8\].

One of those tools is simulation. Recently, many simulation tools (both
continuous and discrete-event simulation tools) have become available on
the market. \[9-15\]. The advances in computing mentioned above have
made it possible to run these tools on desktop computers. This, in
addition to improvements in the tools themselves, is the main reason
that simulation is used heavily in manufacturing plants around the
world.

Nowhere is this more evident than in Japan, where simulation use is on
the rise. This paper, which extends our earlier work \[16-18\],
summarizes the current use of simulation in Japanese industry. It also
discusses some of the criteria used to make purchasing decisions.
Finally, it presents some preliminary views of Japanese industry toward
the use of simulation as an enabling technology for both system
integration and virtual manufacturing.

**2. Simulation practices in Japanese industries**

**2.1 Software Introduction**

Figure 1 displays the evolution of simulation use in Japanese industry.
It shows the number of new manufacturing firms using simulation in their
company. The graph shows very little interest after its introduction
into Japan more than 25 years ago. However, starting in 1985, a gradual
increase occurred.

**Figure 1. New Users of Simulation Software **

Around 1989 or 1990, a dramatic expansion occurred when many
manufacturing enterprises made major investments in computer
technologies and manufacturing equipment. Following this, a sharp
reduction took place. The number of new users declined to pre-89 levels
and remained relatively constant for a few years. The main reason for
this, we believe, was the downturn in the Japanese economy. Then,
remarkably, another dramatic increase took place in 1994 or 1995. We
think that the main reason for this was the rapid advances in computing
technologies mentioned above. These changes included a decrease in
price, growth in processing power, more robust operating systems, and
better user interfaces. Many vendors took advantage of these changes to
release desktop simulation tools. These new tools had graphical user
interfaces that were integrated with existing windows-based operating
systems (see Table 1). This was very attractive to simulation users.

# Table 1. Computing Environments

  ----------- ------------- ------ ----- ------------------- ----------------------------- ----- ---------------- -----------------
  Computers   PC            WS                                                                                    
  OS          DOS/WINDOWS   UNIX   CPU   DX-4, P-90, P-100   various (R4000,MIPS, SPARK)   RAM   16 MB or 32 MB   64 MB or 128 MB
  Disk        500 MB over   2 GB                                                                                  
  ----------- ------------- ------ ----- ------------------- ----------------------------- ----- ---------------- -----------------

**2.2 Target applications of simulation analysis**

The two main users of simulation tools are communications companies and
manufacturing enterprises, with the latter being the most popular. The
target applications within these companies that are the subject of
simulation studies are shown in Figure 2.

**Figure 2. Target application systems **

The most popular applications deal with the movement of raw materials
and finished goods. These include factory material handling systems,
logistics systems including automated warehousing, and transportation to
and from those warehouses.

To date, the least popular applications areas are computer and
communication systems. However, we believe that

**Certain commercial software and hardware products are identified in
this paper. This does not imply approval or endorsement by NIST, nor
does it imply that the identified products are necessarily the best
available for the purpose*.***

this is likely to change since many companies have introduced Local Area
Networks that are connected to the INTERNET. As these systems grow in
complexity and importance, industrial users will require tools to
analyze various design alternatives and to predict performance.

**2.3 Simulation project characteristics**

Figure 3 shows simulation projects organized by department. The people
who actually carry out the project can be classified into two groups:
the developers' group and the users' group. People from the developers'
group analyze potential problems, design simulation models to solve
those problems, and implement those models on target computing
environments. People from the users' group run the developed models to
give a solution to specific instantiations of those problems. That is,
they provide the actual data to run the models. For example, suppose the
plant manager decided that a new simulation-based scheduler was needed.
The developers' group would develop the model with parameters for all of
the required variables. Each time a new schedule was required, a user
would input that data, run the simulation, and get a new schedule.

**Figure 3. Simulation developers and users **

# Figure 4. The work-load of simulation projects

The level of effort required to complete a simulation project varies
between 0.2 worker-months and 24 worker-months, with an average of a
little more than 2 worker-months (Fig. 4). This is difficult to estimate
because 1) the workload changes as the project evolves, and 2) larger
projects require more effort than smaller projects. Typically, the model
development and data collection phases of the project account for
approximately 60% of the entire effort. The remainder is split equally
among the other phases. Table 2 also shows the range for the various
phases.

## Table 2. Typical Phases of a Project

  --------------------------------------------- ----------------------------------- -------------- --------------- ----------- --------------- -------------------- --------------- ------------------------ ----------------
  Table 2. Typical work breakdown of projects   **Work load Ratio (%) \[Range\]**                                                                                                                            
  Data Collection /Gathering                    20 \[15 -25\]                       Model design   40 \[30 -60\]   Animation   10 \[ 5 -15\]   Model modification   10 \[ 5 -15\]   Simulation experiments   10 \[ 5 - 20\]
  Summary of result                             10 \[ 5 -10\]                                                                                                                                                
  --------------------------------------------- ----------------------------------- -------------- --------------- ----------- --------------- -------------------- --------------- ------------------------ ----------------

**2.4 Experimental design and output analysis**

Depending on the complexity of the project, a statistical design might
be required to determine 1) how many simulation experiments to run, 2)
what kind of data to collect, and 3) how to vary the inputs parameters
to minimize the overall variance in that data. Experimental design
methods like orthogonal arrays and Taguchi methods \[19\] are very
popular and powerful methodologies used in Japan. These methodologies
can support multiple views into an enterprise including financial,
accounting, sales, marketing, management, information, and
manufacturing. Table 3 provides some summary information on the use of
experimental design in simulation projects today. Clearly, there is no
widespread use of these techniques in industry at this time. These
results have not changed substantially from those reported in our
earlier work.

.

# Table 3. Practice of experimental design methods

+--------------------------------+----+-------------------------------------+---+
| Use experimental design method | 15 | utilize orthogonal array methods    | 9 |
|                                |    |                                     |   |
|                                |    | do not use orthogonal array methods | 6 |
+--------------------------------+----+-------------------------------------+---+
| Do not use experimental design | 68 |                                     |   |
+--------------------------------+----+-------------------------------------+---+

The final step in a simulation project is the analysis of the output
data \[20-23\]. This will include statistical calculations to compute
estimates of performance measures, a comparison of one set of measures
against another, or both. The knowledge required to carry out these
calculations is very specialized. Since most users will not have this
knowledge, it is important that simulation software packages support a
variety of statistical data analysis routines. These routines should
allow users to generate the required results easily and with confidence.
Table 4 summarizes the use of statistical analysis routines.

# Table 4. Practice of statistical output data analysis

+-----------------------------+----+----------------------------------------+----+
| Do statistical analysis     | 36 | implement own analysis routines        | 6  |
|                             |    |                                        |    |
|                             |    | use supported analysis routines        | 24 |
|                             |    |                                        |    |
|                             |    | use statistical data analysis packages | 8  |
+-----------------------------+----+----------------------------------------+----+
| Not do statistical analysis | 45 |                                        |    |
+-----------------------------+----+----------------------------------------+----+

The users gave the following reasons for not using statistical analysis.

· Statistical analysis output is not required.

· The merits are too small in accordance with its work load.

· The replication of simulation runs is impossible, because modeling
process requires much time.

The implication of this is that most users still perform deterministic
simulations that include no stochastic variables. Motivations for using
this approach include:

· We use deterministic simulation for model validation.

· We use deterministic simulation to validate production plan and
schedules.

· Animation is always done with deterministic simulation.

· We only evaluate the maximum performance of the target system.

· We do not have enough data to implement probabilistic models.

· We get only a simplified estimation of manufacturing lead-time.

· We validate schedules using deterministic simulation.

Many of these are what is commonly called [what if]{.underline}
simulations, because they show [what]{.underline} happens in the system
[if]{.underline} particular plans and schedules are executed. They will
identify problems, workload, lead-time, bottleneck processes, bottleneck
queues and others. They will also provide some insight as to why these
things happen. The interactive parameter setting function will help such
studies. More than half of users have implemented such a function (Table
5).

# Table 5. Interactive parameter setting functions on simulation execution

+-----------------------------------------+----+--------------+----+
| Interactive parameter setting functions |    | Usage        |    |
+-----------------------------------------+----+--------------+----+
| Support the functions                   | 51 | Frequent use | 15 |
|                                         |    |              |    |
|                                         |    | Occasionally | 20 |
|                                         |    |              |    |
|                                         |    | None         | 16 |
+-----------------------------------------+----+--------------+----+
| Not support the functions               | 41 |              |    |
+-----------------------------------------+----+--------------+----+

**2.5 Model maintenance**

About 40 % of simulation models are reused (Fig. 5). Some of them are
copied into new modeling code [as is]{.underline}, and some are modified
for use in new simulation models. Figure 6 shows the various methods
that are used to accomplish this. By far, the most effective of these
methods is a model library. By using a model library, model builders can
use models that have been built and tested previously to address the
problem at hand. The ability to do this reduces the time and cost
associated with the development of a new model. The goal is have models
of each component in enterprise system \[24\]. This way, models of
different systems can be built from these components quickly and easily.
The model library can also include development tools that will support
the integration of simulation tools with other management tools.

Other problems associated with model reuse, and possible methods for
addressing those problems are described below:

· **Input of simulation parameters**: Automatic generation of input
parameters; Standardization of parameter design; External database of
simulation input data.

· **Block modules of simulation models**: Continued maintenance of model
libraries in; Basic building blocks of simulation models;
Object-oriented modeling environment.

· **Re-utilization of existing models**: User implements generic models
so that he can modify the old version of the model to suit current need.

· **Documentation and comments**: Include in source code for model.

**Figure 5. Frequency of model re-utilization**

**Figure 6. The methods of model re-utilization**

**3. Reasons For Buying Particular Software Packages**

**3.1 Current Market shares**

As noted above, there are a large number of simulation software packages
on the market today. During the

last five years, many vendors have developed relationships with Japanese
firms who sell their products and provide consulting services. Even
though the simulation software market in Japan is not as mature as in
the United States, these efforts are beginning to pay off (see Figure
7). The number of different simulation products in use in Japan has
increased sharply since our earlier reports \[16-18\].** **Even so,
there is still a requirement to implement models in packages based on
general programming languages.** **This can be seen from the relatively
large usage of packages like SLAMSYSTEM and WITNESS.** **

**Figure 7. Simulation Software Packages in Japan**

Graphics-based packages such as AUTOMOD, FACTOR/AIM, and ARENA are,
however, becoming increasingly popular

**3.2 User Interface Capabilities**

Table 6 shows some of the user interface factors which users consider
when they purchase simulation software. The top two, modeling support
and output analysis tools, have remained unchanged from our earlier
reports. In those reports, we combined graphical displays and animation
into a single factor. In this report, we have put them into two separate
categories. Since animation appears to be a very important consideration
in the United States, it is perhaps surprising that it is Japanese users
rated it only fourth. We believe that this reflects the current decision
making style of management people in Japanese firms - it is very rare
that these people make important decisions based on the business reviews
and presentations. Consequently, the role of animation will become
gradually more important only if this style changes.

# Table 6. Important user interface facilities

  --------------------------------------------------------- -------------- -------------- ---------------------------------------- ---- --- ------------------------------------------------- ---- ---- -------------------- ---- --- ----------------------------------------------------------------- ---- ----
  **Important interface facilities for simulation users**   **Positive**   **Negative**                                                                                                                                                                                                                      
  Modeling support tool                                     77             6              Statistical simulation output analysis   75   8   Graphical display routine for simulation output   74   10   Animation facility   68   9   Interactive parameter handling facility in simulation execution   61   14
  Others                                                    \-             \-                                                                                                                                                                                                                                
  --------------------------------------------------------- -------------- -------------- ---------------------------------------- ---- --- ------------------------------------------------- ---- ---- -------------------- ---- --- ----------------------------------------------------------------- ---- ----

**3.3 Modeling Capabilities**

Figure 8 summarizes users' views on the different capabilities offered
by various packages.

Modeling support tools are considered most important when choosing
simulation software for particular applications. User-friendly,
icon-based modeling tools reduce the time and cost associated with
building and maintaining simulation models. Some of these tools are
aimed at specific application domains only, like manufacturing or
communications. Others provide a collection of templates for various
domains. The system definition language for these tools is contained in
the domain expertise of the user. This is accomplished by providing:

· An external view that contains modeling elements that are targeted to
the specific domain;

· A hidden abstraction layer which maps those elements the proper
internal simulation concepts;

· A dynamic modeling layer that can link operational models of those
concepts into a coherent simulation of the user's system; and,

· A pragmatic modeling environment

As a result, these tools can generate the simulation model directly from
system configuration data. This gives the user the ability to
automatically model changes in dynamic systems at various levels of
complexity and detail.

**3.4 Animation Capabilities**

Animation facilities enable the user to visualize the results of
simulation directly. Users can see which process is the bottleneck or
which process is not in tune with other ones. Many users indicated the
importance of animation capabilities of simulation software. Currently,
only 60% of users run animation programs every time; while 20% say that
they run them often. Users have summarized their reasons for not using
animation more frequently. They are given below:

· Users do not make business-like presentations to management.

· Users only interact with people having intimate knowledge of
simulation technology.

· Physical models are 3-dimensional, which is difficult to represent in
2 dimensional animation.

· Models are too large to understand using animation.

· Models are simple enough to understand without animation..

· The objective of simulation is performance evaluation based on
statistical calculations.

· The objective of simulation is a schedule that is easier to visualize
using a Gantt-chart.

· The users do not have enough time to implement animation program for
system analysis.

As we said earlier, this is a little surprising, but we expect this
number to grow in the coming years.

**Figure 8. Capabilities of simulation software **

**3.5 Other reasons**

Several other reasons were given for selecting particular packages. They
include: the ability to address problems in a specific target domain;
the ability to handle models of a particular scale; the ability to
provide a particular level of accuracy; the suitability to a user's
modeling skills; and, the ability to finish the project on time. Some
examples are given below

\- Use SIMTOOL for complex and large-scale simulation models.

\- Use FACTOR/AIM to mode manufacturing line or when rapid solution is
required.

\- Use AUTOMOD when an exact layout or 3 dimensional simulation is
required.

\- Use WITNESS when designer use simulation or when rapid solution is
required

\- Use AUTOMOD or EXTEND when the target system is material handling or
AS/RS.

\- Use SIMSCRIPT when the target system is traffic flow simulation, and
use AUTOMOD.

\- Use SIMAN when the target system is a lot-based production system.

**4. Simulation and System Integration**

The requirements for the integration of simulation with other
manufacturing software applications and manufacturing databases are
increasing. The subsequent comments from simulation users provide
support for this view:

· It is necessary to integrate simulation software with production
planning software so that we can provide accurate estimates of delivery
times to our customers.

· It is important to link simulation with real time control system.

· We want to integrate simulation and scheduling

· We need to link simulation with cost estimating.

· Distributed, autonomous simulation will be required in near future.

Fig. 9 shows the current implementation of system interfaces with
simulation. About 30% of the cases connect simulation with other
application systems. Most of these cases combine simulation with
production planning system, floor shop control systems, and engineering
support systems. Implementations are currently realized by using file
transfer mechanisms, but the majority of them are moving toward
real-time data exchange using some sort of messaging protocol or
database access. Some examples are:

· Data transfer from production management database to simulation system

· Data transfer from shop floor control database to simulation system

· Data transfer from simulation system on WS to spread sheet
applications on PC

· Data transfer from spread sheet applications on PC to simulation
system on WS

· Data transfer from CAD input data file to simulation system

We asked users to comment on application systems they planned to
integrate with simulation in the near future. The systems they included
were

· Shop floor monitoring systems,

· Distributed simulation across a LAN,

· Business Process Re-engineering,

· Production Management,

· Large scale logistics, and

· Virtual Production systems

Virtual Production Systems are by far the most ambitious of these plans.
The objective is the synchronization of material-flow with
information-flow across all factory operations. This is achieved through
the integration of manufacturing automation processes, business
processes, and data management systems (Fig 10). The implementation will
require both advances in optimization technologies \[25-29\] and
distributed simulation techniques \[30-32\].

**Figure 9. Current supporting system interfaces**

**5. Conclusions**

The use of simulation in Japanese industry is still modest compared to
the United States, but it is on the rise. With the growing emphasis in
Japan on virtual manufacturing, particularly through the Intelligent
Manufacturing System program \[33\], we expect this trend to continue.
Some of the most important areas of future interest among Japanese users
are:

· real time planning, scheduling, and control using simulation,

· distributed simulation,

· gaming simulation,

· object-oriented simulation, and

· integration of simulation with other applications

# Figure 10. System concept of "Virtual Plant System"

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IMS Intelligent Manufacturing Systems - A Program for International
Cooperation in Advanced

Manufacturing: Final Report of the International Steering Committee
adopted at ISC6, Hawaii,

24 to 26 January, 1994