From: rick@cs.arizona.edu (Rick Schlichting)
Newsgroups: comp.research.japan,comp.ai
Subject: Kahaner Report: Nikkei AI survey of use of expert systems in Japan
Date: 23 Jul 92 03:57:17 GMT
Followup-To: comp.research.japan


  [Dr. David Kahaner is a numerical analyst on sabbatical to the 
   Office of Naval Research-Asia (ONR Asia) in Tokyo from NIST.  The 
   following is the professional opinion of David Kahaner and in no 
   way has the blessing of the US Government or any agency of it.  All 
   information is dated and of limited life time.  This disclaimer should 
   be noted on ANY attribution.]

  [Copies of previous reports written by Kahaner can be obtained using
   anonymous FTP from host cs.arizona.edu, directory japan/kahaner.reports.]

To: Distribution
From: 
 David K. Kahaner
 US Office of Naval Research Asia
 (From outside US):  23-17, 7-chome, Roppongi, Minato-ku, Tokyo 106 Japan
 (From within  US):  Unit 45002, APO AP 96337-0007
  Tel: +81 3 3401-8924, Fax: +81 3 3403-9670
  Email: kahaner@cs.titech.ac.jp
Re: Nikkei AI survey of use of expert systems in Japan
22 July 1992
This file is named "nikkei.ai"

ABSTRACT. Summary of a survey by the Japanese publication, Nikkei AI, on
expert systems in Japan, conducted winter 1991.

Following is a translation and summary of the survey done by the Japanese 
publication, Nikkei AI, on the use of expert systems in Japan, which was 
conducted in 1991. Data were obtained through a questionnaire, and by 
follow-up telephone conversations. A total of 524 systems were reported 
at over 200 companies, more than half in practical use, or sold as 
products. The table below lists the major industries, the number of 
companies responding to the survey in each field and the number of expert 
systems within that industry. Below the table is a rough translation of 
their summary. I have edited it in many places to clarify the ideas 
although some are still poorly stated; nevertheless the main points are 
fairly obvious.  



Field                   Number of Companies     Number of Expert Systems
                           Reporting             Total     In use or product
------------------------------------------------------------------------
Banks, securities                 22               48       34
Insurance                         18               40       30
Distribution, Service             23               34       12
Steel, Metal                      13               58       52
Construction                      23               83       37
Electric Power, Gas                7               40       13
Petroleum, Chemical               28               49       29
Shipbuilding                       4               34       21
Machine, Precision Machinery      20               46       32
Auto                               7               13        5
Electrical machinery              18               48       22
Other manufacturing               22               32       22

     Expert systems in Japanese companies, planning systems in 
particular, are getting more important.  Among the  companies  that have 
a working expert system, two fifths of them  consider  "expert systems as 
important, company-wide." Applications of expert systems to the basic 
framework of business are likely to proceed swiftly as applications 
target "Intelligent Strategic Information Systems" (SIS).  

     Our survey at the end of last year revealed that  205  companies are 
developing/operating 524 expert systems. Almost every system is or is 
planned to be put to practical use; a few are developed purely for 
research.  

     Smaller companies, especially in competitive industries, would not 
make their systems open to the public and we were unable to count them.  
On the other hand, some companies are not capable of counting the number 
of expert systems because every job site is eager to develop its own 
systems.  Consequently, the expert systems we counted here are not all 
the systems in Japan.  We guess the total number of expert systems in 
Japan is 1.2 - 1.3 times the numbers counted.  

     As we have  been gathering data every year since  January  1987, 
this is the fifth survey.  The record in the past are 65 companies/125 
systems ('87), 124/250 ('88), 151/332 ('89) and 190/401 ('90).  We would 
say this approximately reflects the growth of the number of expert 
systems in Japan.  

     We sent a questionnaire to 2000 companies, most of which are listed 
in the first section of Tokyo Stock Exchange and got 261 answers.  Among 
these, 172 companies are developing/operating expert systems.  Based on 
these answers, we tabulated problems on development and evaluation as 
seen by AI vendors.  The list of expert systems at the end contains the 
data we gathered in the past as well.  We confirmed the current status of 
the systems to those who are in charge of them.  We also tried to gather 
data by telephone from companies which did not answer the questionnaire.  
The data is updated/added/deleted  if necessary.  [Nikkei's complete 
survey gives a list of the specific systems that are in use.] 

PRACTICAL USE IS EXPANDING.  PLANNING TYPE SYSTEMS ARE INCREASINGLY USED.

       Many systems  are  diagnostic  and  planning systems.  Diagnostic 
systems come first in terms of the number of systems.  However, examining 
systems in detail, we found planning systems often have more importance 
than other types.  

       A little less than a half of the systems are in practical use, and  
a little more than a tenth of them are sold as products.  About 30% of 
the systems are developed/operated on personal computers.  But we should 
notice the rapid grow of workstations. This implies that Unix 
workstations are becoming anchors as platforms for the development and 
operation of AI products.  

        SYSTEMS IN PRACTICAL USE
    Diagnostic Systems (30%)
        Total: 208         In practical use: 76
    Planning Systems (33%)
        Total: 141         In practical use: 83

       Considering systems  running  on Unix workstations, main frames 
and mini computers, there are 39 (24.8%) diagnostic systems and 58 
(36.9%) planning systems.  Chances are  high  that such hardware is used 
if an expert system needs to be integrated with conventional systems.  

       We would infer from this data that planning systems are the most 
important variety of expert systems.  In other words, the problems that 
planning systems deal with, such as scheduling or planning, are important 
routine work at companies.  

       Designing systems, like intelligent CAD, are also rising steadily; 
43 systems out of 83 are in practical use.  These systems design 
machinery, layouts and semiconductors.  However, they do not 
automatically make a design which  often needs  to  be  expanded  onto 2 
or 3 dimensional space.  Rather, they help a designer make a best choice 
such as designing methods or specifications.  

       Among control systems, 22 systems are in practical use.  This 
number includes a system which supports or supervises plant operation, 
and an automatic control system of a machine.  

       The number of expert systems sold as a product has been increasing 
since last year, and reached 56 systems. 30 of them are diagnostic 
systems that run on a personal computer.  Many are sold to maintain a 
large machine/plant.  

LISP BASED AI REMAINS FIRM AS AN AI TOOL BUILDER BUT C USE GROWING.

       We studied the usage of primary AI tools on the market that are 
used to develop expert systems.  The AI tools of major computer 
manufacturers, such as ES/KERNEL(Hitachi) or ESHELL(Fujitsu) overwhelm 
others.  Other than these, ART and Nexpert Objects have grown remarkably.  

    ES/KERNEL, ESHELL, ART, EXCORE, Nexpert Object and Knowledge Tool are 
mainly used for planning or designing systems. 60% of the systems 
developed with one of these are planning/designing systems.  Conversely, 
BRAINS, Super-BRAINS, Sogen/Dai-Sogen are mainly used for diagnostic 
systems.  Particularly, some diagnostic systems were developed with 
BRAINS or SuperBRAINS several years ago and have been used since then.  

    We also have to point out that ES/KERNEL whose language is C has 
exceeded ESHELL whose language is Lisp.  We could think this reflects the 
trend on AI tools, namely from Lisp to C. Nevertheless, Lisp based AI is 
still significant.  If we categorize AI tools with their descriptive 
languages and add the number of systems directly coded with Lisp or C, 
Lisp based systems come to 40%.  We do not discuss the precise ratio 
since lots of systems are developed with more than one tool or language.  
Roughly speaking, Lisp covers 40%, Prolog does a little less than 10% and 
C does a little less than 30%. (However, if we only see the systems in 
practical use, C will attain a higher ratio.) Other languages used are so 
called AI languages, such as Smalltalk and OPS, and conventional 
languages, such as Fortran or BASIC.  

EXPERT SYSTEMS ARE IMPORTANT COMPANY-WIDE.

    Next, we observe how each company faces expert systems.  Half of the 
172 companies we surveyed have already been operating expert systems.  
80% of these companies acknowledge the importance of their expert 
systems.  The companies who have expert systems in practical use tend to 
find them more important.  However, even among the companies who find the 
expert system important, few companies are explicitly intending 
"Intelligent SIS." Most of them consider that expert systems are still 
used within individual corporate divisions.  

    We also studied the annual budget that a user company is spending on 
hardware/software related to expert systems, and the ratio to the whole 
information-system budget.  22 companies who are spending 50 billion yen 
or more consist of major manufacturer and three financial related 
companies. 9 companies, specialized in developing and consulting in AI, 
have ES/IS ratios of 10% to 30%.  Many companies guess that "Our (expert 
system) budget won't increase abruptly but it will grow on the whole." 

     Information system divisions now lead in the  development  of expert 
systems, replacing Research and Development divisions.  Development at a 
job site has increased a little since last  year  (12 among 127 
companies) but the number is still low.  It would  rise  if AI tools were 
easy to use.  

PROBLEMS ARE KNOWLEDGE ACQUISITION AND DEVELOPMENT METHODOLOGY.

     Two problems were pointed out.  One is that acquiring the domain 
knowledge from domain experts is difficult.  The other is the methodology 
of development is not established yet.  Both are key issues in expert 
systems development.  Among 48 answers which say the company is not 
sympathetic to expert systems, 31 complain particularly about domain 
experts.  Effective knowledge  acquisition  tools which can smoothly 
communicate with domain experts are needed.  

     Some companies who have developed many expert systems are now 
settling on their own methodology.  The methodology for the ordinary 
systems is also being heavily influenced by object oriented analysis.  A 
new methodology and tools covering both conventional systems and expert 
system is hoped for.  

     We surveyed the detailed complaints against the development 
environment.  We find many complaints of interfaces; interface to 
conventional systems, to users and to developers.  There are few 
complaints of inference mechanism such as inference function or speed 
except for several problems of knowledge representation.  Vendors are 
sometimes criticized for not having know-how in application areas.  

     Concerning how AI vendors satisfy their users, the number of answers 
to these questions is not large, so  this  analysis might not be 
accurate, particularly in case we have 10 or fewer answers.  However, we 
should point out that the evaluation has not changed since last year 
except for Nihon Digital Equipment  Corp.  (It used to be evaluated the 
highest.) 

        Company    Number of customer responses    Average Satisfaction
        Fujitsu         29                              2.52
        Hitachi         19                              3.26
        CSK             11                              2.82
        Nichimen Data   10                              2.90
        Personal Media  10                              2.70
        Nihon IBM        8                              2.88
        Nihon DEC        8                              2.63
        AI Soft          7                              3.29

     The difference between Hitachi and Fujitsu, both regarded  as 
leading AI tool vendors, is prominent.  This might indicate the  trend 
Lisp to C. [Hitachi had many fewer respondents who were dissatisfied.] 

"FUZZY"  ATTRACTS  INTEREST  AMONG  RELATED  TECHNIQUES.

     We asked about the degree of interest in related techniques  in 
terms  of what improves expert systems.  Fuzzy  comes  first  as 
"Technique  in progress" and neural nets come next.  Actually, we found 
many systems that use Fuzzy.  It seems that fuzzy is  often more 
effective  than rule-based systems.  

      Object oriented data base dominated responses to the question "What 
kind of technique would you like to try in the future?"  Those who are in 
charge of expert systems also expect this technique to mature.  


---------------------------END OF REPORT------------------------------