Converting computer‐integrated manufacturing into an intelligent information system by combining CIM with concurrent engineering and knowledge management
| Date | 01 October 2000 |
| DOI | https://doi.org/10.1108/02635570010349104 |
| Published date | 01 October 2000 |
| Pages | 301-316 |
| Author | Biren Prasad |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
Converting computer-integrated manufacturing into
an intelligent information system by combining CIM
with concurrent engineering and knowledge
management
Biren Prasad
CERA Institute, Tustin, California, USA
Introduction
The product development environment
typically suffers from a number of
shortcomings. Some are partly due to the
lack of integrated tools that information
technology (IT) management has to deal with
(Stark, 1992; Tonshoff and Dittmer, 1990)
regularly. While others are partly due to the
diverse nature of an enterprise's business
operations (Pawar and Riedel, 1994; Dong,
1995; Bauman, 1990). Too often, tool-related
shortcomings are caused by inappropriate or
inadequate computer groupware or
information aids ± such as hardware and
software tools to needed database
management tools, knowledge-ware,
intelligent technologies and standardization
(Althoff, 1987). Technology is used here in a
generalized sense, similar to its definition in
Webster's Dictionary (1990) ± ``the totality of
the means employed to provide objects
necessary for human sustenance and
comfort.'' For example, by standardizing the
design plans, tools and databases of all
departments, Toyota enabled design work to
overlap between stages. Downstream
processes were started while upstream
design plans were still being completed
(Okino, 1995).
Sources of shortcomings in CIM operations
The operational shortcomings of computer-
integrated manufacturing (CIM) result from
four main sources (Prasad, 1996):
1Process stagnation: Process stagnation
examples include tradition (for example,
why fix if it is not broke), legacy systems,
business operations, management,
technical, or operational 3Ps ± policy,
practices and procedures (Barclay and
Poolton, 1994).
2Influence of infra-structural factors:
Examples include factors such as a
company's culture, mindset, legacy
database, and human factors
(Dimancesen, 1992).
3Communication roadblocks: Lack of
familiarity, product experience, and
training among the CIM teams are some
typical examples of communication
roadblocks (Albin and Crefeld, 1994).
4Organizational roadblocks: Lack of
management support, confidence, and
commitment to apply CIM in full force
(not haphazardly) throughout an
enterprise are some typical examples of
organizational roadblocks (Bajgoric, 1997).
The manufacturing industry today is deeply
in a paradigm shift (Prasad, 1995b):
From an ``economy of scale'':
.to an ``economy of information'' (Kimura,
1994);
.to an ``economy of flexibility (agility)''
(Mortel-Fronczak et al., 1995); and
.to an ``economy of intelligent
manufacturing'' (Lim, 1993; Prasad,
1997).
While the emphasis is constantly changing
from legacy computer systems to new CIM
initiatives, most computer-aided design
(CAD), computer-aided manufacturing
(CAM), CIM and computer-aided engineering
(CAE) tools ± commonly called C4 (CAD/
CAM/CIM/CAE) systems ± are in a state of
flux. Examples of new CIM initiatives
include systems engineering, integrated
product development (IPD), knowledge-based
engineering (KBE) (Abdalla and Knight,
1994), total quality management (TQM),
computer-aided logistics systems/electronic
data interchange (CALS/EDI) (Bauman,
1990), etc. This change (from the legacy
systems to new CIM initiatives) is putting
additional pressure on the C4 tools. These C4
tools are constantly required to provide an
up-to-date knowledge, not just the
information or data, at the right place, at the
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[ 301 ]
Industrial Management &
Data Systems
100/7 [2000] 301±316
#MCB University Press
[ISSN 0263-5577]
Keywords
Computer-integrated
manufacturing,
Information systems,
Simultaneous engineering,
Knowledge management,
Information technology,
Product development
Abstract
Some industrial organizations
using computer-integrated
manufacturing (CIM) for managing
intelligent product and process
data during a concurrent
processing are facing acute
implementation difficulties. Some
of the difficulties are due to the
fact that CIM ± in the current form
± is not able to adequately address
knowledge management and
concurrent engineering (CE)
issues. Also, with CIM, it is not
possible to solve problems related
to decision and control even
though there has been an
increasing interest in subjects like
artificial intelligence (AI),
knowledge-based systems (KBS),
expert systems, etc. In order to
improve the productivity gain
through CIM, EDS focused its
information technology (IT) vision
on the combined potential of
concurrent engineering (CE),
knowledge management (KM) and
computer-integrated
manufacturing (CIM)
technologies. EDS ± through a
number of IT and CIM
implementations ± realized that
CE, KM and CIM do go hand-in-
hand. The three together provide a
formidable base, which is called
intelligent information system
(IIS) in this paper. Describes the
rationales used for creating an IIS
framework at EDS, its usefulness
to our clients and a make-up of
this emerging IIS framework for
integrated product development.
right time, with the right amount, and in the
right format. These tools are continuously
processing a variety of information and
knowledge transactions at many different
places during a product lifecycle evolution,
which also needs to be accessed by other
team members at many more places and
applications. To allow an effective and
efficient processing of knowledge
transactions during product realization,
C4 tools are being redesigned to reflect an
organization's collaborative and competitive
posture. Standardization ± as in common
systems (Althoff, 1987), common methods
(Larsen and Alting, 1992), and common
processes (Jones and Edmonds, 1995) ± is
becoming increasingly more important. The
quest for C4 standardization is rapidly
spreading to all disciplines, organizations
and structures.
As a result, desirable characteristics in C4
tools are changing from their original needs
(Alting, 1986) as ``design tools'':
.to data exchange tools (Tonshoff and
Dittmer, 1990);
.to distributed computing tools
(workstations, mainframe, database)
(Stark, 1992);
.to work-group computing tools available
globally across the networks, local area
network (LAN) wide-area network (WAN),
etc. (Willett 1992; Kimura, 1994).
Toyota, for example, by unifying these
characteristics across all organizational
areas, all departments and work-groups
working on the product, has reduced its
average automobile time-to-market period
from 30 months to 18 months (Okino, 1995;
Shina, 1994). In recent years, there is an
increased emphasis on the use of new or
emerging feature-based standards during
data exchange (Hummel and Brown, 1989;
Jones and Edmonds, 1995). Product data
exchange using STEP (PDES)/standard for
the exchange of product model data (STEP) is
being implemented in newer CAD tools
through the use of a series of application
protocols (APs). One of the APs addressed by
the initial release of STEP is configuration-
controlled design (CCD), formerly designated
as AP203 (Curran, 1994). CCD represents the
dawning of a new era in digital product data
exchange as it specifies how solid models are
to be communicated. Using this protocol, one
CAD system can directly exchange solid
models in a standardized format with
another dissimilar CAD system.
Most research and development (R&D)
efforts toward automation for modern
manufacturing have been independently
developed. For example, creating faster
processors as hardware brains (e.g. silicon
graphics) for running high-end graphics
applications, as in the 1980s, were
independently developed. As a result, these
automated applications were often self-
assertive. It did not work out a panacea, as
initially expected, for reducing design and
development lead-time. In the same decade,
design grew more complex,and the amount of
time required to prepare the corresponding
data (inputs) for each tool to be used in the
design process also increased. At the present
time, highly automated areas in
manufacturing include CAD, computer-aided
process planning (CAPP), CAM,
manufacturing resource planning (MRP), and
computer-integrated inspection techniques
(CII) (Chang and Wysk, 1985). With such tools,
major functions are performed electronically
(using compute power)but the data are nearly
always passed manually. There is a recent
proliferation of ``islands of pre- and post-
processors'' generated from using these tools
due to piece-wise growth of the tools
themselves and lack of in-house standards for
applying them uniformly across the various
tool sets. With the dependency on
computational and logical techniques, the
recent emphasis has been on integrating the
existing CAD, CAM, CAPP, MRP and CII
systems to provide a CIM environment (see
Figure 1). Developments in integration area
include initial graphics exchange standards
(IGES), design for manufacturing and
assembly (DFMA), database management
systems (DBMS), PDES, just-in-time (JIT),
manufacturing automation protocol (MAP)
and cell control software (Prasad, 1995a).
However, they are currently deemed to be
independent contributions to improve
productivity or efficiency in specific CIM
areas or applications.
Today, CIM systems are merely being
applied to integration and processing
(storage and automation) of data,
communication, and processes (common
systems and standards). The communication
part of CIM design is data or information.
1Data or information. This includes many
different categories of product images and
information:
.CAD data;
.CAM data;
.CAPP data;
.CII data;
.design specifications;
.the history of production; and
.interface information in various forms,
including electronic, text, raster
images, video, audio and their mixture,
as well as many different types of paper
formats and methods.
[ 302 ]
Biren Prasad
Converting computer-
integrated manufacturing into
an intelligent information
system by combining CIM
with concurrent engineering
and knowledge management
Industrial Management &
Data Systems
100/7 [2000] 301±316
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