The Leicester Secularist

Integrated Enterprise, Fall 2001 29

The Digital Enterprise

An Integration Maturity Model for theDigital Enterprise

Saeed Paydarfar, Ph.D

Looking for ROI

"We’ve invested in hardware systems, software systems and we've told the staff to use them.So, where is the return on investment?" Ironically, even though the proliferation ofadvanced technology has improved efficiency and accuracy within the many disciplines ofthe product development process, it has also created the new problems of minimized crossdiscipline integration and maximized information-over-load. Arguably, one of today’s great-est engineering challenges is the effective integrated deployment of computer and computer-related technologies to actually achieve enterprise-wide process improvements. Typicaldeployments of process re-engineering and integration solutions seem to elude industry’sefforts, leaving most well-intentioned leaders with only a fraction of the anticipated cost,schedule, and quality improvements and their CEO asking, "Where is the ROI?"

Paradoxically, the introduction of computers and computer-related products, has definitelyand significantly contributed to the improved capabilities of specific industry disciplinesbut, typically, the deployment of these products have failed to provide programs with enter-prise-wide cost, schedule and quality improvements. Indeed, computer technology hasenabled significant capability within disciplines such as mechanical and electrical design,computational fluid dynamics (CFD), finite element analysis (FEA), machining, sheet metalforming, product visualization, process simulation, and manufacturing resource planning(MRP) just to mention a few. However, the added programmatic cost associated with thelack of robust integration of these disciplines has often minimized the sum of their individ-ual benefits.

For years, industry has initiated re-engineering and integration efforts. To the inexperienced,the effort seems straightforward: map the "as-is" process, define a "to-be" process, andimplement. Marred in technical, cultural, and logistical issues, most of these efforts fail andof the successful ones only a few can claim more than just marginal implementation suc-cess.1

Integrated Enterprise, Fall 200130


Simulation of product performance is a key capability of any advanced product develop-ment environment. Simulation enables the evaluation of the product and all its componentsto the product’s performance requirements. The Process Managed Digital Enterprise(PMDE), being introduced here, provides an environment that supports the simulationprocess for product development, manufacture, maintenance, and operations. The PMDEfocus is on global solutions in a distributed collaborative environment through the supplychain (see Figure 1). The PMDE maturity model, a major component of the PMDE, enablesan effective approach for successful technology deployment for the development and simu-lation of product. The maturity model has been developed based on technical, cultural, andlogistical parameters. The maturity model defines discrete, palatable, and measurable stepsthat enable effective planning to transition from one level of maturity to the next (seeFigures 6,7). This approach will significantly increase the probability of achieving the ambi-tious vision of an integrated digital environment, such as PMDE, and enable the robust sim-ulation of the products attributes through its life cycle.

Figure 1: PMDE defines an environment that enables the simulation of the development,manufacturing, operations and management of advanced products throughout theextended enterprise.



Continuing Technological Innovations

Beginning in the 1970s, computer-related technological advancements in software and hard-ware matured enough to intrigue industry leaders to take a serious look at and invest in avast array of computer hardware and software products. At first glance, these technologiesshowed huge potentials to support the product development, manufacturing, maintenance,and operation processes. So the investments began. Drawing boards began to be replaced byCAD systems, structural handbooks replaced by finite element codes, and physical mock-ups (such as "iron birds") replaced by physics-based visualization systems. Although thistransition began over three decades ago, the transition continues today.

More recently, many companies have made major investments in Product Data Management(PDM) systems for data vaulting, process control, and workflow management. Today’s tech-nology advancements include a vast number of offerings, such as intelligent agents,advanced human-to-computer interaction devices, massive parallel computing environ-ments, real-time distributed collaboration, knowledge based engineering, and complete lifecycle simulation of products and their attributes. The transition to these technologies mustconsider: 1) the state and cost of the technology; 2) the effort and cost of migrating legacyprocesses, systems, and data to the new systems; and 3) the ever-perplexing cultural resist-ance to far-reaching changes.2 Undoubtedly, as the technology advances even further, thesechallenges will continue and will remain only marginally mastered unless clear and effec-tive approaches are used.

Process Integration Initiatives

The use of computer hardware, software, and networking technology to achieve the much-anticipated order of magnitude benefits in cost, schedule, and quality can be achieved onlywhen the robust integration of the product development and manufacturing disciplines is astandard engineering practice. Toward this end, many organizations are in pursuit of devel-oping advanced computer environments for product development. Design, analysis, manu-facturing, cost, weights, wiring, and maintenance are just a few of the many process disci-plines that are being targeted for integration. Design activities such as form, fit, and func-tion will be electronically managed and assessed. Analysis groups will be able to "seamless-ly" access the geometry data in the correct form to enable rapid analysis and design feed-back. Manufacturing processes will be assessed in virtual space before any costly material ispurchased or manufacturing steps are begun. By most accounts, the sum of these capabili-ties when deployed with global integration solutions will yield tremendous cost savings,cycle time reductions, and quality assurances.

Many companies, government centers, and academic campuses have embarked on processintegration initiatives. The National Institute of Standards and Technology (NIST) sponsorsa consortium of industry and academic organizations in its Federated Intelligent Product



Environment (FIPER) initiative. This four-year program focuses on reducing cycle time by"intelligently automating elements of the design process in a linked associative environ-ment, thereby providing true concurrency between design and manufacturing." NASA’sIntelligent Synthesis Environment (ISE) is another integration initiative.. "The focus of theISE functional initiative is to research, develop, acquire, validate, demonstrate, and imple-ment revolutionary engineering and science tools and processes for the design, develop-ment, and execution of NASA’s missions."3 Although the ISE initiative is no longer fund-ed, many of its developments and initiatives have taken hold both in the Agency and inindustry.

Software developers today are now offering solutions to support such visions. IBM, forexample, is offering their Product Lifecycle Management (PLM) suite of tools, developedby Dassault Systemes. The PLM solution includes CATIA®, ENOVIA®, and DELMIA® toenable design, analysis, manufacturing, product data management, workflow simulation,and many other key components of a product’s development lifecycle.

Academia also realizes the benefits to improving the product development process.Massachusetts Institute of Technology (MIT), in collaboration with the U.S. Air Force, laborunions, and defense aerospace companies, initiated the Lean Aircraft Initiative (LAI). Inaddition, Purdue University, in collaboration with IBM and Dassault Systemes, created aDigital Enterprise Center. "The research center, a $6 million project, will be part of a work-ing model of PLM, the framework that enables manufacturers to manage their extendedenterprise through a complete e-business environment, from product concept and productionto customer delivery and support. PLM makes it possible for companies to create, manage,simulate and communicate digitally all of the information related to products, processes andresources."

A consortium of companies, dedicated to PMDE, was formed in early 2000, establishing theProcess Architecture Lab (PAL). The PAL is focused on integrating the elements of theproduct development process and offering such environments to industry. (Visithttp://www.mscsoftware.com/services/pal for more information on the PAL.)

These are just a few of the many initiatives related to process re-engineering, automation,and digital enterprise integration.

The PMDE Vision and Approach

The Process Managed Digital Enterprise (PMDE) is a vision and an approach that supportsinitiatives such as ISE and the deployment of the enabling software products to achieve sig-nificant cost, schedule and quality benefits.



PMDE envisions an integrated infrastructure and environment of tools, data, and methodsfor product development. PMDE targets an order of magnitude in improvements throughoutthe product development enterprise and lifecycle. PMDE envisions:

• Distributed collaboration for real time, anytime, anywhere, secure access to data (andknowledge) by geographically dispersed product teams including the supply chain andcustomer

• Effective data/knowledge configuration control and architecture (see Figure 2)• Electronic and automated change update through parametric and associative relationships• Methods for assured quality control of data and product• Automated and rules-based methods for knowledge management, engineering, and man-

ufacturing• Measurement and metric approaches that capture the effectiveness and efficiencies of a

product development environment4

• Effective use of standardization• Minimized data translations• Effective data/knowledge transfer from Conceptual to Preliminary to Detail Engineering

PMDE’s approach to achieving the vision considers the technical, cultural, and logisticalchallenges associated with the rapid and effective deployment of discriminating technolo-gies. PMDE proposes a roadmap of systematic, progressively maturing steps leading to afully realized, integrated environment.

Figure 2: PMDE defines data architecture to support the configuration controlof all data types



Advancing the Product Development Process

In review of software and hardware technology advancements, it appears that the largesthurdle lies not in advancing the technology, but in advancing the product developmentprocess itself. How to select the right tool; craft the appropriate interfaces with the organiza-tion’s current processes, tools, data, and culture; and, finally, deploy the tool into productionhave all become the biggest challenges to realizing expected benefits from these technologyinvestments.

Today’s typical engineering and manufacturing companies will most likely have invested incomputer-aided design (CAD), computer-aided engineering analysis (CAE), and computer-aided manufacturing (CAM) software tools to aid their product development activities. Ingeneral, each tool is procured to enhance the tasks within the respective disciplines ofdesign, analysis, and manufacturing. Industry has benefited from improved quality, lowercycle time, and reduced cost in each discipline. However, little progress has been made toenable information to be effectively managed or communicated from discipline to disci-pline, a requirement to realize the expected cost, schedule, and quality benefits.

Compounding these issues further is that each computer-aided tool (CAx) typically pro-duces orders of magnitude more data than traditional manual methods. As more disciplinesand methods become automated, the flood of data challenges the entire process. Data archi-tecture, data configuration control, data deletion, data translation, data quality, and dataaccess are some of the major challenges that purchasers of CAD/CAM/CAE face and needto address before they can realize the benefits of computer-aided software tools. PMDE sug-gests concepts such as "Pedigree Data" to facilitate data quality measures and data manage-ment issues, such as procedures for data deletion or archiving.

To achieve the desired performance improvement goals of the product development process,the development, procurement, and deployment focus of these technologies must be on theglobal solutions to the product development process and its associated product data man-agement issues. Without an effective global vision and an effective approach to achieve thatvision, these technological advancements will unwittingly promote the much-too-commonailment of "local optimization syndrome" (LOS). LOS is characterized by local improve-ments that cause global slow down, greater costs, and higher risks — just the attributes thatautomation investments try to improve.

Tremendous investments in CAD, CAM, CAE have enabled design, manufacturing, andanalysis, respectively, to operate with greater automation. This automation has improvedlocal or "vertical" efforts in the respective discipline. Unfortunately, today’s practices do nottake advantage of these systems for best efficiencies. For example, CAD models are devel-oped with the sole purpose of creating the engineering drawing. Little attention is paid tothe configuration control of the CAD models, or to how to enable the best use of the CAD



models by downstream functions. With the exception of very few products in a very fewnumber of companies, the product development process does not include the configurationcontrol of the CAD models. It is very common for engineering order (EO) updates to beplaced directly onto the engineering drawings, and not on the CAD models. This action willinevitably render the CAD models of those particular designs obsolete, and significantlyminimize the CAD investment.

This lack of configuration control is not limited to CAD. Analysis models, such as finiteelement models, aerodynamic models, thermal models, and the subsequent results of thesemodels, are all loosely controlled.5 Often, in an analysis organization, the loss of an employ-ee will mean substantial loss of specific knowledge regarding the product and its data.Acquisition and deployment of these analysis tools may, in fact, cost more, take longer, andharbor increased risk if appropriate data configuration control procedures are not in place.

PMDE focuses on these issues by proposing how to deploy these technologies in a con-trolled and integrated fashion. This significantly reduces cost by reducing the time it takesto find information. In other words, it minimizes the "data chase."

Defining the Maturity Model

The PMDE maturity model includes two major paradigm shifts each with a series of sys-tematic, progressively maturing steps. The maturity model steps, or levels, were definedbased on technical and cultural constraints. These steps were selected to enable an organiza-tion to identify its current state of operational maturity and then to define a roadmap fortechnology deployment that will achieve a new desired level of operational maturity. Theexpectation, of course, is that deploying higher levels of process maturity (or PMDE) willprovide lower costs and increased competitive advantage. These paradigm shifts and theconstituent maturity levels within each shift have been crafted to capitalize on availabilityof maturing technology and, a point that is often missed, to progress at a rate within a cul-ture’s ability to acclimate to that progress. Understanding and using these steps can signifi-cantly increase the potential for deployment success and achievement of the expected ROI.



The first paradigm shift moves an organization towards a "CAD-Enabled Enterprise"(CEE). At full maturity, the CAD-Enabled Enterprise: 1) uses CAD models as the designauthority; 2) associates each of the CAD models to its respective spatial position in theproduct thus creating the Digital Mock-up; and 3) electronically delivers the CAD model todownstream disciplines for direct use and application (see Figure 3).

The second paradigm shift builds on the first. It identifies the steps for configuration controlof CAD/CAM/CAE data, effective data quality, parametric and associative relationships,and eventually the full-scale ability for multi-disciplinary optimization (MDO). The DigitalMock-up (DMU) (see Figure 3b), in a CEE environment will be the authority and databasethat maintains the design data. At full PMDE, the DMU will contain all of the product’sinformation, such as design, analysis, manufacturing, and cost data (see Figure 4). In asense, the DMU is the biological equivalent to the product’s DNA.

Central to the DMU is the PDM system, which supports the data management architecture.These data management systems are now enabling control of both the enterprise level docu-ments and data as well as the CAD, CAM, and CAE data.

Figure 3: The engineering drawing is replaced by the digital mock-up



With continued advancements in technology, one can imagine that with a full DMU of aproduct, a next generation of that product can be "morphed" from the existing DMU (seeFigure 5). This future capability will completely change the way products are generated, andwill reduce the development cost and schedule by an additional order of magnitude.

Figure 4: PMDE defines the DMU to configuration controls all data types of the prod-uct’s development process



The First Paradigm Shift: A CAD-Enabled Enterprise (CEE)

The first paradigm shift transitions an organization to a process infrastructure that maintainsthe electronic CAD data as the design authority (as opposed to the traditional engineeringdrawing) in the product’s development process. Today’s technology easily supports this par-adigm shift when a single CAD system is used. For multi-CAD environments and distrib-uted collaboration, there are some technology challenges. The major challenge, however, inachieving this first paradigm shift is cultural. Figure 6 illustrates the four levels of the firstparadigm shift.

The first level depicts an environment that is drawing-based, one that does not use any com-puter-aided software tools in the product development process. Organizations at this leveloften believe they have little need for computer-aided tools or enterprise data integration. Ifdesign or manufacturing change is not anticipated, then it is quite possible that the organiza-tion is operating efficiently for its business model. However, a single change in design,manufacturing, or supplier may warrant reconsideration and an investment in CAD andPDM applications.

Figure 5: As technology and its industry deployment improve, it is envisioned that a DMUin PMDE Level 5 can be used to generate a new derivative product

ProductDMU



An organization that is at the second level of maturity uses computer-aided software tools(CAD, CAM, CAE, etc), but only to support local disciplines. The drawing remains theauthority on the product’s geometry definition. Local disciplines are done with greater effi-ciency, however, at this level, companies are missing significant value from their CAxinvestment. Cross-discipline integration deteriorates and information overload begins.

In the third level, the CAD model has supplanted the drawing, becoming the geometryauthority. The CAD model is configuration controlled in the development and changeprocess. CAD models may be in libraries with model check-in and checkout procedures.However, in Maturity Level 3, downstream users of geometry information still rely on thedrawing as their primary source of input to their processes. Technology constraints alsomake the effective use of CAD models into all downstream processes somewhat of an artrather than a robust practice.

Finally, in Maturity Level 4, the CAD model is the design database authority and all down-stream processes access it directly for use in their processes. Drawing capability still exists,but only as an alternate medium. The development of the Digital Mock-Up (DMU) beginsin Level 4. In the DMU, the CAD models are not just configuration controlled (e.g., as in alibrary), but are controlled in spatial orientation within the context of the product as well asbeing associated with other CAD models.

Figure 6a: The first major Paradigm shift enables effective transition to a CAD-EnabledEnterprise



The Second Paradigm ShiftThe second paradigm shift has five maturity levels (see Figure 7). The first three levels arewithin today’s state-of-the-technology, and their deployment is primarily an exercise inprocess architecture development, process rules development, data management architec-ture, and cultural education. PMDE Levels 4 and 5 require significant technologicaladvancements, especially in the area of enterprise-wide sensitivity analysis. To some degree,parametric and associative capabilities are offered in various CAD/PDM software packages.However, for full enterprise-wide, multi-discipline associativity, today’s capability is insuffi-cient. New approaches are still in the state of algorithm generation.

Desgin AuthorityDesgin Authority

D i A th itDesign Authority

DMU

Prod & MfgProd & Mfg

Disciplines UseCAD to EnableTheir Process

Drawings areredundant

DrawingFacilitatesProcess

Figure 6b: The first major paradigm shift enables effective transition to a CAD-enabledenterprise.



The Five PMDE Maturity Levels

PMDE Level 1 is equivalent to the fourth maturity level in the first paradigm shift, CEE.This level assures that design data is accessed and stored in the controlled DMU allowingdesign visualization at various levels of detail, interference checks, and rapid electronic dataaccess for the extended enterprise. This environment enables concurrent engineering, thussignificantly reducing development times and costs while assuring quality.

PMDE Level 2 places analysis and manufacturing data in the DMU’s configuration con-trolled environment. Analysis data, such as finite element models, finite element results,stress analysis models, margins of safety results, thermal models, and thermal results, andmanufacturing data such as tooling, numerical control machining programs, and wire har-ness information, will be configuration controlled. This control ensures that the productdevelopment community has ready access to this data, and will know its applicability tovarious product designs. All too often, the wrong analysis is used simply because sufficientconfiguration control does not exist.

Figure 7: Strategic maturity levels enable rapid and systematic deployment of a ProcessManaged Digital Enterprise (PMDE)



In this level, automated quality checking of CAD data is deployed to control the quality ofthe design data and ensure optimal utility by downstream disciplines. Without it, down-stream disciplines will inevitably receive non-conforming data that is incompatible with thedownstream discipline application.

PMDE Level 3 ensures the quality content of all the data in the controlled environment. Allkey data regarding a program is configuration controlled. Embedded rules check to ensurethat data meets integrity requirements. Rule checks include those on technical data, data for-mat, and meta-data (i.e. data about data). Technical data checks might include items such asrules to confirm lofting requirements for CFD, removal volumes for manufacturing, and useof the correct application module (e.g. sheet metal function for sheet metal parts). Data for-mat checks ensure that data is usable by downstream users in their respective applications.Finally, examples of meta-data checks include the analyst’s name, date, and version of datathat was processed.

At this level, the DMU contains all the information such as design, analysis, manufacturing,cost and tooling, to create the vehicle.

PMDE Level 4 enables enterprise-wide sensitivity derivatives, enterprise-wide change, andenterprise-wide propagation of change. Some of this capability exists today, however it islimited to a few disciplines and quite often the change across disciplines is not effective. Anunderstanding of sensitivity derivatives for all discipline data (design, analysis, manufactur-ing, facilities, cost, logistics, supply chain, etc) will be needed before enterprise-wide multi-discipline optimization can be applied. These capabilities exist today but are limit by severalfactors. These include: poor CAD user practices, limits in CAD/CAM/CAE/PDM technolo-gy, lack of robust algorithms for enterprise-wide sensitivity derivatives, and poor computespeeds for this immensely large computational problem.

PMDE Level 5 adds Multidisciplinary Optimization (MDO) and Knowledge BasedEngineering (KBE) rules to enable rapid product redesign. If design requirements on a prod-uct change, the existing DMU can be used as a starting point to search for new designs thatmeet the new requirements. Levels 4 and 5 are certainly stretch goals that will need adecade or two before they are available to industry and are understood well enough to useeffectively throughout the enterprise including the supply chain.

Getting ROI on Technology Investments

These two paradigm shifts, and their associated maturity levels, assist in achieving thevision of an integrated product development environment, referred to as a Process ManagedDigital Enterprise (PMDE). These paradigm shifts and their respective maturity levels aredeveloped based on technical and cultural considerations for deployment.



Sometimes, the very best ideas have failed due to cultural resistance that is fueled by grassroots fear, middle management control, or out-of-control executive pride. All too often,many try to achieve higher levels of environment maturity without consideration of lowerlevel environment developments. This undoubtedly leads to failure and/or poor results, andbecomes an endorsement to pessimists that this kind of integration is not achievable.

With careful understanding of deployment strategy, along with a consideration of both tech-nical and cultural constraints, the PMDE vision can be achieved. It will enable an order ofmagnitude improvement in cost, schedule, and quality. The PMDE approach offers an inte-grated and processed managed environment that eliminates data chase, promotes rapiddesign, and ensures product development integrity. All of these translate into savings forcompanies willing to take full advantage of their technology investments.

Contact information:Saeed Paydarfar, Ph.D.

Senior Director for Services, AmericasMSC.Software Corporation

[email protected]

CATIA, ENOVIA, and DELMIA are registered trademarks of Dassault Systemes.

ACKNOWLEDGMENTS The concepts and approaches described here represent years of work in process re-engineer-ing and the deployment of advanced computer related products. I am indebted to all my col-leagues at Rockwell International and The Boeing Company, as well as to all the softwaredevelopment companies that supported the progress we made. Particular mention needs togo to Dr. Thaddeus Sandford, John Sperling, Sharif Noori and Michael Tully who were veryinstrumental, especially in the early years, of trying to understand effective deploymentmethodology. Much credit also goes to the Process Development Group (Dewy Clarke,Michael de Souza, Clara Gubert, Matthew Macias, Carol Melton, Michael Mounier,Michael Paisner) for proving these theories by achieving significant technology deploymentand process re-engineering success. Last, but certainly not least, I am indebted to my familyand close friends who have all supported the long hours and stressful events that were a by-product of the tenacity of this author to progress the science of process re-engineering.



About the AuthorSaeed Paydarfar, Ph.D., is the newly appointed MSC.Software Senior Director forServices, Americas. Formerly, he was Vice President & General Manager of ProfessionalServices and Consulting at Advanced Enterprise Solutions (AES), which was just acquiredby MSC.Software. At AES he managed the Process Development Group (PDG), a high-endconsulting team in the automotive, aerospace, shipbuilding, architectural engineering, andconsumer products industries. He is also the sponsoring executive for the development of anadvanced Process Architecture Lab (PAL), an industry collaborative initiative. Dr. Paydarfarstarted his career at Rockwell International, Space Systems Division (now Boeing), as astructural analyst on the Space Shuttle program. His last position at Boeing was a SeniorManager for Engineering & Manufacturing Process Development, in the EngineeringOperations organization. Dr. Paydarfar graduated from Purdue University with B.S andM.S. degrees in Civil Engineering. At Duke University, he earned his Ph.D. in CivilEngineering, with specialization in Design Optimization.

1 Dennis R. Goldenson and James D. Gerbsleb, "After the Appraisal: A Systematic Survey of Process Improvement, ItsBenefits, and Factors that Influence Success," Software Engineering Institute, Carnegie Mellon University, August 1995.

2 Carol S. Melton, "Cultural Solutions for Successful Implementation," Integrated Enterprise 2:1 (Winter 2001): 19-20.

3 Robert D. Braun, Ph.D., "NASA's Intelligent Synthesis Environment Program:Revolutionizing the Agency’s Engineering and Science Practice," Integrated Enterprise 2:1 (Winter 2001): 2.

4 Matthew Macias and Jairus Hihn, Ph.D., "Proving the Value of Advanced Systems with Metrics," MSC.Software’sWorldwide Aerospace Conference & Technology Showcase, September 24-26, 2001.

5 Matthew Macias, Michael de Souza, Saeed Paydarfar, and Tim Jackson, "Integrated Analysis Architecture: ConcurrentEngineering in the Digital Enterprise," Integrated Enterprise 2:1 (Winter 2001): 11-24.

5 Ken Blakely, Larry Johnson, Boma Koko, Ray Amador, and Arthur H. Fairfull, "Integrating CAE and PDM: A First

Step Towards Providing Simulation Data Management," 2:2 (Spring 2001): 7-16.

Documents

The Leicester Secularist