adding value to enterprise modelling

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Adding value to enterprise modellingRon HysomPublished online: 15 Nov 2010.

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PRODUCTION PLANNING & CONTROL, 2001, VOL. 12, NO. 2, 119 ± 127

Adding value to enterprise modelling

RON HYSOM

Keywords enterprise modelling, value, intellectual capital

Abstract. There is much talk today about ` knowledge manage-ment’ and ` intellectual capital’ . And, looking at enterprisemodelling, one could say that an enterprise model is a visualway to capture knowledge about the enterprise. It can besuggested that much of what is being done in enterprisemodelling is not achieving what enterprise leaders want toachieve, and that is to add signi® cant value to the enterprise.This paper begins to address what is needed to add value bydescribing an enterprise modelling framework comprised of 36diå erent classes of enterprise models. Within this framework,there are six classes that speci® cally address increasinglysophisticated concepts of enterprise value. Examples of someof these classes, called valuemetric enterprise models, aregiven from actual models. But this paper only begins to addressthis challenging area. It is hoped it will trigger furtherdiscussion and development of valuemetric enterprise models.

1. Introduction

1.1. Knowledge management

What is knowledge? This question has been askedcountless times and received perhaps countless answers.Without getting into a debate that would lead us away

from the theme of this paper, it can be suggested that, toan executive of an enterprise, the answer is fairly simple,` knowledge is information that leads to action that addsvalue’ . If the information does not lead to action, then itis perhaps interesting, but not very useful. If theinformation leads to action, but the action does not addvalue, then the information is wasteful, in that it isencouraging the consumption of resources without anybene® t.

A recent Ernst and Young (1998) survey of 431 USAand European executives revealed the following.

. 87% considered their organizations to be a ` knowl-edge-intensive ’ business.

. 94% agreed that ` it would be possible, throughmore deliberate management, to leverage theknowledge existing in my organization to a higherdegree’ .

. When asked to rate their organization ’ s currentperformance in using accessible knowledge in de-cision-making, only 30% rated themselves good orexcellent.

. Even worse, only 4% rated themselves good orexcellent in measuring the value of knowledge assetsand/or the impact of knowledge management.

Author: R. Hysom, 3836, 81st Ave SE, Mercer Island, WA 98040, USA. E-mail: [email protected]

RON HYSOM has over 35 years of experience in complex systems engineering, including over 25years in systems and business management. Some of his past responsibilities related to enterprisemodelling include Partner, Enterprise Design & Development, AT&T GIS; PrincipalInvestigator - Enterprise Modeling Technology, Research & Technology, Boeing ComputerServices; Manager ± Modeling Science, Research & Technology, Boeing Computer Services;Board of Directors ± IDEF Users Group and Board of Directors ± International Society forEnterprise Engineering (ISEE). During the past 10 years, he has wrestled with the challenges ofenterprise modelling within Boeing, AT&T and now NCR. At NCR, he has worked with custo-mers to quickly develop integrated enterprise models, some of which are very large, which arehighly visual and capable of being used to analyse the complexity of an enterprise and to betterunderstand the impacts of decisions on that complexity. He has presented on enterprise modellingsubjects at six international conferences and published four papers on enterprise modelling. He hasreceived many awards, including an Outstanding Contribution Award from IBM, several SpecialAchievement Awards from Boeing Computer Services and a Great Performance Award from NCRProfessional Services.

Production Planning & Control ISSN 0953± 7287 print/ISSN 1366± 5871 online # 2001 Taylor & Francis Ltdhttp://www.tandf.co.uk/journals

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This survey further revealed that the perceived bene® tsto be gained by more active management of knowledgeare:

. innovation (83%);

. eæ ciency (83%);

. improved decision making (83%);

. increased responsiveness (83%);

. ¯ exibility (82%);

. quality improvement (71%);

. reduction in duplication (69%).

Clearly, there is a perceived need for more eå ectiveways to manage ` knowledge’ . And, clearly, these execu-tives are looking for value from ` knowledge’ , not justcollecting information and calling it ` knowledge manage-ment’ . And, ® nally, they seem to suggest that the valuemust be measurable in some way. Put another way, anold business axiom states it as ` you can’ t manage whatyou can’ t measure ’ .

These concepts are presented in the centre portion of® gure 1. Information can become knowledge if it can` lead’ to actions that produce value. You will notice in® gure 1 the use of the term ` valuemetrics’ as the linkbetween ` knowledge’ and ` actions’ . A central premise inthis paper is that the use of ` valuemetrics’ can ` lead’ to

actions that can produce value. But ® rst, let us discussintellectual capital.

1.2. Intellectual capital

Another term being used recently is ` intellectual capi-tal’ . Whatever it means, it is widely recognized that it isimportant. Did you know the following?

. It has been estimated that the intellectual capital ofmost organizations is worth from ® ve to 16 timestangible book value (Stewart 1998).

. A recent poll of executives from 80 mostly largecorporations showed that four out of ® ve believedmanaging knowledge should be essential, . . ., yetonly 15% felt they did it well (Stewart 1998).

. No executive would leave his cash or factory spaceidle, yet if CEOs are asked how much of the knowl-edge in their company is used, they typically say,` about 20%’ (Stewart 1998).

. The reward for investing in intellectual capitalgoods is similar to the return on investment ofR&D, i.e. 800% (Stewart 1998).

. As an example of how investors value intellectualcapital, as of November 1996, every $100 invested

120 R. Hysom

Figure 1. Adding value to enterprise modelling.

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in IBM bought $23 worth of ® xed assets, while thesame $100 in Microsoft bought ® xed assets of justover a dollar. The other $99 bought intellectualcapital. It is interesting to note that as of April1999, over $400 billion was invested in Microsoftalone, for its intellectual capital. Investors mustconsider intellectual capital valuable.

So just what is ` intellectual capital’ ? ThomasStewart, in his excellent book ` Intellectual Capital ’ de® nesit like this:

. ` Intelligence becomes an asset when some usefulorder is created out of free-¯ oating brainpower,i.e. when it is given coherent form (a mailing list,a database, an agenda for a meeting, a descriptionof a process); when it is captured in a way thatallows it to be described, shared and exploited;and when it can be deployed to do something thatcould not be done if it remained scattered aroundlike so many coins in a gutter. Intellectual capital ispackaged useful knowledge (emphasis added).’ (Stewart1998).

Looking at an enterprise from the perspective of execu-tive management, it appears that ` intellectual capital’ isjust another name for what these executives want to beable to manage. Intellectual capital can then be re-described as ` knowledge (packaged in useful ways) thatleads to actions that produce value’ .

Looking at an enterprise from the perspective of inves-tors, it appears that they look at the kind of ` value’ thatenterprises produce and, if they are convinced that theenterprise knows how to leverage its ` intellectual capital’into future ` value’ , they are willing to pay a great deal forthat intangible ` intellectual capital’ . Regardless of which

perspective is used, eå ective management of ` intellectualcapital’ is highly desired.

1.3. Valuemetrics

In ® gure 1, intellectual capital is depicted as the dash-dotted area of a packaged combination of knowledge andvaluemetrics. We have already discussed knowledge.What are valuemetrics? This is a term that is used todescribe the set of metrics, rules and practices that areused to make decisions for the enterprise. Other termsthat describe this general concept are balanced scorecard(Kaplan and Norton 1996), world-class measurementsystems (Brown 1996), etc. However, valuemetricsinclude the additional concept that the network of thesemetrics, with the associated weights, priorities, in¯ uences,etc. work together to de® ne the value the enterprisebelieves it is adding. All of these aspects make up thevaluemetrics. The knowledge network should producevaluemetrics that can be used by management to makeenterprise decisions. These decisions drive actions thatthen produce value of some sort. How well an enterprisemanages all of these factors determines whether theenterprise remains viable.

2. Managing intellectual capital

2.1. The challenges of managing intellectual capital

Enterprise executives report that they experience diæ -culties while trying to manage knowledge. Let us take alook at some of these diæ culties. Figure 2 displays the topsix diæ culties.

Adding value to enterprise modelling 121

Figure 2. Diæ culties in managing knowledge.

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Although it may not be immediately obvious, there is acommon problem underlying these diæ culties. These dif-® culties point to two critical, but often ignored, principleswhen working with knowledge.

(1) Information has the potential to become meaning-ful only when it is related. It is in the network ofhow ` items’ of information relate to one anotherthat the information makes sense. The meaningshidden in the network have the potential to beused to solve real world problems. But that isjust a part of the picture.

(2) Information has the potential to become valuableonly when it is relevant. In order for this networkof information to be capable of adding value, how-ever, it should include the essence of what youwant to use it for. Stewart states it this way. . .` Knowledge assets, like money or equipment,exist and are worth cultivating only in the contextof strategy. You cannot de® ne and manage intel-lectual assets unless you know what you are tryingto do with them.’ (Stewart 1998).

Look at the above diæ culties again and notice thatthey occur because of a violation of one of these twoprinciples, either the ` knowledge’ does not make senseor there is no way to determine the value of the ` knowl-edge’ .

. Changing people’ s behaviour ± people tend to do thingsthat they value (missing value).

. Measuring the value and performance of knowledge assets ±you cannot measure the value of knowledge that isnot connected in some way to what you want to useit for (missing strategy).

. Determining what knowledge should be managed ± if youdo not know which knowledge is of high value, youdo not know which knowledge to manage (missingvalue).

. Justifying the use of scarce resources for knowledge initia-tives ± apparently, the knowledge initiative is notperceived as adding as much value as whateverthe scarce resource is working on (missing value).

. Mapping the organization’ s existing knowledge ± ifyou have not worked out the relationshipsbetween diå erent types of knowledge and howthey impact results, then the knowledge doesnot make sense, you cannot determine valueand you cannot build a useful mapping (missingmetamodel).

. Setting the appropriate scope for knowledge initiatives ± ifyou do not know the knowledge you need to makedecisions, then setting scope is impossible (missingstrategy and value).

2.2. Traditional enterprise modelling

It seems that enterprise models could be a very power-ful way to manage ` intellectual capital’ . It can beobserved that traditional enterprise modelling hastypically included the translation of enterprise informa-tion into a structured network of objects and relationshipsthat represents a set of enterprise rules. The creation ofthis enterprise model is typically governed by a meta-model that de® nes the types of objects and relationships(along with other pertinent information) that can beincluded in the enterprise model. This is shown in ® gure1 within the dotted area labelled ` traditional enterprisemodelling’ . Traditional enterprise modelling has tendedto focus on capturing and displaying the complexity ofthis network, and not so much, if at all, on how theenterprise model can be used to solve problems in theenterprise.

2.3. Valuemetric enterprise modelling

The diæ culties above can be addressed with a properlydesigned, cohesive, value-oriented metamodel. Inaddition to that which is included in the traditionalenterprise metamodel, this metamodel includes thetypes of ` knowledge’ (i.e. ` meta knowledge’ ) that areneeded to make sense of the enterprise information,including all of the valuemetrics needed and used bythe enterprise.

The valuemetric enterprise modelling approach,guided by a cohesive, value-oriented metamodel, andsupported by appropriate tools can:

. help identify and de® ne what is of value to the user;

. help identify and de® ne how to measure that value;

. help identify and collect knowledge that supportsthe achievement of that value;

. help create new knowledge that increases the value;

. help guide decisions on which knowledge has thegreatest value; and eventually

. help implement processes and products that gener-ate added value for the customers of the enterprise.

In the remainder of this paper, such an enterprisemodel will be called a valuemetric enterprise model.This expanded concept of enterprise modelling isdepicted in ® gure 1 as the larger dotted area labelledvaluemetric enterprise modelling (Classes 1F± 6F).Notice that enterprise strategies are included in theknowledge base and an enhanced valuemetric meta-model governs the creation of that knowledge and theresulting valuemetrics. The result is an enterprise modelthat can generate sets of valuemetrics that can be used to

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guide enterprise decisions that lead to actions that addvalue to the enterprise, just what is needed.

To further describe these classes of valuemetric enter-prise models, portions of an enterprise modelling frame-work developed by Hysom (1992) years ago will be used.

3. Enterprise modelling framework

3.1. Enterprise modelling framework overview

The enterprise modelling framework is a cube display-ing three dimensions, two of which are helpful in thispaper. We will show these two dimensions as a square(® gure 3).

. Perspective ± ` the relationship of aspects of a subjectto each other and to a whole’ ± increasing under-standing.

. Eæ cacy ± ` the capacity for producing a desiredresult or eå ect’ ± increasing ability.

3.2. The perspective dimension

The perspective dimension (® gure 4) consists of threeclusters of columns: structure; behaviour; and results.Each perspective cluster consists of two columns makinga total of six columns, each one contributing to increasedperspective:

. Structure:. itemized ± the objects in an enterprise;. con® gured ± itemized with logical relationships

between the objects.. Behaviour:

. Resourced ± con® gured with resource relationships,thereby establishing the means by which be-haviour is accomplished;

. Constrained ± resourced with property valuesapplied, thereby constraining the enterprise toa range of behavioural results.

. Results:. adapted ± constrained with adjustments, usually

toward pre-established targets;. valued ± adapted to add value to the enterprise by

assessing and changing both the adapted resultsand, if necessary, the pre-established targets. Itshould be noted that often simply drivingtowards pre-established targets, without assessingthe potentially complex interactions involved,may not actually add value to the enterprise.This column is intended to evaluate and increasethe value added by any changes.

3.3. The eæ cacy dimension

The eæ cacy dimension (® gure 5) consists of threeclusters of rows: static; dynamic; and eå ecting. Eacheæ cacy cluster consists of several rows, all workingtogether to increase ability.

. Static cluster ± representation of condition at apoint in time:. de® ning ± elements are identi® ed and de® ned;. surveying ± de® ned elements can be selected,

traversed and properties examined and used toperform static-type calculations.

. Dynamic cluster ± representation of change of con-dition across time:. predicting ± surveyed elements can be examined for

properties and dynamic values can be predicted;


Figure 3. Overview of enterprise modelling framework.

Figure 4. Perspective dimension.

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. prescribing ± predicted elements can be con® guredto achieve preferred dynamic behaviour in thereal world.

. Eå ecting cluster ± capability to actuate representa-tion in order to accomplish something:. executing ± prescribed elements can actually execute

to produce an output;. ` thinking’ ± executing elements can ` learn’ to

become competent at producing a quality out-put.

3.4. Enterprise modelling classes

The eæ cacy and perspective dimensions of theenterprise modelling framework describe 36 classes ofmodels (® gure 6). To keep the class codes simple toremember, the eæ cacy dimension is numbered 1-6 andthe perspective dimension is lettered A± F. The result isthe class of the model.

. Static model classes range from 1A to 2F.

. Dynamic model classes range from 3A to 4F.

. Eå ecting model classes range from 5A to 6F.

Observations over the last 8 years have found thatmost, if not all, modelling formalisms produce modelsthat fall into some of these modelling classes.

4. Valuemetric enterprise models

4.1. Traditional enterprise models

Often, enterprise models focus on de® ning structure± key objects and relationships ± within an enterprise(Class 1B). If expanded, they may de® ne thoseobjects and relationships that serve as resources forthe primary structures (Class 1C). This makes itpossible to start de® ning behaviour, because resourcesare necessary to produce behaviour. If properties arede® ned (e.g. quantities, duration, costs, etc.), thenbehaviour becomes constrained to better re¯ ect reality(Class 1D). Then, if the model supports the de® nitionof target metrics (typically goals and objectives) andsteps (strategies) toward meeting those targets, themodel becomes adaptive (Class 1E). This is wheretraditional enterprise models usually stop. Rarely dothey address those aspects that permit the de® nitionof how the enterprise establishes the value that isbeing added by the movement towards targets(Class 1F).

4.2. Class 1F valuemetric enterprise models

Class 1F valuemetric models must support thede® nition of what constitutes value to the enterprise.This de® nition can include many types of measurestailored to address the speci® c strategic needs andplans of the enterprise. It usually includes the speci® cmetrics that are used by senior management to makeenterprise-wide decisions, the de® nition of how thosemetrics are calculated, and the supporting objects,relationships and properties to capture and derivethose metrics. It should also include the rankings ofthose metrics (how important they are) and howcon¯ icting metric behaviour is resolved. Ideally, itwould include a means to combine those metricsinto some overall ` valuemetric’ that can be used tocompare the values of diå erent scenarios and alter-natives.

124 R. Hysom

Figure 5. Eæ cacy dimension.

Figure 6. Classes of enterprise models.

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Class 2F valuemetric models must support the surveyingof the value elements captured in the Class 1F value-metric model. Surveying includes the selection, traversal,hiding/selective showing and static calculation of value-metrics using the de® nitions in the underlying Class 1Fmodel. An example of a static calculation using traversalis the calculation of the critical path through a set ofprocesses.


Class 3F valuemetric models must support theprediction of results of the behaviour of any set of valueelements surveyed in the underlying Class 2F valuemetricmodel. There are a variety of Class 3F methods thatmight be used to predict behaviour, including object-oriented discrete-event simulation. All of these methodsshould report results supporting determination of thevalue added for each scenario. Class 3F valuemetricmodels diå er from Class 2F models by performingdynamic analyses, rather than static analyses. For ex-ample, while a Class 2F calculation of a critical pathmight yield a static calculation, a Class 3F calculationusing simulation would yield a more dynamic result,taking into account resource constraints and bottlenecks.However, a Class 3F valuemetric model would not becapable of driving toward a preferred result, other thanthrough an extensive, manual trial-and-error approach.That kind of capability is accomplished by a Class 4Fvaluemetric enterprise model.


Class 4F valuemetric models must support theprescription of preferred results of the behaviour of anyset of value elements predicted in the Class 3F value-metric model. Prescriptive results can be derived usingvarious methods designed to quickly move towards apreferred solution. Possible methods might includevarious optimization approaches such as geneticalgorithms, genetic programming, neural-net algorithmsor certain heuristic approaches. Class 4F results could beused to feed into Class 3F prediction routines to derivedynamic behaviour. In all cases, in order to qualify asClass 4F valuemetric models, these models must provideresults that report the value potentially added by thepreferred solution.


Class 5F valuemetric models must support theexecution of the preferred solution developed in theClass 4F valuemetric model. This would include thetranslation and implementation of the executablesolution, as well as the actual execution and managementof the model. These types of models are sometimesreferred to as work-¯ ow enactment systems. However,they must provide for on-going reporting and supportof the value being provided in order to qualify as Class5F valuemetric enterprise models.


Class 6F valuemetric models exhibit self-correcting andself-improving kinds of behaviour. They use the value-oriented capabilities of Class 1F± 5F models to determinewhat needs to be improved (in the sense of increasingvalue) and seek to accomplish that within pre-establishedparameters. The author is not aware of any examples ofClass 6F valuemetric models at this time.

5. Examples

5.1. Y2K example ± Class 2F valuemetric model

In 1998, to demonstrate what could be done todetermine the impact of Y2K failures, a simple Class2F valuemetric model was built. This model displayeda set of objects which represented the business goals,objectives and strategies, business activities, ITapplications, IT systems, Y2K projects and Y2K projectmanagers. Each object was linked to other supportingobjects using relationships. Those links established astrategic value chain which linked business goals toobjectives to strategies to business activities to ITapplications. Strategic value weights were assigned tothese links and a strategic value assigned to each objectbased upon the value calculated by traversing thestrategic value chain. In addition, IT applications werelinked to the IT systems on which they ran, and ITsystems were linked to Y2K projects which wereresponsible for making them Y2K compliant. EachY2K project was also linked to the Y2K project managerresponsible for the project. The resulting model was asimple example of a Class 1F valuemetric model. Whenwe calculated the strategic value by traversing the valuechain and used the results to change the colours of Y2Kprojects in trouble (along with the aå ected IT systems,IT applications and business activities), the model


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became a simple example of a Class 2F valuemetricmodel. Figure 7 displays an overview of such an analysis.

5.2. Preferred project process example ± Class 4F valuemetricmodel

Another project within a large enterprise produced anexample of a Class 4F valuemetric model. This projectcaptured project process knowledge that included processoperations, inputs and outputs, methods of performingoperations, and resources required to execute themethods. In addition, the metrics to be used to determinevalue were de® ned, the calculation algorithms designedand the analysis constraints established. The underlyingmetamodel became capable, coupled with the approriatemethods, of producing valuemetric models from Class 1Fto 4F. The de® nition of this knowledge became a Class 1Fvaluemetric model. When that model was traversed andkey portions selected and extracted for analysis, itbecame a Class 2F valuemetric model. This extractedknowledge was evaluated by a special valuemetricanalysis engine that used the analysis constraints todesign a preferred solution, including appropriatemethods and resources producing an example of a Class4F valuemetric model.

The preferred solution was then evaluated by discrete-event simulation and the results displayed using specialgraphs (see ® gure 8 for an example of output from thismodel), providing an example of a Class 3F valuemetricmodel.

6. Summary

Eå ectively managing the intellectual capital of anenterprise is becoming recognized as one of the mostimportant tasks an enterprise executive has to pro-mote the viability of the enterprise.

` Knowledge has become the primary ingredient ofwhat we make, do, buy, and sell. As a result, managingit ± ® nding and growing intellectual capital, storing it,selling it, sharing it ± has become the most importanteconomic task of individuals, businesses and nations. ’(Stewart 1998)

But accomplishing this task in a way that produces valueis not easy. It is believed that valuemetric enterprise model-ling can become a strategic resource to increase the valuebeing generated by that critical asset ± the intellectualcapital of an enterprise. If we can accomplish that, thenthe discipline of enterprise modelling will have become one

126 R. Hysom

Figure 7. Y2K impact model ± example of Class 2F valuemetric model.

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of the more important disciplines in the world ± or at leastwe would like to think so!

References

1998, Knowledge in the Organization. A joint survey by businessintelligence and the Ernst & Young Center for BusinessInnovation http://www.businessinnovation.ey.com/research/knowle/survey/survey.html

BROWN, M. G., 1996, Keeping Score ± Using the Right Metrics toDrive World-Class Performance (New York, NY, USA: QualityResources, Division of The Kraus Organization).

HYSOM, R. R., 1992, Enterprise Process Modeling ± A Statement ofVision. Research & Technology Group, Boeing ComputerServices, Seattle, WA, USA. EPM-92 Framework. Research& Technology Group, Boeing Computer Services, Seattle,WA, USA. 1993, World Class Enterprise Process Modeling.National IDEF Users Group Conference Proceedings.

KAP LAN, R. S., and NORTON, D. P., 1996, The Balanced Scorecard(Boston, MA, USA: Harvard Business School Press).

STEWART, T. A., 1998, Intellectual Capital (London, UK:Nicholas Brealey Publishing), pp. 12, 24, 63± 70.


Figure 8. Example of valuemetric chart produced by Class 4F valuemetric analysis.

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adding value to enterprise modelling

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