Coordinating distributed organizational knowledge

DATA & KNOWLEDGE ENGINEERING Data & Knowledge Engineering 23 (1997) 247-268

ELSEVIER

Coordinating distributed organizational knowledge Matthias Jarke*, Manfred A. Jeusfeld, Peter Peters, Klaus Pohl Informatik V, RWTH Aachen, Ahornstr. 55, 52056 Aachen, Germany Received l September 1996; revised 1 January 1997; accepted 1 January 1997

Abstract

As organizations move from hierarchical towards market-like structures, their distributed units also take a larger role in the design and evolution of organizational information systems. This requires strategies which support the cooperative creation, evaluation and evolution of global information flow structures among autonomous organizational units through local knowledge acquisition and maintenance. Three such strategies are presented: cooperative conceptual modeling, multi-simulation, and explicit process support. These strategies are formally embedded in a meta modeling framework and implemented with a repository-based architecture. They are intended for the analysis of business processes in networked organizations, and as a basis for designing and evolving their federated information systems.

Keywords: Enterprise modeling; knowledge management; cooperative information systems

I. Introduction

Although efficiency is still a central business goal, flexibility and changeability are becoming more and more prominent in business processes [17,44]. Since large monolithic organizations optimized for mass-production efficiency do not satisfy these criteria, organizations are moving from deep hierarchies towards networks of small autonomous units which interact in marketlike structures and processes. These market-like structures enable the creation of much more complex and useful feedback loops than the traditional hierarchical structure. They also create significant challenges for the analysts and designers of organizational structures and information systems. The development of organization information systems has to deal with the problems that result from this change both at the level of software architecture and at the level of knowledge management and evolution [4,23]. Organizational theory has shown that structure and change in organizations are inextricably linked [30]. Thus, a centralized development process for a market-like organization makes little sense. Explicit knowledge is mostly local to the units. * Corresponding author, e-mail: {jarke,jeusfeld,peters,pohl}@informatik.rwth-aachen.de 0169-023X/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved PII S0169-023X (97)00014-1

248

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

Global knowledge is only implicitly available from workflow practice and feedback loops; developing a deep overall model of the organization would not only be very expensive but also subject to rapid aging as the units and their interactions evolve. In this paper, we argue that the analysis and design process must be organized according to the same principles as the whole organization. In other words, it must rely on distributed mechanisms with lightweight coordination achieved through mutual agreement and voluntary use. We discuss knowledge-base management mechanisms for the coordination of such cooperative analysis and design processes. Specifically, we present some experiences with such mechanisms for the tasks of computer-supported conceptual modeling, quantitative impact

analysis, and explicit process support. Two common features appear across all three domains: the reliance on multi-perspective meta models, and the focus on information flow. Multi-perspective meta models attempt to cover a broad range of different modeling formalisms in some domain with a small number of basic concepts. The overlap between the different formalisms is intentional: it is exploited for defining possible relationships between them, and thus for computer-supported conflict analysis and data transfer in the cooperative process. We present meta models for quality-oriented business processes, for the interoperation of simulation techniques, and for modeling the process of developing coordination processes. The focus on information flows between organizational units rather than content of local or global information bases highlights the units' autonomy: Only data that is to be exchanged between units shall be modelled within a common framework formalized in part by the meta model. Another important aspect of focusing on information flows is stressed by organizational research [7] that showed that information flows in design processes are dictated by uncertainty and equivocality between the involved agents. While uncertainty can be reduced by well defined business processes, the reduction of equivocality (which is important especially if knowledge (= interpreted data) is to be used by various cooperating agents) can be achieved efficiently only by direct communication of agents using rich media. In particular, we study the interplay between data exchange and organizational knowledge. We try to capture this interplay through the distinction between different information categories in the conceptual modeling process. We analyze its dynamics through the combination of discrete-event and system-dynamics simulation and support it through explicit separation of process execution and enactment from process modeling and improvement in a process-centered environment. Much of this paper is based on experiences gained in the WibQuS 1 project in which we cooperated with six German engineering and organization science institutes to devise federated system support for total quality management in distributed manufacturing organizations. Some of the support tools for the individual methods can be characterized as knowledge-based systems, others used statistics or other mathematical techniques, yet others were just informal documentation tools. One mission of WibQuS was to relate those methods which apply to different stages of the product lifecycle via carefully designed information flows. The methods range from quality planning techniques such as Quality Function Deployment (QFD) to methods and tools for WibQuS is the German abbreviation "Knowledge-based Systems in Quality Management".

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

249

service engineers [22]. Quality management processes are of special interest in federated knowledge management because information flows occur in various ways (data exchange, transfer of experiences, strategy implementation) between differing method executing agents (automated systems, engineers, teams). Furthermore, the transfer of product and process information does not only follow the product lifecycle, but is crucial also along multiple feedback loops from the manufacturing floor back to work preparation and product planning. The implementation of these feedback flows is essential in order to achieve and maintain high-level product and process quality [10,35]. In the remainder of this paper, we first review the basics of our meta modeling approach and its support by the ConceptBase system [20]. Three further sections present conceptual modeling strategies, simulation-based strategies and explicit process support, all grounded in information flow analyses in combination with the meta modeling approach. We conclude with an outlook on remaining challenges and current research.

2. Meta modeling approach The idea of using meta models to drive large-scale model or system integration processes is not new. For example, in the medical domain, the Unified Medical Language System (UMLS [26]) offers a semantic network structure, metathesaurus and information sources map with the goal of providing uniform access to heterogeneous medical knowledge sources, terminologies and ontologies, and of aiding their integration. In the business process modeling domain, the ARIS system offers reusable reference models for various kinds of business processes under a meta model of event-driven process chains [42] which is technically represented in an extended entity-relationship formalism. In the Knowledge Engineering field, the Carnot project has experimented with using the CYC ontology for the integration of enterprise models, albeit without a specific organizing meta model [18]. Also, the CommonKADS framework can be understood as a set of reference models under an informal meta model of organization, task, agent, expert knowledge and the like [8,43]; in contrast to the approach described in this paper, however, these models are not themselves managed as knowledge bases even though they are meant to aid the development of knowledge-based applications. In all cases, published methodologies recommend the development of specific terminologies and glossaries which may or may not be organized under such a meta model. What appears different about our approach is that the development team can define its o w n meta m o d e l , thus providing a goal-specific focus for the modeling process and for the negotiations. Like other knowledge-representation approaches, but unlike the more informal work in the CASE or BPR domains, the thus-defined meta model can be mapped semiautomatically to rather detailed conflict-analysis mechanisms [27]. The meta modeling approach aims at more coherence than the currently popular proposals for viewpoint-oriented software development [12] or multiperspective environments (like ARIS Navigator) which associate notations and restrictions locally with each viewpoint. In these environments, relationships must be defined for each pair of specific viewpoints while a formal meta model allows you to define relationships between concepts once at the meta level, with automatic specialization to the individual perspectives.

250

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268 f

IRD Definition Scheme Level IRD Definition Level IFID Level

IFIOJ~!lo~on

............ ""

II

,Ro

- ~ L__.J [.-..-~ Application J UJ "'"" J Fig. 1. The IRDS repository.

The basic model of repositories for layered design environments is shown in Fig. 1 which reflects the ISO Information Resource Dictionary Standard IRDS [19]. The IRDS is organized along the classification dimension of semantic data modeling. The concepts on level n + 1 (the defining level) constitute a type system for level n (the defined level). Each information resource is described as a level pair, consisting of a schema and the data managed under this schema. Level pairs can be interlocked (shown by the horizontal dotted lines in the figure) in that the data level of the higher-level pair corresponds to the schema level of the lower-level pair, thus constituting a dictionary for it. Dynamically, a dictionary at level n + 1/n can be used to coordinate a set of autonomous subsystems at level n / n - 1. The four levels are defined as follows. The I R D Application Level holds application data and application process traces. The I R D Level holds the schemata and programs under which these application data and process traces are managed, as well as their interrelationships. The I R D Definition Level defines the languages in which such schemata and programs can be represented, as well as their interrelationships. The I R D Definition Schema Level provides the meta language in which the language descriptions at the IRD Definition Level are represented. Fig. 2 illustrates for the WibQuS case study how we apply this basic idea to the conceptual modeling and simulation-based analysis of information flows in distributed organizations. On the right branch, starting from a suitable meta language defined in the WibQuS repository (cf. Section 3), organizational quality management process models are designed, using the reference method models at the second level and defining desirable information flows between them. These information-flow descriptions can be mapped to a simulation model as described in Section 4. This model is itself based on interoperability between different simulation methods defined through a simulation meta language. The interplay of simulation and real organizational process allows on-going evolution of the federated organizational knowledge base and its usage: The execution of the defined processes results in process traces. The analysis of these traces (or just the observation of the processes) leads to local change proposals. The proposals are tested in the simulation model and the resulting simulation data in turn indicate global implications of changes to the organizational process structure. A similar multi-level repository model has also been defined for maintaining information about the evolution process itself (cf. Section 5). A repository system useful for company-wide integration o f knowledge sources must

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268 Simulation repository

p

251

WibQuS repository

CooperativeDesign of businessprocess and simulationmodels Cooperative Evolutionof processes and knowledge base by trace analysis and simulation

Fig. 2. The design process within the repositories. support different interpretations of Fig. 1. We briefly discuss how they are supported by the ConceptBase deductive object-oriented meta data management system [20]. First, the repository must integrate the cooperatively designed models that describe the possible static and dynamic interrelations among methods. In ConceptBase, this is supported by the Telos language [25]. Telos supports metamodeling by an extensible object structuring mechanism, combined with assertional facilities (rules and constraints) to define the semantics of language extensions. Telos achieves this in a simple and easily implementable manner by a mapping to the perfect model semantics of standard deductive databases. The possibility to specify application-specific rules and integrity constraints is another useful property of Telos which facilitates developing semantically correct repository models. Second, the repository is also a c o m m u n i c a t i o n m e d i u m through which agents can exchange requests, data and services. This has influenced the definition and implementation of query classes, an extended view concept supported by ConceptBase. Query classes can also be defined to analyze models for certain desirable and undesirable properties, and to provide simplified views on overlay complex models. The major advance offered in ConceptBase over normal database query languages is the possibility to define so-called meta formulas, i.e. queries and constraints that span multiple instantiation levels in the IRDS architecture [20]. Meta-formulas are used to enforce or monitor certain modeling disciplines, but they can also be used to adapt the graphical user interface of the modeling environment to user wishes and habits, by using graphical types as rule conclusions (cf. Fig. 3, below).

3. Conceptual modeling strategies In this section, we discuss how to organize and support conceptual modeling of methods used in different departments and of information flows among them. The approach is based on

252

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268 attribute

-

includes

-MII~

knows

\ ', perlorms supportsi ' ' ,,, ' i

,

,

- ~' '\'\needs Task

indud;s ~

includes

i, ",

i

] :

4

generalization

~uses

"

.

generates

(

~ ",, Inf0nnatl0nl , ' ~ L ~nclude~'''

,~

graphical

types

assigned

i::::ii:::l

prodi~i~::if Stra!a::s~ C~e i

modelling

tothe elements

i

Fig. 3. The method modeling language as a semantic network. the following observations: In a federated setting, there is usually a large conceptual and spatial distance among the different groups involved. This distance needs to be bridged to some degree before a successful modeling process can start. This bridge is based on the observation that the cooperative modeling process does not aim at a generic mutual understanding but has a specific purpose. Only after a rough agreement on this purpose has been reached, distributed modeling should start; t h e c o m m o n purpose also guides the local negotiations required to resolve interface conflicts between departments. Our kernel idea is that much of the purpose can be coded in a business process meta m o d e l which defines the language in which cooperating modelers communicate about models and model interactions. O u r strategy therefore has three steps (cf. Fig. 4). The first phase is the joint design of a shared language meta model. In WibQus, it involved significant literature studies as well as

Integrated Method Model Fig. 4. Support of federated method modeling by the meta model and ConceptBase.

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

253

lengthy negotiations and first experiments with the six modeling teams involved. Fig. 3 shows the meta model defined by the team. There are obvious similarities but also significant differences to other well-known meta models. For example, the task-method duality which defines a decomposition structure where a task can be supported by one of several methods, each composed of subtasks, has been used in information systems engineering (CRIS [29]) as well as in task-based approaches to knowledge engineering [5,2]. However, the main point are not these similarities but that this meta model fitted the needs of the quality management engineers to have a focus for their cooperative modeling process. With this goal in mind, the main emphasis shifted from modeling the task-method structure (which tends to describe how a task is accomplished) to the question of what agents and which kinds of information flows between them are involved. In particular, the meta model captures the central observation that methods and their supporting agents are linked by three major types of information f l o w s . 2 1 Task information drives and monitors the operational business processes. It is provided and consumed by the methods along the process chain. 2 Corporate memory is important organizational knowledge about products and processes that results from accumulated execution and analysis of business processes. It is generated and used by the agent that performs the task. 3 The goal of a strategy is the definition of a common context according to which tasks are organized and information is interpreted. It consists of a set of visions, policies and goals under which an organization or department operates. This is not modeled directly but is reflected in the meta model itself; a choice of meta models might be offered to look at processes from different strategic viewpoints but we have not tried this out in practice. In the second phase, each team of engineers developed models of the different methods used along the product lifecycle by instantiation of the modeling language making use of the client-server architecture of ConceptBase. The modeling by instantiation combined with the semantics of the meta model provided the guidelines that helped avoiding modeling errors and kept the models in line with the modeling language. For example, the following Telos definition of a T a s k allows you to derive a transitive subtask relation and prohibits circular subtask relations by defining a rule and a constraint on the i n c l u d e s attribute3: I n d i v i d u a l T a s k in Class w i t h attribute takes : object ; p r o d u c e s : obj ect ; i n c l u d e s : Task; rule

2 It may be interesting to note that this provides a conceptual link between two major streams of recent research in knowledge engineering: the interlinking of reusable methods for tasks, and the construction and usage of organizational memory. 3 As stated above, the expressive power of the deductive object-oriented Telos formalism corresponds to Datalog with stratified negation and integrity constraints; the formal mapping between the frame--like syntax shown here, the adaptable graphical syntax illustrated in Fig. 3, and the underlying Datalog semantics is detailed in [20].

254

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

transitive:$ forall a,b/Task ((a includes b) or (exists z/Task (a includes z) and (forall a,b,z/Task (a includes z) and (z includes b),), (a includes b)))) > (a includes b) $ constraint noncircular: $ forall a,b/task (a includes b)--->~ not (b includes a) $ end The resulting models served two tasks: First, they were an initial requirements specification for the software tools supporting each quality management method, and second, they contributed to the method reference model for the integration of methods along the information flows to be identified. The third phase of our strategy, partially overlapping with the second one, concerns support for the negotiations that are intended to ensure coherence and to identify information flows between the models. The groups discuss the defined concepts in iterating cycles until a common understanding and agreement about the models is reached (either by rational discourse or by decision making of superiors, cf. Fig. 4). ConceptBase offers four techniques for this purpose: (a) Strict monitoring of superficial concept correctness via the axioms of the knowledge representation language (here: Telos). The classification reduced modeling errors. The naming principles of ConceptBase avoid that two different concepts with the same name could be defined. The detection of such inconsistencies led to direct communication between the modeling groups. The concepts detected were presented in meetings in order to discuss if they are possible candidates for interfaces, because what could easily happen within the complex model was that synonymous concepts had been defined. (b) Analysis o f formal conflicts such as inconsistency or incompleteness (under certain assumed constraints within and across perspectives) via query classes. As an example, consider the search for concepts that fulfilled the defined semantics of the modeling language only partially. We were looking for 0 b j e e t s that were not taken or produced, tasks where no agent was attached to, and concepts that were not connected to others at all. A simple example looks as follows:

QueryClass DeadEnd isA ConceptualCategory with comment explanation: ''Information provided but not needed'' constraint qr_deadend: $ exists tl/task (tl produces this) and not exists t2/task (t2 takes this) $ end

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

255

In a commercial business process analysis environment based on ConceptBase, about 80 such query classes were identified to uncover analysis errors, differences in opinion and problems of the business process [27]. This collection of analysis queries encodes the accumulation of analysis knowledge across multiple projects, and is thus a very important base of organizational meta knowledge in its own right. (c) Analysis of subjective conflicts through structured annotations to concepts included in the knowledge base under a simple variant of the IBIS model [6]. We attached voting attributes (accepted_by, rejected_by, not_understood_by) to each concept such that modelers could annotate their opinions about the definition [32]. The results of this annotation process were given to the groups, who resolved problems indicated by negative or inconsistent voting using direct communication. The cycle of voting and discussing was performed until the number of critical concepts was getting small enough to discuss them at a meeting where all groups were represented. (d) Visualization of conflicts within and across perspectives through a multi-matrix interface generator called CoDecide. The idea of CoDecide is to provide a concise overview of the interrelationships between submodels via one or more matrix representations. The toolkit approach allows models to quickly develop specialized visualizations for particular kinds of conflicts. Different styles of cooperative modeling, ranging from fully synchronous shared editing to a asynchronous cooperation with private views can be supported. CoDecide was not yet available during the WibQuS modeling process; an example of its use for cooperative business process analysis is given in [21]. The result of the WibQuS modeling process was a set of almost 600 classes which constituted the first formal model of the quality cycle in engineering-oriented enterprises. The analysis and integration of such a complex model in a federated fashion would have been very costly or even inconductable. The meta modeling approach together with the client server architecture of ConceptBase enabled a rather fast process which lasted about half a year from the first language definition to the integrated model; the comparison with a number of parallel efforts in related fields showed that the intial effort of agreeing on a shared meta model was well worth the subsequent gains in developing and integrating the models. In further work, the model formed the basis for defining a technical infrastructure of federated information systems [34]. It also offered the basic structure upon which quantitative analyses of data flow and organizational learning could be piggybacked, as discussed in the next section.

4. Simulation-based strategies The previous section showed a strategy for the integration of knowledge sources in a federated organization. We now describe how these models can be further exploited for an analysis of information flow impact on organizational performance. We first describe the problem of analyzing the different categories of information flow, then present a method of integrating the required simulation techniques, and finally illustrate the integrated approach with examples from a case study in the packaging machine industry.

256

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

4.1. The problem of analyzing information flows The analysis of the impact of information systems on business performance and the resulting design decisions are complex tasks. The impact of an IS on the performance of federated organizations depends on its adaptation to a variety of tasks, such as transaction processing, decision making and communication. It also depends on values such as organizational philosophy and managerial flexibility [46,13,39] which are performed and implemented at different levels of an organization (user, groups, organization and inter-organizational [3]). With this in mind, the role of the information flow categories defined in Section 2 must be analyzed according to different criteria and with different quantitative modeling techniques. The interaction of corporate memory and task information, and their relation to organizational performance, can be analyzed best by looking at their dynamics4: How long does it take to create and transfer a piece of task information in a given situation? How long does it take until the availability of corporate memory along information flows alters the length and quality of task information creation and distribution? We chose a simulation approach to analyze these effects. Task information criteria can be defined and measured rather easily, because they describe short-term local effects which relate directly to the business process (and, therefore, monetary or time-related business criteria). The analysis of the flow of task information is driven by its transaction costs, its timeliness, its correctness and its completeness. The analysis of such criteria is usually performed by Petri-Net, Queuing System or rule-based simulation [9,28,36]. The analysis of corporate memory is much harder, because its effects are related to long-term feedback loops within an organization: information has to be accumulated, condensed and then transferred to the organizational units where its effects are supposed to happen. These processes are not just related to the task workflows and cannot be measured by hard business variables like time and money, but by the way they influence the variables that produce those time-and-money effects. Examples include task performance, error rate or document production rate. Therefore, the exchange of corporate memory is analyzed to find out its long-term effects on quality, flexibility or personnel qualification. A classical method for the analysis of such systems is System Dynamics (SD) [15]. SD describes the interaction of variables and systems as a flow of resources between levels, influenced by the perimeter of valves (rates). This perimeter in turn is determined by the states of variables throughout the system. The philosophy of SD is that people can describe structure and local behavior of a system well, but fail to predict global behavior, especially if feedback loops of different length and complexity are part of the system. Note that this philosophy is a nice match with our distributed conceptual modeling strategy which also relies on obtaining global models with local work and negotiation. In his work on software project dynamics, Abdel-Hamid [1] showed that SD simulation is well-suited for the analysis of multiple cause-effect feedback loops that describe the productivity of a software development team performing a specific project. Abdel-Hamid developed a set of models for the major factors related to software development productivity 4 We did not attempt to measure the impact of strategies but worked within a single paradigm, viz. Total Quality Management.

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

257

and established the feedback loops within and among them. The parameters of these models were then calibrated in numerous industrial case studies, from which a well-validated and fairly stable overall model emerged. The major elements of his model are the human resource management, where the available manpower is defined, software production, where this manpower is spent on several tasks, the controlling, where the completed work is related to the overall number of tasks and the deadline, and the planning, where conclusions are drawn from the results of controlling. Fig. 5 shows an excerpt of his human resource model structure; it describes the evolution of work force size and work force experience and their interrelationships (e.g. the hiring of many new people takes time from experienced people to train them, thus partly reducing the advantage of having additional personnel). While Abdel-Hamid's model deals with one team performing one project, our goal was to analyze the interaction between multiple organizational units in the quality cycle. Therefore, we had to enlarge the model, as shown in Fig. 6: 1 Communication and cooperation are not modeled in the single team situation. We developed an additional model that describes the effort needed for information management or communication and the effects that result from the existence and quality of information flows. 2 Only in a multi-team-multi-project situation, we can describe the cooperation aspects that define the context of the information flow analysis. One SD model was instantiated for each method along the business process and coupling mechanisms among the method SD models are defined. A Queueing System was coupled to the SD model in order to simulate the flow of discrete tasks that define the schedule. Important for quality management, the Error Management module provides the means to analyze the rate by which errors are generated,

Progress / Status / /

/

/

/

"

tompleted

sc, uie'..

\

Effort Remaining

Fig. 5. Software development subsystems (adapted from [1]).

258

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

Fig. 6. Mapping of the conceptual to the simulation model. detected, reworked or propagated within a method or across methods and the resulting effort in needed manpower. SD variables were defined to couple the models with respect to effort on information flows, the propagation of errors and rework effort needed. The complete structure of the SD model is detailed in [31]. Even though the model was designed to take full advantage of Abdel-Hamid's validation of his original model, careful calibration and validation of the extensions is necessary, before the models can be used as a testbed for analyzing organizational information flow; as of now, this has only been partially accomplished [33]. 4.2. Multi-simulation o f information f l o w s

Our model demands a multi-simulation approach that allows the coupling of discrete (Queueing Systems) and continuous (SD) simulation techniques. While earlier coupling attempts have been criticized as ad hoc and with little theoretical underpinning, we could take advantage of a recent result by Fishwick that every quasi-continuous simulation technique can be mapped on a discrete-event technique if the time increment is sufficiently small [14]. Fishwick has realized this result at the simulation execution level only. We developed a heterogeneous simulation development and execution environment called MultiSim which extends the support to a formal meta model which coordinates a graphical modeling environment and an interpreter for the developed simulation models [24]. The kernel of the MultiSim environment is an IRDS repository for simulation techniques (cf. Fig. 2) defined in Telos using ConceptBase. Its meta model is a definition language for simulation techniques by which simulation languages like SD, Queueing Systems or Petri-Nets can be modelled and connected.

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

259

To integrate the quantitative simulation approach with the conceptual models described in Section 3, a mapping process between the different kinds of concepts in the language model and the simulation submodels was defined which is described here only briefly (but see [33]): Each agent is represented by a Human Resource Management model. The tasks performed by the methods are represented by the Planning and Controlling~Queue models. The method is mapped onto the Method Performance model; it describes how manpower made available by human resource management is spent on various parts of a task. The Information Management model describes the amount of work necessary to access, provide and manage the information flows defined in the conceptual model. It also provides the corporate memory as a resource that influences the productivity of other tasks in the model, e.g. training effort, task productivity or error generation. 4.3. A case study To illustrate the application of the multi-simulation approach, we present an application example from a case study in a medium-sized manufacturing company. The company provides system solutions for packaging machines consisting of mechanical parts and of control software. As Fig. 7a shows, there are two main business processes. The case study concentrates on the mechanical engineering process highlighted in gray. The company wanted to solve some problems with meeting their delivery schedules. The cause of these problems were seen in the design department. While the forward flow of information was satisfactory, the feedback information flows were not working. For example, drawings forwarded by design were often changed by subsequent stages, but these error messages and changes were not made available to the design department. Thus, there was no systematic feedback to avoid that errors were repeated many times--no organizational learning took place. As one possible means of improving the feedback information flow, the introduction of a C A D system was considered. One impact of the new system is local, because a drawing can probably be developed faster, and, if the changes by subsequent process steps are included in the C A D database, with higher quality. But it seemed difficult to predict what global effects the new system plus the added and changed information flows would have. To analyze the effects of possible solution alternatives on the overall process performance, we first calibrated our model with information locally captured from engineers in each department. The resulting simulation was then compared with the real global input-output data of the previous year, with a surprisingly close match [33]. Independently, local estimates about the effects of the expected impact of the three changes shown in italics in Fig. 7 were captured and embedded in the experiment simulation model: local effects of the C A D system within the design department, improved forward task information flow, and improved corporate memory concerning error repetition. Two sample results are depicted in Fig. 7b and Fig. 7c. Fig. 7b compares the simulated throughput of the changed information flow structure with the base case used for validation, and with the actual deadlines that should have been met. In the first half year, the changes show no positive effects on design throughput. Afterwards, the throughput results are significantly better than in the base case and actually meet the deadlines.

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

260

New CAD system

a)Planned ~ ~ ~ / ~ Supportfor ~ ~ ~ s k informationflow changes (Acquisition!, ~o~o~m~4~ ~ = ~ i ~ ~( Corltro,design)

=-(Software/Elec,on'/~)

b) Experiment throughput 11

9

e,

r

.......

tas : deadline

+1., +l.tlvr+ v+=JIt+ (L,=~ ~==.)

c) Effects along the rework feedback loop I

8, i • • ,-x sim iI~Jon re= ~Ults exp~ iment) / .,,.,,~"

slm.llation res ,llt$ (exp(rtment)

f ~ \

/ J"

/7

r - ~ x,

5 4 M a n l factuirn

3

e /

2

throughput/

projects

y

30

60

f--" 90

Desk

120 1so

leo

210

2 4 0 rewo~

time/days

~

30

~ 60

man-days

t

90

120

150

180

210

240

I

270

300

time/days

Fig. 7. Prognosis on IS changes.

A first analysis shows that these results stem from an overlay of two effects: Better support of task information flow and better design support by the C A D system rapidly lead to better performance. However, the additional effort needed to manage and collect the altered design drawings compensates this effect at first. Only after some time of collecting corporate memory the reuse of drawings and design error knowledge leads to an acceleration and quality improvement of the design process. A closer look at the simulation results indicates that this description of the dynamics is in fact true but too simplistic. Fig. 7c graphs the manpower needed for rework in all three departments. It shows that another effect of the change is that the reduction of errors in the design phase by reuse of (altered) drawings leads to a reduction of manpower spent on rework in the late steps of the production process. This effect supports the dynamics of one of the classical assumptions in quality management [35]: Solving a problem in the early stages of the product life cycle is much cheaper than rework in the late phases in terms of needed manpower and material. The second observation is a feedback effect that influences the productivity of the design department. If you take a closer look at the graph for rework in design, you see that a lot of tasks are sent back to design. Otherwise the base case line would go down to the initial value after day 240, the end of the planned design jobs (as indicated by the forking of the base case

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

261

graph). Even though the corporate memory could not show its full effect after nine projects, it is clear that rework in the design department is reduced significantly. Avoiding past errors led to less rework in the assembly, and, therefore, in the design department, too. This in turn leads to yet higher productivity and less errors per task---exactly the kind of positive feedback loop we are trying to establish in quality management.

5. Evolution process support

So far, we have used conceptual modeling technology to help organizational units cooperatively model the structure of their short-term and long-term interaction, and we have used multi-simulation to analyze the dynamic effects (again short-term and long-term) of such interactions. Despite some positive experiences, we should also mention that there were quite a number of problems. In particular, to guide the process, modelers had only the meta language and a few examples available. As a consequence, the created method models initially showed a great variation in features such as granularity (just input-output of methods or detailed step sequences) and focus (e.g. business-oriented or system-oriented). This left the team with a choice between major rework and limited model coherence. Traceability of this process (in WibQuS about nine months) was limited to the product-level and the recording of the discussions in the voting process. One could argue that this approach still makes the old mistake of trying to achieve (model) quality by testing, rather than creating an active process that would produce quality directly. However, there is no easy solution to this problem. As we have mentioned, preserving autonomy and creativity is essential to the organizational setting we are discussing, even more at the level of the development processes themselves. Heavy bureaucratic processes and existing commercial workflow tools would defeat the whole purpose of the exercise. Instead, starting from results gained in the N A T U R E ESPRIT project, we are looking for process support that shares the features we have identified for knowledge in distributed organizations: (a) an inherently distributed way of understanding and tracing the modeling process, (b) explicit model--based process guidance (i.e. interpreted rather than hardcoded), and (c) process support delivered locally in the context of the organizational unit, rather than a uniform centralized framework. 5.1. Understanding the modeling process

We have mentioned earlier that each modeling process is driven by a specific purpose or vision which we have tried to capture partially in cooperatively developed meta models. The modeling process is then the complex task of establishing this vision in the existing, but changing organizational context. A detailed analysis of the requirements engineering literature [37] suggests that this process can be interpreted in three almost orthogonal dimensions: The vision holder establishes her vision in the social context through communication with other people, typically through drawing them into a project team for certain periods of time. The RE process starts with individual views and lacking awareness of all stakeholders, and

262

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

should end with a specification on which sufficient agreement was reached. In Section 3, some tools for capturing and visualizing stakeholder conflicts were mentioned; but it is also important to trace the history of this process, and this was done in WibQuS only by recording the votings. The initially vague and simplistic vision must be confronted with the obstacles of reality through an in-depth understanding. In other words, the vision must be elaborated in a detailed requirements specification which is complete relative to some standard. In WibQuS, the meta model was used as such a standard but turned out not to be rich enough to ensure full coherence. The ideas of all stakeholders are initially expressed informally, if at all. The goal of the R E process is to make the representation sufficiently formal that the subsequent system building tasks have a clear idea what to do. In our approach, coherence of different notations is reached through the common underlying ConceptBase repository.

5.2. Multi-perspective chunked process modeling

The framework of the three dimensions can help to capture the essential issues in a distributed knowledge engineering process, namely social, cognitive and representational ones. Traceability models for all three dimensions have been developed [38]. In [16], the organization of such traces according to individual contribution structures has been discussed. However, one important aspect of autonomy, namely the question of distributed trace ownership, remains to be resolved. To address this problem, we first of all need a distributable process modeling formalism. The process model must not be monolithic but should be freely configurable from basic user-definable process chunks. Further, the process modeling formalism must permit a broad range of different ways-of-working, without sacrificing compositionality. The reader who has followed our argument so far will hardly be surprised that we once again use a meta modeling approach to reach these goals. To achieve chunking and compositionality, process models must be contextual. A t any moment in time, each developer is in a subjectively perceived situation upon which she looks with some specific intention. Situation and intention can change very quickly under control of circumstances that are only partially known. Following [45], plans are just tools for the users which they may want to employ when it suits their current situation, adapt in flexible ways, and discontinue whenever they see fit (possibly within organizationally agreed constraints). The N A T U R E process meta model (Fig. 8) achieves this flexibility with a very small number of basic concepts by following a multi-perspective approach to process modeling [38,40]. It introduces formally the concept of context as an aggregation of situation and intention and then distinguishes three subclasses of contexts which can be nested arbitrarily (composed-of and alternative links in the model). 1 Executable contexts describe immediate action that leads directly to product changes; they correspond to the traditional approach in CASE tools where developers just act without a formal computer-supported plan. 2 A plan is a composite context that imposes a certain control structure over its part-contexts;

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 2 4 7 - 2 6 8

_ ]_~ situation

Situation

i,v_ba,~djm

1 Product

related_intention

Context

t

~vtr~osed_ of

t

t

~\~

263

Intention

proe

~a~um~t

\ =,,,_ Argumen'

t I

changes

Action

applied_ t Executable ]

by

1

Context

Choice Context

Fig. 8. A multi-perspective development process meta model. this corresponds to the way software process modeling is looked at in most of the published models. 3 Modelers often face a choice among multiple alternative contexts about how to proceed further; our model associates the pros and cons with each alternative. Each individual kind of context can be supported by specific kinds of technology: executable contexts and choice contexts by CASE tool actions, plan contexts by state charts or Petri nets. Since the thus defined process model is also an object in the process repository (i.e. a "product" in the terminology of Fig. 8), meta-process chunks for dynamic process redefinition can be defined [41].

5.3. Tool-integrated process support Throughout this paper, we have emphasized the role of information flow analysis as a means of designing organizations and their information systems. We can also apply this kind of analysis to the distributed process of developing and changing organization models. This analysis reveals requirements on tool support for such processes which we address in the P R O A R T process-centered requirements engineering environment [38]. The information flow structure of current process-centered engineering environments has been characterized by Dowson as shown in Fig. 9 [11]. Its three domains can be seen as roughly reflecting the human roles of project workers (performance), project managers (enactment) and researchers (modeling). They can also be linked to categories of technical support such as individual design tools (performance), workflow/process engines (enactment) and process repositories (modeling). Process enactment retrieves instantiated process definitions from the modeling domain, and controls the actual process performance through a process engine. The process performance

264

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268 Modelling Domain

Performance Domain

Enactment Domain

J

Fig. 9. Information flow in a traditional process support environment.

domain actually performs the engineering activity and gives feedback to the enactment domain, a prerequisite for having branches, backtracks or loops in the process. Most commercial environments suffer from weak links between enactment and performance domain. Developers work autonomously on individual tools and are loosely linked to the process engine by some kind of reporting system. On the other extreme, some current workflow systems assume that their process models cover the process rather completely, and enforce it overly rigidly. Neither extreme is justified in our setting where precise process definitions consisting of clearly defined plans or even automated steps only exist in certain chunks and under decentralized control. In P R O A R T , the model is therefore enriched by adding several types of information flows between the domains, as shown in Fig. 10: 1 Guidance request~end-of-enactment. The developer should be in the driver's seat, rather than just giving feedback. If process performers believe that they are in a situation where a process chunk is available for proceeding further, they can ask the process engine for guidance. 2 Process traces. Since we cannot assume anymore that the process engine is continuously active, it should leave a trace of its activity in the process modeling domain. This trace can be used to determine the overall status of a distributed engineering process, but it can also be used for process improvement. 3 Product and tool traces based on tool models. This is the most crucial extension. Outside the defined process chunks, the architecture in Fig. 9 offers no trace of the actual activity. The new information flows generate a meaningful trace from the products created and from the usage of tools that created them. Tool usage patterns are defined as small process chunks so that any tool-supported activity can be traced in terms of the process model. Equally important, by sending tool definitions to the performance domain, the process modeling

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

Modelling Domain (MD)

265

Performance Domain (PD)

Enactment Domain (ED)

Fig. 10. Information flow in the PROART environment.

domain can change tool behavior in line with changing process definitions, thus distributing the process in the local tools. The implementation of this enriched information flow is a non-trivial problem which has consequences both for the organization of the repository and the software architecture of the tools; for details, see [38].

6. Summary and conclusion

The goal of this paper was to discuss formal and computer-supported knowledge management mechanisms for the coordination of short-term and long-term information flows in networked organizations. The market-like structure of these organizations enables the creation of much more complex and useful feedback loops than the traditional hierarchical structure. It also creates significant challenges for the analysts and designers of organizational structures and information systems. O u r formal approach towards solving these problems relies on a combination of multi-perspective meta models (which set the focus for a modeling process) with an emphasis on information flows. Note that knowledge-based technology has been used here to manage the evolution of possibly quite traditional organizational information systems. This complements much work in knowledge engineering, which has tended to focus on quite traditional development techniques but for knowledge-based target applications. Our approach was elaborated for the tasks of conceptual modeling, quantitative analysis and explicit modeling process support in the example application context of computersupported Total Quality Management in a manufacturing organization. It has been technologically supported by multiple interlinked repositories and associated tools, using the ConceptBase deductive object management system as an implementation basis.

266

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

M u c h remains to be done. O n the formal and technical side, a deep integration of the process-centered modeling e n v i r o n m e n t with the other two c o m p o n e n t s is an issue of ongoing w o r k in F O Q U S , a follow-up to the WibQuS project. M o r e o v e r , while initial experiences with applying facets of our approach in industrial settings have b e e n highly encouraging, a m u c h b r o a d e r empirical basis is n e e d e d for serious validation especially of the simulation models. Fortunately, the decentralized nature of the approach itself makes such tests easier than in m o r e monolithic strategies.

Acknowledgment T h e authors would like to thank the participants in the research projects N A T U R E and W i b Q u S for the very valuable discussions on the topics of this paper. We would also like to t h a n k the people at the O S T M A c o m p a n y for answering all our questions and being patient even if they had other important work to do. F u r t h e r m o r e , we would like to thank the referees for their valuable remarks and suggestions.

References [l] T. Abdel-Hamid and S. Madnick, Software Project Dynamics (Prentice Hall, Englewood Cliffs, NJ, 1991). [2] J. Angele, D. Fensel and R. Studer, Domain and task modeling in MIKE, in Proc. of IFIP 8.1/13.2 Joint Working Conf. on Domain Knowledge for Interactive System Design, Geneva, Switzerland (1996). [3] R.D. Banker, R.J. Kauffman and M.A. Mahmood, Information technology, strategy and firm performance evaluation: New perspectives for senior management from information systems research, in R.D. Banker, R.J. Kauffman and M.A. Mahmood (eds.), Strategic Information Technology Management: Perspectives on Organizational Growth and Competitive Advantage (1993) pp. 1-8. [4] M.L. Brodie and S. Ceri, On intelligent and cooperative information systems: A workshop summary, Int. Jour. on Intelligent and Cooperative Information Systems 1(2) (1992) 249-290. [5] B. Chandrasekaran, T.R. Johnson and J.W. Smith, Task-structure analysis for knowledge modeling, Communications of the ACM 35(9) (1992) 124-137. [6] J. Conklin and M.L. Begemann, gibis: A hypertext tool for exploratory policy discussion, ACM Trans. on Office Information Systems (10) (1988) 140-151. [7] R.L. Daft and R.H. Lengel, Organizational information requirements, media richness and structural design, Management Science 32(5) (1986) 554-571. [8] R. de Hoog, R. Martil, B. Wielinga, R. Taylor and C. Bright, The CommonKADS Model Set, Technical report, Deliverable KADS II/WP1-WP2/RR/UvA/018/3.2 (1992). [9] W. Deiters, V. Gruhn and R. Striemer, The FUNSOFT Approach to Business Process Management, Wirtschaftsinformatik 37(5) (1995) 459-466. [10] W.E. Deming, Out of the Crisis (MIT CAES, Cambridge, MA, 1986). [11] M. Dowson, Software process themes and issues, in L. Osterweil (ed.), 2nd Intl. Conf. on the Software Process, Berlin, Germany (1993) pp. 54-62. [12] S. Easterbrook and B. Nuseibeh, Managing inconsistencies in an evolving specification, in 2nd IEEE Intl. Symp. on Requirements Engineering, York, England (1995) pp. 48-56. [13] P. Ein-Dor and C.R. Jones, Information Systems Management: Analytical Tools and Techniques (Elsevier, 1985). [14] EA. Fishwick, Simulation Model Design and Execution (Prentice Hall, Englewood Cliffs, NJ, 1995). [15] J.W. Forrester, Industrial Dynamics (MIT Press, Cambridge, MA, 1961).

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

267

[16] O. Gotel and A. Finkelstein, Contribution structures, in 2nd IEEE Intl. Symp. on Requirements Engineering, York, England (1995) pp. 100-107. [17] A. Heinzl and R. Srikanth, Entwicklung der betrieblichen Informationsverarbeitung, Wirtschaftsinformatik 37(1) (1995) 10-17. [18] M.N. Huhns, N. Jacobs, T. Ksiezyk, W.-M. Shen, M.P. Singh and P.E. Cannata, Integrating enterprise information models in Carnot, in Proc. of the Int. Conf. on Intelligent and Cooperative Information Systems, Rotterdam (1993) pp. 32-43. [19] ISO/IEC, Information Technology--Information Resource Dictionary System (IRDS)--Standard ISO/IEC 10027, Technical report, ISO/IEC International Standard (1990). [20] M. Jarke, R. Gallersd6rfer, M.A. Jeusfeld, M. Staudt and S. Eherer, ConceptBase--A deductive object base for meta data management, Journal of Intelligent Information Systems 4(2) (1995). [21] M. Jarke, M. Gebhardt, S. Jacobs and H. Nissen, Conflict analysis across heterogeneous viewpoints: Formalization and visualization, in 29th Annual Hawaii Conf. on System Sciences, Vol. 3, Wailea, Hawaii (1996) pp. 199-208. [22] M. Jarke, M. Jeusfeld and P. Szczurko, Three aspects of intelligent cooperation in the quality life cycle, Int. Jour. of Intelligent and Cooperative Information Systems 2(4) (1993) 355-374. [23] H.K. Klein and K. Lyytinen, Towards a new understanding of data modeling, in C. Floyd, H. Zullighoven, R. Budde and R. Keil-Slawik (eds.), Software Development and Reality Construction (Springer-Verlag, 1992) pp. 203-219. [24] M. Mandelbaum, Modellierung und Durchfiirung von Informationsflussimulationen auf der Basis eines Simulationsrepositories, Diploma Thesis (Lehrstuhl fiir Informatik V, RWTH Aachen, 1995). [25] J. Mylopouios, A. Borgida, M. Jarke and M. Koubarakis, Telos: A language for representing knowledge about information systems, ACM Transactions on Informations Systems 8(4) (1990) 325-362. [26] National Library of Medicine, Unified Medical Language System, 5th ed., Technical Report (US Department of Health and Human Services, 1994). [27] H. Nissen, M. Jeusfeld, M. Jarke, G.V. Zemanek and H. Huber, Managing multiple requirements perspectives with meta models, IEEE Software (March, 1996). [28] A. Oberweis, G. Scherrer and W. Stucky, INCOME/STAR: Methodology and tools for the development of distributed information systems, Information Systems 19(8) (1994) 643-660. [29] T.W. Olle, J. Hagelstein, I. MacDonald, C. Rolland, H.G. Sol, F.J.M. van Assche and A.A. Verrijn-Stuart, Information Systems Methodologies--A Framework for Understanding (North-Holland, Amsterdam, 1991). [30] W. Orlikowski and D. Robey, Information technology and the structuring of organizations, Information Systems Research 2 (1991) 143-169. [31] P. Peters, Planning and Analysis of Information Flow in Quality Management, Ph.D. Thesis (RWTH Aachen, 1996). [32] P. Peters and M.A. Jeusfeld, Structuring information flow in quality management, in Int. Conf. on Data and Knowledge Systems for Manufacturing and Engineering, Hong Kong (1994) 258-263. [33] P. Peters, M. Mandelbaum and M. Jarke, Simttlation-based method engineering in federated organizations, in Method Engineering 96, Atlanta, Georgia (1996) 246-262. [34] P. Peters, P. Szczurko, M. Jarke and M. Jeusfeld, Business process-oriented information management: Conceptual models at work, in Proc. of Int. Conf. on Organizational Computing Systems, San Jose, CA, (1995) 216-224. [35] T. Pfeifer, Quality Management (Carl Hanser Verlag, M/inchen, 1993) in German. [36] D. Plexousakis, Simulation and analysis of business processes using GOLOG, in Proc. of Int. Conf. on Organizational Computing Systems, San Jose, CA (1995) pp. 311-324. [37] K. Pohl, The three dimensions of requirements engineering: A framework and its applications, Information Systems 19(3) (1994) 243-258. [38] K. Pohl, A Process-Centered Engineering Environment (Wiley and Sons, 1996). [39] L. Raymond, G. Pare and F. Bergeron, Information technology and organizational structure revisited: Implications for performance, in 14th Int. Conf. on Information Systems, Orlando, Florida (1993) 129-143.

268

M. Jarke et al. / Data & Knowledge Engineering 23 (1997) 247-268

[40] C. Rolland, A n approach for defining ways-of-working, Ingenerie des systemes d'information 2(6) (1994) 719-742. [41] C. Rolland, V. Plihon and S. Si-Said, Engineering processes in N A T U R E , in M. Jarke, C. Rolland and A. Sutcliffe (eds.), The N A T U R E of Requirements Engineering, Springer 1997. [42] A.-W. Scheer, Business Process Reengineering--Reference Models for Industrial Business Processes (Springer Verlag, Berlin, Heidelberg, 1994). [43] G. Schreiber, B. Wielinga, R. te Hoog, H. A k k e r m a n n s and W. van de Velde, C o m m o n K A D S : A comprehensive methodology for KBS development, IEEE Expert 9(6) (1994) 28-38. [44] M.S. Scott-Morton, The 1990s research program: Implications for m a n a g e m e n t and the emerging organization, Decision Support Systems 12(2) (1994) 251-256. [45] L. Suchman, Situated Actions and Plans (Cambridge University Press, 1984). [46] D.D. Wilson, Assessing IT Performance: What the Experts Say, Technical report, M I T '90s Working Paper 88-050 (June 1988). Matthias Jarke is professor of Infor-

mation Systems and chairman of the informatics department at Aachen University of Technology, Germany. After obtaining a doctorate from the University of Hamburg, Germany, in 1980, he held faculty positions at New York University and the University of Passau prior to joining RWTH Aachen. His research interests lie in the development and usage of meta information systems for cooperative design applications. He has been coordinator of three European ESPRIT projects in this field, DAIDA (knowledge-based information system environments), NATURE (theories underlying requirements engineering) and CREWS (cooperative requirements en.gineering with scenarios), and has been principal investigator m collaborative projects concerning IS applications in mechanical engineering, chemical engineering, business process engineering and medicine. He is Editor-in-chief of the journal 'Information Systems' and program chair of the 1997 International Conference on Very Large Data Bases.

Peter Peters has since April 1997

been a consultant with McKinsey & Company. He studied computer science and medical informatics at the University of Dortmund, Germany, and obtained his doctorate in 1996 from the 'Graduate College for lnformatics and Engineering' at Aachen University of Technology. In his research, he investigated the modeling, enactment and analysis of information flows and communication in federated organizations. He participated in the interdisciplinary projects WibQuS and FoQuS on federated information systems in organizations, and in the project RegKoKo on IS support for virtual organizations, all funded by the German Ministry of Research. He is a member of AIS and GI.

Jeusfeld is an assistant professor in the Information Systems group at Aachen University of Technology. He received his doctoral degree from the University of Passau, and has served as visiting assistant professor with the Hong Kong University of Science and Technology. His teaching focuses on cooperative information systems, systems analysis methods and knowledge-based systems. His research interests include quality information systems, meta modeling, electronic trading and terminology services. He is member of IEEE, ACM, GI and serves on the management board of the GI interest group EMISA. Manfred

Klaus Pahl is a senior researcher with the Information Systems group at Aachen University of Technology, where he also obtained his doctoral degree in 1995. In 1996 he was a visiting professor at the University of Namur, Belgium. His current research interests include computer support for engineering processes, requirements engineering, process modelling, traceability and process improvement. Klaus Pohl is workgroup leader in the ESPRIT Reactive Long Term Research project CREWS and in the ESPRIT Basic Research Action NATURE. He is initiator of an international workshop series on Requirements Engineering: Foundation of Software Quality (REFSQ), has served in several international program committees and is (co)author of more than 30 refereed journal and conference papers. He is the vice-chair of the requirements engineering group of the GI, and is a member of IEEE and GI.