Driving the Organizational Learning Cycle - Semantic Scholar

Driving the Organizational Learning Cycle: The Case of Computer-Aided Failure Management Ralf Klamma, Matthias Jarke Informatik V, RWTH Aachen, Ahornstr. 55, 52056 Aachen, Germany Tel.: +49 +241 80-21500, Fax: +49 +241 8888-321 email: {klamma|[email protected]}

Abstract: Nonaka describes the process of creating knowledge in enterprises as an interplay of tacit and explicit knowledge. In the interdisciplinary German FOQUS project, industrial engineers and computer scientists have investigated information systems support for this process in the context of a specific knowledge creation strategy, "learning from failures". In the domain of failure management for complex production machinery, Nonaka’s socialization is supported by service-oriented workflows, externalization is supported by a domain-oriented meta model facilitating the construction of failure models, combination and internalization are supported by formal conflict resolution techniques and informal hypermedia representations. All of these representations are held in a knowledge-based repository. A implementation of the approach is operational in the Aditec demonstration factory at Aachen.

1 Introduction It is a well-known fact that for individuals “learning from failures“ is a painful but successful process to create knowledge. The same is true for organizations. A survey of 350 production enterprises showed that failures made in production lines are expensive, e.g. causing a loss up to 10% of personnel and machine usage capacity (Pfeifer 1997). According to (Desatnik 1989), 90% of customers unsatisfied with product quality will avoid that product henceforth. Nevertheless, only organizations which are able to learn from failures can avoid them, improve their performance, and adapt to changes introduced from outside or inside the organization. Failure Management (FM) is thus one of the possible strategies to create and maintain an organizational knowledge base. This strategy was investigated in FOQUS (in German: failure management with object oriented technologies in quality oriented production), an interdisciplinary project conducted by industrial engineers and IS researchers in the year 1995-1996. Following the abovementioned survey, the project investigated the definition, enactment, and maintenance of both reactive and preventive processes for handling failures for complex technical products, such as those used in production machinery, along the product life cycle (Pfeifer 1997). Nonaka's metaphor of organizations as

knowledge-creating enterprises (Nonaka 1994) provided a useful framework for supporting organizational learning processes by FM. In particular, it also guided our design of a repository-based FM environment which we report in this paper. Section 2 presents basic concepts and tools of the organizational learning process from the viewpoint of possible IS support. Section 3 characterizes the FM domain and presents our tools used to support organizational learning processes. Section 4 concludes the paper with a brief summary of the experiences we gained in implementing the approach in a factory environment.

2 Organizations as Knowledge Creating Enterprises Application-oriented metaphors for certain application domains in organizations like workflow management and CSCW have become very successful in the last years. Among them are "electronic circulation folders" (Karbe and Rampsberger 1991) and "workspaces" (Roseman and Greenberg 1996). All these metaphors are related to short-time cooperation within a project or a task. For long-time knowledge management in organizations researchers formulate metaphors like "Organizational Learning" and " Organizational Memory". While Argyris and Schön (Argyris and Schön 1978) defined organizational learning as “the detection and correction of errors“, Huber (Huber 1991) stated that learning occurs in an organization “if through its processing of information, the range of its potential behaviors is changed. A "learning organization" is defined by Dogdson as an enterprise that purposefully constructs structures and strategies to enhance and maximize organizational learning (Dodgson 1993). In contrast to these "western world" definitions of learning as processing of explicit information, Nonaka and Takeuchi (Nonaka and Takeuchi 1995) presented a comprehensive model of how Japanese organizations dynamically create knowledge. Knowledge creation is reached by the interplay of tacit and explicit knowledge in the organization. Tacit knowledge is personal knowledge that is hard to formalize or communicate to others. Explicit knowledge is formal knowledge that is easy to transmit between individuals and groups. Figure 1 shows the continuous spiral of organizational knowledge creation with the four modes of knowledge conversion: socialization, externalization, combination, and internalization. In this model tacit and explicit knowledge are both important for further knowledge creation while tacit knowledge is the primary source of innovation. Socialization is a process of converting tacit knowledge in tacit (sympathetic) knowledge as apprentices learn the craft of their masters through observation, imitation, and practice. Externalization is a process of converting tacit knowledge into explicit concepts (conceptual knowledge) through use of metaphors, analogies, or models. While Nonaka emphasizes the role of metaphors and analogies, we consider conceptual modeling as a excellent technique to externalize knowledge with the aim to collect organized and codified knowledge in an organizational memory (OM) (Mason 1993).

A classical domain of computer science is the process of creating knowledge through formal information processing which was called combination by Nonaka. Explicit knowledge from a number of sources is converted into explicit knowledge by techniques like reasoning, programming, data mining, and information exchange through formal information systems. Closing the circle, internalization is a process of bringing back explicit knowledge into daily work in the form of shared mental models or work practices, which is tacit (operational) knowledge.

Externalization WDFLW H[SOLFLW

Socialization

Combination

WDFLW H[SOLFLW

Internalization Figure 1: Process of organizational knowledge creation

In addition to the epistemological dimension of knowledge creation there is also a ontological dimension describing the units of an organization. Knowledge creation normally begins on an individual level. People have insights and develop new ideas of how to do their work better. Because short-time memory is limited, they write down their ideas thus creating personal information systems. In the last years sophisticated computerized tools have been developed for externalizing and internalizing personal knowledge such as idea managing tools, software development environments, and expert systems. However, as long as the tacit or explicit knowledge cannot be shared with other people, the organization is unable to exploit it (Choo 1996) By socializing individual knowledge through the techniques mentioned above a OM will be created as long as people can share their tacit knowledge by the techniques mentioned above. If these techniques are not applicable - e.g. the organization is distributed or to big to allow the sharing of tacit knowledge - the knowledge will remain personal. But even if it is possible to share the tacit knowledge, there are three threats to the OM if not made explicit. Reorganization can destroy well-known structures, processes, and information. Dismissal, retirement, and fluctuation of well-trained personal can lead to gaps in the OM and changing technologies may lead to system chaos and loss of routine.

As a consequence, OM must be represented explicitly to deal with these threats. Explicit representation can be reached by two methods. First, by combining existing information sources to a representation of the OM and second, by externalizing the tacit OM (cf. Figure 2). This idea differs a little from the approach followed by Nonaka and Takeuchi, even when they did not mention the term organizational memory at all. Isolated explicit information sources of an organization can be used by such techniques as data mining to create an snapshot of the available knowledge in an organization. This approach has certain disadvantages. Because most of the knowledge is not stored in information systems and therefore not explicit, the resulting snapshot is not complete. The evolution of the representation built that way is difficult because acquiring knew data sources, getting knew knowledge, and especially forgetting knowledge which is a very important aspect of an OM must be managed in a distributed manner causing information processing overload. Thus, the representation of the OM created by such methods will be incomplete, permanently out of date, and costly.

explicit

tacit Organization

Group

Externalization Representation of Organizational Organizational Memory Memory Internalization

Socialization

Combination

Externalization

Indvidual

Individual Memory Internalization

Personal Information System

Figure 2: From individual to organizational memory

Another way to create the explicit OM, is the usage of a cooperative modeling approach with formal model resolution strategies (Jarke et al. 1997) which leads to a central conceptual representation of the distributed knowledge in a repository. Evolution can be managed on the schema level as well as on the data level supported by repository technology. The approach of externalizing the tacit but socialized knowledge in failure management to formal and informal representations in an explicit OM repository has been followed in the German project FOQUS. The goal of the externalization step is the identification and description of interdepartmental problems and sources of process or product innovation. The explicit representation of conceptual knowledge allows the application of formal (AIbased) as well as informal (Hypertext-based) techniques to combine the

knowledge systematically for the usage in distributed reactive and preventive failure management processes. Finally, the internalization of that combined explicit knowledge was supported in the project by a repository-based workflow management system and a computer-based training tool.

3 Industrial Failure Management as an Organizational Learning Process Classical FM methods like statistical process control (SPC) and failure mode and effect analysis (FMEA) concentrate on mass product development and large-scale manufacturing which are not suitable for small and medium sized industrial enterprises with small-scale production and intensive support service. The goal of the project FOQUS was to develop solutions for FM with practical relevance for small and medium sized industrial enterprises. The main goal was not the development of new FM methods but bridging the informational, conceptual, and technological barriers which hinders the dynamic creation, share and usage of knowledge in distributed FM processes. An example will illustrate the complexity of the task. When a complaint by a customer is reported to the manufacturer’s call center that his newly bought gear unit loses oil, and the call center cannot give direct advice, a service engineer drives to the customer. The engineer detects the fault (sealing rings are not assembled correctly) and takes measures to correct it (e.g. exchange of gear unit). For preventing further failure occurrences, failure handling is passed on to the departments which are responsible for the correction, due to his assumption about the failure cause. Every department involved needs to look for causes and to take corrective measures whose success is reported back to the process owners. After several assumptions and trials the cause is finally unveiled: the failure was caused in product assembly planning, propagated to product assembly, and not detected by quality control because test routines were not applied effectively. Two types of organizational learning can take place: the call center can be informed how to provide a quick fix, and the product assembly can avoid the problem in future product versions. The realization of these two types of organizational learning in a complex organizational setting requires a formally funded analysis of the cooperative interdepartemental processes. Most of research in CSCW has concentrated on short-time operational processes while there is only little research for modeling long-time knowledge management within the operational processes (Choo 1996, Wargitsch et al. 1997, Peters 1996, Klamma et al. 1998).

3.1 Socialization of knowledge The process of organizational learning starts with socialization of personal tacit knowledge and therefore with the question if there is an ideal organization for FM processes. If a failure must be processed in different departments in order to analyze the problem and define a measure, two problems can be

observed: either there is no allocation of responsibility causing delays in processing and reducing the effects of failure elimination or there are multiple defined responsibilities causing mutual obstructions in processing. For sharing knowledge about responsibilities the project partners in FOQUS proposed the principle of escalation for interdepartmental processes with the aim to enable a direct processing of failures, detect spheres of competence systematically, and give structured support in processing the failure. Sphere of competence Correct

Capture

Sphere of competence

Analyze Correct

Sphere of competence

Escalate failure

Capture Analyze

Correct

Escalate failure

Capture Analyze

Failure detection

Figure 3: Principle of escalation

Escalation describes a socialization mechanism enabling the processing of failures and exchange of knowledge between spheres of competence. The elements of the principle are shown in Figure 3: The distinction between micro processes and macro processes is as follows: Micro processes describe actions to be performed in one sphere of competence. There are three steps, structurally equal for every area but different in implementation depending on the tasks and skills of every agent. The three steps follow the knowledge creation modes externalization, combination, and internalization. 1. The step of capturing the failure Capturing is the process of gathering and documenting (externalizing) available data. The data play an important role in the process. On the one hand, data must be interpretable by machines to support the analyzing process effectively. On the other hand, people later in the escalation chain only rely on the collected data without knowing the "real" facts. Therefore, rich media must be used to create context knowledge thus reducing uncertainty and equivocality (Daft and Lengel 1986). A detailed description of a model to structure failure data and of electronic circulation folders as a moveable encapsulation mechanism for context knowledge can be found in

(Klamma et al. 1998). 2. The step of analyzing the failure Analyzing is the process of interpreting (combining) available information, i.e. determining possible causes and defining applicable measures. As mentioned before, both humans and machines can perform this step. If no final solution is applicable or the agent presumes far reaching consequences, the failure can be escalated. 3. The step of correcting the failure Correcting is the process of defining, performing and tracing measures for failure treatment (Internalization). Success of measures is communicated to agents involved in the escalation. The macro process describes steps of escalation. With every escalation the failure is passed to one or more new spheres of competence. The conditions for passing are negotiated between the agents in the spheres, thus constructing service-oriented workflows (Medina-Mora et al. 1992, Schäl 1996). Systems using this approach, such as the Coordinator by Action Technologies Inc., emphasize the need for closing the contractual loop between customers and providers of services.

3.2 Model based externalization and combination of knowledge But this organizational structure is not externalized and therefore not sufficient. A formal description language to specify micro and macro processes as well as the information to be exchanged is needed to construct a model of the OM. The idea of using a formal modeling language is not to map a putative reality but to give the users a tool to model their own work situation in and between the specific application domains. This modeling process captures or externalized the meanings and knowledge shared by the users. The core of the formal language (cf. Figure 4) is derived from the process model proposed by (McMenamin and Palmer 1984). An input (object) is consumed and manipulated by a system and an output is produced. The system itself is described by processes, describing a workflow, processed by an agent using tools. The formal OM model, defined in the knowledge representation language Telos (Mylopoulos 1990), has three purposes. (1) Defining the flow of FM processes. (2) Defining the interfaces for intra- and interdepartmental coordination and communication. (3) Providing the base for implementing or reusing tools in every step of the micro processes as well as for workflow specification of macro processes. As in the predecessor project WibQus (Peters and Szczurko 1994, Peters 1996) this modeling process was supported by the object base management system ConceptBase (Jarke et al. 1995) that was used as conceptual modeling tool and storage of the repository. With formal model resolution techniques and a voting tool provided by the system, the autonomously described processes can be integrated. Describing and integrating the processes assures a company wide understanding on used concepts and FM flows, which is needed for developing

and coupling systems, defined interfaces between spheres of competence, and identification of agents and exchangeable information in every step.

Figure 4: Modeling concepts for OM representation

In short, the concepts and their relations are presented as follows: 1. The concepts Process/Activity describe workflows (e.g. capture, analysis and correction of failures) performed by agents. Processes can be refined by contains attributes. Refined processes are coupled by objects. An Activity is a specialization of the concept Process describing atomic processes. Agents can perform these steps autonomously as long as the modeled objects are produced. 2. The concept Agent describes humans performing a process. Agent can consist of a team of agents, e.g. the quality control team. 3. Tools are technologies supporting agents performing their tasks. Tools can be structured by a contains attribute. 4. Objects are artifacts consumed, manipulated or produced in FM processes. Objects can be decomposed by a contains attribute. The elements of the language are linked by attributes. So the link between spheres of competence can be defined by exchange objects, providing a defined interface for task relevant information exchange. The problem of responsibility is solved through linkage of agents with processes. In the extended version of the language there are additional concepts for structuring information and workflows which are needed to enact the defined flows on a workflow management system. The additional concepts for information structuring are Medium (for identifying media breaks), Presentation (for context knowledge), and Content (for describing structured objects like part lists used in fabrication of complex products). For workflow modeling concepts like Event (an external trigger), Action (external) and Pre-/Postcondition have been introduced.

With this language a formal representation of the OM can be built which is more resistant to changes. Reorganization of work and information flows can be planned on base of the OM and changes be reflected in repository. Personnel fluctuation and technological change is soften by interweaving concepts agent, process, and tool. In our example we can use the modeling concepts to externalize the shared knowledge in and between the departments involved in the claim process. The call center is modeled as an agent in our language. The tools used in the call centers are telephones and fax machines for communications as well as databases to record incoming complaints and to give direct advice to customers based on the experiences made in former failure cases. The steps performed by the call center are capturing the customer information on the failure, analyzing the failure by simply checking it against the experience database and giving direct advice if possible. So the experience with customer complaints is stored mainly in the database constituting a local and explicit memory of the call center. The input consumed by the complaint process is the customer complaint. Possible outputs are a direct advice to the customer which fixes the failure or a electronic complaint report which is escalated to the next sphere of competence. One possible escalation path is the service center which processes customer complaints by capturing additional data at the customer site, analyzing the problem in a potentially complex way, and taking correcting measures. The tool used by the service engineer is, besides from his product specific diagnostic tools, a laptop with a product specific experience data base. In addition, the complaint process was spooled to the laptop while being connected to the enterprise network. When the engineer fixed the problem and documented the solution in the on the laptop this knowledge can be escalated back to the call center together with the electronic complaint report. The call center then can integrate the solution into its experience data base. This knowledge can be used to give the customer a direct advice or to provide better data for the service center to speed up claim processing in future. Based on the experience made in the service center they can decide to escalate the failure to the product development and production departments. This connects the reactive part of FM with the preventive product-oriented part of FM. In the example the engineer recognized that the sealing rings were not assembled correctly but could not imagine why. If the failure is important enough for further production or even product development, the department can accept the complaint from field. The negotiation protocols for acceptance criteria have been discussed between service center and product departments in detail to use as much as possible from the knowledge gathered in the field. The preventive processes in and between the departments are managed in an analogous manner. The main difference is the usage of knowledge. In reactive FM processes the knowledge is used to speed up and improve the processing of complaints. In preventive processes the organization use the knowledge to improve products or even build new products.

3.3 Tools for the internalization of knowledge Based on the specification, intra- and interdepartmental workflows and information flows, access can be supported by a workflow management system (WFMS) thus internalizing the work practice described in the representation of the OM in a rigid manner. We used the metaphor of "electronic circulation folders" (Karbe and Rampsberger 1991) for claim processing and further escalation to support the share a explicit knowledge with workflow technology based on the OM representations in the repository. The folders are meta documents containing all the task relevant information in different sections.

Figure 5: Segment workplace with multimedia information

The segment project management contains data for controlling macro processes as well as a portfolio for local task management at the workplace. The data describe folder routing, negotiation protocols, schedules, cost estimation plans, the time the folder spent at workplaces, and so on. The segment failure data primarily consists of the descriptions, context, analysis and classification of errors, but also technological data concerning storage and representation of failure data in external data sources. The segment workplace documentation links to workplace-specific documents. Documentation contains textual and multimedia material for describing, analyzing, and correcting the failure (e.g. CAD drawings, machine photographs, products parts, sounds, etc.). Furthermore, catalogues for queries are linked to this segment to give the user the possibility to query the failure in connected experience data bases (Peters and Szczurko 1994). Figure 5 gives an impression of the folder tool used to handle folder at workplaces. In this case

relevant machine information for a failure has been provided by means of a digital image. The segment agents contains temporal assignments from persons to roles in the FM system. The segments refers to the concept Agent in the OM model. The segment tools consists of temporal assignments for applications specific to workplace and role. These tools are linked to the folder based on experience. The segment refers to the concept Tool in the OM. The segment history contains link chains concerning history and version management of folders needed for process controlling and knowledge capturing. Process history is also needed for documentation and monitoring of the performed escalation. A detailed description of the workflow management system and its practical application can be found in (Klamma et al. 1998).

Figure 6: AnswerGarden browsing the organizational memory

To connect the short-time collaborative support given by a workflow management system with long-time organizational learning aspects we used in the project FOQUS another way to internalize explicit knowledge again, is computer-based training. The „AnswerGarden“ metaphor (Ackerman and Malone 1990) facilitates the share of rich media knowledge about failure cases

along the product life cycle. Like in a garden paths to interesting places in the OM are built into a linked system of web pages. So users of the garden in the intranet can „walk on“ these paths to failure cases which are helpful to fulfill a certain task like constructing a variant of an existing product. If paths are not promising to the user or if he has not found documented failure cases he can send an electronic message at every page to the responsible agent defined in the principle of escalation and externalized in the representation of the OM. Figure 6 shows a web browser with a failure case.

4 Summary This paper has investigated the metaphor of organizations as knowledge creating enterprises to provide a framework for organizational learning processes and how industrial FM can support these processes. Figure 7 summarizes the support provided for the organizational knowledge creation process. The framework shows that the transformation of an OM constructed by socialization of personal tacit knowledge into a formal representation is more complete than combination of isolated information sources by methods like data mining. Formal and informal representations (models, notes, web pages, etc.)

Externalization WDFLW

Principle of Escalation

H[SOLFLW

Model resolution,

Socialization

Combination Presentation networking (“Answer Garden”)

WDFLW

Error observation

H[SOLFLW

Internalization

Model-based Workflow Support, Navigation Support (“AnswerGarden”)

Figure 7: Organizational knowledge creation and its support in FOQUS

The principle of escalation provides a reference model for sharing informal or tacit knowledge about responsibilities in FM management processes. The formal OM language was implemented in an repository which supported the modeling processes. The people involved in distributed FM processes convert their tacit knowledge into explicit knowledge autonomously either by modeling

in early knowledge creation phases or by means of the tools provided with the FM system in later phases. The model based design of interfaces in and between departments is handled in cooperative manner. The resulting explicit descriptions of interfaces are contracts used for negotiation support and operational routing support in short-time collaborative tasks. Experiences are stored in knowledge bases which are accessible by means of diagnostic tools. Based on the repository, the explicit representation of the OM can be exploited to combine and to internalize the knowledge by workflow management technology and computer-based training both realized in the project context. The tools provided support for short-time collaborative tasks as well as for long-time organizational learning. The workflow management system is used to populate the OM speeding up and improving complain processing. The "AnswerGarden" uses the populated OM to provide a structured access to the failure cases stored. We have implemented the approach in the Aditec demonstration factory at RWTH Aachen. Using our tools, the engineers at Aditec constructed a simple, but fully operational gear unit with variants and the workers manufactured and assembled the gear unit in small amounts. We decided to construct a new gear unit because we want to publish all available information (Pfeifer 1997). The engineers, workers, and customers simulated by a testing stand discovered and processed a lot of failures which were documented in the experience base during the 15 month process. Repetitive errors in production were reduced significantly. But some of the failures have a more complex nature. In one case, a engineer constructed and programmed the NC program for the drive shaft of a gear unit variant. It was not possible to manufacture the drive shaft with that NC program on the turning machine at the factory. A fact, the workers knew well, but not known by the engineer who was not used to the tailstock of the NC programmable turning machine. A special section in the FOQUS AnswerGarden is reserved for failures according machines and tools to improve the information flow between engineers and workers. In the project partner enterprises the industrial engineers performed additional case studies to study particular problems of product and service oriented failure management. There is a new project on its way to investigate if the support for failure management processes provided by our system can be extended to the core processes of the Aditec which are production and training. Acknowledgments: This work was supported by the German Federal Ministry of Education, Science, Research and Technology (BMBF) under grant 02 PV 710 25. The authors wish to thank their colleagues Manfred Jeusfeld, Peter Peters, and Peter Szczurko for their many fruitful comments and discussions. For the implementation of the FOQUS prototype we thank our students Marco Essmajor, Nico Hamacher, Gregor Lietz, and Axel Stolz.

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