Organisational Knowledge Management in Manufacturing Enterprises - Solutions and Open Issues

Transcription

1 Organisational Knowledge Management in Manufacturing Enterprises - Solutions and Open Issues Ulli FISCHER 1 and Dragan STOKIC 2 1 DaimlerChrysler AG, Mercedesstr. 137, H150/VEM, Stuttgart, phone: , fax: , Ulli.fischer@daimlerchrysler.com 2 ATB-Bremen, Wiener Str.1, Bremen, Germany, phone: , fax: , dragan@atb-bremen.de Abstract. A high importance of effective Knowledge Management (KM) is well recognised in a number of manufacturing enterprises. There is still, however, a clear need to further reinforce applications of KM methods & tools by effectively solving fundamental and specific problems related to KM practice in manufacturing industry. The current IST project PICK intends to meet these challenges by the development of three innovative methods & tools for the management of corporate knowledge needed to support the process improvements in the manufacturing companies (automotive and aerospace industry). The tools are currently under testing in the industrial environment of the three end-users in the consortium. The paper presents the PICK concept and the results achieved, specifically for the business case at the DaimlerChrysler plant, where the tools are used to transfer the knowledge over different life cycles of the production process, and indicates further RTD needs in this domain. 1. Introduction The relevance of an appropriate management of corporate knowledge is rapidly increasing in modern manufacturing. Knowledge of a modern manufacturing enterprise often represents its highest value. This fact is already well recognised by many manufacturing companies, and a great deal of investment is put into the management of knowledge (however the focus varies enormously between sectors and companies). It is often argued that the manufacturing and engineering sectors have been the biggest beneficiaries from the knowledge management (KM) discipline. On the other hand, it is well known that knowledge sharing within both large enterprises and networked small companies is still related to a number of fundamental as well as specific problems. Much negativity centres around the 'people issues', the question is, however, to what extent the technology and methods available are appropriate, and/or whether or not the opportunities offered by IST are adequately used. This results in a frequent application of insufficiently systematic methodologies for KM. Therefore, there is a risk that KM domain in manufacturing companies gets a negative image, leading to a resistance towards its application in other sectors (e.g. civil service, financial sectors etc.) which are still in the initial phase of its adoption. Consequently, there is a clear need to further strengthen applications of KM methods & tools by effectively solving problems related to KM practice in the manufacturing industry. The current IST project PICK (IST : Tools for Process Improvements Based on Corporate Knowledge) intends to meet these challenges by the development of three innovative methods & tools for the effective management of corporate knowledge needed to

2 support the process improvements, specifically for manufacturing companies [1, 2]. The main approach is to combine model based and heuristic methods for KM with advanced multimedia and information/knowledge integration technologies. These methods & tools are successfully tested within the real industrial environments of three large automotive and aerospace companies, indicating high potential benefits. However, a number of further open issues regarding the effective KM in manufacturing industry is identified as well. The paper presents the PICK concept and the results achieved and indicates further RTD needs in this domain. 2. Current Status and Needs on KM in Manufacturing As stated above, knowledge sharing within an enterprise (e.g. among different areas, departments, plants, different players in a virtual company etc.) is related to a number of fundamental and specific problems such as acceptance by employees and motivation issues, ontology problems, correlation of different types of knowledge, treatment of experiencebased and often incomplete and/or ill-structured knowledge etc. Management of tacit knowledge including its capturing, maintenance and sharing over different areas is still not efficiently solved in industrial practice. For example, a typical problem is how to ensure maintenance and continuous update of knowledge systems by employees themselves - missing an efficient maintenance leads to a relatively short life time of the systems for the sharing of knowledge in manufacturing companies, i.e. these systems are quickly becoming non-useful, because the knowledge included becomes obsolete. The problem in manufacturing companies is very often that the knowledge is available, but it is not used either because it is not well structured, or because employees are not aware of its existence or trained to properly use it in their daily work. Communities of Practice are often built horizontally (e.g. over design departments of large companies) but rarely vertically (e.g. between planning and shop-floor areas, including employees with different levels of expertise etc.). KM strategies are often imposed top-down, especially in the large enterprises, and do not reflect specific needs and problems at e.g. the shop-floor or other areas within manufacturing companies. At a first glance KM in manufacturing companies is confronted with the same problems as any organisational knowledge management system. Obviously, efficient solutions for e.g. SW engineering are likely to be relevant for any product design etc. However, there is a number of specific aspects requesting a consideration of KM for manufacturing enterprises as a specific sub-domain, e.g.: - knowledge is centred around specific products(s), processes and technologies, addressing, however, numerous different issues (asking for a unique knowledge management system for the whole (extended) enterprise) - good transparency of business processes and products - great common business interest potentially leading to a solid legal background for knowledge sharing and to an increased motivation of management and employees (however frequently unachievable in current practice) - transition from traditional manufacturing to knowledge driven companies (different to e.g. SW industry) - actors (employees) for internal knowledge management systems in double role: source and user of knowledge - great variety of actors backgrounds (from shop-floor workers, to high specialists, top managers etc.).

3 Due to the specific problems of the manufacturing industry regarding KM, many powerful and generic KM tools and methods available at the market still often do not meet the needs of the manufacturing enterprises. It may be concluded, that many unresolved problems and many specific issues related to KM in manufacturing industry, ask for a further development/enhancement of methodologies and ICT tools to reflect these specific needs. 3. Project PICK: KM in manufacturing industry 3.1 Objectives The consortium of the IST project PICK, which includes DaimlerChrysler AG (Project Coordinator), SAAB, Volkswagen AG, Schneider Electrics, KISS Key Industrial Software Systems and ATB - Institute for Applied System Technology-Bremen, has recognised the requirements of the manufacturing industry w.r.t. KM issues. The project intends to meet the urgent needs of the manufacturing industry w.r.t. KM for process improvements (PI) tasks. The objective of the work is the development of three methods and tools for the effective KM needed to support the main PI steps [1, 2]: The knowledge-based method & tool (PICK-DIAG) for the diagnostic task to support the user to identify (potential) problems when the process/product is (re-) designed, especially in the run-up phase of a process change, as well as support the identification of (potential) reasons of problems. The method & tool (PICK-ACTION) for management of knowledge for action generation task to support the definition of appropriate measures to eliminate the identified (potential) problems. The method & tool (PICK-TRAIN) for training of employees on complex knowledge management tasks (specifically on tolerance analysis task). These methods and tools have to enable an efficient integration of knowledge available in different areas of large manufacturing companies: product and process design, shopfloor, suppliers, etc. By this, the tools have to enable a concurrent solving of problems within an extended manufacturing enterprise. The end-results will include both tools and methodology guidelines for KM and organisational issues related to the considered PI tasks. The tools will provide a means for an effective sharing of knowledge among the employees within (large) manufacturing enterprises. Therefore, the users of the tools will be shop-floor foremen, maintenance experts, process designers, quality assurance, product designers etc, and they should gain most benefit from the work. The conceptual architecture of the PICK system is presented in Fig.1. All three tools share the common knowledge base, and are integrated with different systems in the companies: process and production control systems, process and product design etc. 3.2 Approach The PICK project applies a strictly bottom-up approach: starting from the actual needs on KM for PI tasks at the end-users manufacturing companies, and based upon a thorough analysis of the state of the art in the KM domain, the new methods and tools are developed. The tools are based on a combination of three different approaches: Rule-Based reasoning (RBR), Case- Based reasoning (CBR), as well as so-called Corporate memory approaches (CMA - i.e. approaches supporting the management of documented knowledge and information in a

4 company). However, all approaches are related to a common powerful model of the process, products, production units, technology etc - so called Common Knowledge Base (CKB). Such an approach is selected in order to cover a wide scope of problems addressed: some problems follow the domain rules and therefore application of a rule base reasoning is the most appropriate, certain problems can be solved based on similarity with other cases, etc. Fig.1. PICK system: conceptual architecture The tools are intended to analyse problems reported by the operators on the shop-floor as well as problems reported by PLC and robot controllers, and problems identified on the basis of the monitoring of different product/process variables. A statistical analysis of the problems and the measurement results is also included. In Fig. 2. a general structure of the tool for identification of the reason of problems (PICK-DIAG) is shown. The selection of the different approaches depends on the type of problems to be solved and the availability of knowledge related to the problem,. The project does not intend to develop a new technology, but to apply the advanced technology solutions to meet the needs of the manufacturing enterprises. The methods and tools are specifically oriented to manufacturing and assembly processes. Application of multimedia technology supports the usage of the system in these processes. The PICK tools focus upon the improvements in discrete manufacturing, assembly lines and cells which includes both automatic/machine and manual operations. The tools, however, will be applicable for other manufacturing (e.g. in process industry) and business processes (e.g. in logistics) as well. Special emphasis is put upon the application of these models and tools in the process planning phase, based upon knowledge gathered within the manufacturing of similar products (e.g. previous versions of the same products). This means that the tools have to support the operators to identify the problems in the processes, based upon monitoring of different process parameters and product features, as well as to search for the causes of the problems and disturbances and to define actions to eliminate these reasons. The knowledge gathered will be re-used for the planning of manufacturing processes of new (versions of) products.

5 GUI: Database Set-up GUI: Problem description GUI: Reasoning Statistical Analysis PLC / Robot Control Interface to OPC Common Knowledge Base RBR CBR CMA Fig.2. Basic structure of the tool for identification of the reason of problems PICK-DIAG As indicated above, the core of the tools is the common knowledge base which is used by three approaches. This common knowledge base includes: static knowledge including the detailed models of business process, physical components involved and product parts produced, technology data etc. dynamic knowledge addressing problems that have occurred and dynamic actual states of the processes, physical components and parts The development environment used is: Windows NT 4.0, SP6a Oracle Enterprise Edition JDK / JDK 1.3 JavaHelp / JavaHelp Netscape Communicator 4.76 and/or MS Internet Explorer 5.5 Design/development tools used: Rational Rose 2000e as the main design tool Oracle Designer 6i for development of databases Oracle JDeveloper for development of EJB Visual Cafe 4.0a / Visual Cafe for development of Java code MS Visual Studio 6.0, SP4 for development of C/C++ code MS Visual SourceSafe 6.0 for code control JavaHelp / JavaHelp for implementation of on-line help. Some of the modules (e.g. CBR module etc.) are based upon available commercial tools. The development is scheduled in three main phases: The set of algorithms/ procedures for the management of the main PI tasks was defined within the conceptual phase of the project and critical issues requiring laboratory testing were identified. The laboratory testing of the four main algorithms/ procedures was carried out in the first phase. In the second phase, the early prototype version has been developed in order to test critical issues related to the selected concepts using the real data from the end-users and in real industrial environment, and in order to further verify understanding of the users needs and to provide means for capturing of knowledge at the users companies needed for the introduction of the full prototype versions of the PICK methods and tools within the end-users companies [4]. The full prototype has been developed in the third phase as extensions/updates of the early

6 prototype using the results and the feedback obtained through a thorough testing of the early prototype within the three business cases Results: DaimlerChrysler Business Case The full prototypes of the tools are currently under validation within three industrial sites - in the automotive and aerospace industry. The application area in the automotive industry in DaimlerChrysler is the manufacturing of rear axles. The tools are applied for diagnostics of the problems identified within complex automated lines fully controlled by PLC, NC and robot controllers (Fig. 3). Layout of DaimlerChrysler Business Case Specific Diagnosis-Focus Electric Source 1 2 PLC (S7) RC (Robot) Fixture Welding Control Welding-Cell RC part handling 38 x 38 Welding Cells 62 Welding Robots 14 Handling Robots Fig. 3: Layout of the DC business case This application area involves a very complex part, a rear axle, and complex manufacturing process including aluminium stamping, welding, in-line testing, quality control etc. As the base for the knowledge analysis serves new S-Class production line (launched in 1999). The test field for early prototypes in PICK was the new SL-Roadster (150/day), launched in 2001, while the test field for final methods and tools in PICK is new E-Class (1350/day), launched in Due to a high complexity of the rear axle (many single pieces) DC applies 38 welding cells, 62 welding robots and 14 handling robots to manufacture the axles. Each welding cell contains fixture devices. The robot is controlled by a PLC (Programmable Logic Controller). The PLC also controls the fixture. The robot and welding device is controlled by a separate hardware. The PICK tools are supposed to run on different technical devices, for example on the control or on a multi media platform the so called HUNTER. The PICK tools are primarily intended to support the set-up of the manufacturing process, but later, when the operation is fully running, the tools will be efficiently used as well. The core of the PICK tools is a knowledge database in which all users, those from design, corporate planning, production and maintenance, introduce their knowledge and experience to improve the process. The knowledge on products, production units and processes is collected by interviewing experts and from the available data-bases/tools and documents. The GUI for data base set-up, has been effectively applied for this task. The knowledge on problems that have occurred in other similar manufacturing processes are collected as a base for reasoning tools.

7 The PICK tools were tested to: model the whole production system (part model, production model, process model, technology model) within the PICK knowledge base this means testing of the CKB and user interface for setting of the CKB register problems reported both by operators and PLC - which means testing of the user interface for an error description and interface to PLC reason with both RBR and CBR, i.e. testing reasoning by two selected approaches. The PICK knowledge base includes a complete model of products, production system and processes as well as complex relations among different items. The current status of validation indicates that diagnostics of a high percentage (at least 80%) of the problems, which occur in such a complex manufacturing process can be successfully supported by the developed tools. The main issue for the user acceptance appear to be user-interface. This is well recognised and considerable efforts were needed to adjust GUI to enable an effective capturing of both static and dynamic knowledge. In parallel to the validation process, the tools are being improved and refined with the inputs from the tests in order to assure that end users will input their knowledge, since this knowledge is needed for a full operation of the tools. One of the important lessons learned within the PICK project is that involvement of the end users (e.g. for definition of appropriate GUI etc.) is of the highest relevance for the successful introduction and acceptance of a KM system. Based on the results of the early prototype further needs / possibilities for improvements and extensions of the system were detected and are taken into account during the development and integration of the full prototype. The business case will enable testing on how the tools can be used to transfer the knowledge over different life cycles of the production process. It will also enable to demonstrate the sharing of knowledge on different similar products and processes. The knowledge from a currently running production process - rear axles manufacturing (i.e. knowledge on problems, their causes, actions etc.) is currently gathered using the tools, and this knowledge will be re-used for the start-up phase of the new production process same manufacturing process but for a different car type, as indicated above [2]. Based on the current results of testing, it may be concluded that the PICK methods and tools promise to become efficient and powerful implements for solving process improvements tasks in manufacturing companies. It is expected that the application of PICK tools in such processes will bring a number of direct benefits, such as a reduction of efforts to search for the (potential) reasons of errors and for PI planning by 25%, reduction of time and efforts for the introduction of new (similar) processes in the enterprise related to the preventive elimination of problems by at least 20%, reduction of time/costs (related to problems) for the introduction of new products/product models/variants by 20% etc. It is expected that the costs savings will be at least 150 keuro/year for a plant similar to the testing one. It is likely that the tools will be in full application in the DaimlerChrysler plant by autumn However, a full commercial availability of the PICK tools is planned by the end of 2003 (about 12 months after the PICK project end). 4. Further RTD Needs for KM in Manufacturing Industry The PICK tools are likely to support an important segment of knowledge management practice in manufacturing companies. However, many issues regarding KM in manufacturing companies are still open, particularly those related to motivation and organisational aspects of KM. There is a clear request to provide more methods and tools specifically oriented to the needs of the manufacturing industry.

8 The objective would be to gather stakeholders from industry and academia and technology providers, and, by applying a bottom-up approach, to identify the requirements of the manufacturing industry regarding organisational knowledge management and to define RTD challenges to meet these requirements. This means that it will be necessary to analyse current practices in the manufacturing industry (e.g. analyse in which areas in manufacturing companies KM is appropriately and efficiently used (e.g. design areas) and in which it is still missing (e.g. shop-floor), to which extend there are knowledge islands in companies etc.), define generic KM architecture for both large and small/medium manufacturing enterprises, specifically considering networked companies (to which extend classical information centred architectures are appropriate for KM systems?) and identify the needs of manufacturing companies. The research should focus upon development of a new generation of methods and tools to meet the needs of the manufacturing companies, both large and small ones, specifically addressing extended enterprise issues and requirements regarding an efficient management of the full lifecycle of knowledge within an enterprise in support of both intraand inter-organisational activities, needs on context and location sensitive solutions for acquisition, sharing, trading, and delivery of knowledge etc. As the PICK project indicates, the important issue is the involvement of the end-users in order to assure full acceptance of the new methods and tools. Output of such research will be the holistic concept including a set of methods and tools to address the different requirements of the manufacturing industry and to fit in a unique KM architecture for manufacturing enterprises. 5. Conclusions The main end results of the PICK project will be the integrated kernel of tools applicable for the PI tasks in manufacturing industry. The tools are now tested/evaluated in the industrial environments of the users sites within the partners business cases. The current evaluation indicates that the PICK tools effectively meet the users need to provide a means for sharing of knowledge among the shop-floor area and process and product design area as well as among different plants of an enterprise. The tools enable an efficient collection of knowledge on different problems in manufacturing processes (e.g. problems encountered within the manufacturing process set-up phase) and support users to solve these problems by sharing this knowledge. The work will result in prototype applications in users companies, which would ensure conformity of the solutions and their high transferability to other users in different industrial sectors. Encouraging results of the PICK project imply that such a bottom-up approach is likely to assure an appropriate satisfaction of the industrial requirements. There is a clear need to continue the work on the KM methods and tools requested by the manufacturing industry. References [1] Fischer, Ulli; et. all; System Concept, The project PICK (IST ), Public report, October [2] Fischer, Ulli, Stokic, Dragan; Beckmann, Thomas; Management of Corporate Knowledge for Process Improvement in Manufacturing Companies, ebusiness and ework Conference, Madrid, October, [3] Thie, Michael; Stokic, Dragan; Chapter on Corporate Knowledge Management in Agile Manufacturing, in the book Agile Manufacturing: 21st Century Manufacturing Strategy, Elsevier, [4] Stokic, Dragan, Doerr, Michael; Beckmann, Thomas; 'Knowledge Management for Diagnostics and Action Planning in Manufacturing Companies', ICE-2001, Bremen, June, 2001.