Towards Core Knowledge Management: Challenges

Towards Core Knowledge Management: Challenges from Motorsports Stefania Bandini Department of Computer Science, Systems and Communication, University of Milano – Bicocca [email protected]

Summary This work reports about a research project (P-Race) conducted by the Artificial Intelligence Lab of the University of Milano-Bicocca to explore the role of knowledge engineering in core knowledge management and the experimental application of Knowledge Based Systems (KBS) technology to support the design of tread batches for motor-sports tires, which is the core competence of the Motorsports Department of Pirelli Tires. During the P-Race project, knowledge engineering techniques have been applied to acquire and represent core knowledge of Pirelli compound designers and race engineers that are involved in the design of new tires for motor-sports competition of IMSA and GT championships (a subset of championships on which Pirelli Tires is involved). The result of this project has been the development of a system based on Case-Based Reasoning approach, a specific type of KBS that deals with experiential knowledge.

Keywords Knowldege Management, Knowledge Engineering

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Introduction Product innovation is a key factor for industrial enterprizes survival. It runs in parallel with production processes requiring procedures, protocols, structures, and investments to be suitably designed, engineered, applied, and kept under strict control to achieve efficiency. Production processes must guarantee the reproducibility and the reuse of already existing successful products, that is, concrete solutions to previously defined sets of needs, requirements, and constraints, according to which their performance is evaluated. On the contrary, innovation is change [Utterback, 1996], and implies the creation of new products, involving the alteration of the strong requirements and constraints imposed by industrial production processes, at the same time satisfying new market needs or inducing them. Inducing needs on the market means creating products that customers need but have not yet even imagined [Prahalad, 2000], and the ability of infusing products/needs is becoming a critical task in the global market (where boundaries are changing ever more quickly, and targets are elusive and their capture is at best temporary). The competitive advantage roots its essence in this framework, and innovation plays a leading role on this stage, as the core ability creating new products or significantly modifying by adaptation products without ignoring production constraints. Designing new products by adaptation is the very challenge of competitive advantage. This means valorizing and exploiting to the maximum degree the core competencies owned by a company, focusing on its own core knowledge. For enterprizes, the successful management of core competencies (as its most valuable not tangible asset) implies focusing on knowledge management (that is, on the ability to identify, cultivate and exploit knowledge instead of merely restructuring, decluttering and delayering the company organization) [Liebowitz 1999, Davenport 1989]. From the knowledge management perspective, pointing out a core competence (as it is suggested in [Prahalad 2000]) means identifying the competence that provides potential access to a wide variety of markets, gives a significant contribution to the actual or potential customer's benefits of the end product, and is difficult to imitate for competitors. The concrete tie between identified core competencies and the end product is the core product (i.e., those components or subassemblies that actually contribute to the value of the end product). For instance, in computer industry, a new CPU is a core product; likewise, a new sculptured tread is a core product in tyre industry. In this conceptual framework it is easy to consider as core knowledge the organized set of core competencies, whose value is greater than the mere sum of all its elements. From this perspective, core knowledge directly supports the core business. Another basic feature characterizing core competence is its experiential nature. Experience means the apprehension of objects, thoughts, or emotions through the senses or mind. It is the active participation in events or activities, leading to the accumulation of knowledge or skill. The role of experience in knowledge creation is crucial. It is derived from the direct application of theoretical knowledge in problem solving on a specific domain, and allows to structure explicit knowledge and to accumulate tacit knowledge. The latter is the most valuable one, and can be considered a measurement of the skills of an expert. Experience means having dealt with several cases during time, regardless whether successful or not. It strongly depends on an event or a series of events participated in or lived through. Moreover, it concerns with the totality of such events in the past of an individual or group. To ``treasure" knowledge derived from experience in a production environment is a very hard problem in knowledge management. The idea that structured collections of documents and reports on cases (often supported by automated systems, i.e. databases) can provide a solution to the problem is just the tip of an iceberg (for more details on the role of experience in business, see [Pine 1999]). More considerations about the role of knowledge engineering in the design of computerbased systems to support knowledge management must be taken in account. Core competencies working by solving, by learning from experience, and by sharing innovation problems, are in several cases defined as communities-of-practices [Seely 2000, Wenger 1998, Barab 2000]. In most of the cases, innovation depends on these communities, and represents the interface between the organization and its environment. If this interface perceives change as the very challenge in competition, the focus on knowledge is naturally guaranteed. On the contrary, if conflicts emerge, the core knowledge is just a fragmentary collection of notions. Within this framework, shared experience can be viewed as the “melting pot” where the communities-of-practices, producing innovation, can create core knowledge. However, core knowledge coming from shared experience is the most difficult to be captured, structured, represented and managed, and computational approaches to knowledge management find here a major challenge. Managing core knowledge in a community-of-practices 23rd CADFEM Users’ Meeting 2005 International Congress on FEM Technology with ANSYS CFX & ICEM CFD Conference November 9 – 11, 2005, International Congress Center Bundeshaus Bonn, Germany

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often implies the deep analysis of the nature of such process of knowledge creation, that means to support the possibility of discover new modelling practices, such as in scientific environment. In the case of new products design, models are revised or, sometimes, replaced by new methods of creative changes such as in scientific discovery process. Performing creative reasoning within a computational framework in order to support innovation process requires investigating computational methods that allow discovery to emerge. The development of Knowledge Based Systems supporting a community-of-practice requires the design and the implementation of knowledge models dynamically dealing both with well-structured notions and with formal knowledge, and heuristic, tacit and not formalized knowledge and experience. Knowledge engineering must take into account organizational issues for the design of the general architecture of the system, for the adoption of Knowledge Acquisition techniques, and for the choice of the best knowledge representation methods in order to guarantee knowledge sharing and support problem solving activities. The development of knowledge acquisition tools dedicated to a specific community-of-practice is a task that implements this scenario, allowing common views of a problem to converge in a unique computational framework and, at the same time, every single standpoint to be preserved. The main aims of this work concern the presentation of the general computational framework that has been adopted in order to support knowledge creation process in product innovation. It allows capturing the experiential nature of core knowledge involved in knowledge creation activity (namely, the Case Based Reasoning (CBR) approach [Kolodner 1993]). General considerations about the role of this computational approach supporting communities-of-practice will be based on a successful application.

Knowledge Management and Case Based Reasoning The computational approach to knowledge management concerns the design, the implementation and the application of computer-based systems to support the representation, the sharing and the computation of data and knowledge within an organization. Several branches of computer science are devoted also to the development of computer-based systems supporting knowledge management (networking, data-mining, databases, neural networks, computer systems for cooperative working, artificial intelligence, and so on). In the case of core knowledge management and its role in innovative product design, we focus on the artificial intelligence approach, and in particular, on the Case Based Reasoning approach, a very promising research topic, with increasing importance for the development of computer-based solutions to knowledge management [Davenport 1989, Aha 1999, Aha 2000, Bandini 2002].

Case Based Reasoning (CBR) [Kolodner 1993, Slade 1991] is a problem-solving paradigm based on specific knowledge about previously experienced real problem situations (cases). From a conceptual standpoint, reasoning on cases means to identify the current problem situation, to find a past case similar to the new one, to use that case to suggest a solution to the current problem, to evaluate the 23rd CADFEM Users’ Meeting 2005 International Congress on FEM Technology with ANSYS CFX & ICEM CFD Conference November 9 – 11, 2005, International Congress Center Bundeshaus Bonn, Germany

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proposed solution either by suggesting the adoption of the solution or the revise by adaptation of the solution to the current situation, and finally to update some case memory by incremental learning with this experience. From a computational standpoint, Case Based Reasoning is a cyclic and integrated problem solving process. At the highest level of generality, the CBR cycle may be divided into the following four steps (see the Figure above, from [Aamodt 1994]): • • • •

RETRIEVE the most similar case(s); REUSE the information and knowledge in the case retrieved to solve the problem; REVISE the proposed solution; RETAIN the parts of this experience likely to be useful for future problem solving by incorporating them into the existing knowledge-base.

CBR allows experiential knowledge to be captured when models cannot or are hard to be formalized. Experience is the center of CBR, and CBR deals with it through its main idea of case, that is, an episode where a problem or a problem situation was partially or totally solved. Several applications of CBR have been developed, like, for example, classification, diagnosis, planning, decision support, information retrieval, configuration, and design. Case Based Reasoning supporting design is an increasing application area, since most of design knowledge comes from the experience of previously solved situations [Maher 1995, Borner 1998]. Successful examples of application of CBR to chemical product design can be found in [Cheetham 1997, Craw 1998, Craw 2001]. From the point of view of knowledge management for the support of innovation through design and adaptation of core products, CBR offers a computational paradigm to study knowledge models involved in competitive product design and to develop applications dedicated to communities-ofpractices. In particular, when the aim of knowledge management is to support a community-ofpractice, Case Based Reasoning allows to capture, represent and manage the experiential knowledge that the community-of-practice largely uses. The development of a knowledge management project for the support of a community-of-practice must take in account the following main factors: • how to structure significant information and documents concerning the activities involved; • how to acquire, classify and represent knowledge allowing information to be used; • how to capture both the experiential and the model based knowledge involved; • how to propose reuse of previously adopted solutions to analogue situations; • how to support the innovative adaptation of previously adopted solutions; • how to support the process of knowledge creation; • how to support the incremental learning deriving from the accumulation of experience; • how to spread information, documents and knowledge among the members of the communityof-practices; • how to address the right information or knowledge to each member in order to personalize the individual work; • how to maintain the knowledge management process.

Supporting a Community-of-Practice Dedicated to the Design of Rubber Compounds for Motor Racing The example of community-of-practice that is used throughout this work concerns a team dedicated to the design of tyres for motor racing. Several competence are involved in the decision making process about the right rubber compound to be provided to each racing team, the main ones owned by car drivers, racing team components, tyre designers, race engineers, and compound designer. The whole team acts according to the experience of its components on the field and their knowledge about a very complex decision making problem. In particular, race engineers and compound designers are usually the ones that collects knowledge and experience of the whole community that can be physically distributed and directly interact in order to provide a solution to problem concerning the whole community (see Figure below for a schematic representation of the interaction between race engineers and compound designers). 23rd CADFEM Users’ Meeting 2005 International Congress on FEM Technology with ANSYS CFX & ICEM CFD Conference November 9 – 11, 2005, International Congress Center Bundeshaus Bonn, Germany

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The experience of this community-of-practice is strongly dependent on performance and results obtained in previous races on tracks they consider similar to the current one. Moreover, even if in a previous race their choice had led to success, the improvement of some factors (grip, warm-up, thermal and mechanical stability, resistance to wear) is anyway advisable, because of possible improvements in the products of other tyre makers. Therefore, the choice of a tread for a given race depends mostly on the results of previously solved cases: the general problem solving mechanism used by race engineers and compound designers is strongly based on reasoning about past cases in order to solve a new case. In motor racing the role of tyres is crucial. Among the parts that must be assembled in order to build a tyre, tread is one of the most important. It is a chemical compound represented by a recipe, which determines its major properties. The basic material composing tread obtained by the recipe is called in jargon batch. Tread batch comprises a set of ingredients: artificial or natural elastomers (rubber), active fillers (carbon black, silica), accelerants, oils, and some others. All these ingredients are essential for the acquisition of the desired chemical-physical properties determining tyre performance. The knowledge about the recipes is the chemical formulation of rubber compounds, and is a large part of core knowledge of a tyre company (while the knowledge about the structure of the tyre is another large portion). Tread batch is a core product and any innovation of it represents an innovation involving the whole tyre. Any innovation on tread batch influences the production process, both in the case of tyres dedicated to car racing, and all the other products of a tyre company (large-scale products, as tyres for cars, trucks, motorbikes, and so on). The production of motor racing tyres is a production niche: generally the production yield is limited and it does not represent a sizable slice of in core business of a tyre company. Notwithstanding this, the role of the a motor-sport department is anyway crucial since, in many cases, innovation of core products starts from innovation experienced in racing (i.e. the most demanding case of tyre employment, in very particular test conditions). For instance, in large-scale production, a set of standard lab tests is usually performed in order to obtain the best performance from a tread batch. In motor racing, however, only few tests can be performed, because of the particular raw materials used, and the characteristics of some basic chemical ingredients of the batch. The global performance of tread can be verified therefore only during the trials or directly during the race. The evaluation of the performance is not absolute, but depends on several factors. The most important of them, characterizing each single race, concerns car set-up, road ground, geometrical profile and severity of the track, weather conditions, and racing team. Quite obviously, the skills of the people involved in the design of motor racing tyres (a real community-ofpractices composed by race engineers and compound designers) consist of their experience on the field and their knowledge about a very complex decision making problem. More in detail, the race engineer and the compound designer make final decisions about the reuse, the innovation, or the creation of the tread batch. Since their choice is bound to a single race, it is usually strongly dependent on performances and results obtained in previous races on similar tracks (usually in previous seasons of the championship, such as Sports Racing World Cup, American Le Mans Series, or others). Thus, the choice of a tread for a particular race depends on the results of previously solved cases: that is the general problem solving mechanism used by race engineers and compound designers is strongly based on reasoning about past cases in order to solve a new case. Moreover, decisions about the choice of a tread often involve innovation, that is the adaptation of a previously used tread to meet new requirements in order to improve its performance. Improvement is a constant: 23rd CADFEM Users’ Meeting 2005 International Congress on FEM Technology with ANSYS CFX & ICEM CFD Conference November 9 – 11, 2005, International Congress Center Bundeshaus Bonn, Germany

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even if in a previous race the choice had led to success, the improvement of some required performance (grip, warm-up, thermal and mechanical stability, resistance to wear) is anyway advisable, because of possible improvements in the products of other tyre makers (competitors). It is easy to transpose this example to the case of large-scale production: the competitiveness on the tyre market strongly depends on the innovation of tyre core products (such as tread). The innovative design of the rubber compound of a tread is a fundamental activity in defining and capturing new markets through the improvement of tyre performance. In the case of this community-of-practice, the involved knowledge that has to be acquire, structured, classified and represented regards, for instance, morphological features of the racing tracks, weather and track conditions, data about the type of the race, data concerning of the car team, the adopted recipes, time measurements for each test or race, comments about the race, and so on. A computer based tool for the support of this community-of-practice has to propose the reuse of previously adopted tyres to analogue races and teams and to support the adaptation of previously adopted rubber compounds of tyres. Knowledge creation and sharing among the members of the community-of-practices, together with incremental learning, are other main problems that the supporting tool has to address. The following section will introduce the P-Race system, a supporting tool developed for the support of the motorsport department of company that provides tyre to racing teams [Bandini 2000a, Bandini 2000b].

The P-Race System Figure below shows the general architecture of the P-Race system. It can be seen that the structure mirrors the CBR cycle. In particular, it can be divided into three main parts (A, B, and C, drawn by dotted lines in the figure).

Part A contains the main components dedicated to the race engineer: • a user interface for system users; • a database containing all meaningful data about past racing activity (dates, championships, cars, teams, drivers, trial and race times, coded recipes of the used tread batch, coded information about the tyre structure, and so on); • a dedicated Knowledge Acquisition and Representation Module (KARM) for the management of the main knowledge that is involved in the race engineer decision making process. Part B is made of components supporting the activity of the compound designer: • a dedicated integration interface with the recipes database and other confidential data contained in the information system of the company; • the Abstract Compound Model (ACM) module, that adapts retrieved solutions to the current problem; • a user interface allowing to system users to access and use the ACM module. Finally, the Part C constitutes the Case Based Reasoning core, and comprises: 23rd CADFEM Users’ Meeting 2005 International Congress on FEM Technology with ANSYS CFX & ICEM CFD Conference November 9 – 11, 2005, International Congress Center Bundeshaus Bonn, Germany

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• •

the Case Memory, where the pragmatic features of races are indexed and structured in cases; the Case Memory Manager indexing data from track descriptions and races database in form of cases, and evaluating the similarity between the current case and the stored ones.

The race engineer can interact with the system in three different ways, that is, he can input a track description, update the database, or activate the case based engine. The first two activities have the purpose to provide the system with all the information needed to support the race engineer's choice of the most suitable tyre for a given race. The KARM has been designed in order to let users express their knowledge about tracks both in qualitative and quantitative ways, avoiding as much as possible subjective descriptions. In particular the profile of tracks where races take place is acquired by both a dedicated user interface (see Figure below) and a telemetric data acquisition module.

The qualitative description provided by system users about weather and track condition forecasting is acquired by another dedicated user interface. Each track is described through the graphical user interface of the KARM as a set of blocks. Each block is characterized by a type (stretch, bend, chicane, and so on) and by the level of stress tyres have to withstand (computed by the system according, for instance, to features of the road ground). The resulting description of tracks allows to capture race engineer experience and knowledge in terms of crucial information about tracks. The main ones are, for example, the features of track bends according to their severity and the required tyre performance, the characteristics of the track surface, the thermal variation from a straight stretch to a bend and vice versa. This type of representation allows the system to compare tracks according to their morphological profiles in terms of race heuristics (e. g., initial and final speed in a bend, gear used in a given part of the track, weight supported by each wheel, and so on). Another dedicated interface allows the race engineer to update the database containing data about the racing activity (dates, kind of championship, car, team, drivers, trial times, warm-up times, race times, coded recipe of the used tread batch, coded information about the tyre structure, tyre performances, and so on). This type of activity is usually done directly on the field, that is, at the circuit during the competition. The system uses these data about tracks and races to support the race engineer in the solution of new problems. The reasoning process starts with the representation of the current problem as a new case to be solved. A case represents a set of chronometrical measurements, concerning a race or a trial, relevant for the performance or the technical solution adopted. As in any CBR system, the three major parts of a case are problem/situation description, solution, and outcome. In P-Race, the description of the current problem contains both qualitative and quantitative information (date, time and location of the event, weather forecast and track conditions) used by the system to retrieve from the case memory the most similar cases. The solution for a case describes the coded recipe of the batch used in that case, while the outcome represents the resulting state in terms of performances obtained when the solution was applied. Starting from the description of the current problem, the system examines the case memory containing past problems already solved, and proposes a list of solutions (the most similar cases) to the race engineer. The main task of the retrieval algorithm is to apply a function giving a measure of similarity among cases. In the P-Race system, the similarity function has been defined as the weighted sum of differences between attributes, some of which are 23rd CADFEM Users’ Meeting 2005 International Congress on FEM Technology with ANSYS CFX & ICEM CFD Conference November 9 – 11, 2005, International Congress Center Bundeshaus Bonn, Germany

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the result of a fuzzy interpretation of the user inputs. Case retrieval is based on knowledge about tracks, weather conditions, and type of track surface. The list of solutions proposed by the system could, at this point, include a feasible solution for the problem at hand that could be directly applied. Otherwise, an adaptation process has to modify one of the solutions proposed. The system also supports users in feasibility evaluation, reporting in a structured way the outcomes of proposed cases, including all documents associated to each case (comments of race engineers and drivers after a race or a test on track; quality-values vectors stating results in terms of performances obtained applying the solution; and so on). Thus, the system offers the view of all the current conditions the user needs to make his decision on which performance must be reached. At this point, if some modification to the basic recipe is needed, in order to improve or achieve some desired performance, the adaptation process is invoked. Adaptation could be necessary, for instance, when the proposed solution contains ingredients no longer available for tyre production or when the past use of the solution had led top undesired outcomes. A dedicated module of the P-Race system has been developed in order to support the compound designer in product innovation (i.e., the chemical formulation of a batch). It provides access to the information contained in a database of recipes and other confidential data about raw materials. The formulation of a new compound is guided by the requests of the race engineer that asks for the improvement of some performance of an existing batch. Starting from the solution and the outcome of a case retrieved by the P-Race module dedicated to race engineer, the compound designer examines the recipe and the race conditions in order to fulfil the race engineer's requests. The decision process of the compound designer can be divided in three separate stages: •

•

•

Batch evaluation: the expert is usually able to assess the performance of a compound from its parameters. The evaluation of the compound designer is usually different from the one given by the race engineer: the former judges according to his theoretical knowledge about materials, while the latter examines the results of the races. Definition of the objective: starting from to the results of the analysis of the previous point, and to the information about the race context, the expert decides which property of the batch has to be changed in order to obtain the desired performance. The properties usually involved are grip, thermal stability, mechanical stability, and warm-up. At the end of this stage, the compound designer has a set of possible options leading to the needed improvement of the performance. Choice of the ingredient: finally, the compound designer describes the batch recipes contained in the archive of the company as lists of ingredient together with their respective quantities. Then, according to the chemical and physical properties of the raw materials, chooses an ingredient, and decides whether (and how) its quantity has to be changed in quantity or if the ingredient must be substituted by another one.

P-Race supports the activities described above with a dedicated adapter module, called ACM Adapter. The ACM Adapter activates the integration interface with the recipes archive of the company in order to provide to the ACM component the decoded recipe expressed in terms of quantity of ingredients (see [Bandini 2001b, Bandini 2001c] for more details about the product innovation support provided by the ACM component). In other words, it modifies the recipe of the proposed batch in order to improve the performance observed in the outcomes of the past case, or to obtain new performances in relation with the description of the new case.

Concluding Remarks Knowledge has been recently recognized as a very important asset for enterprizes [Abecker 1998], needing knowledge engineering to: • identify knowledge sources (people, data sets, texts, programs and so on); • build an incremental knowledge base for the acquisition and representation of knowledge; • share and reuse knowledge among different applications for various types of users (i.e., share existing knowledge sources and future ones). P-Race System is the result of a three years project deriving from the co-operation between the Artificial Intelligence Lab of the University of Milan-Bicocca and the Motorsports Department of Pirelli Tyres. It is currently in use and supports the decision making process for the main championships in 23rd CADFEM Users’ Meeting 2005 International Congress on FEM Technology with ANSYS CFX & ICEM CFD Conference November 9 – 11, 2005, International Congress Center Bundeshaus Bonn, Germany

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which Pirelli takes part. The authors acknowledge all the people that have contributed and supported the project.

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