Armen Zakarian

Sequential pattern mining algorithm for automotive warranty data

This paper presents a sequential pattern mining algorithm that allows product and quality engineers to extract hidden knowledge from a large automotive warranty database. The algorithm uses the elementary set concept and database manipulation techniques to search for patterns or relationships among occurrences of warranty claims over time. These patterns are represented as IF-THEN sequential rules, where the IF portion of the rule includes one or more occurrences of warranty problems at one time and the THEN portion includes warranty problem(s) that occur at a later time. Once sequential patterns are generated, the algorithm uses rule strength parameters to filter out insignificant patterns, so that only important (significant) rules are reported. Significant patterns provide knowledge of one or more product failures that leads to future product fault(s). The effectiveness of the algorithm is illustrated with the warranty data mining application from the automotive industry. A discussion on the sequential patterns generated by the algorithm and their interpretation for the automotive example are also provided.

Process analysis and reengineering

Abstract To achieve meaningful improvements of the process performance measures such as quality, speed, service, and cost, fundamental rethinking and redesign of the underlying process is required. Numerous corporations have been forced to change their processes in order to survive in a highly competitive market. To perform analysis and reengineering of processes, a structured and unified approach is required. In this paper, a framework based on the IDEF methodology, stream analysis approach, and dynamic simulation for process analysis and reengineering is presented. The stream analysis approach is used for analysis, diagnosis, and management of process changes represented with an IDEF model. To evaluate the impact of changes considered, support the process analysis, and to model performance of the proposed process, a dynamic simulation is used. This study extends the IDEF methodology by including quantitative information. The latter improves IDEF process analysis and reengineering capability, and facilitates the formulation of a dynamic simulation model. The significance of the results presented in the paper arises from the fact that many companies, e.g. Lockheed–Martin, General Motors, Rockwell International, are using IDEF for representing their processes.

Association rule-generation algorithm for mining automotive warranty data

A new association rule-generation algorithm is presented for mining automotive warranty data. The algorithm uses elementary set concept and database manipulation techniques to develop useful relationships between product attributes and causes of failure. These relationships (knowledge) are represented using IF–THEN association rules, where the IF portion of the rule includes set of attributes representing product features (e.g. production date, repair date, mileage-at-repair, transmission, engine type, etc.) and the THEN portion of the rule includes set of attributes that represent decision outcome (e.g. problem-related labor code). Once association rules are developed, the algorithm applies a statistical analysis technique to evaluate the significance of each rule. The rules that pass the significance test are reported in a solution. Application of the association rule-generation algorithm is presented with a data-mining case study from the automotive industry. The knowledge (rules) extracted from the automotive warranty data are used to identify root causes of a particular warranty problem or to develop useful conclusions. Detailed discussion on the source and characteristics of warranty data is also presented.

Modeling manufacturing dependability

In this paper, an analytical approach for the availability evaluation of cellular manufacturing systems is presented, where a manufacturing system is considered operational as long as its production capacity requirements are satisfied. The advantage of the approach is that constructing a system level Markov chain (a complex task) is not required. A manufacturing system is decomposed into two subsystems, i.e. machining system and material handling system. The machining subsystem is in turn decomposed into machine cells. For each machine cell and material handling subsystem, a Markovian model is derived and solved to find the probability of a subset of working machines in each cell, and a subset of the operating material handling carriers that satisfies the manufacturing capacity requirements. The overall manufacturing system availability is obtained using a procedure presented in the paper. The novelty of the approach is that it incorporates imperfect coverage and imperfect repair factors in the Markovian models. The approach is used to evaluate transient and steady-state performance of three alternative designs based on an industrial example. Detailed discussion of the results and the impact of imperfect coverage and imperfect repair on the availability of the manufacturing system is presented. Possible extensions of the work and software tools available for model analysis are also discussed.

Development of modular electrical systems

Modular systems provide the ability to achieve product variety through the combination and standardization of components. A methodology that combines system modeling, integration analysis, and optimization techniques for development of modular systems is presented. The approach optimizes integration and interactions of system elements and creates functional and physical modules for the electrical system. The Hatley/Pirbhai methodology (1987) is used for modeling functional requirements of a system. The model defines system interfaces (interactions) to support its functions. Once the interactions among functions are identified, an incidence matrix of the interfaces is developed. A clustering algorithm is developed to identify clusters in the incidence matrix, group the functions, and create modules. A Hatley/Pirbhai architecture model is developed to represent modular system design. A detailed discussion on the importance of system modeling in design of modular systems and on the constraints that limit development of modular vehicle systems is also presented. The approach presented is systematic and can be used to support product development and decision-making in engineering design.

Data modelling with IDEF1x

The idea of integrating business, product development, and manufacturing activities has gained momentum in manufacturing industry. To remain competitive, companies must optimize the utilization of their resources. This integration of various functional areas, e.g. computer-aided design, computer-aided process planning, computer-aided production planning and control, computer-aided quality control, and computer-aided manufacturing requires a large volume of data to be stored by the users contributing to those areas. IDEF1x provides a formal framework for consistent modelling of the data necessary for this integration. This paper provides insights into the IDEF1x modelling technique. The paper concludes with a discussion of several relevant research issues.

Analysis of process models

Process modeling tools, such as the integrated definition (IDEF) methodology, allow for a systematic representation of processes in manufacturing, product development, and service applications. Most of the process modeling methodologies are based on informal notation, lack mathematical rigor, and are static and qualitative, and thus can be difficult to use for analysis. In this paper, a new analysis approach for process models based on signed directed graphs (SDGs) and fuzzy sets is presented. A membership function of fuzzy sets quantifies and transforms incomplete and ambiguous information of process variables into an SDG qualitative model. The effectiveness of the approach is illustrated with an industrial example. The architecture of an intelligent system for qualitative/quantitative analysis of process models is presented.

Risk assessment of process models

The IDEF methodology has been extensively used for modeling various processes. Qualitative and quantitative reliability analysis and risk assessment of IDEF models is of interest to industry for several reasons. It identifies critical activities in a process, improves the process performance, and decreases downtime and operating cost of the process. To evaluate the risk associated with an IDEF3 model formal tools and techniques are required. In this paper, the fault tree analysis technique and minimum cut and path sets generation algorithms are applied for reliability evaluation and risk assessment of the parent activities in an IDEF3 model. A structural and reliability importance measure for parent activities in an IDEF3 model as well as for the elementary activities in a decomposed model are presented.

Reliability evaluation of process models

The integration definition (IDEF) methodology has been extensively used for modeling of various processes. Reliability analysis of IDEM models is of interest to practitioners and researchers for several reasons. It identifies critical activities in the process, improves the process performance, and decreases downtime and operating cost of the process. This paper extends the system reliability evaluation techniques, i.e., the system reduction approach and minimum path and cut sets method for reliability evaluation of IDEF3 models. Representation of IDEF3 models as reliability graphs, generation of the minimal path and cut sets of IDEF3 models with a path tree algorithm, and reliability analysis of IDEF3 models are the issues discussed in this paper. An algorithm for computing reliability of an IDEF3 model from a path set-activity incidence matrix is also presented.

Modelling and analysis of system robustness

Modelling and analysis of system robustness is of interest to researchers and practitioners for several reasons. Robust systems can minimize product development time, warranty costs, and improve product quality. The problem of attaining system robustness is often not addressed formally because design teams work independently of each other when designing system components. In this paper, a framework is presented for robust system development based upon system modelling, integration analysis, and quality engineering techniques. System robustness is achieved by specifying sub-system configurations within the overall system to minimize subsystem-to-subsystem interactions and overall system sensitivity to noise factors. System modelling represents system structure, captures system requirements and identifies required functional interfaces among system components. Once the interfaces among system components are identified, a component—component interaction matrix of interfaces is developed. Integration analysis then groups system components into subsystems and identifies interfaces (main elements of robustness study) between subsystems. Experimental design techniques are used to identify the combinations of levels of these interfaces, if any, that result in a robust system specification. This identified combination of levels then is incorporated into designing product subsystems and components. The application of the approach is illustrated with an automotive example of the design of a vehicle seat system. Discussion on the importance of system modelling and traditional robust design procedures and on the difficulties associated with the development of robust systems is also presented.