Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

Exploitation des connaissances issues des processus de retour d'expérience industriels

Depuis plusieurs années, dans le secteur industriel, l’amélioration continue constitue un aspect important de la famille de normes ISO 9000 maintenue par l’organisation ISO (International Organization for Standardization). Elle se concentre sur l’amélioration de la satisfaction du client en passant par des améliorations continues et incrémentales des produits, des services et des processus. Afin de répondre à ces exigences, un point clé consiste à optimiser le processus de résolution de problèmes qui vise à analyser et résoudre les problèmes courants pour éviter de nouvelles occurrences. Différents processus de résolution de problèmes ont été définis et sont implantés dans les entreprises. L’un des plus connu est sans doute la méthode PLAN-DO-CHECK-ACT (PDCA), également connue sous le nom de « Roue de Deming ». D’autres méthodes sont également utilisées comme : 8 Disciplines (8D) également appelée TOPS (Team-Oriented Problem Solving), Six sigma ou DMAIC (Define, Measure, Analyze, Improve and Control), 7 step, etc. Les activités principales dans ces processus sont : la formation d’une équipe de résolution de problème, la description et l’évaluation de la criticité des événements, l’analyse des événements afin d’en rechercher les causes racine et valider cette analyse, la proposition d’une solution au problème et son application (solution curative), la suggestion d’actions pour éviter une nouvelle occurrence du problème (solution préventive, leçons apprises, etc.). Dans cette logique d’amélioration continue, un processus de Retour d’Expérience (Rex) est une représentation générique focalisé sur l'acquisition des connaissances des experts en phase de résolution de problème et sur la réutilisation de ces connaissances pour résoudre ou éviter de nouveaux problèmes. Une base de connaissances de retour d'expérience va servir de pivot entre la phase d'acquisition et la phase d'exploitation. Les points abordés dans le travail de thèse seront les suivants : Représenter les différentes composantes d'une expérience en utilisant les processus de résolution de problème comme support de capitalisation. Instrumenter les processus de capitalisation et d’exploitation Formaliser des mécanismes de recherche d’expérience, Formaliser des mécanismes de réutilisation d’analyses expertes - Développer un outil support de retour d’expérience sur une architecture Web. ABSTRACT : Continuous improvement of industrial processes is increasingly a key element of competitiveness for industrial systems. Management of experience feedback takes place in this framework to build, analyze and facilitate the reuse of immaterial potential of an organization in order to make it better in achieving its processes and / or products. For several years, the need for continuous improvement of products and processes has led many companies to set up standardized problem solving processes. For this purpose, different Problem Solving Processes are commonly used in the industrial field such as: 8D, PDCA (Plan Do Check Act), DMAICS (Define Measure Analyze Improve Control Standardize) or, more recently, the 9S process (9Steps). The main activities in the problem solving process are: The composition of the problem solving team, the description and assessment of the problem highlighted by events, the analysis of events to identify their root causes and their validation, the formulation of the problem solutions and their application checking (corrective actions), the action suggestions to prevent from a new occurrence of the problem (preventive actions, lessons learned, etc.). During the Problem Solving Processes, the intellectual investment of experts is often considerable. We propose to define mechanisms to reuse previously performed analysis (already solved issues) to guide the resolution of a new problem. The main contributions of this research work are : The structuring of a cognitive experience feedback framework allowing a flexible exploitation of expert knowledge: we propose a formal representation of an experience (according to the problem solving processes). - The definition of two mechanisms to exploit the context and analysis in these experiences. The specification and development of Experience Feedback Support Framework ProWhy offering methodological and software support for knowledge management (KM), and in particular for capitalization and exploitation phases of experience feedback processes

Exploitation des connaissances issues des processus de retour d'expérience industriels

Depuis plusieurs années, dans le secteur industriel, l amélioration continue constitue un aspect important de la famille de normes ISO 9000 maintenue par l organisation ISO (International Organization for Standardization). Elle se concentre sur l amélioration de la satisfaction du client en passant par des améliorations continues et incrémentales des produits, des services et des processus. Afin de répondre à ces exigences, un point clé consiste à optimiser le processus de résolution de problèmes qui vise à analyser et résoudre les problèmes courants pour éviter de nouvelles occurrences. Différents processus de résolution de problèmes ont été définis et sont implantés dans les entreprises. L un des plus connu est sans doute la méthode PLAN-DO-CHECK-ACT (PDCA), également connue sous le nom de Roue de Deming . D autres méthodes sont également utilisées comme : 8 Disciplines (8D) également appelée TOPS (Team-Oriented Problem Solving), Six sigma ou DMAIC (Define, Measure, Analyze, Improve and Control), 7 step, etc. Les activités principales dans ces processus sont : la formation d une équipe de résolution de problème, la description et l évaluation de la criticité des événements, l analyse des événements afin d en rechercher les causes racine et valider cette analyse, la proposition d une solution au problème et son application (solution curative), la suggestion d actions pour éviter une nouvelle occurrence du problème (solution préventive, leçons apprises, etc.). Dans cette logique d amélioration continue, un processus de Retour d Expérience (Rex) est une représentation générique focalisé sur l'acquisition des connaissances des experts en phase de résolution de problème et sur la réutilisation de ces connaissances pour résoudre ou éviter de nouveaux problèmes. Une base de connaissances de retour d'expérience va servir de pivot entre la phase d'acquisition et la phase d'exploitation. Les points abordés dans le travail de thèse seront les suivants : Représenter les différentes composantes d'une expérience en utilisant les processus de résolution de problème comme support de capitalisation. Instrumenter les processus de capitalisation et d exploitation Formaliser des mécanismes de recherche d expérience, Formaliser des mécanismes de réutilisation d analyses expertes - Développer un outil support de retour d expérience sur une architecture Web.Continuous improvement of industrial processes is increasingly a key element of competitiveness for industrial systems. Management of experience feedback takes place in this framework to build, analyze and facilitate the reuse of immaterial potential of an organization in order to make it better in achieving its processes and / or products. For several years, the need for continuous improvement of products and processes has led many companies to set up standardized problem solving processes. For this purpose, different Problem Solving Processes are commonly used in the industrial field such as: 8D, PDCA (Plan Do Check Act), DMAICS (Define Measure Analyze Improve Control Standardize) or, more recently, the 9S process (9Steps). The main activities in the problem solving process are: The composition of the problem solving team, the description and assessment of the problem highlighted by events, the analysis of events to identify their root causes and their validation, the formulation of the problem solutions and their application checking (corrective actions), the action suggestions to prevent from a new occurrence of the problem (preventive actions, lessons learned, etc.). During the Problem Solving Processes, the intellectual investment of experts is often considerable. We propose to define mechanisms to reuse previously performed analysis (already solved issues) to guide the resolution of a new problem. The main contributions of this research work are : The structuring of a cognitive experience feedback framework allowing a flexible exploitation of expert knowledge: we propose a formal representation of an experience (according to the problem solving processes). - The definition of two mechanisms to exploit the context and analysis in these experiences. The specification and development of Experience Feedback Support Framework ProWhy offering methodological and software support for knowledge management (KM), and in particular for capitalization and exploitation phases of experience feedback processes.TOULOUSE-INP (315552154) / SudocSudocFranceF

Analysis reuse exploiting taxonomical information and belief assignment in industrial problem solving

To take into account the experience feedback on solving complex problems in business is deemed as a way to improve the quality of products and processes. Only a few academic works, however, are concerned with the representation and the instrumentation of experience feedback systems. We propose, in this paper, a model of experiences and mechanisms to use these experiences. More specifically, we wish to encourage the reuse of already performed expert analysis to propose a priori analysis in the solving of a new problem. The proposal is based on a representation in the context of the experience of using a conceptual marker and an explicit representation of the analysis incorporating expert opinions and the fusion of these opinions. The experience feedback models and inference mechanisms are integrated in a commercial support tool for problem solving methodologies. The results obtained to this point have already led to the definition of the role of ‘‘Rex Manager’’ with principles of sustainable management for continuous improvement of industrial processes in companies

Knowledge reuse integrating the collaboration from experts in industrial maintenance management

Distributed environments, technological evolution, outsourcing market and information technology (IT) are factors that considerably influence current and future industrial maintenance management. Repairing and maintaining the plants and installations requires a better and more sophisticated skill set and continuously updated knowledge. Today, maintenance solutions involve increasing the collaboration of several experts to solve complex problems. These solutions imply changing the requirements and practices for maintenance; thus, conceptual models to support multidisciplinary expert collaboration in decision making are indispensable. The objectives of this work are as follows: (i) knowledge formalization of domain vocabulary to improve the communication and knowledge sharing among a number of experts and technical actors with Conceptual Graphs (CGs) formalism, (ii) multi-expert knowledge management with the Transferable Belief Model (TBM) to support collaborative decision making, and (iii) maintenance problem solving with a variant of the Case-Based Reasoning (CBR) mechanism with a process of solving new problems based on the solutions of similar past problems and integrating the experts’ beliefs. The proposed approach is applied for the maintenance management of the illustrative case study

Exploitation of knowledge extracted from Industrial Feedback Processes

Depuis plusieurs années, dans le secteur industriel, l’amélioration continue constitue un aspect important de la famille de normes ISO 9000 maintenue par l’organisation ISO (International Organization for Standardization). Elle se concentre sur l’amélioration de la satisfaction du client en passant par des améliorations continues et incrémentales des produits, des services et des processus. Afin de répondre à ces exigences, un point clé consiste à optimiser le processus de résolution de problèmes qui vise à analyser et résoudre les problèmes courants pour éviter de nouvelles occurrences. Différents processus de résolution de problèmes ont été définis et sont implantés dans les entreprises. L’un des plus connu est sans doute la méthode PLAN-DO-CHECK-ACT (PDCA), également connue sous le nom de « Roue de Deming ». D’autres méthodes sont également utilisées comme : 8 Disciplines (8D) également appelée TOPS (Team-Oriented Problem Solving), Six sigma ou DMAIC (Define, Measure, Analyze, Improve and Control), 7 step, etc. Les activités principales dans ces processus sont : la formation d’une équipe de résolution de problème, la description et l’évaluation de la criticité des événements, l’analyse des événements afin d’en rechercher les causes racine et valider cette analyse, la proposition d’une solution au problème et son application (solution curative), la suggestion d’actions pour éviter une nouvelle occurrence du problème (solution préventive, leçons apprises, etc.). Dans cette logique d’amélioration continue, un processus de Retour d’Expérience (Rex) est une représentation générique focalisé sur l'acquisition des connaissances des experts en phase de résolution de problème et sur la réutilisation de ces connaissances pour résoudre ou éviter de nouveaux problèmes. Une base de connaissances de retour d'expérience va servir de pivot entre la phase d'acquisition et la phase d'exploitation. Les points abordés dans le travail de thèse seront les suivants : Représenter les différentes composantes d'une expérience en utilisant les processus de résolution de problème comme support de capitalisation. Instrumenter les processus de capitalisation et d’exploitation Formaliser des mécanismes de recherche d’expérience, Formaliser des mécanismes de réutilisation d’analyses expertes - Développer un outil support de retour d’expérience sur une architecture Web.Continuous improvement of industrial processes is increasingly a key element of competitiveness for industrial systems. Management of experience feedback takes place in this framework to build, analyze and facilitate the reuse of immaterial potential of an organization in order to make it better in achieving its processes and / or products. For several years, the need for continuous improvement of products and processes has led many companies to set up standardized problem solving processes. For this purpose, different Problem Solving Processes are commonly used in the industrial field such as: 8D, PDCA (Plan Do Check Act), DMAICS (Define Measure Analyze Improve Control Standardize) or, more recently, the 9S process (9Steps). The main activities in the problem solving process are: The composition of the problem solving team, the description and assessment of the problem highlighted by events, the analysis of events to identify their root causes and their validation, the formulation of the problem solutions and their application checking (corrective actions), the action suggestions to prevent from a new occurrence of the problem (preventive actions, lessons learned, etc.). During the Problem Solving Processes, the intellectual investment of experts is often considerable. We propose to define mechanisms to reuse previously performed analysis (already solved issues) to guide the resolution of a new problem. The main contributions of this research work are : The structuring of a cognitive experience feedback framework allowing a flexible exploitation of expert knowledge: we propose a formal representation of an experience (according to the problem solving processes). - The definition of two mechanisms to exploit the context and analysis in these experiences. The specification and development of Experience Feedback Support Framework ProWhy offering methodological and software support for knowledge management (KM), and in particular for capitalization and exploitation phases of experience feedback processes

Ermenek Mine Accident in Turkey: The Root Causes of a Disaster

Mining accidents are one of the critical safety concerns all over the world. From the general point of safety, it is important to identify human factors, especially violations, and other types of barriers with respect to the occurrence of an accident. Root Cause Analysis helps to identify the mechanism behind accidents and develop future countermeasures for prevention. In the current analysis, Ermenek Mine Accident in Turkey was evaluated by using Root Cause Analysis Tool Kit and Manchester Patient Safety Framework (MaPSaF). Safety issues were structured by using Five Whys, Fishbone Diagram, and Barrier Analysis and safety culture were evaluated by using some of the dimensions of MaPSaF. Main factors were structured by using Five Whys, Barrier Analysis, and Manchester Safety Framework. According to these main factors, fishbone diagram was constructed. In general, natural, personnel, general policy in mining industry, and management issues in mining industry were determined as main four deficiencies affecting the occurrence and consequences of the accident. These main four factors were detailed in the fishbone diagram. The results indicated the importance of including different agents in the process of mining and working in cooperation to develop necessary policies and actions. Some methodological and practical suggestions were made for safety related issues. It is important to state policies related to basin in mining by considering both economic factors and safety factors. Moreover, reports related to safety issues should be more detailed by considering individual and organizational safety culture factors