H. Jabrouni12, B. Kamsu1 and L. Geneste1
1 Production Engineering Laboratory, National Engineering School of Tarbes 47, Avenue Azereix, BP 1629, 65016 Tarbes Cedex
2Alstom Transport, Rue du Docteur Guinier - BP 4 - 65600 Séméac {jabrouni, bkamsu, laurent.geneste}@enit.fr
Abstract - During the Problem Solving Processes, Intellectual investment of experts is often considerable.
The opportunities for exploitation of expert knowledge are numerous: decision making, problem solving under uncertainty, expert configuration, etc. It is then necessary to assist experts in their tasks of solving problems by dispensing them to produce new work involving a high level of expertise. This can be structured in the process of feedback experience. In this paper, we present experience feedback as an alternative solution to usual knowledge management systems. We propose a structural-model approach for reasoning with Root Cause Analysis. Based on the Fuzzy and probabilistic Theory, our approach presents a general framework that enables the representation of uncertainty in a structure of causality.
Index Terms – Industrial feedback experience; Root Cause Analysis; structural model
I. INTRODUCTION AND PROBLEMATIC
Industrial products currently developed are more and more complex and make use of several technologies at the same time. Moreover, design times are reduced, bringing new constraints during pre-industrialization phases. Companies have to solve many problems by involving experts who have a partial knowledge of product limited to their field of specialty. These new constraints are rarely taken into account in traditional problem solving methods.
The process of problem solving are generally cumbersome to implement and are often triggered for solving complex problems (requiring a high level of expertise) and critical (with a very negative impact on the client, safety or performance of the company for example). However, one major inconvenient of these processes is the inability to reuse knowledge devoted to solving a past problem, especially that of experts using in analysis phase.
Thus, the ability to capitalize and reuse this knowledge represents a powerful way for optimizing and streamlining the process of problem solving. We call experience the fragment of knowledge capitalized during the experts’ activity to solve a problem and we call experience feedback the process that allows to organize the capitalization and exploitation of these experiences.
In the following, this paper will be focused on the proposed relevant mechanisms of analysis promoting efficient reuse of knowledge capitalized with better management aspects of similarity and uncertainty.
II. POSITIONINGSTATE OF THE ART
A. Positioning in relation to problem solving approaches
In the context of continuous improvement of products, services and processes in enterprises, the establishment of the experience feedback process aims to provide a practical solution for accelerating the resolution of problems already encountered and their non-repetition.
In the case of complex problems, an approach of problem solving is often helpful. We recall the definition of a problem solving approach presented by [6]: a problem solving process is a set of planned and systematic activities that can address complex problems. This approach is usually based on the use of rules, principles, expert knowledge. It can mobilize, in a structured and logical, a set of tools and techniques. Whatever the chosen approach [2], we find the same steps of reasoning:
- The composition of problem solving team;
- The description and assessment of the problem highlighted by events;
- The analysis of events to identify their root causes and to validate this analysis;
- The formulation of a solution to the problem and the verification of its application (corrective actions);
- The suggestion of actions to prevent a new occurrence of the problem (preventive actions, lessons learned, etc.).
There are two main approaches for problem solving process [6]:
- The theoretical approach is also called deductive method. This approach is used to solve problems by applying inference mechanisms based on research algorithm (e.g.
simulated annealing, the spread of constraints, etc...).
- The process of problem solving using inductive mechanism. This approach allows identifying the causes of problems which are identified by applying a series of tools allowing tracing the source of problem starting from the observed facts.
In response to our study, we retain the latter approach. It is often manipulated by tools that vary according to the method used. In all cases, we find the Deming PDCA cycle, regardless of the method used, as shown in Figure1.
Figure 1. Standard problem solving processes
The nature of information capitalized was characterized in particular in [10]-[14]-[15] during the application of traditional approaches of problem solving. The methods involve four major categories of information: context, analysis, solutions and lessons learned. As a result, this paper will be focused to propose a mechanism ensuring a relevant analysis promoting efficient reuse of knowledge capitalized with better management aspects of similarity and uncertainty.
During problem solving phase, the identification of causes, often called the "analysis phase" is the most important step in a methodology of solving problems. Whatever the problem solving method adopted, the analysis phase is always guided by the process of Root Cause Analysis (RCA). This process relies on the fact that it is judicious to treat the root causes of a problem than to treat the immediate symptoms. First, analyzing the causes of the problem, and second, choosing the most important causes to solve, and thus preventing its repeated again.
RCA can be considered as an element contributing to the iterative process of continuous improvement and represents a common approach for solving problems in a rational and methodical way [6]. We often notice that these specific actions are conducted with lack of knowledge of the main cause that occasions unsatisfactory work performance, that is, a failure to perform the resolution of the problem.
Therefore, the real causes must then be diagnosed at the stage of root cause analysis to initiate appropriate corrective actions.
Generally, the main steps of the RCA are:
- Studying the relationship between cause and effect, based on past experience and technical data then summarize them in cause– effect diagram.
- Gather factual information using appropriate tools (e.g. sheet statements).
- Investigate the relationship between cause and effect using the methods of quality, analyze past experimental data, organize data to observe daily and analyze using graphs, histograms, control charts, analysis of variance, regression analysis, etc.
- Synthesize causes to retain those who have been validated. This validation is usually done by contextually appropriate tools (experimental design, Pareto chart, industrial testing ...).Depending on the level of expertise involved, three possible situations can be identified during the analysis phase [29]:
- The group of experts knows, without ambiguity, the real causes of the problem. In this case, the group may propose directly the solution.
- The group of experts has some doubts about causes. In this second case, the group checks the presumed causes before going in search of a potential solution.
- The group of experts has no specific ideas about the real causes of the problem. In the third case, the group does a search of all possible causes. It collects facts and clues that will identify the real causes of the problem.
We consider this last possibility to be the most likely situation, in the rest of this document.
B. Positioning in relation to the techniques of reuse experiences
The exploitation process of feedback experience consists of activities to disseminate and use capitalized knowledge in an organization in order to make possible expert’s knowledge reusability [8]. In the same time, exploitation of experience stored may be done by experience feedback techniques. Indeed, initially the expert knowledge stored in the form of experience can save both the context of emerging knowledge and accurate information on its explanation that from the point of view and knowledge of the expert concerned. Ina second step, techniques for the re-use of experience can use past experiences to assist the expert in the resolution of problems. These include Case Based Reasoning, noted CBR, which we present in the next section.
Other methods, derived from CBR incorporate more elements of the experience. We can cite here the trace-based reasoning (RAPT), which based on temporal recording of units of information called
"Trace" of the reasoning process [25]. Finnie and Sun [24] propose a more general idea of Experience
Based Reasoning (EBR). This research approach aims to formalize any type of reasoning based on experience from one or more rules of inference.
However, the paradigm of Case-Based Reasoning [23] remains the most popular of these tools previously mentioned. The guiding principle of CBR is to provide a basis for reasoning through previous cases already tested and validated. This approach consists in retrieving an existing solution that enabled, in the past, to solve a similar problem to that which is being addressed. From the description of the current problem to solve, it is a matter of provide sufficient information to enable a function of similarity to access the cases in the proximity of the current case described. Specific tools allow adapting solutions of retrieved cases in the database. We can note that this method is based on the fact that two similar problems have similar solutions.
We propose a new alternative of Case-Based Reasoning as showing in Fig. 2. We are primarily interested in the classical approach of CBR in which the inference mechanism based on the reuse of the solution of solved cases with a context (described by a set of attributes) similar to the new case. However, this type of reasoning is not particularly appropriate to the context of continuous improvement in which our problematic is inscribed. Two problems occurred in similar circumstances often have different solutions especially in the context of complex problems and the adaptation process becomes increasingly difficult. As for the new alternative, the description of an event and its context can serve as an input element to the expert analysis in which the analysis is described by a hierarchy of attributes.
Figure 2. Illustration on the use of a case for resolution of problems
III. MODELING ANALYSIS
A. Representation of the causal analysis by quality tools
In the next section we briefly introduce some tools from the quality approaches dedicated to the representation of the analysis phase. This allows us to position the Experience Feedback from these methods.
1) Cause-effect diagram / Ishikawa diagram The cause-effect diagram [2] provides a simple way to visualize all potential causes for the finding of
an effect regardless of the nature of the concerned problem. It comes in the form of fishbone whose head oversees the effect that we want to know the causes (see Figure 3). Causes are arranged according to their level of importance or detail, resulting in a depiction of relationships and hierarchy of events.
Figure 3. Cause-effect diagram or Ishikawa diagram
After identifying the issue in terms of effect, causes are frequently arranged into four major categories (Manpower, Equipment, Material, Environment), but they can also be replaced with other classifications specific to particular context.
2) ACE Diagram
The ACE diagram (Action on the Causes of Errors)[2] is a variant of a cause-effect diagram method process. It can handle a problem using the cause-effect diagram as support for monitoring the action plan (see Figure 4). At each step of the process. Each step of the process is associated with a Pareto chart of non-conformities updated periodically. In addition, the effect is associated with a temporal chart representing the percentage of defective units observed during the last period of time. The figure below shows this type of diagram.
Figure 4. ACE Diagram
3) Cause tree / fault tree
The cause tree (fault tree) is generally used in the field of occupational hazards. The method is to construct a graphical representation of sequences of potentially hazardous events that led to the main fault. The tree branches are built and based on the Why Why Analysis, also called "5 why", which identify the origin of a problem (root issue) by bringing the experts to wonder about the problem by asking gradually the question “why?” in several times. The graphical representation of the list of
potential causes can be presented as arborescence.
Figure 5 shows this diagram.
Figure 5. Cause tree / Fault tree
B. A formal model of analysis
In the proposal approach, we propose a simple method of analysis. It is a filter that allows considering only the relevant information. Indeed, if events are not represented in an appropriate manner (modeled precisely), the application of reuse tools is likely to be too limited in some cases and not specific enough in others. This is the reason why we propose a mechanism of the analysis phase to better describe the quality of data and modeling expertise appropriately.
1) The quality of data
In the domain of feedback experience, the quality of data is crucial because it impacts directly on the reliability of results and interpretation. Before any study or analysis of feedback experience, a checking data quality must be done. Three checking criteria allow an analyst to secure
data quality management:
- Their consistency.
- Their validity, representativeness and homogeneity in the case of multi-expertise.
- Exhaustiveness.
Data quality must be evaluated on two levels:
- At the level of collection, before the
introduction of information in priori analysis of event.
- At the level of processing or statistical analysis of information in posteriori analysis of event.
All these constraints must be incorporated as part of the formalization of an analysis model.
2) Modeling expertise
We propose a functional diagram of the analysis phase which includes the following steps based on the model using tree causes:
- The description of the main problem is divided into many contextualized hypothesis.
- During the analysis phase, these assumptions must be detailed in the form of several elementary assumptions (H11, H12, H13, etc.). To better
understand the problem, each of these assumptions can be more finely detailed in other hypotheses (H11, H12, H13, etc.) by using appropriate tools such as "5 why".
- These sub-assumptions include uncertainty [30]
which can be represented by a measure reflecting the degree of confidence or certainty of the expert.
- The experts should naturally validate the potential assumptions in priority (hypothesis with the highest degrees of plausibility). This validation phase consists in applying a filter to determine the assumptions considered as the most relevant root causes of the issue.
Fig. 6 summarizes these steps of analysis described previously.
Figure 6. Block diagram of the proposal analysis phase
The experience can be considered as a collection of information that reflects a context in which information is rarely known with precision. They may be totally unknown in some cases [5].
Formalism to model experiences should allow representing the imperfections of information and integrating them in different stages of functional diagram of the analysis phase in order to provide adequate results. The uncertainty must then be spread from the original data until the final result.
The information is rarely given as reliable and perfect data. Many defects, such as uncertainty, vagueness and incompleteness are often associated with them. The unreliability of information can be translated in several forms [7]:
- The uncertainty is related to truth of information, and characterizes its degree of conformity to reality. It refers to the nature of the subject or the fact concerned, its quality, its essence or its occurrence,
- The vagueness regarding the information content and thus indicates its lack of quantitative knowledge,
- The incompleteness that characterizes the absence of information provided by the source on some aspects of the problem.
- The ambiguity reflects the ability of information to allow different interpretations.
This brief description allows us to better understand the different facets that may take on imperfect information[26] from the real world. We will now introduce several families of means representation of these limitations:
- The Bayesian approach provides a framework for a priori subjective probabilities. The Bayesian inference allows to calculate (or revise) the probability of a hypothesis. The probabilities are based on the weight distribution of a trust unit on singletons in the field of possible values. The Bayesian formulation introduced conditional probability and a priori probability revision. This framework is largely based on the concept of probability [13].
- The possibility theory [22] provides mathematical tools that allow representing incomplete, imprecise or fuzzy information. It is an extension of decision-making of Fuzzy Set Theory [27] that models categories of natural language. The possibility distributions [19] were meant to provide a graded semantics to natural language statements and imply ‘‘judgment’’ in the feeling of
‘‘possibility’’, ‘‘achievability’’, ‘‘acceptability’’
and “capacity of the events to occur’’.
- The theory of belief functions [7] provides, in turn, of the mathematical tools to process information with random and imprecise nature. It is a theoretical framework that generalizes the two previous frameworks. The developments are based on the weight distribution of a trust unit on any sub-set of the domain of possible values. There are several variants including the Dempster-Shafer [27] and the Transferable Belief Model(TBM) [28].
In this paper, although the third approach seems promising and generic, we will deal only with the first two approaches. Indeed, we have not yet improved the mechanisms for proper implementation of the theory of belief function. Thus we propose below an approach combining quantification of expertise by fuzzy concepts and conditional probabilities.
3) Quantifying the uncertainty of the expert by fuzzy concepts
The knowledge of the expert is difficult to quantify and is also uncertain [1]. It is therefore necessary that the analyst can model a probability distribution from the information collected.
Several approaches exist [4] and we can quote the most common of these approaches:
- The simplest method is to ask the expert to express the probability of validating a hypothesis by choosing a number between 0 and 1: value 0 means that the hypothesis is
impossible, however, the value 1 means that the hypothesis is absolutely certain.
- If the experts are not able to quantify a subjective value on the scale [0, 1], they can generally express their uncertainty using fuzzy concepts such as "probable”, “very likely" or with symbols like "+ +" "+", "-". These fuzzy concepts are often used in industrial environments and their implementation is easy [11]. [18] define a verbal–numerical probability scale corresponding to these concepts. We present here an extract from a table (see Table 1) estimate the correlation between declarative and probabilistic expressions.
TABLE1: CORRESPONDENCE BETWEEN DECLARATIVE AND PROBABILISTIC ESTIMATION
Expression of
Finally, if the experts are familiar with statistical methods, they can apply directly an average value or standard deviation of a distribution. These values are generally considered as the parameters of log-normal distribution, normal or those of Student rule [12].
4) Estimation of expertise by using probabilistic approaches
In general, the analysis phase, we seek to determine an uncertain quantity associated with each plausible cause, by interviewing persons having knowledge of this quantity [4]. These people, in the language of psychology, are "subjects" but they are more commonly called “experts". They are represented in the following by E1,E 2, E 3,… E n.
The Analyst D
,
identified as the "decision maker"in the problem solving process must combine expert opinions with his own knowledge [21], in order to evaluate the distribution or the most likely a priori value of θ. That is the person who makes the final
in the problem solving process must combine expert opinions with his own knowledge [21], in order to evaluate the distribution or the most likely a priori value of θ. That is the person who makes the final