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Many companies can today attest to having obtained significant progress in their performance using some improvement processes (six sigma, 5S, business process reengineering, etc.). But they can also attest to experiencing difficulties in sustaining the use of these processes over time. The aim of this paper is to describe all the specific actions which can contribute to the sustaining of these processes.

An empirical research methodology is used by carrying out a survey of 40 Swiss and French manufacturing companies regarding their approach to the sustaining of some of their improvement processes.

A model of sustainability for an improvement process is proposed. This model is founded on three axes: organic state, return on effort and facilitation. To sustain an improvement process over time consists in taking these three axes into account by managing their relative importance in space and over time. The paper details the generic actions associated with each of the three axes. These actions are then illustrated using the context of a sustained statistical process control project.

The survey detailed in this paper confirms the difficulty of companies have in sustaining their improvement processes over time, since the average sustainability ratio for all the different processes mentioned rarely exceeds 40 per cent. The paper gives all the specific actions which can contribute to sustain these processes better.

Visual inspection is used to assess a product’s quantitative characteristics (physical inspection) and/or to assess a product’s qualitative characteristics (sensory inspection). Due to the complexity of the product, inspection tasks are often performed by humans and are therefore prone to errors. It is particularly the case when controllers have to detect aesthetic anomalies, to evaluate them and decide if a product must be rejected or not. The paper details how to improve visual inspection.

This paper details how the performance of visual inspection can be measured. It then lists the actions which can be carried out to improve the detection and the evaluation of aesthetic anomalies. Finally, it describes how can be made the knowledge about visual inspection more explicit in order to be shared by controllers. The methods we propose are illustrated with a concrete example detailed throughout the paper.

The gage R2E2 we developed can be used to decide which corrective actions to carry out. The four generic descriptors and the list of their attributes we list are usable by a controller to both describe and characterize any aesthetic anomaly on the surface of any product. The paper details then how evaluate an anomaly with a grid or with a neural network when the link between attributes values and the overall intensity of the anomaly is not linear. Finally, a method to formalize the expertise of controllers is described.

The proposed approach has been applied in companies which are part of an european research program (INTERREG IV). The practices we suggested have significantly reduced the variability of the visual inspection results observed up to now.

The paper shows how to improve inspection vision of products.

Traditionally, tolerances are defined by an interval [LSL; USL] which can lead to several ambiguous interpretations of conformity. This paper examines an alternative method for setting specifications: “inertial tolerancing”. Inertial tolerancing consists of tolerancing the mean square deviation from the target rather than the distance. This alternative has numerous advantages over the traditional approach, particularly in the case of product assembly, mixed batches and conformity analysis. Coupled with a capability index Cpi, this alternative method leads to minimizing production costs for a specified level of quality. We propose to compare both approaches: traditional and inertial tolerancing.

The purpose of this paper is to present a benchmarking process that can assist small to medium‐sized enterprises (SME).

The paper describes how the steps of a benchmarking process can be positioned with the steps of the plan‐research‐observe‐analyzes‐adapt‐improve cycle (PROAAI) and shows that the tools proposed to carry out these steps are mostly reserved for big companies. We therefore detail a set of tools and methods to assist SMEs in the deployment of the steps of a benchmarking process (observe and analyse steps).

The tools and methods which are described in this paper especially target the description of the processes (process to be improved and reference process) using the description of the current practices used, and the comparison of these processes leading to suggestions of improvements to carry out on the process to improve.

In this paper, the identification of the subject to be benchmarked is currently based on a description of the differences observed between a reference process and the process to be improved. Our future research is to determine how just one interview could be carried out instead of two.

The methods and tools have been applied in several manufacturing plants at TECUMSEH Europe.

Practical help to a SME to carry out a benchmarking.

The variability of the results of a visual control is often high. This paper aims to propose a new tool to give information about what improvement actions can be carried out to reduce this variability.

The variability of a visual control can be measured by Kappa's Fleiss which measures the level of agreement between appraisers and experts. The R&R Gage is then classically used to give information about corrective actions which can be carried out in order to improve this level of agreement. The paper demonstrated that this information is not always sufficient.

By considering the two essential steps of a visual control (exploration and evaluation), the R2&E2 Gage proposed gives more precise information about the improvement actions to carry out to reduce the variability of a visual control. Repeatability and reproducibility, for detection and evaluation purposes, are considered separately.

This R2&E2 gage is one result of a European research program called INTERREG. The aim of this program, which brings together two laboratories from the University of Savoy and EPFL, two institutional partners (CTDEC and CETEHOR) and some Swiss and French industrial companies, is to create methodological support and the tools needed to improve the visual control of high added‐value products.

This R2&E2 gage has been used in six industrial companies involved in the European program INTERREG. Significant improvement of the visual control has been observed over a short time.

The paper fulfils an identified need of industrial firms to have efficient tools improving the visual control of their products.

The paper aims to provide guidelines of companies in identifying their best practices with reference to a French example.

The paper describes first the evolution of benchmarking, which nowadays is more and more based on the identification of good practices to acquire or transfer. Then we present a typology of best practices which can help a company to discern more effectively what could be relevant to exchange in benchmarking. Finally, we describe the best practice specification (BPS) method, which helps a company to locate and specify its good practices likely to be transferred within the framework of benchmarking.

The paper underlines the difficulty of a company to clearly define what a “best practice” is and the lack of methods which could help it to identify its best practices.

Future research will be to develop a method of acquisition and representation of the best practices. In particular, it will be a question of studying if certain models that are currently proposed to represent knowledge (GAMETH, KADS, MKSM, MEREX, …) can be used for the acquisition and the formalization of these best practices.

The BPS method is presently applied in TECUMSEH Europe on its Cessieu site (France). The company is identifying the best practices currently put into place by the various sectors of manufacturing of the site on the process “To deploy progress effort (SPC and TPM)”. The long term objective of the company is to apply these practices in all of the manufacturing sectors of the site, as well as on those other three sites in the group.

This paper offers practical help to a company to identify and characterize its best practices.

The purpose of this paper is to establish a tolerance optimization method based on manufacturing difficulty computation using the genetic algorithm (GA) method. This proposal is among the authors’ perspectives of accomplished previous research work to cooperative optimal tolerance allocation approach for concurrent engineering area.

This study introduces the proposed GA modeling. The objective function of the proposed GA is to minimize total cost constrained by the equation of functional requirements tolerances considering difficulty coefficients. The manufacturing difficulty computation is based on tools for the study and analysis of reliability of the design or the process, as the failure mode, effects and criticality analysis (FMECA) and Ishikawa diagram.

The proposed approach, based on difficulty coefficient computation and GA optimization method [genetic algorithm optimization using difficulty coefficient computation (GADCC)], has been applied to mechanical assembly taken from the literature and compared to previous methods regarding tolerance values and computed total cost. The total cost is the summation of manufacturing cost and quality loss. The proposed approach is economic and efficient that leads to facilitate the manufacturing of difficult dimensions by increasing their tolerances and reducing the rate of defect parts of the assembly.

The originality of this new optimal tolerance allocation method is to make a marriage between GA and manufacturing difficulty. The computation of part dimensions difficulty is based on incorporating FMECA tool and Ishikawa diagram This comparative study highlights the benefits of the proposed GADCC optimization method. The results lead to obtain optimal tolerances that minimize the total cost and respect the functional, quality and manufacturing requirements.