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

PurposeThe purpose of this paper is to present a benchmarking process that can assist small to medium‐sized enterprises (SME).Design/methodology/approachThe 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).FindingsThe 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.Research limitations/implicationsIn 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.Practical implicationsThe methods and tools have been applied in several manufacturing plants at TECUMSEH Europe.Originality/valuePractical help to a SME to carry out a benchmarking.

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value The paper shows how to improve inspection vision of products.

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Abstract. For some companies, visual inspection has become an essential step when seeking to improve the quality of their products. The aim of this control is to be sure of the perceived quality of the product, which often goes well beyond the quality expected by the customer. For this type of control, the controller should be able to detect any anomaly on a product, characterize this anomaly, and then evaluate it in order to decide if the product should be accepted or rejected. This paper describes how this characterization can be carried out and, more specifically, how to measure the impact of the local environment of an anomaly on the perceived quality of the product.