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This paper aims to provide a strategic approach to the improvement of customer service performance and apply it to a food supplier that distributes its products to food multiple retailers' stores (from now on, the term “retailers” instead of “food multiple retailers' stores” will be used to represent hypermarkets, supermarkets or neighbourhood stores).

To attain the performance's enhancement, the requirements are: the identification, evaluation and selection of customer service elements and the establishment of measures as well as performance standards. To identify service elements, previous research and the food supplier's as well as the retailers' viewpoints were taken into consideration. To evaluate service elements, 40 retailers were asked to rate the elements' importance and the two competitors' performance. The usable questionnaires were subjected to correlation analysis, paired‐samples t‐test and multiple ANOVA. To select the appropriate service elements, on which measures and performance standards would be established, positioning matrices were also formed.

With respect to sausage market in Greece, the analysis points out that the supplier should apply measures in the service elements order completeness, invoice error‐free, on‐time delivery, delivery of products without defects, efficient handling of returned products, informing about shortages in the orders, providing technical information and efficient handling of customers' requests. The performance should be improved in the first three elements and maintained as it is in the remaining five elements.

The study provides insight into the customer service elements which a supplier should measure and into which of them a supplier should improve its performance or maintain it. Moreover, the elements that constitute customer service in the Greek sausage sector are identified.

Additive manufacturing (AM) technologies are gaining immense popularity in the manufacturing sector because of their undisputed ability to construct geometrically complex prototypes and functional parts. However, the reliability of AM processes in providing high-quality products remains an open and challenging task, as it necessitates a deep understanding of the impact of process-related parameters on certain characteristics of the manufactured part. The purpose of this study is to develop a novel method for process parameter selection in order to improve the dimensional accuracy of manufactured specimens via the fused deposition modeling (FDM) process and ensure the efficiency of the procedure.

The introduced methodology uses regression-based machine learning algorithms to predict the dimensional deviations between the nominal computer aided design (CAD) model and the produced physical part. To achieve this, a database with measurements of three-dimensional (3D) printed parts possessing primitive geometry was created for the formulation of the predictive models. Additionally, adjustments on the dimensions of the 3D model are also considered to compensate for the overall shape deviations and further improve the accuracy of the process.

The validity of the suggested strategy is evaluated in a real-life manufacturing scenario with a complex benchmark model and a freeform shape manufactured in different scaling factors, where various sets of printing conditions have been applied. The experimental results exhibited that the developed regressive models can be effectively used for printing conditions recommendation and compensation of the errors as well.

The present research paper is the first to apply machine learning-based regression models and compensation strategies to assess the quality of the FDM process.