Management and Production Engineering Review (MPER) is a peer-refereed, international, multidisciplinary journal covering a broad spectrum of topics in production engineering and management. Production engineering is a currently developing stream of science encompassing planning, design, implementation and management of production and logistic systems. Orientation towards human resources factor differentiates production engineering from other technical disciplines. The journal aims to advance the theoretical and applied knowledge of this rapidly evolving field, with a special focus on production management, organisation of production processes, management of production knowledge, computer integrated management of production flow, enterprise effectiveness, maintainability and sustainable manufacturing, productivity and organisation, forecasting, modelling and simulation, decision making systems, project management, innovation management and technology transfer, quality engineering and safety at work, supply chain optimization and logistics. Management and Production Engineering Review is published under the auspices of the Polish Academy of Sciences Committee on Production Engineering and Polish Association for Production Management. The main purpose of Management and Production Engineering Review is to publish the results of cutting-edge research advancing the concepts, theories and implementation of novel solutions in modern manufacturing. Papers presenting original research results related to production engineering and management education are also welcomed. We welcome original papers written in English. The Journal also publishes technical briefs, discussions of previously published papers, book reviews, and editorials. Letters to the Editor-in-Chief are highly encouraged.

The paper provides statistical analysis of the photographs of four various granular materials (peas, pellets, triticale, wood chips). For analysis, the (parametric) ANOVA and the (nonparametric) Kruskal-Wallis tests were applied. Additionally, the (parametric) two-sample t-test and (non-parametric) Wilcoxon Rank-Sum Test for pairwise comparisons were performed. In each case, the Bonferroni correction was used. The analysis shows a statistical evidence of the presence of differences between the respective average discrete pixel intensity distributions (PID), induced by the histograms in each group of photos, which cannot be explained only by the existing differences among single granules of different materials. The proposed approach may contribute to the development of a fast inspection method for comparison and discrimination of granular materials differing from the reference material, in the production process.