Additive manufacturing (AM), or popular scientific 3D printing, disseminates in more and more production processes. This changes not only production processes themselves, e.g. by replacing subtractive production technologies, but AM will in all likelihood also impact the configuration of supply networks. Due to a more efficient use of raw materials, transportation relations may change and production sites may be relocated. How this change will look like is part of an ongoing discussion in industry and academia. However, quantitative studies on this question are scarce. In order to quantify the potential impact of AM on a two-stage supply network, we use a facility location model. The impact of AM on the production process is integrated into the model by varying resource efficiency ratios. We create a test data set of 700 instances. Features of this data set are, among others, different geographical clusters of source nodes, production nodes, and customer nodes. By means of a computational study, the impact of AM on the supply network structure is measured by four indicators. In the context of our experimental setup , AM reduces the overall transportation costs of a supply network compared to subtractive production. However, the share of the transportation costs on the second stage of a supply network in the total costs increases significantly. Therefore, supply networks in which production sites and customer sites are closely spaced improve their cost-effectiveness stronger than other regional configurations of supply networks. Keywords Supply network Á Additive manufacturing Á 3D printing Á Quantitative assessment Á Two-stage capacitated facility location problem This article is part of a focus collection on ''Dynamics in Logistics: Digital Technologies and Related Management Methods''.
Background Standardized training prescriptions often result in large variation in training response with a substantial number of individuals that show little or no response at all. The present study examined whether the response in markers of cardiorespiratory fitness (CRF) to moderate intensity endurance training can be elevated by an increase in training intensity. Methods Thirty-one healthy, untrained participants (46 ± 8 years, BMI 25.4 ± 3.3 kg m−2 and $${\dot{\text{V}}}$$ V ˙ O2max 34 ± 4 mL min−1 kg−1) trained for 10 weeks with moderate intensity (3 day week−1 for 50 min per session at 55% HRreserve). Hereafter, the allocation into two groups was performed by stratified randomization for age, gender and VO2max response. CON (continuous moderate intensity) trained for another 16 weeks at moderate intensity, INC (increased intensity) trained energy-equivalent for 8 weeks at 70% HRreserve and then performed high-intensity interval training (4 × 4) for another 8 weeks. Responders were identified as participants with VO2max increase above the technical measurement error. Results There was a significant difference in $${\dot{\text{V}}}$$ V ˙ O2max response between INC (3.4 ± 2.7 mL kg−1 min−1) and CON (0.4 ± 2.9 mL kg−1 min−1) after 26 weeks of training (P = 0.020). After 10 weeks of moderate training, in total 16 of 31 participants were classified as VO2max responders (52%). After another 16 weeks continuous moderate intensity training, no further increase of responders was observed in CON. In contrast, the energy equivalent training with increasing training intensity in INC significantly (P = 0.031) increased the number of responders to 13 of 15 (87%). The energy equivalent higher training intensities increased the rate of responders more effectively than continued moderate training intensities (P = 0.012). Conclusion High-intensity interval training increases the rate of response in VO2max to endurance training even when the total energy expenditure is held constant. Maintaining moderate endurance training intensities might not be the best choice to optimize training gains. Trial Registration German Clinical Trials Register, DRKS00031445, Registered 08 March 2023—Retrospectively registered, https://www.drks.de/DRKS00031445
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