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Robust solutions are needed in the context of warehouse planning in contract logistics. To achieve this goal, knowledge about dominant factors is a prerequisite and thus presents the aim of this research. The factors to be evaluated are identified through a literature analysis, which leads to more than 40 factors from over 30 sources. To explore a broad range of factors, including their interaction, a design of experiments approach is used in a generic simulation model applied in a multiple-case study. The results indicate the overall dominance of demand-related factors and also the high importance of the equipment specifications across cases. Consequently, managerial insights include a shift of planning resources towards these most dominant factors. Based on the research results, the focus should be on a thorough analysis of the demand structure and the characteristics of the technical equipment. Also, when employing simulation models for the planning of order picking systems, it is strongly advised to use a parameter variation which draws a multifaceted picture of the situation in order to enable a justified decision. Implications for warehouse research are the consideration of variable input, especially regarding demand characteristics, to create relevant order picking scenarios when testing the optimisation potential of new developments. [ABSTRACT FROM AUTHOR]

This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a Portuguese cold chain logistics company was selected for the analysis of the allocation of the products in the warehouse and of the corresponding outbound movements. Data with 8525 movements that occurred during 2021 were collected for 228 different product references. For this case study, the products that had a picking place in the initial scenario now have pallets with all the goods in the reserve area, and vice versa. The mathematical models were permitted to obtain savings for the logistics operator costs of around 30.9%. The proposed models can, in the future, be applied in other warehouse scenarios to companies in completely different sectors of activity. [ABSTRACT FROM AUTHOR]

Purpose: Selecting the optimal supplier is a challenging managerial decision that involves several dimensions that vary over time. Despite the considerable attention devoted to this issue, knowledge is required to be updated and analyzed in this field. This paper reveals new opportunities to advance supplier selection (SS) research from a multidimensional perspective. Moreover, this study aims to formalise SS knowledge to enable the appropriate selection of sustainable, resilient and circular criteria. Design/methodology/approach: This study is developed in two stages: first, a systematic literature review is conducted to select relevant papers. Descriptive and thematic analyses are employed to analyze criteria, solving approaches and case studies. Second, a criterion knowledge-based framework is developed and validated by experts to be implemented as ontology using Protégé software. Findings: Evaluating the viability of suppliers need further studies to integrate other criteria and to align SS objectives with research advancement. Artificial intelligence tools are needed to revolutionize and optimize the traditional techniques used to solve this problem. Literature lucks frameworks for specific sectors. The proposed ontology provides a consistent criteria knowledge base. Practical implications: For academics, the results of this study highlight opportunities to improve the viable SS process. From a managerial perspective, the proposed ontology can assist managers in selecting the appropriate criteria. Future works can enrich the proposed ontology and integrate this knowledge base into an information system. Originality/value: This study contributes to promoting knowledge about viable SS. Capitalizing the knowledge base of criteria in a computer-interpretable manner supports the digitalization of this critical decision. [ABSTRACT FROM AUTHOR]

This systematic review presents a comprehensive analysis of the existing literature examining training approaches and resources intended for workforce development in increasingly digital workplaces, a trend that has been expedited by the COVID-19 pandemic. The review adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and incorporates 29 articles that met the inclusion and exclusion criteria from a preliminary pool of 1,095 publications spanning the years 2020 to 2022. A thematic synthesis was employed to evaluate the included articles. The examined literature focused on (1) establishing guidelines for designing digital transformation training programs, (2) mapping and analysing training programs and resources for digital transformation, and (3) describing the design and implementation processes of these training programs across different sectors. The synthesized findings present an overview of the educational theories and learning methodologies underlying the training programs, the technologies implemented in training, and other key characteristics of these programs. The review acknowledges certain limitations, as many articles do not systematically report negative outcomes or drawbacks of the described lifelong training interventions. However, the review provides valuable insights and research-based recommendations for training employees in highly digitalised workplaces. [ABSTRACT FROM AUTHOR]

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The present scoping review investigated the current state of the art concerning factors affecting physical and mental health and well‐being of workers using collaborative robots (cobots) in manufacturing industries. Each identified factor was classified using the SHELLO (Software‐Hardware‐Environment‐Liveware‐Liveware‐Organization) conceptual model. Strengths and limitations of such an approach were outlined. A total of 53 papers were included in the scoping review and analyzed following PRISMA guidelines. In 35 papers at least one risk factor referred to the SHELLO Liveware‐Hardware interaction, followed by factors concerning Liveware‐Software (16 papers), Liveware‐Liveware (11 papers), Liveware intrinsic factor (10 papers), Liveware‐Organization (8 papers), and Liveware‐Environment (8 papers). This work highlighted that methodological research is still primarily focused on traditional risk assessment and physical safety. However, several research directions concerning the design of cobots as active collaborators were identified, promoting workers' mental health and well‐being, too. The SHELLO model proved to effectively highlight human factors relevant for the design of cobots and can provide a systemic approach to investigate human factors in other complex sociotechnical systems. To the best of our knowledge, this is the first time the model is applied in the field of human–cobot interaction. [ABSTRACT FROM AUTHOR]

Nowadays, the diffusion of digital and industry 4.0 (I4.0) technologies is affecting the manufacturing sector with a twofold effect. While on one side it represents the boost fastening the competitive advantage of companies, on the other hand it is often accompanied by several challenges that companies need to face. Among all, companies are required to invest in technologies to empower their production activities on the shopfloor without lagging behind their workforce in order to undertake a linear, aware, and structured path toward digitization. The extant literature presents some research conducted to support companies toward digitization, and they usually rely on maturity models in this intention. Nevertheless, few studies included the assessment of workforce skills and competencies in the overall assessment, and in this case, they provide a high level perspective of the investigation, mainly based on check lists which may limit the objectivity of the assessment, and usually they do not customize the assessment based on companies' requirements. Therefore, considering the importance to balance investments in technologies with those in the workforce to move toward the same direction, this contribution aims to develop a structured, customizable, and objective skill assessment model. With this intention, it has been first clarified the set of job profiles required in I4.0, together with the needed related skills based on the extant literature findings; second, it has been identified the set of key criteria to be considered while performing the assessment of the workforce; third, it has been defined the method to be integrated in the maturity model to enable the initial setting of the weights of the criteria identified according to the company needs; and fourth, based on these findings, it has been developed the assessment model. The developed model facilitates the elaboration of the proper workforce improvement plans to be put in practice to support the improvement of the skills of the whole workforce based on company's needs. [ABSTRACT FROM AUTHOR]

Recently, a revolutionary change is taking place in manufacturing and production systems thanks to the development of various advanced technologies such as IIoT (Industrial Internet of Things), CPPS (Cyber-Physical Production System), digital twins, big data analytics, AI (Artificial Intelligence), and so on. One of the change is that manufacturing and production systems are now trying to transform into the ZDM (Zero-Defect Manufacturing) system. For a manufacturing company, quality takes precedence over any other competitive factors, so the implementation of a ZDM system is very important. For the implementation of ZDM, many fundamental technologies are required. Among them, the advanced maintenance approach for the facilities/equipment of the manufacturing and production system is much more important because it could support the zero-defect and high-efficiency operation of manufacturing and production systems. The advanced maintenance approach, which is often called by various terms such as predictive maintenance, condition-based maintenance plus (CBM+), and PHM (Prognostics and Health Management), requires various interdisciplinary knowledge and systematic integration. In this study, we will review previous works mainly focusing on advanced maintenance subject among ZDM research works, and briefly discuss the challenging issues for applying PHM technologies to the ZDM. [ABSTRACT FROM AUTHOR]

Fe3O4-HDTMS nanocomposites were prepared and studied using Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy, X-ray analysis, thermal analysis (TGA), dynamic light scattering analysis, magnetic and specific loss power (SLP) measurements. FTIR results showed that during the modification, the formation of the silane coating occurs due to the appearance of the magnetite–O–Si–R bond. According to TGA results, the mass loss in the range of temperatures 410–650 °C is due to the destruction of covalent bonds Fe–O–Si. The Si–O–R coating leads to the decrease in the absolute value of the effective saturation magnetization due to the presence of a non-magnetic phase (coating) in the sample, but the coercivity increases with the coating thickness due to higher effective values of the magnetic anisotropy of the magnetostrictive nature. The thermal response of NP-based dispersions in silicone and oleic acid was shown that SLP value is higher for magnetic material dispersions in Lipiodol and oleic acid compared to silicone-based dispersions. This can be explained by the contribution of both Neel and Brownian relaxation processes. However, in the case of silicone-based dispersion, Brownian relaxation is negligible because of NP immobilization in viscous silicone matrix. As it is to the effect of coating on SLP, this is clearly evident in the case of silicone dispersions. The study of the heating ability of dispersions based on HDTMS-modified Fe3O4 NPs showed that the coating does not significantly decrease the SLP values. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is a non-invasive imaging modality used in longitudinal cell tracking. Previous studies suggest that MagA, a putative iron transport protein from magnetotactic bacteria, is a useful gene-based magnetic resonance contrast agent. Hemagglutinin-tagged MagA was stably expressed in undifferentiated embryonic mouse teratocarcinoma, multipotent P19 cells to provide a suitable model for tracking these cells during differentiation. Western blot and immunocytochemistry confirmed the expression and membrane localization of MagA in P19 cells. Surprisingly, elemental iron analysis using inductively-coupled plasma mass spectrometry revealed significant iron uptake in both parental and MagA-expressing P19 cells, cultured in the presence of iron-supplemented medium. Withdrawal of this extracellular iron supplement revealed unexpected iron export activity in P19 cells, which MagA expression attenuated. The influence of iron supplementation on parental and MagA-expressing cells was not reflected by longitudinal relaxation rates. Measurement of transverse relaxation rates (R2* and R2) reflected changes in total cellular iron content but did not clearly distinguish MagA-expressing cells from the parental cell type, despite significant differences in the uptake and retention of total cellular iron. Unlike other cell types, the reversible component R2′ (R2* ‒ R2) provided only a moderately strong correlation to amount of cellular iron, normalized to amount of protein. This is the first report to characterize MagA expression in a previously unrecognized iron exporting cell type. The interplay between contrast gene expression and systemic iron metabolism substantiates the potential for diverting cellular iron toward the formation of a novel iron compartment, however rudimentary when using a single magnetotactic bacterial gene expression system like magA. Since relatively few mammalian cells export iron, the P19 cell line provides a tractable model of ferroportin activity, suitable for magnetic resonance analysis of key iron-handling activities and their influence on gene-based MRI contrast. [ABSTRACT FROM AUTHOR]

Coprecipitation of FeCl2 and FeCl3 with aqueous ammonia was used to prepare iron oxide nanoparticles dispersible in aqueous medium. Oxidation of the particles with sodium hypochlorite then yielded maghemite (γ-Fe2O3) nanoparticles which were coated with two types of coating - d-mannose or poly( l-lysine) (PLL) as confirmed by FTIR analysis. The particles were <10 nm according to transmission electron microscopy. Their hydrodynamic particle size was ∼180 nm (by dynamic light scattering). The d-mannose-, PLL-coated, and neat γ-Fe2O3 particles as well as commercial Resovist® were used to label rat macrophages. The viability and contrast properties of labeled macrophages were compared. PLL-coated γ-Fe2O3 nanoparticles were found optimal. The labeled macrophages were injected to rats monitored in vivo by magnetic resonance imaging up to 48 h. Transport of macrophages labeled with PLL-γ-Fe2O3 nanoparticles in rats was confirmed. Tracking of macrophages using the developed particles can be used for monitoring of inflammations and cell migration in cell therapy. © 2014 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 103B: 1141-1148, 2015. [ABSTRACT FROM AUTHOR]