The composition of the vacuolar sap of Chara vulgaris growing in a brackish water lake was estimated weekly over 2 years (1984-1985). The ionic concentrations of the main cations Na, K, Ca, and Mg and the anion Cl varied depending on cell age, developmental state, and season. The average of all measurements (in mM) was Na: 35, K: 106, Ca: 7, Mg: 23, Cl: 101, SO : 20, and PO : 5. At the onset of growth in May/June the ionic content was lower compared to the mean value for the year, steadily increasing until it reached its maximum above the annual mean in winter. During the period of fructification (sexual reproduction: formation of antheridia and oogonia), when up to 100 mM sucrose was accumulated in the vacuolar sap, ionic content was lowest. This resulted in a fairly constant osmotic potential throughout the year. Mg and Ca concentrations were correlated with the physiological age of the cells.
Cyber-physical systems (CPS) are able the collect huge amounts of data about themselves, their users, and their environment during their usage phase. By feeding these usage data back into product planning, manufacturers can optimize their engineering and decision-making processes. Despite promising potentials, most manufacturers still do not analyze usage data within product planning. Also, research on usage data-driven product planning is scarce. Therefore, this paper aims to identify the main concepts, advantages, success factors and challenges of usage data-driven product planning. To answer the corresponding research questions, a comprehensive systematic literature review is conducted. From its results, a detailed description of usage data-driven product planning consisting of six main concepts is derived. Furthermore, taxonomies for the advantages, success factors and challenges of usage data-driven product planning are presented. The six main concepts and the three taxonomies allow for a deeper understanding of the topic while highlighting necessary future actions and research needs.
Cyber-physical systems generate and collect huge amounts of usage data during operation. Analyzing these data may enable manufacturing companies to identify weaknesses and learn about the users of their products. Such insights are valuable in the early phases of product development like product planning, as they facilitate decision-making for product improvement. The analysis and exploitation of usage data in product planning, however, is a new task for manufacturing companies. To reduce mistakes and improve the results, companies should build upon a suitable reference process model. Unfortunately, established models for analyzing data cannot be easily applied for product planning. In this paper, we propose a reference process model for usage data-driven product planning. It builds on three well-established models for analyzing data and addresses the unique characteristics of usage datadriven product planning. Finally, we customize the model for a manufacturing company and demonstrate how it could be implemented in practice.
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