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In times of crisis, cash and liquidity play an essential role. This paper analyzes the working capital measures over the course of a business cycle. We examine (1) how companies behave in economic downturns regarding their working capital components and (2) whether firms with higher financial constraints behave differently in economic downturns regarding their working capital components. The analyses were conducted with descriptive statistics and generalized linear mixed-effects modeling. Our dataset consists of 2111 stock-listed firms and 10,555 observations spread over the period of five years during the financial crisis era. The findings indicate that days sales outstanding and shorter days inventory held are related to better financial performance while days payable outstanding had no observable effect. Furthermore, financially constrained firms have shorter days sales outstanding than average firms. In economic downturns, firms seem to reduce both working capital and fixed investments to asset ratios. The financially constrained firms pushed down their fixed investments ratio more aggressively than average firms while, in contrast, the financially strongest firms pushed down the working capital to asset ratio in comparison to average firms. Interestingly, neither the cash conversion cycle, days payable outstanding, nor company performance or fixed investments to asset ratios fully returned to the pre-shock level. The behavior of non-financially constrained firms, which also perform better, indicates a stronger supply chain orientation than that of average firms. This might indicate that the supply chain-oriented view of working capital management could provide a more favorable and resilient alternative to the prevailing self-orientation. Keywords: supply chain financing; financial constraints; economic downturn; trade credit; insolvency risk; generalized linear mixed-effects model

The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloudshadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloudshadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are both conceptual and pragmatic in the definition of remote sensing best practices, which is the focus of efforts made by intergovernmental organizations such as the Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS). First, the ESA EO Level 2 product definition is recommended for consideration as state-of-the-art EO Analysis Ready Data (ARD) format. Second, systematic multi-sensor ESA EO Level 2 information product generation is regarded as: (a) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (b) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2 (proposed as Supplementary Materials), the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed. (VLID)3411565