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The outbreak of the Covid-19 pandemic led to restrictions in production worldwide. Numerous firms were affected and unable to keep up production due to lockdowns. In disruptive events like this, the resilience of the production system is of central importance, as the survivability of the entire firm depends on it. In this context, the literature argues that cutting-edge technologies, such as Artificial Intelligence (AI), raise the proactive and reactive capabilities of firms, enabling them to better resist and recover from disruptive events and thus, show a higher resilience. This paper takes up this topic and observes the Covid-19 pandemic with the aim to analyse whether a firm's AI-readiness had an impact on its production resilience during the spring 2020 lockdown in Germany. For this purpose, we combine two large-scale surveys containing data from 237 manufacturers in Germany and test hypotheses based on quantitative analyses. Our results show that firms could indeed benefit from AI-enabled production during the lockdown. However, it is also clear that manufacturers have to exceed a certain AI threshold to significantly increase their resilient capabilities and realise positive effects. Our findings not only hold implications for research, but also provide recommendations for the resilience management of manufacturers. [ABSTRACT FROM AUTHOR]

This study examines the dynamic causal relationship between financial stress index and economic activity in the UK and Germany for the period from 2003 to 2018. Unlike the previous studies which ignore the consideration of all quantiles of distribution, we employ a Granger causality in quantiles that captures causal links in each quantile of distribution. Hence we are able to disseminate between the causality in the median and the tails of the conditional distribution. Our findings indicate that there is a significant, negative causal relationship running from financial stress to economic activity in both countries. However, the changes in the financial stress level in Germany start influencing the industrial production earlier (when economic activity is at lower levels) than in the UK. Our results highlight the emphasis on the consideration of the entire conditional distribution to avoid the risk of misleading inferences on the causality analysis. [ABSTRACT FROM AUTHOR]

This article looks into the ‘fine print’ of boosting for economic forecasting. By using German industrial production for the period from 1996 to 2014 and a data set consisting of 175 monthly indicators, we evaluate which indicators get selected by the boosting algorithm over time and four different forecasting horizons. It turns out that a number of hard indicators like turnovers, as well as a small number of survey results, get selected frequently by the algorithm and are therefore important to forecasting the performance of the German economy. However, there are indicators such as money supply that never get chosen by the boosting approach at all. [ABSTRACT FROM PUBLISHER]

By the summer of 2011, Germany industry was back at levels last achieved before the economic crisis, thanks to continuous growth in production since early 2009. The recovery was driven by strong domestic and foreign demand for German capital and intermediate goods. However, the Ifo Business Climate Index, which has contracted since July 2011, and fewer orders suggest that industrial production will decline by the end of 2011. The duration and intensity of the slowdown will depend not least on how quickly policy-makers take convincing measures to resolve the debt crisis in the eurozone and thus allay widespread uncertainty among investors and consumers. [ABSTRACT FROM AUTHOR]

Macroeconomic variables, such as industrial production or GDP, are regularly and sometimes substantially revised by the official statistical offices. Nevertheless, there are only few attempts in the previous literature to investigate whether it is possible to forecast these revisions systematically. In this article, it is illustrated how revisions of German industrial production can be forecasted with respect to both the direction and the level of the revision. We are the first to use a large data set for this purpose. [ABSTRACT FROM AUTHOR]

By the end of 1998, Babcock Noell Nuclear (BNN) as part of the Consortium Wendelstein was awarded with the fabrication of the nonplanar module field coils for the Wendelstein 7-X experiment to be built at Greifswald. In 1999 and 2002, BNN received orders for the production of LHC dipoles to be installed at CERN. Both projects are performed in parallel; therefore challenging tasks had to be solved not only from a technical but also from an industrial point of view. We present the solutions we found for the set-up of the production facilities, which include the development of tools suitable for series production and the recruitment of capable staff. An overview of the different production steps for both magnet types will be given as well as an outline about the quality measures that has been established. As in total 416 dipoles and 50 nonplanar coils consisting of a lot of high-quality components are fabricated, logistics within the company and the consortial partners as well as to subsuppliers is an issue, too. Both types of magnets are on the forefront of what is technically feasible. Therefore feedback from test results and possible improvements of the fabrication processes are always an issue. Besides that, technical changes introduced for knowledge gained only at a later stage of the project had to be introduced in the fabrication process. [ABSTRACT FROM AUTHOR]

We apply a relatively novel leading–lagging (LL) method to four leading and one lagging indexes for industrial production (IP) in Germany. We obtain three sets of results. First, we show that the sentiment-based ifo index performs best in predicting the general changes in IP (−0.596, range −1.0 to 1.0, −1.0 being best). The ZEW index is very close (−0.583). In third place comes, somewhat unexpectedly, the behavioral-based unemployment index (−0.564), and last comes order flow, OF (−0.186). Second, we applied the LL method to predefined recession and recovery time windows. The recessions were best predicted (−0.70), the recoveries worst (−0.32), and the overall prediction was intermediate (−0.48). Third, the method identifies time windows automatically, even for short time windows, where the leading indexes fail. All indexes scored low during time windows around 1997 and 2005. Both periods correspond to anomalous periods in the German economy. The 1997 period coincides with "the great moderation" in the US at the end of a minor depression in Germany. Around 2005, oil prices increased from $10 to $60 a barrel. There were few orders, and monetary supply was low. Our policy implications suggest that the ZEW index performs best (including recessions and recoveries), but unemployment and monetary supply should probably be given more weight in sentiment forecasting. [ABSTRACT FROM AUTHOR]

The Industry 4.0 (I4.0) concept is concerned with the fourth industrial revolution in manufacturing, in which technological trends such as digitalization, automation and artificial intelligence are transforming production processes. Since the concept's introduction at the Hannover Fair in Germany in 2011, I4.0 has enjoyed a meteoric rise in popularity and is currently high on the agenda of governments, politicians and business elites. In light of these observations, some commentators have asked the question of whether I4.0 is a concept that is hyped up and possibly just the latest in a long line of fashionable management concepts introduced over the course of the last few decades. Therefore, the aim of this paper is to provide a critical outside-in look at the emergence and rise of I4.0. Theoretically, these processes are viewed through the lens of management fashion, a theoretical perspective well suited to examinations of evolutionary trajectories of management concepts and ideas. The findings indicate that the I4.0 concept has quickly become highly popular and is dominating much of the popular management discourse. The concept has migrated out of the specialized manufacturing discourse to become a more general concept with mainstream appeal and applicability, evidenced by a multitude of neologisms such as Work 4.0 and Innovation 4.0. The numbers 4.0 have spread in a meme-like fashion, evidenced by the fact that the combination of a noun and the numbers 4.0 are used to signal and usher in discussions about the future of business and society. While there is much evidence that clearly shows that the concept has had a wide-ranging impact at the discursive level, the currently available research is less clear about what impact the concept has had so far on industries and organizations worldwide. [ABSTRACT FROM AUTHOR]