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Highlights We provide predictive analytics tools for immediate use during COVID-19. We use data from the UK, USA, India, Germany, Singapore up to mid-April 2020. We forecast COVID-19 growth rates at country-level. We use auxiliary data (Google trends) to model excess demand. We forecast the excess demand for products and services at country-level.

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HIGHLIGHTSThe literature on supply chain forecasting is critically reviewed;The process of involving the forecasting community towards that task is described;Gaps between theory and practice are identified; Data and software related issues are explicitly considered; Challenges are summarised followed by suggestions for further research. ABSTRACTSupply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products).This paper provides a comprehensive review of the literature and aims at bridging the gap between the theory and practice in the existing knowledge base in SCF. We highlight the most promising approaches and suggest their integration in forecasting support systems. We discuss the current challenges both from a research and practitioner perspective and provide a research and application agenda for further work in this area. Finally, we make a contribution in the methodology underlying the preparation of review articles by means of involving the forecasting community in the process of deciding both the content and structure of this paper.

This study examines the relationship between internationalization orientation and international performance of small and medium-sized enterprises (SMEs), and the mediating effect of technological innovation. Prior research suggests that internationalization is a prominent strategic choice for SMEs growth and profitability. However, there is still no explicit agreement on how internationalization affects international performance. Similarly, the role of innovation on performance has long been emphasized, but the implications of technological innovation on international performance are still eluding us. Our investigation of 116 SMEs in the United Kingdom reveals that internationalization orientation has a significant effect on their international performance. SMEs that have a strong international orientation can achieve better international firm performance. We further demonstrate that there is an inverted U-shaped relationship between technological innovation and international firm performance among SMEs. In addition, the results indicate that technological innovation positively mediates the effect of internationalization orientation on international firm performance, particularly for the SMEs exhibiting moderate levels of technological innovation activities. The findings of this study suggest that managers can improve international performance by combining inward and outward internationalization orientation with technological innovation activities in their strategic decisions.

Intermittent demand patterns are characterised by infrequent demand arrivals coupled with variable demand sizes. Such patterns prevail in many industrial applications, including IT, automotive, aerospace and military. An intuitively appealing strategy to deal with such patterns from a forecasting perspective is to aggregate demand in lower-frequency 'time buckets' thereby reducing the presence of zero observations. However, such aggregation may result in losing useful information, as the frequency of observations is reduced. In this paper, we explore the effects of aggregation by investigating 5,000 Stock Keeping Units (SKUs) from the Royal Air Force (RAF, UK). We are also concerned with the empirical determination of an optimum aggregation level as well as the effects of aggregating demand in time buckets that equal the lead time length (plus review period). This part of the analysis is of direct relevance to a (periodic) inventory management setting where such cumulative lead-time demand estimates are required. Our study allows insights to be gained into the value of aggregation in an intermittent demand context. The paper concludes with an agenda for further research in this area.

In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also examined the efficacy of a judgmental model‐build approach, in which experts were asked to decide on the existence of the structural components (trend and seasonality) of the time series instead of directly selecting a model from a choice set. Our behavioral study used data from almost 700 participants, including forecasting practitioners. The results from our experiment suggest that selecting models judgmentally results in performance that is on par, if not better, to that of algorithmic selection. Further, judgmental model selection helps to avoid the worst models more frequently compared to algorithmic selection. Finally, a simple combination of the statistical and judgmental selections and judgmental aggregation significantly outperform both statistical and judgmental selections.