## 1 Introduction  
The ability to anticipate supply chain (SC) risks has always been pivotal in supply chain risk management (SCRM). Indeed, the necessity not only to consider reactive strategies but also to consider proactive ones has been highlighted in several studies in this field [1, 2]. However, this historical need has recently become increasingly relevant. Several supply chain disruptions occurred in recent years, leading unprepared industries nearly to default and obliging even big companies to shut down their plants or cut their production rates due to the shortages of parts experienced during the pandemic period [3, 4]. For this reason, adopting a proactive risk-management perspective in the current shortage economy [5] by structuring SC with resilience [6] has become a fundamental aspect if they are to survive. In addition to the increased risk exposure of supply chains, this current time period is also characterized by outstanding advancements in the field of artificial intelligence (AI) [7, 8] and especially in machine learning (ML) and deep learning (DL) [9, 10], which represent technological solutions with high potentials for gaining visibility over supply chains. Visibility is one of the antecedents of resilience [11], and industries' correct adoption of these technologies is a fundamental step toward more resilient systems. Thus, researchers should guide practitioners through the intelligent adoption of ML technology in this era. More specifically, assuming a design research perspective [12], new studies should provide information about how to effectively design ML artifacts tailored to deal with different supply chain risks. Unfortunately, while most studies proposed ML artifacts to forecast demand risks proactively, less attention has been dedicated to designing approaches able to predict supply risks. However, similar to demand risks, supply risks can lead to severe disruptions. In particular, predicting supplier delivery risk is particularly relevant for companies that rely heavily on outsourcing and lean manufacturing practices. Indeed, when operating with a low level of stock, delivery delays can lead to shortages of components and thus block production lines. As a result, small delays have been demonstrated to decrease sales by up to 10% for three to four weeks, while longer delays can have an even larger negative impact [13]. Studies proposing innovative ML artifacts to predict supplier delivery delay risks are thus necessary to ensure more resilient supply chains. However, despite this need, the literature proposing ML approaches that can effectively predict supplier delivery risks is in its infancy, and a definitive solution to the problem thus remains elusive. In particular, most studies on this topic proposed approaches to solve the problem in a classification manner (i.e., to predict whether a delivery delay will occur or not) [14; 15; 16; 17; 18]. Predictions provided in this form are useful, but to effectively prioritize risk action, a punctual estimation of the amount of delay needs to be defined. Moreover, very little attention has been dedicated to integrating macroeconomic indicators to support predictions [18]. However, according to [19], relying only on internal company data or past historical delivery data does not allow for anticipating market changes. Lastly, even if autocorrelation in data has often been considered in demand forecasting [19; 20; 21], autocorrelation in delivery delay data has never been adopted to build a forecasting model to solve the problem. Thus, the main motivation for this study is to propose an approach that, for the first time, simultaneously covers all these gaps. Therefore, a deep learning model able to capture autocorrelation in data, specifically a Long Short-Term Memory (LSTM) model, has thus been proposed to predict delivery risk in a regression manner (i.e., by punctually estimating the number of days of delay or the advance a component will be delivered with).