The Blockchain technology can be defined as a distributed ledger database for recording transactions between parties verifiably and permanently. Blockchain emerged as a leading technology layer for financial applications. Nevertheless, in the past years, the attention of researchers and practitioners moved to the application of the Blockchain technologies to other domains. Recently, it represents the backbone of a new digital supply chain. Thanks to its capability of ensuring data immutability and public accessibility of data streams, Blockchain can increase the efficiency, reliability, and transparency of the overall supply chain, and optimize the inbound processes. The literature concerning Blockchain in non-financial applications mainly focused on the technological part and the Business Process Modeling, lacking in terms of standard methodology for designing a strategy to develop and validate the overall Blockchain solution and integrate it in the Business Strategy. Thus, this paper aims to overcome this lack. First, we integrate the current literature filling the lack concerning the digital strategy, creating a standard methodology to design Blockchain technology use cases, which are not related to finance applications. Second, we present the results of a use case in the fresh food delivery, showing the critical aspects of implementing a Blockchain solution. Moreover, the paper discusses how the Blockchain will help in reducing the logistics costs and in optimizing the operations and the research challenges.

The role of a relatively small cadre of high-tech startup firms in driving innovation and economic growth has been well known and amply celebrated in recent history. At the same time, it is well recognized that, while the overall contribution of startups is crucial, the high-risk and high-reward strategy followed by these startups leads to significant failure rates and a low ratio of successful startups. So, it is curious to notice that literature tends to focus on successful startups and on quantitative studies looking for determinants of success while neglecting the numerous lessons that can be drawn by examining the stories of startups that failed. This paper aims to fill this gap and to contribute to the literature by providing a repeatable and scalable methodology that can be applied to databases of unstructured post-mortem documents deriving startup failure patterns. A further and related contribution is the analysis carried out with this methodology to a large database of 214 startup post-mortem reports. Descriptive statistics show how the lack of a structured Business Development strategy emerges as a key determinant of startup failure in the majority of cases.

City Logistics has attracted considerable interest from the operations research and logistics communities during past decades. It resulted in a broad variety of promising approaches from different fields of combinatorial optimisation. However, research on urban freight transportation is currently slowing down due to two different lacks, limiting the exploratory capacity and compromise the technology transfer to the industry. First, the majority of the instances in the literature are based on the generalisation of classical instances, often not created for urban applications, or on artificial data, i.e. data not coming from any historical or empirical datasets. Thus, the validation of models and methods becomes more difficult, being the results not directly compared with real or realistic settings. Second, even when some data sources become available, there is no standard way to mixing data gathered from different sources and, from them, generate new instances for urban applications. This study aims to overcome these issues, proposing a simulation–optimisation framework for building instances and assess operational settings. To illustrate the usefulness of the framework, the authors conduct a case study, in order to evaluate the impact of multimodal delivery options to face the demand from e‐commerce, in an urban context as Turin (Italy).