Blockchain in Additive Manufacturing and its Impact on Supply Chains

Additive manufacturing (AM) appears to be a particularly attractive use case for blockchain. This research combines inductive in‐depth interviews with the Delphi method to explore what potentials blockchain technology in AM creates, which adoption barriers firms need to overcome, and how supply chains will be affected by the integration of these two potentially disruptive technologies. The results suggest opportunities that are related to intellectual property (IP) rights management, the monitoring of printed parts throughout their lifecycle, process improvements, and data security. The most important barriers for blockchain adoption in AM are an absence of blockchain‐skilled specialists on the labor market, missing governance mechanisms, and a lack of firm‐internal technical expertise. By addressing important limitations of AM, blockchain is expected to improve the competitiveness of AM in parts’ production, catalyzing the trend toward more decentralized manufacturing resulting in more agile, resilient, and flexible supply chains and reduced logistics costs. Beyond that, blockchain‐based AM platforms are expected to enhance supply chain visibility, drive supply chain digitalization, support supply chain finance, and contribute to the emergence of shared factory systems.     

The LeChatelier-Samuelson principle revisited

Within the context of a linear Leontief model, the LeChatelier-Samuelson principle examines the effects of an increase in some final demand on the output levels under the constraint that the production of certain goods is held at its original value. The principle states that the increase in any output is larger when fewer output levels are kept constant. The present paper discusses bounds for such incremental changes, second-order effects, the consequences on the markets for the products with restricted output levels, and generalizations of the original assumptions.I would like to thank two anonymous referees for their helpful comments.     

On invariant parametric covariance families

Summary In this note we characterize all linear transformations of random vectors which are invariant for a given parametric covariance family. Two one-dimensional covariance families are discussed in detail.