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.     

Tacit meaning in disguise: Hidden metaphors in new product development and market making

This article explores the role of metaphor in product development processes and market making. Based on a sociocognitive perspective of innovation dynamics and required learning by market actors, the potential of metaphors for mental model development during new product development (NPD) processes is investigated. Three roles for metaphors as cognitive focusing devices for the co-evolution of producers' and consumers' mental models are inferred: mental model communication, mental model matching, and mental model creation. These roles are illustrated by examples that reinforce the need for creativity in applying metaphors as cognitive focusing devices in NPD and market making.     

Exact and approximate D-optimal designs in polynomial regression

If the sample sizen is large enough, then the exact polynomial regression designs obtained by rounding the weights of the approximate D-optimal design to integral multiples of 1/n are D-optimal. This was shown by alaevskiî (1966) and Gaffke (1987). In this note, an efficient algorithm to determine the minimum sample sizen _d for a polynomial model of degreed is derived from a condition given by Huang (1987). Under an additional assumption we show that the conditions of Gaffke and Huang are equivalent; we verify the additional assumption for polynomial degreed40.