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.     

Initial coin offerings (ICOs) to finance new ventures

In an initial coin offering (ICO), new ventures raise capital by selling tokens to a crowd of investors. Often, this token is a cryptocurrency, a digital medium of value exchange based on the distributed ledger technology. Both the number of ICOs and the amount of capital raised have exploded since 2017. Despite attracting significant attention from ventures, investors, and policy makers, little is known about the dynamics of ICOs. This initial study therefore assesses the determinants of the amount raised in 423 ICOs. Drawing on signaling theory, the study explores the role of signaling ventures' technological capabilities in ICOs. The results show that technical white papers and high-quality source codes increase the amount raised, while patents are not associated with increased amounts of funding. Exploring further determinants of the amount raised, the results indicate that some of the underlying mechanisms in ICOs resemble those found in prior research into entrepreneurial finance, while others are unique to the ICO context. The study's implications are multifold and discussed in detail. Importantly, the results enable investors to more accurately understand crucial determinants of the amount raised (e.g., technical white papers, source code quality, token supply, Ethereum-standard). This reduces the considerable uncertainty that investors face when investing in ICOs and enables more informed decision-making.     

An Empirical Comparison of Sales Forecasting Models

There has been a veritable industry of forecasting models in recent decades, spurred partly by planners' search for certainty and partly by the data crunching capabilities of computers. In this article, Sanjay-kumar Rao tests two families of models on some common data sets to see how they compare. He examines how well the models fit the data and how reliable they are as forecasting tools.     

基于电力交易现货市场企业配售电测控管理系统

本文是基于电力交易现货市场企业配售电情况,研究其测控管理系统。本系统的构建是通过传感器(智能表计)等获取各回路用电量等能源信息,进而建立智能网关。采用DLT645协议对数据进行采集,处理数据后利用4G网络通过因特网与云端服务器相连,将数据打包发送,利用json或帧数据格式上传至云端服务器。从而实现监控、运维、能管一体化且具有良好的可维护性和可拓展性。