Amandine Herbe¹, Zarah Estermann², Valentin Holzwarth³ and Jan vom Brocke¹

Distributed Ledger Technology (DLT) refers to multi-party systems that enables saving encrypted data across a peer-to-peer network of nodes, without central authority. While DLT applications have been mainly studied in finance, we conduct empirical research on DLT application in supply chain management, combining theory testing and theory elaborating case research. Applying the Theory of Affordance Actualisation, we identify five DLT affordances: (1) verify product origin and history, (2) exchange data on digital product models, (3) track and trace products’ logistics, (4) simplify supply chain finance, and (5) automate payments. We identify and evaluate these affordances and also outline how these affordances can be actualised. We contribute to the discourse of DLT value creation and provide practical guidance to assess DLT potential in supply chains. We integrate our findings into the academic discussion on collaboration in viable, intertwined supply networks.

¹Institute of Information Systems, University of Liechtenstein, FL, Vaduz, Liechtenstein

²School of Management, Law, Economics, SocialSciences and International Affairs, University of St. Gallen (HSG), St.-Gallen, Switzerland

³Network & Innovation Department, Rhysearch, Buchs (SG), Switzerland

Sandro Widmer^1,2,3 , Marcel Kossel¹, Giovanni Cherubini¹, Stanislaw Wozniak¹, Pier Andrea Francese¹, Ana Stanojevic¹; Mathias Brändli¹, Klaus Frick², Angeliki Pantazi¹.

In biologically inspired spiking neural networks (SNNs) neurons communicate by short pulses, called spikes. SNNs have the potential to be more power efficient than artificial neural networks (ANNs), thanks to the fewer computational steps required by the spike transmission and processing, as compared to the multiply-and-accumulate (MAC) operations with wide bit-vectors usually adopted in ANNs. We present the design of two types of SNNs with integrate-and-fire dynamics and single-spike per neuron operation, where neural communication is based on synchronous time-to-first-spike (sTTFS) and time-to-first-spike (TTFS) encoding schemes. In the considered time-encoded SNNs, the information is carried by the timing of the spikes with respect to a reference time. In 7nm CMOS technology both designs are synthesized as VHDL-based random-logic-macros (RLMs) and compared to an equivalent ANN design in terms of power consumption, latency and silicon area, using the Iris data set for inference. A cost function expressed as a product of energy consumption and silicon area is introduced to compare the three network designs. With respect to this cost function, it turns out that the SNN-TTFS implemented for the considered classification task outperforms the ANN used as baseline model.

¹ IBM Research GmbH, Säumerstrasse 4, Switzerland

² OST-Eastern Switzerland University of Applied Sciences, Werdenbergstrasse 4, Switzerland

³ RhySearch, Switzerland

Valentin Holzwarth^2, 3, Christian Hirt¹, Joy Gisler¹, Andreas Kunz¹

Digital twins (DTs) provide numerous opportunities for value creation in manufacturing. Services enabled by DTs include remote monitoring of assets’ conditions and predictive maintenance. In this paper, we introduce novel, previously unexplored services based on a fully virtualized machine tool, which are targeted at increasing machine operators’ productivity. This allows conducting procedures, such as operator training at a virtual machine tool, which results in the real machine tool being available for value adding activities. Beyond operator training, we envision further potential applications of the virtual machine tool including the run-in of new processes and collision detection.

¹ Innovation Center Virtual Reality (ICVR), ETH Zurich, 8092, Zurich, Switzerland

² Institute of Information Systems, University of Liechtenstein, 9490, Vaduz, Liechtenstein

³ RhySearch