Measuring the performance of human-machine symbiosis

In today’s economy, machines are evolving and becoming increasingly important for supporting human’s daily life and work. Recently, scholars even discovered a symbiotic relationship between humans and machines, learning from each other to improve their joint processes and outputs. However, to our knowledge, this effect has not yet been rigorously measured. With this research in progress, we directly address this gap and propose to create a method consisting of evaluation criteria as well as an instrument for gathering data to measure human-machine symbiosis by interrogating users

Conceptualization of the Human-Machine Symbiosis – A Literature Review

The vision of a symbiotic partnership between humans and machines has existed since the 1960s. With this paper we provide the first conceptualization of the human-machine symbiosis (HMS) and make three important contributions: we present the fundamentals of HMS by focusing on objectives, requirements, and boundaries; we propose a framework for the design of HMS; and we review HMS research and, specifically, what the literature says with respect to whether HMS has already been achieved

An Intelligent Decision Support System for the Empty Unit Load Device Repositioning Problem in Air Cargo Industry

Unit load devices (ULDs) are containers and pallets used in the air cargo industry to bundle freight for efficient loading and transportation. Mainly due to imbalances in global air transportation networks, deficits and surpluses of ULDs are the result and require stock balancing through the repositioning of (empty) ULDs. Following a design science research approach, we (1) elaborate the hitherto uninvestigated problem class of empty ULD repositioning (EUR) and (2) propose an intelligent decision support system (IDSS) that incorporates a heuristic for the given problem and combines artificial intelligence (i.e., rule-based expert system technology) with business analytics. We evaluate the IDSS with real-world data and demonstrate that the proposed solution is both effective and efficient. In addition, our results provide empirical evidence regarding the positive economic and ecological impact of leveraging the potential of ULD pooling in multi-carrier networks