Digital twin providing new opportunities for value co-creation through supporting decision-making

This article belongs to the Special Issue Smart Resilient Manufacturing.The application of digital twins provides value creation within the fields of operations and service management; existing research around decision-making and value co-creation is limited at this point. Prior studies have provided insights into the benefits of digital twins that combined both data and simulation approaches; however, there remains a managerial gap. The purpose of this paper is to explore this research gap using input from a multiple case study research design from both manufacturing environments and non-manufacturing environments. The authors use ten cases to explore how digital twins support value co-creation through decision-making. The authors were all involved in the development of the ten cases. Individual biases were removed by using the literature to provide the assessment dimensions and allowing a convergence of the results. Drawing on the lessons from the ten cases, this study empirically identified eight managerial issues that need to be considered when developing digital twins to support multi-stakeholder decision-making that leads to value co-creation. The application of digital twins in value co-creation and decision-making is a topic that has developed from practice and is an area where a research gap exists between theory and practice. A cross-case analysis was developed based on the literature and the ten cases (eight industrial and two pilot-scale cases) providing the empirical findings. The findings describe how firms can design, develop, and commercialize digital-twin-enabled value propositions and will initiate future research

Larger capacity for unconscious versus conscious episodic memory

Episodic memory is the memory for experienced events. A peak competence of episodic memory is the mental combination of events to infer commonalities. Inferring commonalities may proceed with and without consciousness of events. Yet what distinguishes conscious from unconscious inference? This question inspired nine experiments that featured strongly and weakly masked cartoon clips presented for unconscious and conscious inference. Each clip featured a scene with a visually impenetrable hiding place. Five animals crossed the scene one-by-one consecutively. One animal trajectory represented one event. The animals moved through the hiding place, where they might linger or not. The participants' task was to observe the animals' entrances and exits to maintain a mental record of which animals hid simultaneously. We manipulated information load to explore capacity limits. Memory of inferences was tested immediately, 3.5 or 6 min following encoding. The participants retrieved inferences well when encoding was conscious. When encoding was unconscious, the participants needed to respond intuitively. Only habitually intuitive decision makers exhibited a significant delayed retrieval of inferences drawn unconsciously. Their unconscious retrieval performance did not drop significantly with increasing information load, while conscious retrieval performance dropped significantly. A working memory network, including hippocampus, was activated during both conscious and unconscious inference and correlated with retrieval success. An episodic retrieval network, including hippocampus, was activated during both conscious and unconscious retrieval of inferences and correlated with retrieval success. Only conscious encoding/retrieval recruited additional brain regions outside these networks. Hence, levels of consciousness influenced the memories' behavioral impact, memory capacity, and the neural representational code

Digital twin-enabled decision support services in industrial ecosystems

The goal of this paper is to further elaborate a new concept for value creation by decision support services in industrial service ecosystems using digital twins and to apply it to an extended case study. The aim of the original model was to design and integrate an architecture of digital twins derived from business needs that leveraged the potential of the synergies in the ecosystem. The conceptual framework presented in this paper extends the semantic ontology model for integrating the digital twins. For the original model, technical modeling approaches were developed and integrated into an ecosystem perspective based on a modeling of the ecosystem and the actors’ decision jobs. In a service ecosystem comprising several enterprises and a multitude of actors, decision making is based on the interlinkage of the digital twins of the equipment and the processes, which is achieved by the semantic ontology model further elaborated in this paper. The implementation of the digital twin architecture is shown in the example of a manufacturing SME (small and medium-sized enterprise) case that was introduced in. The mixed semantic modeling and model-based systems engineering for this implementation is discussed in further detail in this paper. The findings of this detailed study provide a theoretical concept for implementing digital twins on the level of service ecosystems and integrating digital twins based on a unified ontology. This provides a practical blueprint to companies for developing digital twin based services in their own operations and beyond in their ecosystem

Development and Application of an Eco-design Tool for Machine Tools

Improving the energy efficiency of machine tools is one of the challenges regarding the European energy saving goals. This work presents a new tool, enabling an effective quantification of a machine tool's (MT) energy consumption during all life phases. Scope of the presented tool is the fast and efficient estimation of a MT's cumulated energy demand and the systematic derivation of improvement measures regarding ecological performance. This work will present a framework, as well as the required calculations for this task. Using model and rule-based procedures, only a minimal set of input parameters is required to identify the hot-spots regarding energy consumption and improvement potential. Applications of this tool as well as a systematic approach to derive measures to increase the energy efficiency based on the output of the tool are presented on practice-oriented examples from industry.ISSN:2212-827

Correction: Implicit Vocabulary Learning during Sleep Is Bound to Slow-Wave Peaks

(Current Biology 29, 541–553.e1–e7; February 18, 2019) In the original version of the article’s Supplemental Information, the cell values of Table S4 were erroneously duplicated from Table S2. The marginal means of Table S4 correctly represented the data but did not represent the cell values of the table. This error and a small typographical error (missing word “potential” in “event-related potential”) in the description of Table S6 have been corrected online. The error in Table S4 has no impact on the main results or conclusions of this publication. All statistics and analyses were based on the correct raw data, not on the values as seen in the table. Interpretations of the results pertained to the marginal means, which were correctly represented in Table S4. The authors thank an astute reader for bringing the error in Table S4 to their attention and apologize for the errors

Energy equivalents to quantify the total electricity consumption of factory-integrated machine tools

Energy efficiency in industries is one of the dominating challenges of the twenty-first century. Since the release of the first eco-design directive 2005/32/EC in 2005, great research effort has been spent on the energy efficiency assessment for energy using products. A missing piece for finalizing the ISO 14955-2 is the quantification of a machine tool’s non-electric power demand due to external support systems such as compressed air systems, water cooling systems, air conditioning systems, and exhaust air systems. These systems are comprised to the technical building service and cause additional electrical power demand that can be assigned to a machine tool. A model is set up that links the machine tool and the technical building services. The model enables to deduce the electrical power demand of the technical building service caused by non-electric power demand of a machine tool using electrical energy equivalents. Hence, the total electrical power demand caused by a factory-integrated machine tool can be derived. The applicability of the model and the electrical energy equivalents is proven in a practical case study on a grinding machine.ISSN:0268-3768ISSN:1433-301

Subliminal messages exert long-term effects on decision making

Subliminal manipulation is often considered harmless because its effects typically decay within a second. So far, subliminal long-term effects on behavior were only observed in studies which repeatedly presented highly familiar information such as single words. These studies suggest that subliminal messages are only slowly stored and might not be stored at all if they provide novel, unfamiliar information. We speculated that subliminal messages might affect delayed decision making especially if messages contain several pieces of novel information that must be relationally bound in long-term memory. Relational binding engages the hippocampal memory system, which can rapidly encode and durably store novel relations. Here, we hypothesized that subliminally presented stimulus pairs would be relationally processed influencing the direction of delayed conscious decisions. In experiment 1, subliminal face-occupation pairs affected conscious decisions about the income of these individuals almost half an hour later. In experiment 2, subliminal presentation of vocabulary of a foreign language enabled participants to later decide whether these foreign words are presented with correct or incorrect translations. Subliminal influence did not significantly decay if probed after 25 vs. 15 minutes. This is unprecedented evidence of the longevity and impact of subliminal messages on conscious, rational decision making