Openness of Digital Twins in Logistics – A Review

Openness is becoming increasingly important in scientific research and practice. It describes the phenomenon of sharing information with other internal or external stakeholders by using different technologies, e.g., cloud computing, distributed ledger, or digital twins. Hence, many researchers investigate and evaluate the openness of platforms. Alongside these platforms, digital twins are gaining influence in industrial processes. A digital twin is a virtual representation of a physical entity connected through a bi-directional data linkage. Its primary purpose is to visualize, analyze, and optimize production and logistics systems. Nevertheless, research shows a lack of knowledge in the domain of the openness of digital twins and that the topic has not been addressed adequately. To approach this research gap, this paper provides a review of literature-based work on digital twins focusing on logistical contexts. It aims to answer the question of how open digital twins are, depending on their use case, purpose, and status as digital twin or digital shadow. Through a comprehensive research approach, this paper provides researchers and practitioners with meaningful insights into the openness of digital twins

Practical Requirements for Digital Twins in Production and Logistics

Companies are under tremendous pressure to analyze and optimize their productional and logistical networks in today's global business world. Hence, practitioners and researchers show great interest in digital twins. A digital twin is a virtual construct that mirrors real-world objects and conceptual ideas while it processes, handles, distributes, and optimizes data streams. Its main purpose is to visualize, analyze, and optimize objects and systems, making a digital twin highly suitable to help companies gain an advantage over their competitors through a great degree of transparency over their production and logistics. Therefore, almost every company evaluates the usage of digital twins. Nevertheless, many companies struggle to instantiate digital twins since they lack fundamental knowledge about all necessary components of a digital twin and the individual requirements for the operation of the digital twin. This lack of knowledge hinders the broad implementation in practice. Research shows many descriptions of theoretical use cases and field studies but rarely describes digital twins in real operational settings. To address this research gap between theoretical concepts and practical challenges of the implementation of digital twins, this paper investigates the practical requirements of digital twins in real-life usage. Based on a thorough interview series with international manufacturing and logistics experts, we identify and analyze the requirements for data handling, data policy, and services of digital twins and cluster them according to the requirements engineering approach. Through a comprehensive overview of the different requirements, the paper delivers profound insights into the needs of companies from various fields and, therefore, gives practitioners a guideline on crucial aspects of implementing digital twins

Opportunities And Challenges Of The Asset Administration Shell For Holistic Traceability In Supply Chain Management

Due to changing regulatory environments, evolving sustainability requirements, and the need to perform effective supply chain risk management, traceability systems have become an increasingly important aspect of supply chain management. However, globalized, interconnected supply chains require a dynamic mapping of direct and indirect relationships between companies and assets, driving traceability systems' complexity. Here, the standardization of data formats provides an essential aspect to facilitate asset-related information sharing across companies. In this regard, the Asset Administration Shell is available as a holistic standardized digital representation of an asset. The representation of an asset via an Administration Shell includes data ensuring a clear identification of the Administration Shell and its assets as well as data describing aspects of the asset's technical functionality in so-called submodels. Based on current literature and available prototypical concepts, this paper identifies the opportunities and challenges of the Asset Administration Shell when aiming to map interconnected multi-tier supply chains holistically and contextualizes their role in achieving holistic supply chain traceability

Towards the Internet of Behaviors in Smart Cities through a Fog-To-Cloud Approach

Recent advances in the Internet of Things (IoT) and the rise of the Internet of Behavior (IoB) have made it possible to develop real-time improved traveler assistance tools for mobile phones, assisted by cloud-based machine learning and using fog computing in between the IoT and the Cloud. Within the Horizon2020-funded mF2C project, an Android app has been developed exploiting the proximity marketing concept and covers the essential path through the airport onto the flight, from the least busy security queue through to the time to walk to the gate, gate changes, and other obstacles that airports tend to entertain travelers with. It gives travelers a chance to discover the facilities of the airport, aided by a recommender system using machine learning that can make recommendations and offer vouchers based on the traveler’s preferences or on similarities to other travelers. The system provides obvious benefits to airport planners, not only people tracking in the shops area, but also aggregated and anonymized view, like heat maps that can highlight bottlenecks in the infrastructure, or suggest situations that require intervention, such as emergencies. With the emergence of the COVID-19 pandemic, the tool could be adapted to help in social distancing to guarantee safety. The use of the fog-to-cloud platform and the fulfillment of all centricity and privacy requirements of the IoB give evidence of the impact of the solution. Doi: 10.28991/HIJ-2021-02-04-01 Full Text: PD

Data Ecosystem Governance: A Conceptual Framework

Data need to be created, collected, stored, exchanged, integrated, and processed among a diverse set of actors and infrastructures to create value. Such reliance on other actors leads to the emergence of data ecosystems. Despite the focus on data ecosystems in the literature, little is known about who governs what data activities and how. Data ecosystem governance aims to ensure the alignment of activities with different goals and strategies of ecosystem actors. We contribute to the understanding of data governance by expanding the conceptual model for data ecosystem governance. The framework draws on an extensive review of data governance and ecosystems. We show governance is multi-layer, multi-actor and multi- dimension which creates complexity and interdependencies. The conceptual framework provides a guide for managers to fully understand and implement data ecosystem governance

UNDERSTANDING DATA TRUSTS

Finding ways to share data while upholding data sovereignty is a key issue to succeed in building the digital economy. One way to achieve this is to install data intermediaries – so-called data trusts – that facilitate this process. Their role is to orchestrate data sharing for organizations and individuals by ensuring that data sovereignty (i.e., the right to decide how data can be used) remains with the data provider. However, while the concept is promising, research on it is still in its infancy. The paper tackles exactly that issue as we collect data on data trusts (interviews and publically available information) and construct a general solution space for designing data trusts. With our research, we provide an overview as well as options for designing data trusts