Conference Agenda

Abstract:

Purpose: The purpose of this study is to operationalize Digital Twin (DT) technologies into real-life project-based supply network environments by investigating how practical implementation can improve visibility, enable real-time decision support, and enhance coordination across operations (Ivanov, 2021) (Zaidi, 2024). The study emerges from recurring operational issues in the supply network, such as missing or incorrect materials, and poor adherence to schedules, all of which lead to significant resource waste and delays. The study therefore aims to identify and structure a set of relevant KPIs that capture lean performance, e.g., value creation, waste reduction. These KPIs will be examined in terms of how they can be integrated into a Digital Shadow artifact, enabling real-time monitoring, analytics, and decision support.

Methodology/approach: The study adopted a qualitative approach, and the Design Science Research (DSR) is considered a research methodology which is appropriate for research aimed at creating and evaluating innovative artifacts. DSR methodology combines real-life challenges (relevance) and scientific knowledge (rigor) or expertise in design, development, and evaluation (design) of the artifact (Hevner et al. 2004).

Findings: The results of this study indicate that digital shadow can significantly enhance visualization and real-time situational awareness of material location over supply network. The developed digital shadow enables both real-time material location tracking and detailed pallet-level traceability, thereby improving transparency and control over supply network. However, the findings also highlight that the accuracy and precision requirements for material location may constrain freedom to select IoT solutions. These constraints are particularly influenced by the operational environment and surrounding conditions where artifact is deployed. During the design phase, the digital shadow was conceptualized and iteratively refined. Resulting artifact integrates multiple technologies, including IoT-sensors, wireless communications 5G/GPS networks, cloud-based data platforms, Geographical Information System (GIS), and visualization dashboards. Furthermore, the rigorously developed dashboard incorporates performance analytics, supporting both lean value stream analysis and the monitoring of key performance indicators (KPIs).

Originality: This study focuses on identified a lack of empirical implementation of network level material tracking and dashboard solutions within a digital twin (DT) of supply network. The managerial perspective, study introduces practically relevant KPIs to support lean waste reduction and supply networks operational targets in project business context and provides a dashboard level visibility with real-time situational awareness for decision making. At the network level, study contributes not only to the development of the artifact but also to broader knowledge creation regarding digital shadow applications in material tracking. From a scientific perspective, it contributes by empirically testing Digital Twin (DT) operationalization in a real-life supply network context, bridging the gap between conceptual DT models and their practical implementation.