All Publications
The Physical Internet (PI) envisions a hyperconnected cyber-physical logistics system inspired by the Digital Internet, yet adoption remains limited due to persistent trust and governance challenges. This study addresses these barriers by conceptualizing systemic trust as a foundational requirement for broad participation in the PI. We apply the echeloned Design Science Research methodology to develop the Governance and Trust Orchestration Layer (GTOL), an extension of existing layered PI models that incorporates mechanisms for rule enforcement, credentialing, dispute resolution, and fair matching. Based on empirical interviews with logistics practitioners and evaluated through scenario-based walkthroughs, the GTOL addresses core commercial, institutional, and technological trust concerns. We contribute mid-range design theory through six design principles that provide transferable guidance for embedding trust in decentralized logistics networks. They serve as a practical and theoretical blueprint for enabling governance structures that are transparent, enforceable, and adaptable across diverse logistics contexts.
This paper introduces the PI-link protocol, a novel process inspired by the operational principles of the Internet, to address node-to-node transshipment within the Physical Internet (PI). Recognizing the critical need for efficient, standardized protocols to manage the complexities of logistic flows in the PI, we propose a simple, adaptable protocol designed to facilitate seamless collaboration among diverse logistics service providers. By drawing analogies to the TCP/IP model, our protocol emphasizes streamlined processes for dynamic and static information exchange, analogous to the Internet Protocol (IP) ensuring and supporting effective shipment processing, routing, and monitoring across interconnected logistic networks. The technical operations of the protocol are clearly demonstrated through pseudocode, providing a detailed blueprint for its implementation. This exploration aims to contribute to the ongoing development of the PI, by providing a first step towards a simple baseline protocol stack, as TCP/IP for the Internet.
Del Marshall, manager of the customer support operations for Leucadia Outfitters, finds himself in a difficult and challenging position at the end of Leucadia Outfitters' summer selling season. His team of customer support personnel has just set a record for booked revenue for the season of USD150,000,000, while struggling through the integration of their catalog and e-Commerce operational management systems. Unfortunately, this sales record was USD50,000,000 short of what Del had expected to generate during the period and now would have to be made up during the holiday selling season. In addition to the problems caused by the implementation of the new integrated system and the shortfall in booked revenue, two of Del's most senior customer support personnel have tendered their resignations, an event that was unexpected and very disturbing. Del must determine why these senior and historically loyal customer support personnel have decided to leave so that he can stop any issues that are causing problems in the customer service organization before the holiday season arrives.
Despite the increasing academic interest and financial support for the Physical Internet (PI), surprisingly little is known about its operationalization and implementation. In this paper, we suggest studying the PI on the basis of the Digital Internet (DI), which is a well‐established entity. We propose a conceptual framework for the PI network using the DI as a starting point, and find that the PI network not only needs to solve the reachability problem, that is, how to route an item from A to B, but also must confront a more complicated optimality problem, that is, how to dynamically optimize a set of additional logistics‐related metrics such as cost, emissions and time for a shipment. These last issues are less critical for the DI and handled using relatively simpler procedures. Based on our conceptual framework, we then propose a simple network model using graph theory to support the operationalization of the PI. The model covers the characteristics of the PI raised in the current literature and suggests future directions for further quantitative analyses.
Sustainability and efficiency of logistics operation in a world with fast-evolving demand from mass distribution to ecommerce and home delivery raise significant challenges. Among solutions, collaboration is often cited but remains marginal. To overcome current limits a new concept was introduced several years ago: the Physical Internet (PI) — in other words, the universal interconnection of logistics services. Initial research has shown a large potential for improvement by switching from current organisations to more interconnected ones. The concept attracted attention from researchers and professionals and several results, industry roadmaps and first applications are now available. This paper reviews the main contributions on the subject by detailing impact levels, examines the potential of new technologies in the PI framework and identifies avenues for further research efforts to overcome current barriers, particularly from business models.
Increased volume, velocity, and variety of data provides new opportunities for businesses to take advantage of data science techniques, predictive analytics, and big data. However, firms are struggling to make use of their disjointed and unintegrated data streams. Despite this, academics with the analytic tools and training to pursue such research often face difficulty gaining access to corporate data. We explore the divergent goals of practitioners and academics and how the gap that exists between the communities can be overcome to derive mutual value from big data. We describe a practical roadmap for collaboration between academics and practitioners pursuing big data research. Then we detail a case example of how, by following this roadmap, researchers can provide insight to a firm on a specific supply chain problem while developing a replicable template for effective analysis of big data. In our case study, we demonstrate the value of effectively pairing management theory with big data exploration, describe unique challenges involved in big data research, and develop a novel and replicable hierarchical regression‐based process for analyzing big data.
Wir stellen einen experimentellen Vergleich von Prognosetechniken für das Predictive Business Process Monitoring vor. Ausgehend von unseren Experimentergebnissen schlagen wir eine geeignete Kombination von Prognosetechniken vor.
Predictive business process monitoring aims at forecasting potential problems during process execution before they occur so that these problems can be handled proactively. Several predictive monitoring techniques have been proposed in the past. However, so far those prediction techniques have been assessed only independently from each other, making it hard to reliably compare their applicability and accuracy. We empirically analyze and compare three main classes of predictive monitoring techniques, which are based on machine learning, constraint satisfaction, and Quality-of-Service (QoS) aggregation. Based on empirical evidence from an industrial case study in the area of transport and logistics, we assess those techniques with respect to five accuracy indicators. We further determine the dependency of accuracy on the point in time during process execution when a prediction is made in order to determine lead-times for accurate predictions. Our evidence suggests that, given a lead-time of half of the process duration, all predictive monitoring techniques consistently provide an accuracy of at least 70%. Yet, it also becomes evident that the techniques differ in terms of how accurately they may predict violations and nonviolations. To improve the prediction process, we thus exploit the characteristics of the individual techniques and propose their combination. Based on our case study data, evidence indicates that certain combinations of techniques may outperform individual techniques with respect to specific accuracy indicators. Combining constraint satisfaction with QoS aggregation, for instance, improves precision by 14%; combining machine learning with constraint satisfaction shows an improvement in recall by 23%.
Share a warehouse… with a competitor? Once it would have seemed unthinkable. Not any more. Transport costs, environmental factors and supply chain logistics are changing the game.
Die Logistik hat sich in den letzten Jahrzehnten weitreichend verändert. Zunächst geprägt von der klassischen funktionalen Strukturierung Beschaffung - Produktion - Absatz besteht die Aufgabe der Logistik heute darin, Wertschöpfungsketten in globalen Netzwerken zu integrieren. In Zukunft wird sich die Veränderung der Anforderungen an die Logistik noch schneller vollziehen. Logistiker aus Wissenschaft und Praxis schätzen, dass die nächsten 10 Jahre so viele Neuerungen bringen werden wie die vergangenen 50 Jahre. Um mit diesem dynamischen Wandel mithalten zu können, ist das Wissen über zukünftige Entwicklungen und Trends, die die Logistik prägen werden, erfolgsentscheidend. Vier globale Entwicklungen werden aus unserer Sicht zukünftig die Planung, Koordination und Abwicklung von Warenströmen wesentlich beeinflussen: Supply Chain Risiken, Technologie, Umweltorientierung sowie geopolitische und gesellschaftsstrukturelle Veränderungen. Einerseits eröffnen diese Entwicklungen Chancen, vielmehr beinhalten sie aber große Herausforderungen, denen sich die Logistik stellen muss.
