Prof. J. Rod Franklin, PhD

Publications

Adjunct Professor of Logistics

Academic Director of Executive Education

Prof. J. Rod Franklin, PhD

Publications

Adjunct Professor of Logistics

Academic Director of Executive Education

Publications

Journal Articles (Peer-Reviewed)

DOI: ../KLU publications current/doi.org/10.1111/jbl.12253 

Abstract: 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.

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Open reference in new window "From the Digital Internet to the Physical Internet: A Conceptual Framework with a Stylized Network Model"

DOI: ../KLU publications current/doi.org/10.1111/jbl.12187 

Abstract: 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.

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Open reference in new window "Thirsty in an Ocean of Data? Pitfalls and Practical Strategies when Partnering with Industry on Big Data Supply Chain Research"

Abstract: 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.

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Open reference in new window "Improving Logistics by Interconnecting Services in a Physical Internet: Potential Benefits, Barriers and Developments"

Abstract: 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.

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Open reference in new window "Vergleich und Kombination von Techniken des Predictive Business Process Monitoring"

DOI: 10.1109/TSMC.2014.2347265 

Abstract: 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%.

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Open reference in new window "Comparing and Combining Predictive Business Process Monitoring Techniques"

DOI: 10.1007/s12146-011-0070-3 

Abstract: 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.

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Open reference in new window "Shared Warehouses – Sharing Risks and Increasing Eco-efficiency"

Journal Articles (Professional)

Books

Conference Proceedings

Abstract: Proactive event processing constitutes the next phase in the evolution of complex event processing. Proactive event processing makes it possible to anticipate potential issues during process execution and thereby enables proactive process management. One industry domain that can expect relevant benefits from applying proactive event processing is transportation. Transportation companies face numerous stochastic issues when managing the shipment of goods. One such issue faced in airfreight is the exact volume, weight, and number of pieces that a shipper wants to have shipped. Because of the high cost of air shipments, discrepancies between what has been booked by a shipper and the actual volume that is delivered impose costs that create problems for all participants in a shipment. One potential approach to addressing this problem is to use real-time monitoring and proactive alerting to assist air freight companies in anticipating actual delivered weights, volumes, and piece counts. In this paper we address the issue of cargo shipments by leveraging real-time monitoring data collected from an industry-standard monitoring system of a large freight forwarding company. Our evidence indicates that by using a novel proactive event-driven software engine, prediction about the weight of shipments can be developed and used in a proactive manner to assist air freight planners in making better estimates and plans for the shipment of goods. We demonstrate that through the use of this proactive approach, predictions concerning over and under-weight loads can be made days in advance of a shipment, thus enabling the air freight planner to optimize their load plans and thus maximize the revenue that they generate from shipments.

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Open reference in new window "Proactive Event Processing in Action: A Case Study on the Proactive Management of Transport Processes"

Book Chapters

DOI: 10.1007/978-3-662-45391-9_49 

Abstract: SLA management of non-computational services, such as transport and logistics services, may differ from SLA management of computational services, such as cloud or web services. As an important difference, SLA management for transport and logistics services has to consider so called frame SLAs. A frame SLA is a general agreement that constitutes a long-term contract between parties. The terms and conditions of the frame SLA become the governing terms and conditions for all specific SLAs established under such a frame SLA. Not considering the relationships between frame SLAs, specific SLAs and QoS monitoring information may lead to partial conclusions and decisions, thereby resulting in avoidable penalties. Based on a real industry case in the transport and logistics domain, this paper elaborates on a multi-level run-time SLA management approach for non-computational services that takes into account those relationships. We describe a cloud-based software component, the BizSLAM App, able to automatically manage multi-level SLAs by extending SLA management solutions from service-oriented computing. We demonstrate the feasibility and usefulness of the SLA management approach in an industrial context.

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Open reference in new window "Runtime Management of Multi-level SLAs for Transport and Logistics Services"