Rod Franklin is Full Professor of Logistics and Academic Director of Executive Education at Kühne Logistics University. Professor Franklin, an engineer and operations manager by training and experience, received his Doctorate in Management from the Case Western Reserve University in Cleveland, Ohio. Professor Franklin has held management positions at Kühne + Nagel, USCO Logistics, ENTEX Information Services, Digital Equipment Corporation, and Cameron Iron Works. In addition, he has been a consultant for Booz-Allen & Hamilton, Theodore Barry & Associates and Arthur Young & Co. Professor Franklin was also a development engineer for the Saginaw Steering Gear Division of General Motors Corporation.
Franklin’s research focuses on the application of modern management techniques to the efficient and effective operation of supply chains, sustainable business models, green logistics, corporate social responsibility and cloud based supply chain management.
In his role as Academic Director of Kühne Logistics University’s Executive Education program, Franklin’s focus is on ensuring that executives educated at the University receive both the rigor and relevance in their academic training that they expect from a world class research institute like Kühne Logistics University.
Up Close & Personal
“KLU is near and dear to my heart, because I was one of the individuals that helped plan the university.”
– Prof. Rod Franklin, Ph.D.
(2020): From the Digital Internet to the Physical Internet: A Conceptual Framework with a Stylized Network Model, Journal of Business Logistics: .
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.
(2018): Thirsty in an Ocean of Data? Pitfalls and Practical Strategies when Partnering with Industry on Big Data Supply Chain Research, Journal of Business Logistics, 39 (3): .
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.
(2017): Vergleich und Kombination von Techniken des Predictive Business Process Monitoring, Software Engineering: .
(2015): Comparing and Combining Predictive Business Process Monitoring Techniques, IEEE Transactions on Systems, Man and Cybernetics: Systems, 45 (2): 276-290.
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%.
: Runtime Management of Multi-level SLAs for Transport and Logistics Services: International Conference on Service-Oriented Computing, , 560-574.
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.
Adjunct Professor of Logistics and Academic Director of Executive Education at Kühne Logistics University, GER
|2014 - 2016||Dean of Programs at Kühne Logistics University, Hamburg|
Vice President, Product Development at Kühne + Nagel Management AG, CH
Vice President, Solutions Engineering at Kühne + Nagel Inc., USA
Vice President, Product Development at USCO Logistics, USA
Vice President, Professional Services at Viacore, USA
Vice President, Product Development at ENTEX Information Services, USA
Director, Professional Services at ENTEX Information Services, USA
Systems Integration and Management Consulting Manager at Digital Equipment Corporation, USA
Consulting Manager at Arthur Young & Co., USA
Managing Associate at Booz-Allen & Hamilton, USA
Managing Associate at Theodore Barry & Associates, USA
Manufacturing Manager at Cameron Iron Works, USA
Development Engineer at Saginaw Steering Gear Division of General Motors Corporation, USA
Board of Directors for Del Mar Community Connections, Del Mar, California, USA
Computer & Telecommunications Advisory Committee for the City of Del Mar, California, USA
Sandpiper Editorial Board of Directors, Del Mar, California, USA
Mayor for the City of Del Mar, California, USA
City Council Member for the City of Del Mar, California, USA
City Finance Committee for the City of Del Mar, California, USA
Doctorate of Management at the Case Western Reserve University, USA
Master of Business Administration at the Harvard Graduate School of Business, USA
Master of Science in Mechanical Engineering at the Leland Stanford Junior University, USA
Bachelor of Science in Mechanical Engineering at the Purdue University, USA