Condition-Based Logistics: Internet of Things Solutions for Resilient Supply Chains

Various disruptive events have demonstrated how fragile today's supply chains are on a global scale, and how valuable but nearly impossible it is to anticipate these unforeseen incidents in a timely manner. Therefore, the concept of ``Supply Chain Resilience” became popular for building up capacity to absorb disturbances and respond to them as quickly as possible, which implies more empirical observations are needed to monitor logistics performances. One way to address the dynamic and stochastic nature of modern-day supply chains is to let the physical resources monitor themselves (e.g., machinery, vehicles, containers, etc.). These physical objects can be empowered with wireless sensors, actuators and communication devices, resulting in an interconnected network of uniquely addressable objects that is better known as the ``Internet of Things'' (IoT). The IoT paradigm prescribes enhanced visibility of logistics operations, while enabling more autonomous behavior of physical assets with the implementation of data processing techniques. But will this condition-based approach stimulate logistics operations to be rescheduled more effectively once a significant change is either observed or predicted?

The objective of this dissertation is to investigate how the IoT paradigm can improve supply chain resilience. We propose an applicable solution that supports logistics managers to effectively restore operational performances in response to disturbances. We have decomposed the main question ``How to enhance supply chain resilience by means of IoT solutions’’ into four specific objectives, each of which is worked out in a separate chapter:

1.To understand why IoT systems could enhance supply chain resilience, resulting in a state-of-the-art overview of various IoT applications reported in academic literature.
2.To explore what objects to empower with electronic devices, leading to the development of an ontology that merges both the supply chain resilience and IoT paradigms.
3.To investigate how to align hardware, software, and business processes to proactively respond towards disruptions, resulting in the creation of an IoT-based reference architecture by means of an Enterprise Architecture modelling approach.
4.To learn when to intervene in case performance deteriorations are observed by developing an online severity assessment approach that integrates both simulation and machine learning techniques.