Emergent Behaviors in a Resilient Logistics Supply Chain

This PhD dissertation addresses vulnerabilities in logistics supply chains, such as disruptions from pandemics, natural disasters, and geopolitical tensions. It underscores the complexity of supply chains, likening them to socio-technical systems where resilience is key for managing unexpected events and thriving amidst adversity. The focus is on leveraging smart business objects—exemplified by “smart pallets” with sensing and computational capabilities—to augment real-time decision-making and resilience in supply chains. When strategically positioned within the supply network, these smart pallets can provide key insights into the movement of goods, enabling a rapid response to disruptions through real-time monitoring and predictive analytics. The dissertation investigates centralized, decentralized, and hybrid approaches to decision-making within these networks. Centralized methods ensure uniformity but may neglect local specifics, while decentralized ones offer adaptability at the risk of inconsistency. A hybrid model seeks to balance these extremes, combining broad guidelines with local autonomy for optimal resilience. This research aims to explore how such smart objects can anticipate and react to emergent behaviors, thereby augmenting supply chain resilience beyond mere performance indicators to actively managing and adapting to disruptions. Through various chapters, the dissertation offers an exploration, from designing resilient architectures and evaluating business rules in real-time to mining these rules from data and adapting them to evolving circumstances. Overall, this work presents a nuanced view of resilience in supply chains, emphasizing the adaptability of business rules, the importance of technological evolution alongside organizational practices, and the potential of integrating novel techniques such as process mining with multi-agent systems for better decision-making and operational efficiency

Business rule extraction using decision tree machine learning techniques:A case study into smart returnable transport items

Decision support systems are becoming increasingly sophisticated (e.g., being machine learning-based), attempting to automate decisions as much as possible. However, it remains challenging to extract meaningful value from large quantities of data while also maintaining transparency in seeking justification for the choices made. Instead of creating methods for increasing the interpretability of black box models, one way forward is to design models that are inherently interpretable in the first place. Rule-based methods can automate decisions with great transparency and accuracy, helping to ensure compliance with regulations and adherence to organizational guidelines. In this paper, we propose an approach that uses a decision tree machine learning classification technique for extracting business rules from IoT-generated data to predict the asset status of Smart Returnable Transport Items (SRTIs). We report on an industrial case study that uses two years of historical data, obtained from an SRTI provider in the Netherlands, to predict the status of smart pallets. We compare the performance with the results obtained by using a support-vector machine (SVM) technique. Our experiments show that our solution is both accurate and flexible in terms of business rule elicitation. The obtained decision trees are human-interpretable, can easily be combined with other decision-making techniques, and provide a prediction accuracy marginally higher than an SVM technique

Discovering Agent Models using Process Mining: Initial Approach and a Case Study

Agent-based modeling is widely used for modeling and simulation of self-organizing sociotechnical systems that are composed of distributed autonomous agents. In these systems, macro level behaviors emerge from local micro level behaviors of agents that follow rules and interact with each other and the environment. Although the individual agents' behaviors are typically described by sets of simple rules, the many interactions, heterogeneous populations, and complex topologies can make it challenging, or even impossible, to predict or steer the emergent behaviors beyond micro levels. Hence, the actual behaviors of such systems are generally hard to know beforehand, and they need to be observed to extract realistic models. In this paper, we propose a proof-of-concept approach to discover agents' underlying models from log data generated from their behaviors, utilizing process mining. To conceptualize and demonstrate our initial approach, we use an illustrative example of the popular Schelling's model of segregation. Our findings provide encouraging initial evidence on how agent models can be extracted utilizing process mining techniques

Using process mining for workarounds analysis in context: Learning from a small and medium-sized company case

Workarounds are deviations in the execution of designed, de jure, work processes. Process mining research has developed methods for unobtrusive workaround analysis using process-aware systems’ datasets. This study applies process mining for workaround analysis in a medium-sized enterprise (SME). SME contexts can be challenging for workaround mining because SMEs often lack de jure process designs and their process-supportive information systems may have ambiguous semantics. The identification of de jure models and the solving of systems data ambiguities are the first steps in workarounds identification. A semantically well-defined information system may enable factual, de facto, process mining. Comparing the de jure and de facto process models may give candidate workarounds. Our study shows that (1) incomplete de jure models hinder the use of process mining for detecting workarounds, and (2) human interpretation of process mining outcomes is needed to realize a useful triple loop organizational learning from workarounds mining

Assessing Factory’s Industry 4.0 Readiness:A Practical Method for IIoT Sensor and Network Analysis

Manufacturing industries are aware of the benefits of Industry 4.0 (I4.0) and the pivotal role of the Industrial Internet of Things (IIoT) to facilitate the transition. However, they often underestimate the intricate connection between the implementation of IIoT and their existing sensing and communication systems. Hindering their progress towards I4.0 implementation, many factories still rely on rigid sensing systems, characterized by limited adaptability and inflexibility. This raises questions regarding the industrial readiness for I4.0 implementation and its ability to meet the sensing and connectivity standards of IIoT. Specifically, it is unclear whether the current brownfield installations, consisting of sensors and networking systems, adequately support the demanding I4.0 applications that necessitate a multi-functional and collaborative IIoT system. Our objective is to assess a factory’s progress towards I4.0 by examining the readiness of its sensing system and the communication capabilities of its network. We propose a two-stage analysis of factory readiness, including a functional segment-based sensor analysis and an application class-based network analysis. We present a case study conducted at an iron-making plant in The Netherlands to illustrate our method. Key findings of the case study include: (1) a lack of multi-functionality across segments for the majority of sensors (90%), (2) a considerable portion of network traffic (73%) requires high reliability, and (3) only 3% of the current network traffic necessitate ultra-reliable, low latency communication. Furthermore, we discuss how our method provides decision-makers with valuable guidance for the digital transformation of established and newly built manufacturing industries