A Platform Architecture for Industry 4.0 Driven Intelligence Amplification in Logistics

Piest, Jean Paul Sebastian

doi:10.1109/edocw.2019.00038

Cited by 3 publications

(9 citation statements)

References 18 publications

(18 reference statements)

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“…The demonstration incorporates multiple topics in the field of enterprise computing, e.g., enterprise architecture, business intelligence and business analytics, and business rules. Both researchers and practitioners can see validations of earlier work and real deployments in the context of distributed enterprise applications and systems, originating from crossdisciplinary research projects of [4,13,14]. The demonstration is considered of interest to both researchers and practitioners active in the field of enterprise computing.…”

Section: B Discussionmentioning

confidence: 81%

“…Real-time streaming event data are used to identify, assess, and (if possible) resolve logistical disturbances and develop dynamic optimization algorithms as proposed by [13]. In addition, the input and involvement of the users will be incorporated using the principles of intelligent amplification, as proposed by [13,14].…”

Section: Process Mining and Event Streaming Algorithmsmentioning

confidence: 99%

“…Furthermore, interoperability with IDS [10] will be examined, as well as the integration with sophisticated analytical decision support systems. Another promising direction is the incorporation of an intelligence amplification approach, as positioned in [14]. Such an extension would be useful for determining which parts of the system can be taken over by intelligent agents, which parts humans may need to intervene with the system and how symbiosis supports operational decision making and contributes to the overall system performance.…”

Section: Future Workmentioning

confidence: 99%

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Demonstrating the Architecture for Situation-aware Logistics using Smart Returnable Assets

Piest

Bemthuis

Charismadiptya³

2020

2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)

Self Cite

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Building on earlier work, this paper aims to demonstrate and discuss an instantiated architecture for situation-aware logistics in an operational environment using smart returnable assets. The demonstration is based on a motivation scenario focusing on exception management. The system outline and its components, interfaces, and enabling technologies are described and linked to the different layers of the architecture. This paper documents and illustrates the use of the system with detailed models and screenshots. Earlier work is extended using business rules to identify and quantify exceptions and potential disruptions. Specifically, it is shown how shipment data are enhanced with data from IoT sensors of smart returnable assets to provide situation-aware decision support based on data analytical methods. This demonstration provides scholars and practitioners, active in the fields of enterprise computing, insights into the concepts, models and engineering technologies used to implement an architecture for situation-aware logistics. The instantiated architecture provides a rich testbed for experiments, measurements, and incorporate the ideas promoted by the international data space initiative. An online recording is available to support the demonstration and ignite discussion about the potential of IoT technologies and future research directions in pursuit of the smart logistics vision.

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Section: B Discussionmentioning

confidence: 81%

Section: Process Mining and Event Streaming Algorithmsmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Demonstrating the Architecture for Situation-aware Logistics using Smart Returnable Assets

Piest

Bemthuis

Charismadiptya³

2020

2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)

Self Cite

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“…In earlier work, IA is applied for schema matching (Buis, 2019;Piest et al 2020;Boerrigter, 2021). In current research, an industry platform is realized to develop IA applications for the logistics industry (Piest, 2019).…”

Section: Applying Iamentioning

confidence: 99%

Designing intelligence amplification: a design canvas for practitioners

Piest¹,

Iacob²,

Wouterse³

2022

AHFE International

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Designing, developing, and implementing applications based on the concepts of Intelligence Amplification (IA) is a complex process. Although some design theories are present in literature, to our best knowledge, no comprehensive IA design approach exists for practitioners. Based on action design research, an IA design canvas is developed and guiding design principles are derived in two iterations. The main contribution of this research is a comprehensive IA design approach, consisting of an IA design canvas and four guiding design principles. Evaluation of the IA design canvas in three concurrent design workshops with 25 participants representing, 14 organizations, provides empirical support that the proposed IA design approach can ease the design processes, especially during the emphasize, ideate, and conceptualize stages of design thinking. Generalization is however not possible. Future research can explore the broader use of the IA canvas for explanation, analysis, prediction, and quantification, and formalize the IA design approach in a design theory.

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“…2) While real-time data and sophisticated RL approaches are emerging, logistic organizations, in particular SMEs, lack the tools and expertise [5] to effectively identify whether (parts of) their business processes are suitable for using RL for decision making and adopt these approaches in their daily practice [4].…”

Section: Introductionmentioning

confidence: 99%

A Reinforcement Learning Platform for Small and Medium-sized Enterprises in Logistics

Piest

Iacob

Sinderen

et al. 2021

2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW)

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While real-time data and sophisticated Reinforcement Learning (RL) approaches are emerging, logistic organizations, in particular Small and Medium-size d Enterprises (SMEs), lack the tools and expertise to effectively identify whether (parts of) their business processes are suitable for using RL and adopt these approaches in their daily practice. This paper presents the results of our efforts to design, develop, test and implement a RL-based decision support platform based on the Open Trip Model (OTM) for the logistics industry . The main contribution of this paper is a potentially generalizable platform architecture and instantiation of the following main functional components: 1) a graphical user interface to access platform services, 2) APIs to automate the data collection based on the OTM, 3) an OTM compliant data storage, 4) a repository and tool for testing multiple algorithms and perform (hyper)parameter tuning, and 5) infrastructure provisioning to run and monitor agents. The platform is complemented with RL guidelines to transfer the platform, knowledge, and practices to SMEs. The platform architecture is validated with a panel of experts and demonstrated in use in a case study at a logistics services provider. The platform demonstration and case study contribute to increasing awareness of potential applications of RL in the logistics industry. The platform provides a foundation for empirical research and experimental development of RL approaches in logistics. Future research will focus on alternative and hybrid approaches, federated learning, and incorporating data sharing concepts as part of the envisioned federated data sharing infrastructure for the Dutch logistics industry.

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A Platform Architecture for Industry 4.0 Driven Intelligence Amplification in Logistics

Cited by 3 publications

References 18 publications

Demonstrating the Architecture for Situation-aware Logistics using Smart Returnable Assets

Demonstrating the Architecture for Situation-aware Logistics using Smart Returnable Assets

Designing intelligence amplification: a design canvas for practitioners

A Reinforcement Learning Platform for Small and Medium-sized Enterprises in Logistics

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