Complex Collaborative Physical Process Management: A Position on the Trinity of BPM, IoT and DA

In the modern economy, we see complex business processes with a physical character executed collaboratively by a set of autonomous business organizations. Examples are international container logistics, integrated supply and manufacturing networks, and collaborative healthcare chains - all of which handle physical objects. Over time, these processes have become more complex, more business-critical, more time-critical, and at the same time heavily mass-customized. This implies that the processes need to be managed more explicitly in an increasingly real-time fashion, with ample attention to individual process cases. To support this kind of processes, no single existing technology class suffices. Therefore, we propose to integrate technologies from the areas of business process management (BPM - to manage the processes), internet of things (IoT - to sense and actuate the physical objects) and distributed analytics (DA - to take the right decisions at the right place in real-time) into a trinity. We illustrate our position with an example from the domain of container logistics.

[1]  Lorenz M. Hilty,et al.  The Things of the Internet of Things in BPMN , 2015, CAiSE Workshops.

[2]  V. Bhakoo,et al.  Collaborative implementation of e‐business processes within the health‐care supply chain: the Monash Pharmacy Project , 2011 .

[3]  Ponciano Jorge Escamilla-Ambrosio,et al.  Distributing computing in the internet of things: Cloud, fog and edge computing overview , 2018 .

[4]  Paul W. P. J. Grefen,et al.  A Software Architecture for Transportation Planning and Monitoring in a Collaborative Network , 2015, PRO-VE.

[5]  Larry A. Wasserman,et al.  Random Differential Privacy , 2011, J. Priv. Confidentiality.

[6]  Mathias Weske,et al.  The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges , 2017, ArXiv.

[7]  Rik Eshuis,et al.  Internet-based support for process-oriented instant virtual enterprises , 2009, IEEE Internet Computing.

[8]  Jiang Zhu,et al.  Fog Computing: A Platform for Internet of Things and Analytics , 2014, Big Data and Internet of Things.

[9]  Remco M. Dijkman,et al.  Business models for the Internet of Things , 2015, Int. J. Inf. Manag..

[10]  Rik Eshuis,et al.  A Reference Framework for Advanced Flexible Information Systems , 2018, CAiSE Workshops.

[11]  Paul W. P. J. Grefen,et al.  Correlation Miner: Mining Business Process Models and Event Correlations Without Case Identifiers , 2017, Int. J. Cooperative Inf. Syst..

[12]  Zakaria Maamar,et al.  RE-ENGINEERING OF SMART CITY'S BUSINESS PROCESSES BASED ON SOCIAL NETWORKS AND INTERNET OF THINGS , 2018 .

[13]  Rik Eshuis,et al.  Creating Agility in Traffic Management by Collaborative Service-Dominant Business Engineering , 2015, PRO-VE.

[14]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[15]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[16]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[17]  Larry A. Wasserman,et al.  Differential privacy for functions and functional data , 2012, J. Mach. Learn. Res..

[18]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[19]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .