Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
SE01 - SIG SCM5: Empirical Supply Chain Management
Time:
Sunday, 26/June/2022:
SE 17:00-18:30

Session Chair: Rachel Chen
Session Chair: Luyi Gui
Location: Forum 12


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Presentations

Using Internet-of-Things Point-of-Consumption Data for smart Replenishment

Sandria Weißhuhn1, Yale T. Herer2, Kai Hoberg1

1Kühne Logistics University, Germany; 2Technion – Israel Institute of Technology, Israel

Newly emerging smart replenishment systems at the point-of-consumption track product usage via smart, connected devices and use this data to automate order processes. Based on a large industry dataset from the professional coffee industry, we develop models for demand forecasting, inventory control, and replenishment under inventory inaccuracies.



Project networks and reallocation externalities

Vibhuti Dhingra1, Harish Krishnan2, Juan Serpa3

1Schulich School of Business, York University, Canada; 2Sauder School of Business, University of British Columbia, Canada; 3Desautels Faculty of Management, McGill University

Project networks involve several participants; clients, contractors, and subcontractors; each working on multiple projects concurrently. By tracking a network of 2.6 million public projects over a five-year span, we show that when a project suffers a localized disruption, other projects in the network get delayed because participants reallocate resources to the disrupted project. This creates a domino-effect externality that ripples through the network, causing delays across unrelated projects.



Predictive 3D printing with IoT

Jing-Sheng Song1, Yue Zhang2

1Duke University, United States of America; 2Pennsylvania State University, United States of America

We consider the problem of a 3D printer supplying a critical part installed in multiple machines embedded with sensors and interconnected via IoT. We show that it is optimal to print-to-stock predictively in advance of demand, triggered by a system-lifetime-status dependent threshold. We further quantify the impact of IoT on system cost and inventory by separately assessing the impact of advance demand information from embedded sensors and that of IoT's real-time information fusion.



 
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