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).
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.