There are two ways to enter the Simio Student Case Competition. You can either enter directly or through class participation. This contest, we had 26 teams enter directly (80 students) and 37 instructors with 313 teams (1263 students) for a total of 339 teams (1343 students).
We also had representation from the following 16 countries:
The 1343 students came from these 44 schools:
The Simio Shelving Shop is a shelving manufacturer and wholesaler specializing in several types of shelving units with design variations. The shop is currently experiencing low order fill rates, affecting the shop’s bottom line. Management recently hired an outside consultant to provide recommendations for revamping the inventory and buffering system. The consultant recommended a new buffering and inventory paradigm, Demand Driven Materials Requirement Planning (DDMRP), to address the rampant stockout issue.
The factory currently uses static buffer levels, where all stations have a fixed amount of raw material buffered at their station, and the factory maintains a fixed number of each end item ready to ship. The current buffering method, combined with variations in lead time and quality throughout the factory and its suppliers, drives stockouts of finished goods and raw material. Management embraced the consultant’s recommendation to incorporate a DDMRP buffering solution into the factory because they are convinced that a dynamic buffering solution will improve a collection of selected Key Performance Indicators (KPIs), especially fill rate and average inventory cost. As a secondary objective, management also seeks better methods of monitoring risk in the buffer levels, which the DDMRP buffer profiles will provide.
The challenge is to set up dynamic DDMRP buffers in the factory. This will include leveraging pre-existing data from Sales, Quality, machines, and suppliers to first determine the locations of the buffers, then fine-tune the buffer parameters to improve the selected KPIs. After the buffers are integrated, management wants to predict the change in the factory’s KPIs. Additionally, management would like to reevaluate potential suppliers, which have varying lead times and product quality. This assessment will include recreating optimal buffers for the potential suppliers to determine the impact the supplier could have on the company’s fill rate and average inventory costs.
The judging was based on:
For more detail, see the Contest Judging Criteria.
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| Edward Williams Lecturer II at Business Analytics, College of Business, University of Michigan - Dearborn |
Sven Guzman Dean of Business Engineer at ESEN |
Ivan Vilaboa Associate Professor at Instituto Tecnológico de Buenos Aires |
Maria Diaz Business Process Optimization Manager at Spirit Airlines |
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| Cassidy Shaffer Advanced Operations Research Analyst at Eastman |
Mustafa Gocken Associate Professor at Adana Alparslan Türkeş Science and Technology University |
Ted Allen Associate Professor at The Ohio State University |
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| David Sturrock Technical Fellow |
Elizabeth Millar Applications Engineer |
Alex Molnar Applications Engineer |
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| Caleb Whitehead Applications Engineer |
Rylan Carnery Applications Engineer |