Sponsor:

Project Name:

Cold Chain Inbound Optimization

Students:

Phillip Jonay
Alaiyi Lawson
Colette Lerkantitham
Jennifer Helga Mulia
Valentina Valero Nieto
Jimmy Tan
Haifeng Xia

Faculty Adviser:

About the Project:

Cold chain logistics refers to the transportation and handling of temperature controlled products such as groceries, pharmaceuticals, and chemical goods. Amazon’s current Freight Inbound (AFI) Network for cold chain delivery is dependent on third party shippers and managed by suppliers. This is not cost effective and prevents Amazon from managing their inbound cold supply chain. The student team created an automated optimization tool for inbound cold chain transportation network that processes the input variable and generates the most cost efficient execution plan for inbound deliveries based on constraints creating a more transparent, informed, timely, accurate and cost effective transportation process.

Sponsor:

Project Name:

Standardization of Boeing Tooling Communication and Information

Students:

Noah Adamek
Travis Folta
Raymond Fung
Lien Nguyen
Jordan Sims

Faculty Adviser:

About the Project:

Tooling at Boeing refers to unique structures and equipment built for very specific production needs (these are individualized and do not refer to traditional tools). Tooling fulfillment is currently inconsistent and fails to meet projected schedules in the 767 Line, resulting in an increasingly large backlog. The student team worked to improve communication, accountability, and order visibility in the Tooling Value System (TVS) to prevent and address tooling order delays.

Sponsor:

Goodwill

Project Name:

Seattle Goodwill E-Commerce Physical Growth Plan

Students:

Apoorv Bansal
Nathan Klaff
Joleen Lawson
Carisa Lin
Tiffany Yam

Faculty Adviser:

Patty Buchanan, Assistant Teaching Professor, Industrial & Systems Engineering

About the Project:

Seattle Goodwill’s e-commerce has been expanding rapidly with increased growth expected. The student team worked to design a streamlined facility, improve existing processes, and create a flexible design that allows for expansion and future improvements to optimize the effectiveness of Seattle Goodwill’s e-commerce sector. The team created a process-based layout with a CAD drawn floorplan that significantly increases the items that are processed per month, improved the flow of items, and allows for future expansion.

Sponsor:

MilliporeSigma

Project Name:

Bag Handling Optimization

Students:

John Boyle
Siena Firestone
Marina Hutchison
Christopher Lo
Sam Rich

Faculty Adviser:

About the Project:

MilliporeSigma currently uses two lab technicians to perform sampling, which involves holding the bags, sampling the media and returning the bag to the holding bin. The student team worked to develop a solution to improve the long-term efficiency and reduce the number of lab technicians by assisting them in the bag handling process without causing cross contamination. The team built and tested a final prototype featuring an ambidextrous handle and ratcheting clamp that is used by a single lab technician, supports media filled bags upright without spilling, and makes media sampling easier.

Sponsor:

Project Name:

Remote In-Vehicle User Testing

Students:

Lisa Beresford
Yuxi Chen
Langston Golver-Castro
Neil Lockwood
Amy Pham
Kristen Jayne Rudnick
Rui Yang

Faculty Adviser:

About the Project:

With the onset of COVID-19, user testing for PACCAR trucks significantly decreased. The student team worked to improve the user testing process, meet safety guidelines, and practice social distancing to allow truck testing to resume remotely. Their solution was required to be completely remote, flexible, maintain WiFi connection at all times, and not distract the driver.

Sponsor:

Project Name:

Automation of Time and Motion Data Collection Processes

Students:

Daelyn Bergsman
Will Locatelli
Audrey Slater
Sara Stavaski
Payam Vafadari
Bill Zhao

Faculty Adviser:

Patty Buchanan, Assistant Teaching Professor, Industrial & Systems Engineering and Prashanth Rajivan, Assistant Professor, Industrial & Systems Engineering

About the Project:

Time and motion studies are the basis of determining how much labor is required to staff Starbucks stores to support customers and inform business decisions on new beverage, food and equipment programs. While important, time and motion studies are resource intensive, and can be unreliable and prone to error. The student team worked to prove the feasibility of a system that could accurately recognize one key object manipulation activity in the Starbucks store and time how long it took to complete by utilizing transfer learning with machine learning models to classify human actions with video data.

Sponsor:

Project Name:

Kitting Facility Supply Chain Analysis and Refrigerated Freight Prediction

Students:

Kibrom Berhane
Hirza Nazhari
Connor Nehls
Amy Shao
Emma Utley

Faculty Adviser:

Kyle Caldwell, John C. Berg Endowed Lecturer, Chemical Engineering and Patty Buchanan, Assistant Teaching Professor, Industrial & Systems Engineering

About the Project:

Talking Rain is a fast-growing beverage company known for their popular brand, Sparkling Ice. They are interested in reducing costs in their supply chain by scheduling fewer unnecessary refrigeration trucks and implementing a centralized kitting system. The student team built an optimization model that finds the optimal city to locate the kitting facility to minimize total transportation costs.

Sponsor:

Project Name:

Logistics of the Implementation of a Concentrate Kit

Students:

Chyson Acoba
Will Christison
Gabe Enberg
Christina Europa
Mariam Nader Hussein
Daniel Le
Julius Liang
Patricia Rustam
Alexandra Salim
Gannon Short
Algird Zalpys

Faculty Adviser:

Kyle Caldwell, John C. Berg Endowed Lecturer, Chemical Engineering and Patty Buchanan, Assistant Teaching Professor, Industrial & Systems Engineering

About the Project:

The Talking Rain Beverage Company is the creator of the beverage Sparkling Ice. Currently, Sparkling Ice is created directly from raw material at both the Talking Rain facilities and copackers. This process could be improved using a concentrate kit which would decrease cost, processing time, error rate, and required storage space. The student team worked to calculate the required storage space for the current demand, find the feasibility of implementing the concentrate kit system, and give concentrate implementation recommendations.

Sponsor:

Project Name:

Optimization of Scheduling for Manufacturing Processes

Students:

Ryan Cheng
Kyle Luiten
Yash Raj Sinha
Eugene Tou
Max Yang

Faculty Adviser:

Patty Buchanan, Assistant Teaching Professor, Industrial & Systems Engineering

About the Project:

TE Connectivity designs and manufactures connectivity and sensor parts. The Automation Manufacturing Technology team is looking to minimize total inventory and production costs on their Universal Mate-n-Lock manufacturing processes. Currently, connector assembly machine schedules do not include low volume parts resulting in a sub-optimal process. The student team built a tool to improve demand forecasts based on historical values, recommend optimized sequences for scheduling, and recommend minimum order quantities for low volume parts.