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AMAZON.COM, INC.- MAINTENANCE Dynamic Preventive Maintenance
AMAZON - ROBOTICS
Developing A Supplier Assessment Framework
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Student Team: Shreyas Parab – Master of Engineering in Industrial & Operations Engineering Jackie Whittaker – Master of Business Administration
Project Sponsors: Mike Anderson – Senior Manager, Technical Operations Paul Seay – Senior Manager, Advanced Manufacturing Engineering
Faculty Advisors: Ravi Anupindi – Ross School of Business David Kaufman – College of Engineering
Amazon Robotics (AR) is a subsidiary of Amazon.com, Inc., responsible for providing automation and robotic integration within Amazon’s fulfilment, sortable, and last mile delivery centers. AR designs, manufactures, and tests warehouse robotics solutions. Amazon has 175+ fulfillment centers, of which AR services 50+ as robotic fulfillment centers. To support an upcoming 100+ site launches over the next year, AR is rapidly expanding in complexity and volume.
To meet the pressures of AR’s rapid growth and scope expansion, AR charged the Tauber team to develop a holistic framework for supplier selection that enhances the decision-making process, mitigates biases in supplier selection, and supports the planned expansion of the supplier base by 2x year over year. While each decision-maker in the supplier selection process offers a unique and important perspective, AR lacks a structured method to capture these stakeholders’ judgement, make trade-offs, and reach a consensus efficiently. To facilitate effective cross-functional decision-making at scale, the Tauber team’s primary goal focused on developing holistic assessment mechanisms.
The Tauber team used a DMAIC (Define, Measure, Analyze, Improve, and Control) approach to conduct 38 interviews with key stakeholders as part of a root cause analysis. The analysis identified seven common pain points and refined the following four recommendations through 3 pilot tests with different commodities: (1) implement go/no-go filters based on industry standards, (2) incorporate cross-functional criteria for holistic assessment, (3) implement an Analytical Hierarchy Process decision-making model to reduce biases through data-driven tools, and (4) use a Rapid Site Assessment tool to consistently assess efficiency and effectiveness.
By implementing the recommended mechanisms to establish data-driven processes and controls, AR will reduce the time and costs associated with searching for qualified suppliers, evaluating pre- assessment data, and conducting on-site audits with suppliers that do not meet AR’s standards. The benefits of implementing these piloted recommendations are reducing decision biases while improving agility and effectiveness in supplier selection.
AMERICAN INDUSTRIAL PARTNERS (AIP)
Additive Manufacturing Opportunity Identification
Student Team: Alyssa Dern – EGL (BSE & MSE in Industrial and Operations Engineering) Aaron Kaplan – Master of Business Administration
Project Sponsors: Jeff Birenbaum – Partner (Operations) Daniel Davis – Partner (Operations) Dan Saberton – Engineering Manager
Faculty Advisors: Damian Beil – Ross School of Business Grant Kruger – College of Engineering
American Industrial Partners (AIP) is an operationally-oriented middle market private equity firm that is distinctively focused on buying and improving industrial businesses with operations in the United States and Canada. Backed by AIP, ADDMAN Engineering leverages the expertise and knowledge of the mid-sized manufacturing companies in AIP’s portfolio to apply Additive Manufacturing (AM) and advanced technologies to enable breakthroughs in product development and manufacturing for their customers. ADDMAN currently operates facilities in Bonita Springs, FL and Westfield, IN.
A critical component in the identification of additively manufactured parts is a deep knowledge and understanding of the technologies and its potential uses. ADDMAN had fewer than twenty employees across both locations and seeked to enhance their processes for part identification to improve sales performance and limit dependence on engineers. The company focused on expanding their customer base to all of AIP’s portfolio companies and needed assistance to quickly identify potential value within a company as well as specific parts that would be feasible to print.
To address this opportunity, the Tauber team accumulated knowledge from the ADDMAN team and relevant AIP portfolio companies to create a set of tools that could be used to decrease the time it takes for identification of 3D printing opportunities and assessment of value creation. These tools included discrete rapid (plant) assessments as well as an engineered sales questionnaire tool to comprehensively review a given company. The outputs of the tools included top value drivers, readiness for AM implementation, service fits within the company, specific opportunity areas, and immediate next steps for execution. The tools limited the need for a deep understanding of additive manufacturing. They also decreased the time and variability of the opportunity discovery process when engaging a potential new customer.
After assessing possible implementations for these tools, ADDMAN’s 3-year impact included additional revenue generation of up to $73M from improved lead conversion rates. ADDMAN also acquired value from reduced customer acquisition costs due to a 20% reduction in discovery time amounting up to $668K. The tools created by the Tauber team will be implemented immediately in ADDMAN’s operations.
BEYOND MEAT
Student Team: Gilbert Pasquale – Master of Business Administration Channing Wan – EGL (BSE & MSE in Industrial and Operations Engineering)
Project Sponsor: Victor Davis – Vice President of Internal Manufacturing
Faculty Advisors: John Branch – Ross School of Business Debra Levantrosser – College of Engineering
Beyond Meat is the first manufacturer of plant-based meat, having debuted its plant-based burger product in 2015. Since then, it has expanded its product offerings to include plant-based sausage, meatballs, chicken and many others. Its revenue grew to $400M in 2020 showing strong customer demand for this new category. In selling plant-based meat products, Beyond’s goals are multi-faceted: to improve human health, decrease the impacts of climate change, limit the reliance of humans on natural resources and respect animal welfare. Additionally, in 2020, Beyond expanded its footprint by adding three manufacturing sites. Amidst this growth, Beyond’s manufacturing team has committed to optimizing their production capabilities. The Tauber team was tasked with identifying and prioritizing opportunities to cut costs, as well as piloting one of the identified priorities.
To measure opportunities for improvement, the Tauber team leveraged the Overall Equipment Effectiveness (OEE) framework at the Devault, PA facility. OEE evaluates opportunities on a machine level by measuring availability, quality, and performance. The Tauber team manually measured OEE and provided recommendations for Beyond to automate OEE measurement long term. While measuring OEE, the Tauber team identified a specific machine on the plant floor that had significantly lower availability and performance. The Tauber team planned an Autonomous Maintenance Event (AME) and included the machine’s manufacturer, Devault’s management, the maintenance team, and the production team.
During the event, operators were trained on proper machine use, the technician inspected and made machine adjustments, and preventative maintenance was standardized. The Tauber team provided documentation and standard operating procedures following the event.
By executing the AME, the Tauber team was able to improve the performance and availability of the machine and ultimately increase the output of the line. The team also recommended additional strategies to improve OEE including rotating operator breaks and adding a pull system between two process steps. With these improvements, Beyond can expect $3.3M in savings and 35M lbs of additional product produced over the next three years at the Devault facility. BEYOND MEAT
