A leading medical device company has experienced a consistent decline in profit margins across its product categories. Factors such as high inventory costs, complex Supply Chain operations, and challenges in managing a highly complex product portfolio have contributed to the erosion of gross profit. This study aims to uncover and...
Cell and Gene Therapy (CGT) treatment require significant upfront R&D investments coupled with lower patient demand volumes compared to traditional blockbuster drugs while not benefiting from the economies of scale principles for cost reduction. Therefore, ensuring the economic viability of a CGT treatment is one of the most pivotal considerations...
To enhance understanding of congestion points at ports and provide visibility into the incoming goods flow into the USA, this study focuses on maritime ports, using the Port of Boston and New York/New Jersey as case studies. Based on the Automatic Information System (AIS) data, we aim to develop predictive...
The logistics supporting life-saving Cell & Gene Therapy treatments, such as autologous CAR-T, face significant challenges due to strict constraints like time sensitivity, temperature control, and regulatory compliance. These constraints make the supply chain vulnerable to disruptions that could result in the loss or damage of these delicate, high-value therapies...
This project presents an innovative approach to estimating On-Shelf Availability (OSA) within nanostores, key components of retail channels in emerging markets like India. Utilizing sales data and field study findings in Mumbai, we developed and validated two distinct models: a probabilistic model and a Machine Learning model. The probabilistic model...
In today’s competitive business environment, companies are increasingly seeking ways to optimize their supply chain networks to reduce costs, improve responsiveness, and enhance sustainability. This capstone project explores the potential of co-location strategies, specifically the Supplier Park Model, in addressing supply chain inefficiencies for ABC Corporation, a leading food and...
This capstone project streamlines the demand forecasting process for drilling bits in the oil and gas industry, crucial for optimizing supply chains and ensuring equipment availability. Focusing on a leading service provider, the study utilized causal and time-series modeling to predict bit runs and revenue for the next 4–6 quarters...
Consumers frequently reach behind supermarket shelves to find products with the latest expiration dates. This instinctive behavior highlights a universal desire for fresh produce. Therefore, our capstone sponsor is eager to consistently deliver fresh produce to maximize customer satisfaction. One innovative approach to increasing freshness is to minimize the duration...
Access to cancer medicines remains a significant challenge in many Low- and Middle-Income Countries (LMICs), limiting patients' ability to receive timely and affordable treatment. This study aims to analyze the impact of the pharmaceutical downstream supply chain on patient access to oncology medicines in LMICs. Utilizing a system dynamics approach...
As the field of artificial intelligence (AI) advances rapidly, its application within the supply chain arena has seen a significant surge. This capstone project focuses on Artificial Intelligence/Machine Learning (AI/ML) predictive algorithms applied in demand forecasting, a pivotal process for managerial decisions such as inventory control and production planning. The...
Food insecurity is a major issue for many Americans. Food banks and food pantries strive to provide enough food to satisfy the needs of people suffering from food insecurity. These organizations are also trying to distribute healthy food, not just meeting the caloric intake necessary to sustain the community. The...
In response to global climate warming, corporations have solidified their sustainability commitments and intensified their efforts to reduce greenhouse gas (GHG) emissions. In partnership with a global CPG company, this work focuses on the feasibility of engaging wholesalers in the distribution network as third-party logistics providers (3PLs) to reduce emissions...
This paper explores the integration of generative artificial intelligence (AI) technology into the procurement operations of a global healthcare company. Driven by a large procurement spend of $35BN with diverse sourcing information and massive amounts of data, the research aims to help our sponsor company develop a real-world proof-of-concept of...
For our project sponsor, a leading global Fast Moving Consumer Goods (FMCG) company, the goal goes beyond simply meeting complex demands. The focus is on optimizing inventory management strategies to balance the crucial trade-off between avoiding stockouts and minimizing the costs associated with excess inventory. Our project explores machine learning's...
Rising inventory costs and lower inventory turnover due to long lead times and supply/demand volatility have led to a need for our sponsor company, Tempur Sealy International, to reassess its sourcing, inventory, and deployment strategy for its adjustable base category. We develop a linear program optimization model capable of analyzing...
More than 85 million tons of cardboard waste are created annually, with most ending up in landfills. This capstone project aims to develop a mathematical optimization model to help last-mile delivery companies reduce their carbon dioxide (CO2) footprint by collecting and reusing cardboard cartons. Specifically, the optimization model suggests from...
In the oil and gas industry, the fluctuations of semiconductor component delivery time significantly impact Printed Circuit Board Assembly (PCBA) production planning. The discrepancies between the supplier quoted lead time and actual delivery lead time present a substantial challenge, as the sponsor company`s MRP system lacks a robust safety stock...
In the U.S. trucking industry, freight brokerages act as vital intermediaries between shippers and carriers, but they face financial risks due to write-offs from unpaid services. Despite the recognized importance of mitigating these financial risks, the sponsoring company does not currently have a predictive model to assess the likelihood and...
This capstone project addressed the challenges faced by GlaxoSmithKline’s (GSK) supply chain in managing sequential delays, essential for ensuring timely healthcare delivery in the pharmaceutical industry. The key objectives included pinpointing planned dates within GSK’s system and developing a robust machine learning model to predict sequential delays accurately. Through an...
Newell Brands, a conglomerate with a diverse product portfolio, has traditionally relied on financial metrics for SKU rationalization. Now, they are seeking a SKU rationalization strategy for “Pen” portfolio to eliminate SKUs that add more complexity to the Bill of Material (BOM) with least value creation. How can Newell Brands...
Anticipating fluctuations in Less-than-Truckload (LTL) volume presents challenges for shippers, carriers, and freight brokers alike. This capstone addresses this issue by developing a predictive model for LTL volume, leveraging insights gained from the cyclical nature of demand shifts between Truckload and LTL freight. Through analyzing various truckload metrics, this study...
The US Bureau of Labor Statistics states that food and beverage manufacturers have experienced annual 0.5% decreases in labor productivity and annual 7% increases in unit labor costs since 2019 (US Bureau of Labor Statistics, 2023). These statistics underscore a growing inefficiency in the manufacturing and distribution processes of food...
Consumer demand for frozen products from The J.M. Smucker Company has grown dramatically over recent years, driving an evident need to increase total ship output. The overarching problem that we attempted to solve in this project was how to maximize the amount of a specific frozen product loaded onto trailers...
Truckload procurement practices vary widely across industry and firm spend levels. This research utilizes a framework to identify shipper behaviors that represent state-of-the-practice in truckload procurement while also highlighting shipper behaviors that represent state-of-the-art. The presented anlaysis uses data collected from 1) a survey of 300 shippers and 2) semi-structured...
Despite concerns over distracted driving, many Americans still engage in risky activities while driving, leading to crashes and fatal outcomes. This study aims to investigate the impact of individual risk attitudes and in-vehicle technologies on various types of distracted driving behaviors (DDB), providing insights into the factors that contribute to...
