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Articles 1 - 7 of 7
Full-Text Articles in Business
Enhancing Customer Support Operations Through Gpt & Q-Learning: A Model Study, Adam Alidra, Bob O'Brien, Dalton Young
Enhancing Customer Support Operations Through Gpt & Q-Learning: A Model Study, Adam Alidra, Bob O'Brien, Dalton Young
SMU Data Science Review
Abstract. “Growth strategies that are purpose-led, customer-centric, experience-driven, data/AI-enabled, and technology-scaled require new mindsets…” (Cornfield, 2021). What can we take from this? Business growth and customer experience are inextricably tied together. Therefore, thriving, as an organization, is dependent on reimagining enterprise operations through modern, scalable data and AI technologies. Our study aims to enhance support operations with emerging AI capabilities, including OpenAI’s LLM models, built on self-attention mechanism transformer architecture, and tailored for business needs through prompt engineering. Our research uses Markov Decision Process and the Q-learning algorithm to evaluate synthetically created support incidents. Through this set of methods, our …
Analysis Of First-Time Completion In The Field Service Environment, Gavin Rick, Scott Englerth, Marc Carter, Hayley Horn
Analysis Of First-Time Completion In The Field Service Environment, Gavin Rick, Scott Englerth, Marc Carter, Hayley Horn
SMU Data Science Review
First-time completion is an important measure of service quality and efficiency in the field service industry. Customers call upon field service providers to repair their equipment in a timely manner so it can be put back into service for their business demands. Responsiveness can be measured through first-time completion and is defined as completing the repair on the first visit of a service call. This research is exploring the first-time completion in the forklift service industry. This research found the primary factors that impact first-time completion percentage in this industry include parts on hand, parts backorder process, technician experience, and …
Enhanced Data Science Methods For Freight Optimization At Kelly-Moore Paints, Lance Dacy, Reannan Mcdaniel, Shawn Jung
Enhanced Data Science Methods For Freight Optimization At Kelly-Moore Paints, Lance Dacy, Reannan Mcdaniel, Shawn Jung
SMU Data Science Review
Kelly-Moore Paints is a paint manufacturing company founded in San Carlos, California in 1946 by William Kelly and William Moore. It has stores located in California, Texas, Oklahoma, and Nevada. They currently own 11 42’ trailers, contract 4 distinct drivers, and service 44 stores Monday-Thursday from its Texas Distribution and Manufacturing Center in Hurst, TX. Given that transportation costs are typically the highest in the supply chain costs, this study will employ data science techniques to ensure the transportation routing, store ordering mechanism, and trailer utilization are at the best efficiency possible given the current ordering patters of the stores. …
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater
SMU Data Science Review
In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied …
Demand Forecasting For Alcoholic Beverage Distribution, Lei Jiang, Kristen M. Rollins, Meredith Ludlow, Bivin Sadler
Demand Forecasting For Alcoholic Beverage Distribution, Lei Jiang, Kristen M. Rollins, Meredith Ludlow, Bivin Sadler
SMU Data Science Review
Forecasting demand is one of the biggest challenges in any business, and the ability to make such predictions is an invaluable resource to a company. While difficult, predicting demand for products should be increasingly accessible due to the volume of data collected in businesses and the continuing advancements of machine learning models. This paper presents forecasting models for two vodka products for an alcoholic beverage distributing company located in the United States with the purpose of improving the company’s ability to forecast demand for those products. The results contain exploratory data analysis to determine the most important variables impacting demand, …
Demand Forecasting: An Open-Source Approach, Murtada Shubbar, Jared Smith
Demand Forecasting: An Open-Source Approach, Murtada Shubbar, Jared Smith
SMU Data Science Review
In this paper, we compare demand forecasting methods used by the supply chain department at Bilports to open-source forecasting methods. The design and implementation of the open-source forecasting system also attempts to use several external datasets such as consumer sentiment, housing permit starts, and weather to improve prediction quality. Additionally, the performance of the forecast is evaluated by the reduction of shipment lead times from China, the company’s primary vendor. The objective of our paper is to improve Bilports’s forecasting capabilities. The primary motivation of this paper is to increase forecasting accuracy and identify the weaknesses of the methods used …
Evaluating Feasibility Of Blockchain Application For Dscsa Compliance, Tracie Scott, Armand L. Post, Johnny Quick, Sohail Rafiqi
Evaluating Feasibility Of Blockchain Application For Dscsa Compliance, Tracie Scott, Armand L. Post, Johnny Quick, Sohail Rafiqi
SMU Data Science Review
Abstract. We evaluated the feasibility of using a blockchain technology to create a traceability solution for pharmaceutical drugs that would promote compliance with recent legislation. Counterfeit and other illegitimate pharmaceutical drugs threaten patient safety, drug efficacy, and patient trust. The purpose of the Drug Supply Chain Security Act (DSCSA) is to greatly reduce distribution of illegitimate drugs by requiring all pharmaceuticals to be serialized and traceable from the manufacturer through the supply chain to the dispenser. A software application to serialize and track pharmaceuticals must overcome numerous obstacles. In particular, the solution must provide a high degree of trust while …