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- All Subjects: Data Analytics
- All Subjects: Supply Chain Management
- Creators: Department of Information Systems
- Resource Type: Text
Before the COVID-19 pandemic, there was a great need for United States’ restaurants to “go green” due to consumers’ habits of frequently eating out. Unfortunately, COVID-19 has caused this initiative to lose traction. While the amount of customers ordering takeout has increased, there is less emphasis on sustainability.<br/>Plastic is known for its harmful effects on the environment and the extreme length of time it takes to decompose. According to the International Union for Conservation of Nature (IUCN), almost 8 million tons of plastic end up in the oceans at an annual rate, threatening not only the safety of marine species but also human health. Modern food packaging materials have included a blend of synthetic ingredients, trickling into our daily lives and polluting the air, water, and land. Single-use plastic items slowly degrade into microplastics and can take up to hundreds of years to biodegrade.<br/>Due to COVID-19, restaurants have switched to takeout and delivery options to adapt to the new business environment and guidelines enforced by the Center of Disease Control (CDC) mandated guidelines. Some of these guidelines include: notices encouraging social distancing and mask-wearing, mandated masks for employees, and easy access to sanitary supplies. This cultural shift is motivating restaurants to search for a quick, cheap, and easy fix to adapt to the increased demand of take-out and delivery methods. This increases their plastic consumption of items such as plastic bags/paper bags, styrofoam containers, and beverage cups. Plastic is the most popular takeout material because of its price and durability as well as allowing for limited contamination and easy disposability.<br/>Almost all food products come in packaging and this, more often than not, is single-use. Food is the largest market out of all the packaging industry, maintaining roughly two-thirds of material going to food. The US Environmental Protection Agency reports that almost half of all municipal solid waste is made up of food and food packaging materials. In 2014, over 162 million tons of packaging material waste was generated in the states. This typically contains toxic inks and dyes that leach into groundwater and soil. When degrading, pieces of plastic absorb toxins like PCBs and pesticides, and then each piece will, in turn, release toxic chemicals like Bisphenol-A. Even before being thrown away, it causes negative effects for the environment. The creation of packaging materials uses many resources such as petroleum and chemicals and then releases toxic byproducts. Such byproducts include sludge containing contaminants, greenhouse gases, and heavy metal and particulate matter emissions. Unlike many other industries, plastic manufacturing has actually increased production. Demand has increased and especially in the food industry to keep things sanitary. This increase in production is reflective of the increase in waste. <br/>Although restaurants have implemented their own sustainable initiatives to combat their carbon footprint, the pandemic has unfortunately forced restaurants to digress. For example, Just Salad, a fast-food restaurant chain, incentivized customers with discounted meals to use reusable bowls which saved over 75,000 pounds of plastic per year. However, when the pandemic hit, the company halted the program to pivot towards takeout and delivery. This effect is apparent on an international scale. Singapore was in lock-down for eight weeks and during that time, 1,470 tons of takeout and food delivery plastic waste was thrown out. In addition, the Hong Kong environmental group Greeners Action surveyed 2,000 people in April and the results showed that people are ordering out twice as much as last year, doubling the use of plastic.<br/>However, is this surge of plastic usage necessary in the food industry or are there methods that can be used to reduce the amount of waste production? The COVID-19 pandemic caused a fracture in the food system’s supply chain, involving food, factory, and farm. This thesis will strive to tackle such topics by analyzing the supply chains of the food industry and identify areas for sustainable opportunities. These recommendations will help to identify areas for green improvement.
Created predictive models using R to determine significant variables that help determine whether someone will default on their loans using a data set of almost 900,000 loan applicants.
Corporate buzzword terms like “big data” and “data analytics” are vague in meaning, and are thrown around by media sources often enough to obfuscate their actual meanings. These concepts are then associated with company-wide initiatives beyond the reach of the individual, in a nebulous world where people know that analytics happens, but don’t understand what it is.
The power of data analytics is not reserved for company-wide initiatives, or only employed by Silicon Valley tech start-ups. Its impacts are visible down at the team or department level, and can be conducted by the individual employees. The field of data analytics is evolving, and within it exists a rapid transition in which the individual employee is becoming a source for insight and value creation through the adoption of analytics based approaches.
The purpose of this thesis is to showcase an example of this claim, and demonstrate how an analytics based approach was applied to an existing accounting process to create new insights and information. To do this, I will discuss my development of an Excel based Dashboard Analytics tool, which I completed during my internship with Bechtel Corporation throughout the summer of 2018, and I will use this analytics tool to demonstrate the improvements that small-scale analytics had on a pre-existing process. During this discussion, I will address conceptual aspects of database design that related to my project, and will show how I applied this classroom learning to a working environment. The paper will begin with an overview of the desired goals of the group in which I was based, and will then analyze how the needs of the group led to the creation and implementation of this new analytics-based reporting tool. I will conclude with a discussion of the potential future use of this tool, and how the inclusion of these analytical approaches will continue to shape the working environment.
Keywords. Supply Chain Management, Social Responsibility, Sustainability, Economics, Supply Management, Blockchain, Intelligent Technology
Paper Type. Conceptual Paper
My recommendations to the company will be based on the findings of the analyses’ used. There may be multiple conclusions in the recommendations for demand forecasting based on each individual forecast used. However, I will give my insight on which forecast I think is more accurate and why this one would be the best to implement in terms of accuracy. Going forward, the company will be capable of implement these models and fine tune them as necessary to help streamline their inventory needs.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.