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- All Subjects: Executive Summary
- Creators: Moore, James
- Creators: Chang, Karen
- Creators: Civil, Environmental and Sustainable Engineering Programs
- Member of: Theses and Dissertations
- Status: Published
Description
E-commerce has rapidly become a mainstay in today's economy, and many websites have built themselves around providing a platform for independent sellers. Sites such as Etsy, Storenvy, Redbubble, and Society6 are increasingly popular options for anyone looking to open their own online store. With this project, I attempted to examine the effects of four different marketing techniques on sales in an online store. I opened a shop on Etsy and tracked sales in connection with promotion through social media, selling products in-person at a convention, holding a holiday tie-in sale, and using price anchoring. Social media accounts were opened on Facebook, Tumblr, and Instagram to promote the shop over the course of the project period, and Etsy's web analytics were used to track which sites directed the most traffic to the shop. I attended a convention in mid-January 2016 where I sold my products and distributed business cards with a discount code to track sales resulting from being at the convention. A holiday sale was held in conjunction with Valentine's Day to look at whether holidays influenced purchases. Lastly, a significantly more expensive product was temporarily put in the shop to see whether it produced a price anchoring effect \u2014 that is, encouraged sales of the less expensive products by making them seem affordable in comparison. While the volume of sales data was too small to draw statistically significant conclusions, the project was a highly instructive experience in the process of opening a small online store. The decision-making steps outlined may be helpful to other students looking to open their own online shop.
ContributorsChen, Candice Elizabeth (Author) / Moore, James (Thesis director) / Sanford, Adriana (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
New Venture Group, a student-run consulting organization at ASU, collaborated with representatives from Intel Corporation to determine current best supplier management practices in the area of capital equipment procurement. The New Venture Group team accomplished this goal by completing the following deliverables: (1) Research and consolidate best practices for managing capital equipment suppliers. (2) Interview suppliers of capital equipment in the semiconductor industry to understand their motivators. (3) Examine top supply chain companies that utilize capital equipment manufacturers within their procurement systems. (4) Gather data and knowledge in conjunction with Intel Corporation's current practices to improve the effectiveness of the company's supplier management techniques regarding capital equipment manufacturers. The thesis report outlines the key insights and recommendations that our team extracted from the research that we performed. Our team analyzed peer-reviewed journal articles, conducted interviews with suppliers of capital equipment to semiconductor manufacturers, and surveyed buyers at top companies to reach important key insights. We then used these insights to develop the following strategies to improve Intel's capital equipment supplier management structure: All Suppliers 1. Allow high-performance suppliers to select one reward from an established portfolio of incentives. 2. Increase measurement frequency for specific metrics. 3. Use collaborative two-way measurement with a corresponding balanced scorecard. Key Suppliers of Critical Products 4. Conduct gap analysis through supplier self-assessments. 5. Implement collaborative target pricing. 6. Delegate an Ombudsman. 7. Create a value map to determine the strengths and incentivize collaboration. 8. Create comparison charts comparing supplier technological competencies versus Intel's product developments. 9. Establish a systematized product development process and strategic sourcing strategy that supports the continuation of Moore's Law.
ContributorsSantiago, Bryce (Co-author) / Chen, Jenny (Co-author) / Chang, Karen (Co-author) / Baldridge, Stephen (Co-author) / Laub, Jeffrey (Thesis director) / Brooks, Daniel (Committee member) / Department of Information Systems (Contributor, Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The problem is that children in developing countries are doing our dirty work. Electronic waste that end up in landfills in these developing countries pose a danger to the children extracting metals that are then resold in local markets. The dumping of solar panels in these landfills is sometimes the only alternative for some manufactures because there is no viable option for silicon wafers. Solar panel installations started to peak in the early 1990's . With the lifespan of a solar panel being 25 years, recycling these panel is not a priority task in government policies. First Solar is currently the only company in the United States that executes the full recycling process. However, there is an environmental hotspot and an energy intensity phase identified in their process. The second stage in First Solar's recycling method consist of hammering and shredding the solar panel to reduce the surface area to then move on the chemical path stage. This stage currently uses 1.1 kWh for a meter by meter solar cell. A thermal processing method was explored and found to be the most environmentally conscious chose in terms of emissions and energy cost. The thermal method uses a conventional furnace to burn away the EVA, leaving the internal components of the cell intact and ready for the remaining process of recycling. SLICE method aims to introduce an industry tailored, low energy cost process, that initiates a solar panel recycling infrastructure in the United States. The recycling infrastructure is needed to sustain the exponential growth of solar panels and avoid third party recycling to developing countries. This new method transitions from lab tested batch processes to a continuous process.
ContributorsMartinez, Mariana (Co-author) / Grayson, Madison (Co-author) / Seager, Thomas (Thesis director) / Ravikumar, Dwarak (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Only an Executive Summary of the project is included.
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.
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.
ContributorsVerma, Ria (Author) / Goegan, Brian (Thesis director) / Moore, James (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05