Statistical Reasoning & Analytical Problem Solving

The past two years I have been able to participate in two professional work experiences that reflect my desired career ambitions. In these roles, being an efficient leader has included making smart and effective decisions that benefited the teams I worked with. In both work environments, a multitude of factors and options needed to be considered before solutions were created, and then each potential solution was tested to determine the best option. Once solution(s) were reached, it was critical to implement methods to ensure their continued success. During summer 2021, I worked as an area manager intern for Amazon in Florence, NJ and in the fall 2021 semester I worked as a consulting analyst for ABB in Mebane, NC. In both roles I led the teams I worked with and completed process improvement projects that experienced massive success through following the lean six sigma DMAIC problem-solving approach discussed in the previous key insight. The solutions created and implemented were backed by the analysis of statistics and measurements, which were also used to assess the impact the solutions had on operations and finances. Through using past leadership experiences, academics, and statistical reasoning, I have been able to create large-scale impacts for large companies. Using data to make decisions is a crucial business practice and implementing the use of data and statistical reasoning has helped me in my problem-solving methods and decision-making for different roles.
 

My business classes at UofSC have allowed me to work with a vast amount of data and understand how to use analytical tools/programs to make sense of the data and use it to make decisions. In my Service Operations Management class (MGSC 486) and Financial Statement Analysis class (FINA 470), I have worked on assignments that require analyzing data and testing solutions to make recommendations for different business aspects. In MGSC 486, we worked with different data sets and utilized Microsoft Excel and Power BI to make statistical decisions with the use of hypothesis testing, regression analysis, demand forecasting, and queuing. Utilizing these different methods ensured that our results were valid and allowed us to make managerial suggestions through the interpretation of data using visuals and reports that the audience could understand. For example, one of the assignments required us to find the best location for a new hotel based on how different factors affected occupancy rate, using regression analysis. I tested the significance level of each independent factor on the dependent factor (occupancy rate) and picked the best location based on the factors that were significant and what hotel had them*. Below you can see the regression analysis output and the location decision based on factor significance, along with our report for management.

*Regression analysis tests what independent variables can explain variation in the dependent variable through interpretation of the r-squared. Significant factors have p-values of less than the level of significance (Queenan, slide 7).

 

 

 

 

 

 

 

 

 

 

 

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From this assignment, I learned how to use data and statistical programs to make business decisions and report findings in a way that the audience could understand and use. To make an informed and educated decision, a process like this is crucial in order to consider all aspects, such as finances, operations, and feasibility.
 

In FINA 470, we learned about financial planning for different projects and how to incorporate this into project planning. Implementing a cost/benefit analysis and calculating net present value of a project before it starts allows you to make an economically responsible decision and assess if the benefits and solutions of your project will outweigh the costs to complete it. Throughout the completion of a project, it is critical to assess different costs and the budget to ensure feasibility and determine if any changes need to be made. Combining both aspects of operations and finances ensures a broad understanding of your project and reduces the need for rework after completion. In my internship and professional work opportunities utilizing concepts from supply chain and finance have allowed me to make educated decisions.
 

While working as an Area Manager intern at Amazon, I managed the outbound dock department, which is responsible for ensuring packages in the warehouse leave within three days on trailers and are safely secured. My role entailed supervising 25 associates with my mentor in the department while simultaneously completing a process improvement project that aimed at reducing the time it took for trailers to be swapped on the outbound dock doors. For my project, I collected associate feedback and watched the process first-hand in order to identify root causes and better understand variations in the process to identify ideas for solutions. Next, I collected and organized data for the time it took to close each dock door for all each shift in the building, along with other warehouses in our network to compare times and understand disparities. After these two steps were completed, I began implementing solutions based on data collected and associate feedback. After a month of testing solutions and collecting data, my mentor and I were able to come up with a collaborative solution with the Transportation Operations Management team that played a key role in reducing our times from an average of 46 minutes in May to 27 minutes in July. Working and leading the project with cross-functional teams in the warehouse allowed me to drive larger-scale results by implementing solutions that resulted in benefits for both parties. If the solutions are fully implemented across all shifts in the warehouse, the labor cost savings would total over $500,000 per year and reduce traffic for trailers in the yard outside by around 30%. Completing each step of the DMAIC problem-solving approach allowed me to utilize my past leadership experiences (in leading teams and taking ownership of projects) and academics (using data to make decisions and create reports) to experience successful results.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

In the fall 2021 semester, I took the Supply Chain Capstone project (MGSC 497), a capstone consulting class where I was able to gain college credits by working as a consulting analyst with a group of 5 business students. Our group worked for ABB and completed a process improvement project that aimed to increase raw material inventory turns for the most profitable line in their warehouse. Our goal was to create a way to analyze all the material parts and to improve the inventory replenishment policies. We were given five large Excel files containing data about each material part (cost to order and hold in warehouse, number of items in warehouse, demand/usage, etc.) at the beginning of the project and went on a site tour at the warehouse to understand the problems first-hand and talk with different parties affected. Next, we created an Excel-based tool that calculated different inventory metrics for each material part based on the initial data given to assess current versus optimal conditions. Using current demand (usage) data and forecasting, we recommended different purchasing practices based on active versus inactive parts and the demand trends each part exhibited. We also recommended ordering different quantities (economic order quantities) and updating frequency of orders placed per year based on the demand pattern they exhibited. Our team gave ABB the Excel-based model and a manual that showed them how to use it by “dumping” the data sets each time they wanted updated optimal calculations and recommendations. Based on model calculations and updated inventory replenishment policies, ABB could save over $3 million in inventory value and over $1.5 million in inventory holding and ordering costs if the model is implemented fully. As a result of the success my group and I experienced with the project and the my successful completion of the green belt exam administered by Sonoco, I earned the Lean Six Sigma Green Belt Certification, as seen to below.

 

 

 

 

 

 

 

 

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This certifies my knowledge of lean six sigma concepts and my ability to use them in my professional career. The certificate is a testament to the academic success I experienced during college and my ability to apply what I learned in class and other experiences to a supply chain role.
 

Reflecting on this accomplishment has allowed me to acknowledge the importance of my peer leadership experiences and academics towards my professional work and goals. I am confident in making decisions and leading others because of the strong foundation I created the past four years. I led both the projects (discussed above) and ensured the teams I worked on were collaborating and effectively contributing to the projects. Taking ownership over the projects and being an inclusive leader allowed me to gain confidence from my teammates and superiors about my solutions for the process improvements. This, along with the statistical reasoning and testing behind each solution, allowed me to attain desired results and solutions that are currently being implemented at Amazon and ABB. By employing academic learning from my supply chain and finance classes, I was able to consider multiple economic and feasibility aspects before beginning a project and throughout to ensure the solutions would be successful and reduce variation and rework afterwards. As I continue with my professional and civic engagement pathway, I want to continue to make effective and successful decisions for those I lead and the projects I work on with the use of statistical reasoning. Utilizing the skills I learned in the classroom and from my past peer leadership experiences allows me to employ both soft and hard skills in my professional work experiences.