Exam Solutions

My learning journey in Derivatives Markets My learning journey in Derivatives Markets has been an exciting and challenging one. As someone with a background in finance, I have always been interested in learning more about derivatives markets and the various strategies used to mitigate risks and maximize returns. The course began with an overview of derivatives markets and the different types of instruments traded in these markets. We then delved deeper into the mechanics of options, futures, and swaps, and learned how these instruments can be used to hedge risks and speculate on market movements. One of the most challenging aspects of the course was learning about the advanced mathematical models used to price derivatives. We spent several weeks studying Black-Scholes and other models, and I was amazed by the level of complexity involved in these calculations. However, with the help of my instructor and classmates, I was able to grasp these concepts and gain a deeper understanding of the pricing mechanisms underlying derivatives markets. Another aspect of the course that I found particularly interesting was learning about the various trading strategies used by market participants. We studied everything from simple long and short positions to more complex strategies such as straddles and spreads. We also learned about the importance of risk management and how to construct portfolios that minimize risk while maximizing returns. Throughout the course, we worked on several case studies and group projects that allowed us to apply the concepts we had learned to real-world scenarios. These projects were challenging but rewarding, as they helped us to develop our analytical skills and gain practical experience in derivatives trading. Week 1: Forward Contracts In our first week, we focused on Forward Contracts and learned about the following key points: We started by understanding what Forward Contracts are and how they differ from other types of financial instruments such as options and futures. We then delved into the key features of Forward Contracts, such as their fixed price, delivery date, and customized nature. Finally, we explored real-world examples of how Forward Contracts are used in different industries, such as agriculture and energy, and how they can be combined with other financial instruments to create more complex trading strategies. I found some problems quite interesting: Part 3 Suppose that there are no storage costs for borrowing or lending is 2.57% per annum(continuously compounded). Consider the transactions that would allow you to make an arbitrage profit in March by trading in May and December forward contracts. Use the forward prices provided in the table below and assume you can lock in the settlement prices on either a long or short position. Week 2: Futures and Swaps As a student of Derivatives Markets, I found our second week of studying Futures and Swaps to be incredibly insightful. Here are three key points that stood out to me: We started by learning about what Futures and Swaps are and how they differ from each other and other types of financial instruments. Futures contracts involve standardized terms and are traded on exchanges, while Swaps are customized agreements between two parties and are traded over-the-counter. We then discussed the key features of Futures and Swaps, such as their fixed price, delivery date, and underlying asset. Finally, we explored the risks and benefits associated with Futures and Swaps, including counterparty risk, margin requirements, and liquidity. I found some problems quite interesting: Part 4 Find the fixed rate for a 1-year semi annual interest rate swap for the following. Week 3: Duration-based Strategies I found our third week of studying Duration-based Strategies to be incredibly interesting. Here are three key points that stood out to me: Definition and Importance: We started by learning about what Duration is and how it measures the sensitivity of a bond’s price to changes in interest rates. Strategies: We then explored different Duration-based strategies, such as matching the Duration of assets and liabilities, barbell and bullet strategies, and immunization strategies. Real-world Examples: Finally, we looked at real-world examples of Duration-based strategies used by institutional investors, such as pension funds and insurance companies. We also discussed how changes in interest rates and economic conditions can affect the performance of these strategies. I found some problems quite interesting: Part A Assume that the treasury yield curve is flat at 3% on an annual basis (i.e., an investment of $100 receives a 3% interest payment at the end of the year). Utilize the duration and convexity calculator and answer the following. Overall, my learning journey in Derivatives Markets: Advanced Modeling and Strategies was a challenging but incredibly rewarding experience. I gained a deep understanding of the mechanics of derivatives markets and the various strategies used to trade in these markets. I also developed my analytical skills and gained practical experience through case studies and group projects. I am excited to continue exploring this fascinating field and applying what I have learned to my future endeavors in finance. To view the full journey, please visit: Click here

My learning journey in Mathematical Methods for Quantitative Finance My learning journey in Mathematical Methods for Quantitative Finance was an intensive and exciting experience. Over the course of the program, I gained a deep understanding of the mathematical concepts and techniques that underpin quantitative finance. During the early weeks of the program, we focused on core topics such as calculus, linear algebra, and probability theory. These foundational topics were crucial for building a strong understanding of the more advanced concepts that we would encounter later on. As the program progressed, we delved into more specialized topics such as stochastic calculus, Monte Carlo simulation, and partial differential equations. I found these topics challenging but incredibly rewarding, as they helped me to gain a deep understanding of the tools and techniques used in quantitative finance. Throughout the program, we had the opportunity to work on real-world problems and case studies, which helped to cement our understanding of the concepts and techniques that we were learning. We also had access to industry experts who provided valuable insights into the practical applications of the mathematical techniques that we were studying. Week 1: Probability Distributions Week 1 focused on the important topics of random variables and probability distributions. Here are my main takeaways from the week: A random variable is a variable whose value is determined by chance. Probability distributions are mathematical functions that describe the likelihood of different outcomes for a random variable. We learned about important probability distributions, such as the normal distribution and the binomial distribution, and how to calculate probabilities using these distributions. I found some problems quite interesting: Part 2 Let R0 be tomorrow’s one-day return on the S&P 500. This question will walk through how long it will take, on average, to observe a return Rt>R0. We will not need a market data for this question. Assume each day’s return is independent and identically distributed. Week 2: Stochastic processes Week 2 focused on stochastic processes and linear time series models. Here are my main takeaways from the week Stochastic processes are mathematical models that describe the evolution of a random variable over time. We learned about different types of stochastic processes, such as random walks and Brownian motion, and how to simulate them using computer programs. Linear time series models are a type of stochastic process that describe how a variable changes over time based on its past values. I found some problems quite interesting: Part C What is the lag-k auto-covariance, for three cases k=0, k=1 and k≥2.   Week 3: Model Estimations Week 3 focused on the important topics of model estimation, forecasts, and binomial trees. Here are my main takeaways from the week: Model estimation is the process of using data to estimate the parameters of a mathematical model. We learned about different techniques for model estimation, such as maximum likelihood estimation and Bayesian estimation. Forecasting is the process of using a mathematical model to make predictions about future values of a variable. I found some problems quite interesting: Part A Write a function MCpaths to generate a set of simulated price paths for a stock with lognormally distributed returns. Overall, my learning journey in Mathematical Methods for Quantitative Finance was an enriching experience that provided me with a deep understanding of the mathematical foundations of finance. I feel confident that the knowledge and skills that I gained through the program will be valuable in my future career in the finance industry. To view the full journey, please visit: Click here

My learning journey in Supply Chain Dynamics Throughout my learning journey in Financial Accounting, I have gained a deep understanding of the principles and practices that underlie the management and reporting of financial information. From my earliest encounters with the subject, I have been fascinated by the ways in which accounting provides a window into the inner workings of organizations and the economy as a whole. As I delved deeper into Financial Accounting, I began to appreciate the importance of accuracy, clarity, and transparency in financial reporting. Through coursework, reading, and practice exercises, I learned to construct and interpret financial statements, to assess the financial health of organizations, and to make informed decisions based on financial data. One of the most challenging aspects of my learning journey was mastering the various conventions and standards that govern accounting practices. From GAAP to IFRS to tax regulations, there seemed to be a dizzying array of rules and guidelines to follow. However, I found that with perseverance and attention to detail, I was able to navigate this complex landscape and produce accurate and reliable financial reports. Another important aspect of my learning journey was the development of critical thinking and problem-solving skills. In Financial Accounting, there are often multiple ways to approach a problem, and it was up to me to determine the best course of action based on the available information and the specific context. Through case studies and group projects, I learned to analyze financial data, identify trends and patterns, and make informed decisions based on sound reasoning.   Week 1: Balance Sheet Equation Week 1 of my financial accounting learning journey focused on the Balance Sheet Equation. The Balance Sheet Equation expresses the relationship between a company’s assets, liabilities, and owner’s equity. I learned how financial transactions affect the balance sheet equation, such as when a company purchases inventory on credit. We also discussed the different components of the balance sheet and how they relate to the balance sheet equation, including current and long-term assets and liabilities. I found some problems quite interesting: Part 1 At the beginning of a recent year, The Walt Disney Company’s liabilities equaled $32,940 million, during the year assets increased by $6,379 million, and year end assets equaled $81,242 million. Liabilities increased $171 million during the year. What is the beginning amounts for equity? Week 2: Accounts Receivable Week 2 focused on the subject of Accounts Receivable. Here are my main takeaways from the week Accounts Receivable is a type of asset that represents money owed to a company by its customers for goods or services sold on credit. We learned about the process of recording and managing Accounts Receivable, including how to create and send invoices to customers, and how to monitor and collect on outstanding balances. I found it interesting to learn about the different methods of accounting for bad debts and how they impact a company’s financial statements. I found some problems quite interesting: Part 2a Zolton Company analyzes its accounts receivable at December 31, 2017, and arrives at the following totals for its two customer groups along with the percentages for each customer group that are estimated to be uncollectible. Week 3: Inventory-LIFO In Week 3 of my financial accounting learning journey, we delved into the subject of Inventory and specifically the LIFO method. Here are my main takeaways from the week: Inventory is a type of asset that represents the goods that a company has on hand and available for sale. We learned about the LIFO method (last-in, first-out), which assumes that the most recently purchased inventory items are the first to be sold. I found it fascinating to learn about the impact of using the LIFO method on a company’s financial statements, particularly in times of inflation. I found some problems quite interesting: Part A Analyzing Inventories using LIFO Inventory Footnote. The footnote below is from 2017 10-K report of Whole Foods Market, Inc., a retail grocery chain. Overall, my learning journey in Financial Accounting has been challenging, rewarding, and eye-opening. It has equipped me with the skills and knowledge to understand and communicate financial information effectively, to make informed decisions based on data, and to contribute meaningfully to the success of organizations. I look forward to continuing my exploration of this fascinating and ever-evolving field. To view the full journey, please visit: Click here

My learning journey in Capstone in Statistics and Data Taking a Capstone course in Statistics and Data has been a challenging but rewarding experience for me. The course has provided me with a comprehensive understanding of statistical methods and their applications in data analysis.   At the beginning of the course, I was introduced to advanced statistical concepts such as multivariate analysis, time-series analysis, and Bayesian statistics. These concepts were new to me, and I found it challenging to understand the underlying theory and their practical applications. However, with the guidance of my instructor and the support of my peers, I was able to gain a deeper understanding of these concepts.   Throughout the course, I was required to apply my knowledge to real-world data sets. For example, in one assignment, I analyzed a dataset of hospital patients to investigate the relationship between patient satisfaction and hospital quality. I used multivariate analysis techniques such as regression and factor analysis to analyze the data and draw conclusions. Through this assignment, I learned how to identify and control for confounding variables and how to interpret the results of statistical analyses.   In another assignment, I analyzed a time-series dataset of the stock market to predict future market trends. I used time-series analysis techniques such as autoregression and moving averages to make predictions. This assignment taught me the importance of selecting appropriate models and how to validate and test these models to ensure their accuracy.   One of the highlights of the course was a group project where we had to design and execute our own research study. My group chose to investigate the impact of COVID-19 on mental health among college students. We collected data through surveys and used Bayesian statistics to analyze the results. Through this project, I gained valuable experience in designing research studies, collecting data, and using advanced statistical methods to analyze the results.   Throughout the course, I also learned how to use statistical software such as R and Python to analyze data. This was a valuable skill that will be useful in future research projects. I found some problems quite interesting:   Probability:   A fair coin is tossed repeatedly until 2 tails are observed. (The 2 tails need not be consecutive.) What is the probability that at least 2 heads were observed?     Solution:   The expectation of getting two consecutive tails is 6 tosses.     The last two will be tails.     So the question is equal to the probability of getting at least two heads in 4 tosses.     HHHH, HTTT, HHTT, HHHT, HTHT, TTTT, THHH, TTHH, TTTH, TTHT, HHTH, HTHH, THTT, TTHT, HTHT, THTH     P = p(>=2) = 9/16 = 0.5625       Statistics Theory:   Assume we don’t actually get to observe X1,…,Xn. instead, we only observe the random variables Yi = 1 (Xi <=32), which indicates if lightbulb I have failed before 32 thousand hours   Expectation of Y  Compute E [Y1]   E [Y1] = 1   Distribution of Y What kind of distribution does Y = 1 (Xi <=32) follow:   o   The same distribution that X follows o   Bernoulli o   Binomial o   Normal   Identifiability Is the parameter λ identifiable in this model? o   Yes o   No o   Not enough information to determine     Statistics:   In the problems in this exam, you will investigate various aspects of the following data set, and answer questions such as how different factors affect the health insurance cost of a person in the United States.   The description of the field is listed below: age: the age of the person sex: binary variable describing the sex of the person (1 if male, 0 if female) bmi: body mass index (BMI) given by the weight divided by the height squared, of the person (in units of kg/m2) children: number of children that person has smoker: binary variable indicating whether the person is a smoker (1) or not (0) region: the region where the person lives charge: the amount of insurance cost charged to the person (in US dollars)   Preliminaries Load the data set (insurance.csv) and answer the following questions about the data set 1.     How many observations are there in the data set?  Answer: 1338   2.     How many people are smokers? Answer: 274   3.   What is the sample mean charges of the insurance cost? (Enter numerical answers correct to the nearest integer) Answer: 13270         Machine Learning:   For each of the question below, select the best: option.   1 (a) Which of the following is/are difference(s) between supervised and unsupervised learning? (Choose all that apply.)  q Supervised learning uses linear models only, but unsupervised learning use both linear and non-linear models. q Supervised learning uses labelled data. Unsupervised learning uses unlabeled data q In supervised learning, we minimize or maximize an objective function. In unsupervised learning, we do not need to solve any minimization or maximization problems.     1 (b) What is the purpose of regularization? q To decrease training error q To increase testing error q To prevent overfitting q To tune hyperparameters Overall, taking Capstone in Statistics and Data has been a challenging but rewarding experience. I feel much more confident in my ability to analyze and interpret complex data sets, and I am excited to apply these skills in my future academic and professional endeavors. To view the full journey, please visit: Click here

My learning journey in Supply Chain Comprehensive Exam Taking a comprehensive exam covering all the key concepts from supply chain analytics, supply chain dynamics, supply chain fundamentals, supply chain technology and systems, and supply chain design was a challenging yet rewarding experience. It allowed me to bring together all the knowledge and skills I had acquired throughout the different courses and put them to the test. In preparation for the exam, I reviewed my notes from each of the courses and identified the key concepts and techniques that were most important. I also worked through practice problems and quizzes to assess my understanding of the material. During the exam, I was presented with a variety of scenarios and questions that required me to apply my knowledge from multiple courses to solve supply chain problems. The exam tested my ability to analyze data, design efficient supply chain networks, manage inventory, mitigate risks, and incorporate technology and systems into supply chain operations. One of the key takeaways from this comprehensive exam was the importance of a holistic approach to supply chain management. Each course provided a unique perspective on supply chain operations, and it was essential to understand how they all fit together. For example, the knowledge of supply chain dynamics helped me understand how supply chain disruptions could affect network optimization and inventory management. Similarly, the skills acquired in supply chain analytics enabled me to identify patterns and trends in the data that could be used to inform supply chain design decisions. I found some problems quite interesting: Problem 1Question 1You have been hited as the inventory manager of a department store company in US. Your first task is to set a continuous (S, Q) policy for the luxury handbag Texas 108. It has a cost of 120$ /item. The annual demand appears to be relatively stable at 245 items/year. The forecast error or RMSE for this product is 98 item/year. You can assume that this error is normally distributed around the mean. The annual holding rate is estimated at 20%. The estimated administrative cost to manage each new order for this product is $600. From the moment an order is place, it takes 4 weeks to receive the luxury handbags from the supplier.For simplicity, assume that there are 4 weeks in a month, 12 months in a year, and 48 weeks per year. Maintain this assumption through the whole problem.What would be the parameters of the optimal (S,Q) policy to provide a CSL of 80%? Problem 2Question 1After gaining your MicroMasters in SCM credential, you finally get your dream job in England as Procurement Manager at PickHero, the musical instruments seller preferred by all your favority bands. You oversee their most demand digital piano model name ‘Lucy 49’ and need to define the supplier to source it from, at the lowest total cost, for the next year.Your manufacturers are in New York and once you purchase them, you take ownership of the digital piano, being responsible for the logistics from the port of New York, to the port of Tilbury in London. Your team provide you the following data on the usual carrier:• PickHero purchanses the digital pianos in batches of 50(Q), PickHero pays$25000 for each piano• The Carrier charges you $1165 for shipping each batch (Ct).• Lead time from New York to Lpndon: 40 days.• The daily demand of this product in England is normally distributed, of 13 digital pianos with a standard deviation of 3.• Assume a CSL of 96% and a holding charge of 20% on annual basis.• Consider 350 days in a year all through this question.• PickHero policy is to NOT include the cost of transportation in product cost when calculating inventory cost, so you shouldn’t include transport costs as part of the value “c” when calculating inventory cost.• Do not round intermediate steps in your calculations. You should only round your answers as requested in each question.What is the annual pipeline inventory cost forthis product? Problem 3Yerbar Inc. manufactures and distributes baby food in the US. Traditionally, the company ships their products directly from 3 manufacturing facilities to 30 markets all over the country. Similarly, raw materials are shipped from 20 different suppliers to the manufacturing plants. The same is dipicted in the image below. Question 1Before making thefinal decision, you are asked to modify the existing mathematic model (suppliers->manufacturing plants->markets) to reflect the new situation (suppliers-> manugfacturing plants->distribution centers->markets). The current model minimixes the transportation cost while fulfilling markets demand and satisfying production capacity. The new model should optimize the flow of products to help decide how many distribution centers to open.What are some considerations you should include in your mathetical model?Check all that apply Overall, the comprehensive exam was a valuable learning experience that tested my knowledge, skills, and ability to apply them in real-world scenarios. It also demonstrated the importance of a well-rounded education in supply chain management, where each course provides a unique perspective and contributes to a holistic understanding of supply chain operations. I am grateful for this learning journey and look forward to applying this knowledge in my future career. To view the full journey, please visit: Click here

My learning journey in Supply Chain Analytics As someone who has always been interested in the world of business and logistics, I was drawn to the field of supply chain analytics early on in my academic career. Throughout my learning journey in this field, I have been exposed to a wide range of concepts, theories, and practices that have allowed me to develop a deep understanding of how businesses manage their supply chains in order to stay competitive and profitable. My journey began with a foundational understanding of the supply chain, which involves the movement of goods and services from their source to the end consumer. I learned about the various components of the supply chain, including sourcing, procurement, manufacturing, transportation, and distribution, and how they work together to create an efficient and effective process. From there, I began to explore the role of data and analytics in supply chain management. I learned about the importance of collecting and analyzing data from across the supply chain in order to identify inefficiencies, optimize processes, and make informed decisions. This involved learning about different data collection methods, such as RFID tags and sensors, as well as various tools and technologies used for data analysis, such as machine learning algorithms and predictive modeling. As my knowledge and understanding of supply chain analytics grew, I began to explore more complex topics, such as supply chain network optimization and risk management. I learned about the various factors that can impact the efficiency and effectiveness of a supply chain, including demand variability, supplier disruptions, and transportation constraints. Through case studies and simulations, I developed the skills needed to identify potential risks and develop contingency plans to mitigate them. Throughout my learning journey, I also had the opportunity to work with industry experts and practitioners through internships and research projects. This allowed me to gain real-world experience and apply my knowledge to real-world problems. I was able to see firsthand how businesses use supply chain analytics to improve their operations and achieve their goals. Week 1:  Introduction to supply chain analytics and its importance in supply chain management. Learned about the basics of data analysis and visualization. Discussed how data analytics can be used to improve supply chain performance. I found some problems quite interesting: Part 3: Special Applications KNIT’ting Industries also has innovative applications for the industry. One of their applications include developing color baths for knitting wools. The color baths are mixed using two different ingredients – the color and an agent that makes the color more UV stable. Other ingredients go into the color baths that are not important to the analysis. The mix of the two ingredients depends on the product type (knitting wool variety). Consider two products SKU M50 (Product A) and Z43 (Product B). You are asked to calculate the ‘amount of blue color bath’ for Product A and Product B. The following table indicates the amount of blue color (in liters) and UV agent (in liters) needed to prepare one litre of Product A and one litre of Product B. Suppose you have to prepare the blue color baths for Product A and Product B at the same time. You have 308 liters of blue color and 586 liters of UV agent available. How many liters of blue color bath for Product A and Product B can you prepare using the available inventory of the color blue and the UV agent? Week 2:  Explored the use of statistical methods in supply chain analytics. Learned about different types of statistical methods, such as regression analysis and hypothesis testing. Discussed how statistical methods can be used to solve supply chain problems. I found some problems quite interesting: Brinell & Rockwell Brinell & Rockwell (B&R) is a high-end manufacturer of cutting and drilling bits for lathe machines, drill presses, and other metal-working equipment. They pride themselves in producing only bits of the very best quality, guaranteed to last longer than the bits from any of their competitors. Brinell & Rockwell has a hotline where customers can call to report when a B&R bit fails before it is expected. At that time, Brinell & Rockwell will ship a replacement bit to the customer free of charge, and the customer will return the failed bit for examination. Back at the Quality Control Department of Brinell & Rockwell, a team of scientists and engineers – including several with PhDs in Metallurgy – will examine the failed bits under the microscope, X-Ray machines, and other diagnostics equipment to determine exactly what went wrong. Brinell & Rockwell’s quality control team keeps the failed bits that they have collected from customers in large glass containers, using one glass container per bit model. They have gathered thousands of failed drilling and cutting bits of 73 different models, including 62 models of drilling bits. These 62 models of drilling bits can be grouped into four families: In summary, they have collected failed bits of 73 different models, and they use one glass container for every bit model. Therefore, Brinell & Rockwell has in the lab a total of 73 different glass containers. Taking into account that drilling bits can be grouped into families, we know that 14 glass containers contain Diamond bits, 19 glass containers contain Tungsten bits, 20 glass containers contain Iridium bits, and 9 glass containers contain Adamantium bits. Part 2 Gloria Perez is the second worker to arrive to the lab. Following Brinell & Rockwell policy, she will pick two bits from randomly and independently selected containers. She is equally likely to select any of the containers. Even the container selected to pick up the first bit has the same chance of being chosen as any other container at the time she picks the second bit (meaning that Gloria could grab the same model of bit twice).What is the probability that Gloria will pick up at least one Iridium bit? Week 3:  Learned about forecasting techniques in supply chain analytics. Explored different types of forecasting methods, such

My learning journey in Data Analysis in Social Science As a social science student, I’ve always been interested in understanding human behavior and social phenomena through data analysis. Taking a course in Data Analysis for Social Science has allowed me to gain a deeper understanding of statistical methods and their applications in social science research.   At the beginning of the course, I was introduced to basic concepts such as descriptive statistics, probability distributions, and hypothesis testing. These concepts formed the foundation of the course, and I found it essential to master them before proceeding to more advanced topics.   As the course progressed, I was introduced to more complex statistical techniques such as regression analysis, factor analysis, and ANOVA. I learned how to use these methods to analyze social science data, and how to interpret the results in a meaningful way. For example, in one assignment, I analyzed a survey dataset to examine the relationship between social media usage and political engagement. Using regression analysis, I found that social media usage was positively correlated with political engagement.   One of the highlights of the course was a group project where we had to design and execute our own research study. We chose to investigate the impact of peer pressure on academic performance among high school students. We collected data through surveys and used regression analysis to analyze the results. Through this project, I gained valuable experience in designing research studies, collecting data, and analyzing results.   Throughout the course, I also learned how to use statistical software such as SPSS and R to analyze data. This was a valuable skill that will be useful in future research projects. I found some problems quite interesting:   Conditional Probability   Imagine you are on a gameshow and you’re given the choice of three doors: behind one door is a car and behind the other two doors are goats. Of course, you would want to win the car. You have the opportunity to pick a door (say door A), which is not opened. The host, who knows exactly what is behind each door, then opens another door (say door B) that has a goat behind it. Note that the host will not open the door that will reveal the car, which means that if the car is not behind door A, the host will open the door that has a goat behind it. Finally, the host ask you to decide whether to stay with your original choice or switch to the other unopened door. Overall, taking Data Analysis for Social Science has been a challenging but rewarding experience. I feel much more confident in my ability to analyze and interpret social science data, and I am excited to apply these skills in my future academic and professional endeavors. To view the full journey, please visit: Click here

My learning journey in Supply Chain Technology and Systems My learning journey in supply chain technology and systems has been a fascinating and enlightening experience. At the beginning of the course, I had a basic understanding of technology and systems used in the supply chain. However, through the weeks of study, I have gained a deep understanding of the role of technology and systems in the supply chain. I started by learning about the different types of supply chain technologies and systems, including enterprise resource planning (ERP), warehouse management systems (WMS), transportation management systems (TMS), and supply chain visibility systems. I was able to understand the functionalities and benefits of each of these systems and how they can be used to improve supply chain performance. Next, I learned about the importance of data analytics in the supply chain. I gained knowledge about different types of data analytics techniques, such as predictive analytics and prescriptive analytics, and how they can be used to optimize supply chain operations. I realized that accurate data analytics can be a powerful tool in supply chain management as it can help companies make informed decisions and improve their operations. I also learned about the role of automation and robotics in the supply chain. I was introduced to different types of automated systems, such as automated storage and retrieval systems (ASRS) and automated guided vehicles (AGVs). I was able to appreciate the significance of implementing automated systems in the supply chain to improve efficiency, accuracy, and cost-effectiveness. Furthermore, I learned about the importance of supply chain visibility and how it can be achieved through the use of technology and systems. I was able to understand the benefits of supply chain visibility, such as improved inventory management and better collaboration between supply chain partners. In addition, I learned about the importance of cybersecurity in the supply chain. I was introduced to different types of cybersecurity threats and how they can impact supply chain operations. I was able to appreciate the significance of implementing robust cybersecurity measures in the supply chain to protect against cyber-attacks and data breaches. Week 1:  Introduction to supply chain technology and systems and their role in supply chain management. Learned about different types of supply chain technologies and systems, such as ERP, WMS, TMS, and SCV. Discussed how to select the right technology and systems based on a company’s needs and goals. I found some problems quite interesting: Part 2 In crow’s foot notation, which relationship is indicated by the notation above? Week 2: Explored the role of data analytics in the supply chain. Learned about different types of data analytics techniques, such as predictive analytics and prescriptive analytics. Discussed how data analytics can be used to improve supply chain performance. I found some problems quite interesting: Graded Assignment 2 Remember that you cannot use Excel for this assignment. You may only use MySQL or SAS. Do not open this SQL file except with MySQL. You manage data for Screwy, a screw manufacturer in southern Oklahoma City. The Screwy staff aren’t too analytical and only maintain simple records of Orders in a single table. They also maintain two separate tables to keep track of their customers and part offerings. All of this is saved in a single database creatively named “Screwy”. Open this MySQL code and run it. This will create the database Screwy for you to work with.  Or Copy and paste the following SAS code (Step 1 -> Step 2) to your SAS on demand instance to import the database. You should only need to use Step 1 the first time you download the data. Step 2 will need to be run each time your reset your SAS session. The Orders table includes: Order_Number (Numeric) (PK) Customer_ID (Numeric) (FK) Part_Number (Numeric) (FK) Quantity (Numeric) Total_Cost (Numeric) (Total_Cost is in USD) The Parts table includes: Part_Number (Numeric) (PK) Part_Name (Text) The Customers table includes: Customer_ID (Numeric) (PK) Customer_Name (Text) Customer_Credit_Line (Numeric) (Credit line is in USD) PK- Primary Key, FK- Foreign Key Part 2 – SQL Coding How many orders has the customer named “Lows” placed in total (as recorded in Orders)? Week 3: Inventory Management Learned about the importance of automation and robotics in the supply chain. Explored different types of automated systems, such as ASRS and AGVs. Discussed how automation can be used to improve efficiency, accuracy, and cost-effectiveness. I found some problems quite interesting: Part 1 Harrison-Charles Electronics makes a wide variety of tiny electronic components known as ‘transistors’. Each transistor type that Harrison-Charles Electronics makes is identified by a unique 5-digit alpha-numeric SKU identifier. APDL4, SORYQ, and G2U0H are three examples of Harrison-Charles Electronics’s five-digit SKU identifiers. Which MySQL core data type is most appropriate for these SKUs? Select a response below Part 2: Unsupervised Machine Learning Imagine that you are applying an unsupervised machine learning algorithm to a data set containing 4 attributes, (1) information on the location, (2) the length, (3) the number of customers served, and (4) the total delivery cost for each of your company’s 10,000 global delivery routes. Which insight below should you expect? Select the best answer. Overall, my learning journey in supply chain technology and systems has been a valuable experience. I am now equipped with the knowledge and skills necessary to design and implement effective supply chain technologies and systems. I am confident that I can apply this knowledge in the real world to help companies optimize their supply chain operations and achieve their business goals. To view the full journey, please visit: Click here

My learning journey in Probability (Uncertainty and Data) My learning journey in Probability (Uncertainty and Data)   Probability is a branch of mathematics that deals with uncertainty and randomness. It plays an essential role in various fields, such as science, engineering, finance, and social sciences. In my learning journey of Probability (Uncertainty and Data), I have gained valuable knowledge and skills in probability theory and its applications in data analysis.   In the course, I learned about the fundamentals of probability, including the definition of probability, probability axioms, and the concept of random variables. I also learned about probability distributions, such as the binomial, Poisson, and normal distributions, and their applications in data analysis. The course provided an overview of hypothesis testing and confidence intervals, which are crucial in statistical inference.   One of the most valuable lessons I learned in the course was the importance of understanding the underlying assumptions of probability models. I gained an understanding of the limitations of probability models and how to test their assumptions to ensure their validity. I also learned about the central limit theorem and its applications in sample statistics, which helped me understand the concepts of standard error and sampling distribution.   The course also covered the use of probability in decision making, including decision trees and expected value. I gained an understanding of the principles of decision theory and how to apply them in real-world scenarios.     Week 1: Probability models and axioms   Probability Models: The course provides an introduction to probability models, including discrete and continuous models. The module covers the definition of probability, sample spaces, events, and probability axioms. The module also covers probability distributions such as the binomial, Poisson, and normal distributions, and their applications in data analysis. Axiomatic Approach: The module explains the axiomatic approach to probability theory, which is the mathematical framework that underlies the theory of probability. The module covers the three axioms of probability: non-negativity, additivity, and normalization. The module also discusses how these axioms are used to derive important properties of probability, such as conditional probability, independence, and Bayes’ theorem. Applications in Data Analysis: The module highlights the importance of probability theory in data analysis and its applications in various fields, such as finance, engineering, and social sciences. The module shows how probability models are used to model real-world phenomena, such as coin tosses, dice rolls, and insurance claims. The module also covers the use of probability in decision making, including decision trees and expected value. I found some problems quite interesting:     Set operations and probabilities Week 2: Conditioning and independence   Conditional Probability: The module covers conditional probability, which is the probability of an event occurring given that another event has already occurred. The module explains how to calculate conditional probability using the multiplication rule and Bayes’ theorem. It also shows how to use conditional probability to solve real-world problems, such as medical diagnosis and weather forecasting. Independence: The module explains the concept of independence between events, which occurs when the occurrence of one event does not affect the probability of the other event occurring. The module covers the definition of independence, the relationship between independence and conditional probability, and how to determine whether two events are independent. Applications in Data Analysis: The module highlights the importance of conditioning and independence in data analysis and its applications in various fields, such as finance, engineering, and social sciences. The module shows how to use probability models to model real-world phenomena that depend on multiple factors. The module also covers the use of probability in decision making, including decision trees and expected value. Overall, the module provides a solid foundation for understanding how to use probability models to analyze and solve problems that involve multiple factors. I found some problems quite interesting:       Two five-sided dice   You roll two five-sided dice. The sides of each die are numbered from 1 to 5. The dice are “fair” (all sides are equally likely), and the two die rolls are independent. Overall, my learning journey in Probability (Uncertainty and Data) has provided me with a solid foundation in probability theory and its applications in data analysis. I am confident that the knowledge and skills I gained in the course will be valuable in my future endeavors, whether in academia or industry. To view the full journey, please visit: Click here

My learning journey in Supply Chain Design My learning journey in supply chain design has been an exciting and insightful experience. At the beginning of the course, I had little knowledge of the complexities involved in designing an efficient supply chain. However, through the weeks of study, I have gained valuable insights into the various components that make up a successful supply chain. I started by learning about the different types of supply chain networks and their advantages and disadvantages. This allowed me to understand how different supply chain designs can impact a company’s performance. I was able to recognize the importance of selecting the right network design based on the specific needs of a company. Next, I learned about the importance of demand planning and how it relates to supply chain management. I gained knowledge about the different types of forecasting techniques and how to apply them to supply chain management. I realized that accurate forecasting can be a powerful tool in supply chain design as it can help a company optimize production and minimize costs. I also learned about the different types of production planning strategies and how they can be used to optimize supply chain performance. I was introduced to the importance of materials management and how it relates to inventory management. I was able to appreciate the significance of managing inventory effectively and the impact it has on a company’s cash flow. Furthermore, I learned about the importance of logistics and transportation in the supply chain. I was able to review different modes of transportation and their costs and benefits. I learned how to select the right mode of transportation based on the specific needs of a company. In addition, I learned about the role of warehousing and distribution in the supply chain. I was able to appreciate the importance of selecting the right type of warehouse operations and their key performance indicators. I gained knowledge about different warehouse management strategies and how to optimize them. Week 1: Introduction to Supply Chain Design Introduction to supply chain design and the importance of a well-designed supply chain. Learned about the different types of supply chain networks and their advantages and disadvantages. Discussed how to select the right supply chain network based on a company’s needs and goals. I found some problems quite interesting: Graded Assignment 1:  Locky Locke Inc. You have finally been promoted to Senior Manager for the nuts and bolts category at Locky Locke Inc., a manufacturer with a large demand for bolts at its three (3) factories. You have surveyed the market and found four (4) suppliers for a certain steady selling SKU, ZA27R46.  Your factories’ weekly demand for bolts are: Factory 1: 9,500 cases/week Factory 2: 6,500 cases/week Factory 3: 17,000 cases/week Each of the four suppliers can provide the ZA27R46, but they differ in unit price, weekly capacity, and distance to Locky Locke Inc. three factories. The unit prices and the weekly capacity for each of the four suppliers are shown below:  Supplier 1:  U.S. Ainbolt  – Unit cost:  2.50 $/case  Capacity  9,500 cases/week Supplier 2:  Der Bolt, Thun  – Unit cost:  3.20  $/case  Capacity  10,000 cases/week Supplier 3: Li Tningbolt –  Unit cost:  1.90  $/case  Capacity  7,000 cases/week Supplier 4:  M. Bolton –   Unit cost:  2.80  $/case  Capacity  9,000 cases/week You are trying to plan the weekly flow of bolts from these suppliers to your factories. Based on the current contract with your transportation carrier, the unit transportation cost for a case of bolts is $0.05 per mile from any of the suppliers to any of the factories.  The distances between the four suppliers and the three factories are given in the table below. Part 4 You want to discuss the situation with the general manager of Factory 2. How much is the difference between Factory 2’s total landed cost per week in Part 1 compared to the total landed cost per week from Part 3? Part 5 1.0/1.0 point (graded)You decide to re-negotiate with Supplier 1 (U.S. Ainbolt) to increase their capacity. They agree to increase it to 14,000 cases per week. How many cases per week does Supplier 1 (U.S. Ainbolt) send to Factory 2 in the new optimal solution?Input your answer in integers. Week 2:  Explored demand planning and forecasting techniques used in supply chain design. Learned about the importance of accurate demand planning in supply chain design. Discussed different forecasting techniques and how they can be used to make supply chain design decisions. I found some problems quite interesting: Graded Assignment 2:  Colombian Premium Coffee A traditional Colombian Coffee manufacturer called CPC has hired your services as a supply chain consultor to analyze the distribution of its brand-new premium product, named Coffee Indeed ®. This product is 1 package containing 1-kilogram of ground and roasted Arabica coffee to be commercialized and distributed in three cities: Bogotá (DC 1), Medellín (DC 2), and Cali (DC 3). Due to governmental tax benefits, the company roasts and packs the product in two factories: Barranquilla (Plant 1) and Santander (Plant 2). CPC hires carriers to transport the products from the plants to each of the three distribution centers (DCs). Each DC has a different demand, and each plant has a different production capacity. Demand: Bogotá: 345,000 units per week Medellín: 263,000 units per week Cali: 172,000 units per week Plant Capacities: Barranquilla: 610,000 units per week Santander: 350,000 units per week Part 3 You are informed that due to a severe fire in plant 2, some packaging machinery was lost. To keep both plants producing both types of products, the maintenance manager Ms. Tiffany Rivera has redistributed the remaining machinery in both plants. This move enabled both plants with the flexibility of producing both types of products; however, it restricted the production capacities as described below: Demand: Barranquilla (Plant 1) cannot produce more than 300,000 500-grams Coffee Indeed ® (product 2) per week. Santander (Plant 2) cannot produce more than 250,000 1-kilogram Coffee Indeed ® (product 1) per week. Create a linear program that