LDR 5301, Methods of Analysis for Business Operations 1

Course Learning Outcomes for Unit I Upon completion of this unit, students should be able to:

1. Differentiate the steps of the quantitative analysis approach. 1.1 Explain the quantitative analysis approach. 1.2 Perform a profit analysis and breakeven analysis. 1.3 Determine an action plan for a company using the quantitative analysis approach.


Learning Outcomes Learning Activity

1.1 Unit Lesson Chapter 1 Unit I Case Study


Unit Lesson Chapter 1 Video Segment: Breakeven Analysis Unit I Case Study

1.3 Unit Lesson Chapter 1 Unit I Case Study

Required Unit Resources Chapter 1: Introduction to Quantitative Analysis In order to access the following resource, click the link below. TV Choice Ltd. (Producer). (2011). Breakeven analysis (Segment 7 of 9) [Video]. In Accounting & finance

clips 1: Accounting, forecasting, and breakeve. Films on Demand. https://libraryresources.columbiasouthern.edu/login?auth=CAS&url=https://fod.infobase.com/PortalPlaylists.aspx?wID=273866&xtid=128753&loid=450485

The transcript for this video can be found by clicking on “Transcript” in the gray bar to the right of the video in the Films on Demand database. Unit Lesson Today’s business world is globally connected. From Canada to Mexico, and from China to the United States, each country is connected to others in many ways. Imagine the business decisions that are made on a daily basis from every type of organization—finance, manufacturing, video production, automobile production, retail, fashion, gambling, and food services. Literally, there are hundreds of thousands of decisions that must be made every day in these organizations regarding cost analyses, debt analyses, return-on-investment analyses, breakeven analyses, trend analyses, correlations, consumer preferences and change to products, and market share analyses. Also, think about the entertainment industry with regard to professional and college sports (football, basketball, hockey, soccer, and more). Both professional and college sports use business analytics from a business standpoint and as a strategy application of the game. Data are recorded regarding where points are

UNIT I STUDY GUIDE Introduction to Quantitative Analysis

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scored, the distance from which they were scored, what play worked in what given defensive or offensive situation. There is much effort put into major league and college sports with data gathering and analysis. Think about this application to minor league baseball: two scouts are sitting behind home plate, each with a notebook, pencil, and speed gun. They record the speed of each pitch, the call by the umpire, and if there are runners in position. Why? They are gathering data for future utilization of that pitcher by the parent major league team. So, where do leaders and executives begin? It starts with the comprehension of managerial science. They can begin with the application of quantitative analysis, as mentioned above, with qualitative analysis, which includes things such as weather, federal and state legislation, and economic disruptors that create positive change with new product and technological developments. All of these operations require data and information. This is referred to as the application of management science. Data drive the decision-making process because there is no room for guesswork or emotions to take part in quantitative analysis (Render et al., 2018). When corporate leaders look at data, it is usually in the form of numbers with regard to cost, revenue generated, profit, and market share. To have a good grasp on decision-making, leaders use a business analytics approach. Render et al. (2018) have broken down this process into three major areas:

• descriptive analytics: statistical methods mean, standard deviations, mode; • predictive analytics: decision trees, regression models, forecasting, project scheduling, and waiting

line models such as McDonald’s, Chick-fil-A, and bank drive-through operations; and • prescriptive analytics: inventory modeling, transportation modeling.

So, what do they do? What is their purpose? How do they help a business leader use both quantitative and qualitative analysis? Descriptive analytics involves the study and consolidation of historical data for a business and industry (Render et al., 2018). An example of this would be in the automotive industry. How many cars were sold last year? What models sold best? What were the regional and world economic positions (growth, recession)?

Predictive analytics relies on forecasting to determine future outcomes based on patterns from past data (Render et al., 2018). A few examples of predictive analytics are business operations in marketing campaigns conducted by determining customer purchase patterns based on billboard ads, television ads, and radio ads. In addition, predictive analysis can be used to better manage a company’s inventory based on seasonality and weather changes. One more example is insurance companies’ analyses of data when predicting insurance rates for their drivers based on past driving records, age, accidents, and citations. The final analytic is prescriptive. According to Render et al. (2018), this analytic focuses on using optimization models such as linear programming, transportation modeling, and inventory control through economic order quantities. The next time you are

driving on the interstate highway in your area or on a main traffic route in your city, look at the trucks that are

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on the road. You will likely see a Walmart, Target, Publix, or other such truck. Companies use transportation modeling for deliveries to maximize time with route and fuel consumption savings. A great example of prescriptive analytics is from Google’s self-driving, autonomous car called the Waymo. John Krafcik, Waymo CEO, began work on Waymo in 2009 under the ownership of Google (Waymo, n.d.; Wakabayashi, 2020). According to Wakabayashi (2020), Google spun out the project in 2016, after vehicles were driven more than 20 million miles on public roads and more than 10 billion miles in computer simulation since 2009. Given Google’s work on this project, other companies have followed their lead because that is what it takes in business decision-making. Decision makers use and employ business analytics as described above to comprehend the needs of the consumer and the market. This is done through data gathering, statistics, and models that assist in forecasting the future needs and sales. Now, all the big names in cars are engaged in this technological development of designing autonomous cars for the consumer. Your exposure to the three types of business analytics will expand your perspective on what types of tools and formulas are used for each analytic, thereby, helping you make a better decision on what quantitative technique works best. Now that you have an understanding of what business analytics are and how they are used, let’s see how they are integrated into the quantitative analysis approach. Render et al. (2018) do a great job of providing a quality diagram on page 3 in the textbook. If you have taken any courses in business management, the quantitative analysis approach closely follows the business management decision-making model. Let’s step through Render et al.’s (2018) quantitative analysis approach. Defining the Problem This could be very easy; then again, it could be difficult because many problems may exist. Leaders and managers need to focus on the operation at hand. What is causing the disruption, lack of sales, or delays? Developing the Model Simply put, this is determining whether to use a mathematical model for solving the problem (more than likely yes), or it could be a physical model on building a bridge. Regardless of the type, there will be data, variables, and parameters that will be measured. Acquiring the Data It is now time to gather data that pertain to the problem at hand. Depending on the problem’s breadth and depth, the data gathering could be easy or difficult. There is no set number of data points; it depends on the problem to be solved. Developing a Solution You have defined your problem, you have determined the correct model to use, and you have gathered your data. You now load the data into the equation or computer algorithm selected and let the program crunch the numbers for you. Once the data application is completed, leaders, managers, and executives will review the data, determine the parameters, and do a few things:

• accept the data, • reject the data, or • tweak the data by broadening the data gathering and parameters set.

Testing the Solution Depending on the outcome in the previous step above when the final solution is selected, it will need to be tested. This usually takes place in a simulation or a test marketing application. A great example is the

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marketing practices of the cola wars in the 1970s (predictive analysis) where consumers were put to a taste test to determine what they liked better, Coca-Cola or Pepsi Cola. The catch was that the survey was a blind taste test (Little, 2020). In the example above, hundreds of thousands of dollars were spent by each company to promote their product. The follow-up in the 1980s was when Coca-Cola did away with “Old Coke” and introduced “New Coke” (Little, 2020). This is, again, business analytics in motion. Analyzing the Results Here is where decision makers earn their worth. They have to make a decision based on the final evidence provided. It could mean many things. For example, a company might decide to spend a million dollars in LED lights to save the company overhead costs of $4 million over 2 1/2 years in electrical costs. The decision could even be to gain more shelf space in supermarkets, drug stores, or gas stations. A company could change their product taste and name by creating a new marketing hype to gain market share. The costs of this could be in the billions of dollars. Implementing the Results This is putting the plan into action. Of course, after the data is analyzed, the numbers do not do the work. Leadership, human capital, strategies, planning, resources, and timelines must be put into place to implement, execute, and measure the results in the real world.

Putting the Data and Decision-Making Into Action This is where the mathematical model you selected takes action. There are two simple business questions that are always at the forefront of decision-making:

• How much profit will we make? • What is our breakeven?

Your assignment this week will allow you to compute the breakeven and profit analysis for the BAT Car Company. The assignment will expose you to the business analytics model, as well as the decision-making model in the textbook, and allow you to apply what you have learned in this lesson.


Little, B. (2020, March 12). How the ‘blood feud’ between Coke and Pepsi escalated during the 1980s cola wars: History. https://www.history.com/news/cola-wars-pepsi-new-coke-failure

Render, B., Stair, R. M., Jr., Hanna, M. E., & Hale, T. S. (2018). Quantitative analysis for management (13th

ed.). Pearson. https://online.vitalsource.com/#/books/9780134518558 Wakabayashi, D. (2020, March 2). Waymo includes outsiders in $2.25 billion investment round. The New

York Times. https://www.nytimes.com/2020/03/02/technology/waymo-outside-investors.html Waymo. (n.d.). Our journey. https://waymo.com/journey/ Suggested Unit Resources In order to access the following resources, click the links below. The Chapter 1 PowerPoint Presentation will summarize and reinforce the information from this chapter in your textbook. You can also view a PDF of the Chapter 1 presentation.

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Learning Activities (Nongraded) Nongraded Learning Activities are provided to aid students in their course of study. You do not have to submit them. If you have questions, contact your instructor for further guidance and information. For an overview of the chapter equations, read the Key Equations on page 17 of the textbook. Complete the Self-Test on pages 17–18 of your textbook to test your knowledge of concepts and terms in this unit. Use the key in the back of the book in Appendix H to check your answers. Complete the Unit I Practice Problem Activity to practice the math skills presented in this unit. These concepts will be used in your unit assignment, so practicing them here will help you to successfully complete that assignment. You can also view a PDF of the Unit I Practice Problem Activity.

  • Course Learning Outcomes for Unit I
  • Required Unit Resources
  • Unit Lesson
    • Defining the Problem
    • Developing the Model
    • Acquiring the Data
    • Developing a Solution
    • Testing the Solution
    • Analyzing the Results
    • Implementing the Results
    • Putting the Data and Decision-Making Into Action
    • References
  • Suggested Unit Resources
  • Learning Activities (Nongraded)