Assignment Overview You are the lead consultant for the Diligent Consulting Group. It is mid-October. One of your top clients, Sunshine Floor Barn, has just closed the books for the first three quarters of the year (January through September). Sunshine Floor Barn requests that you analyze the sales performance of its 5 product lines over this 3-quarter period. From past consulting work you have done for the company, you know that Sunshine Floor Barn has 4 regions and 18 total store locations. Each Regional Manager at the company has compiled the data for his/her region. The raw data provided consists of the sales revenue for each of the 5 premium flooring lines for all 4 regions and 18 locations for the first three quarters of the current year. Case Assignment The data have been provided in list format. Generate a Pivot Table Report with Charts. Use the Pivot Table and Charts to analyze the data. Following your in-depth analysis of the data, write a report to Sunshine Floor Barn in which you discuss and analyze the data, and make appropriate recommendations relative to how Sunshine Floor Barn should improve its sales performance going forward. Assignment Expectations Data: To begin, download the list data here: Data chart for BUS520 Case 3 Excel Analysis: Provide accurate and complete Excel analysis (Pivot Table with Charts). Written report: • Length requirement: 4–5 pages minimum (not including Cover and Reference pages). NOTE: You must have 4–5 pages of written discussion and analysis. This means you should avoid use of tables and charts as “space fillers.” • • • • • • • • • • Provide a brief introduction to/background of the problem. Using the Pivot Table and Pivot Charts, discuss and analyze the data, noting key highs and lows, trends, etc. Include charts from your Pivot Table to support your written analysis. (Please do not use charts as “space fillers.” Instead, use them strategically to support your written analysis.) In a “Recommendations” section, give clear, specific, and meaningful recommendations that Sunshine Floor Barn should use to improve overall company sales. Be sure to consider highs, lows, and trends in the data. Which cities are the highest performers? Lowest? Which regions and quarter had the highest sales? Lowest sales? Consider what may be driving the numbers: Poor marketing? Outstanding marketing strategies? Inventory management? Seasonal sales? Other? There are innumerable possibilities. Your role is to reflect on the data, and ultimately, to use the data to give useful recommendations. Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12 pt. type size. Have an introduction at the beginning to introduce the topics and use keywords as headings to organize the report. Avoid redundancy and general statements such as "All organizations exist to make a profit." Make every sentence count. Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if absolutely necessary, should rarely exceed five words. Upload both your written report and Excel file to the Case 3 Dropbox. PIVOT TABLE AND MULTI-ATTRIBUTE DECISION ANALYSIS Modular Learning Outcomes Upon successful completion of this module, the student will be able to satisfy the following outcomes: • o o o o • o o o o • o Case Explain when to use a Pivot Table. Enter data and set up the structure of a Pivot Table and Pivot Graph. Manipulate a Pivot Table and Graph, and change the pivot factors. Discuss the results of a Pivot Table analysis in a business report. SLP Explain when to use Multi-Attribute Decision Analysis. Identify the various aspects of a MADM and set up the decision table, (i.e., criteria, alternatives, raw data/outcomes, utilities, and criteria weights). Evaluate the alternatives using MADM. Perform sensitivity analysis to validate and verify the best choice. Discussion Discuss the concept of bounded rationality and how it can affect decision making. Module Overview Businesses must survive in the fast-paced climate of the 21st century. Managers and leaders must make decisions about what to do now so that their businesses can survive (and even grow) into the future. But the future is uncertain, and at best, managers may be able to estimate with some degree of accuracy what the future might be. Data and information are a necessary part of this process. Most data is about the past and the past is the best predictor of the future. This course is about using data to understand the past, to forecast the future, and to make the best informed decisions to move ahead. This module teaches how to develop and use an Excel Pivot Table for data analysis and how to develop a decision table for making a decision under assumed certainty. Some key terms and definitions: Decision Making—the process of determining a set of feasible choices and choosing the one that is judged to provide the best outcome for the specified goal. Data—factual information (as measurements or statistics) used as a basis for reasoning, discussion, or calculation; information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful; and, information in numerical form that can be digitally transmitted or processed (www.merriam-webster.com/dictionary/data) Note that the definition of data above indicates that data and information are synonymous. Some experts believe that for data to be considered information, it must be analyzed and interpreted for some useful purpose. This course also takes this approach and will provide data and analysis that mostly resembles the third definition. Data analysis and decision making are accomplished by people, often with the aid of computing devices and software or information technology (IT). While IT has progressed in the last 25 years to a phenomenal state of processing speed and storage capability, humans are ultimately needed to direct IT toward specific goals and decision making, and yet the human is bounded in the ability to make rational decisions. This idea, called bounded rationality, was first introduced in 1957 by Herbert Simon, Nobel in economics in 1978. This is a key concept in this course. Bounded Rationality—humans, either individually or in a group, are limited in their rationality and ability to make decisions due to limited information, limited cognitive processing ability and limited time. This definition now requires that we define rationality. Rationality—the normative concept of human thinking, reasoning, and cognitive processing that will determine the optimal choice or behavior to achieve a specific outcome or goal. In other words, humans, being constrained by bounded rationality, are not capable of making optimal decisions, at least not very often. Only if the decision situation provides all finite and identifiable options with all specified and finite information and the time allotted is sufficient can the decision be considered truly optimal. These kinds of decisions are few, very few, in the real world. The kind of usual decision making that takes place is considered to be satisficing (another term coined by Simon) and usually done using heuristic methods. These and other topics are covered in this course. Pivot Table—a table of data in rows and columns in an Excel spreadsheet that organizes the data in such a way that it can be viewed from several different viewpoints. PIVOT TABLE AND MULTI-ATTRIBUTE DECISION ANALYSIS Required Reading One of the most useful tools in Excel that you can use for data analysis is the Pivot Table also called the Pivot Table Report. This is an interactive way to quickly summarize large amounts of data. You can query large amounts of data, do subtotaling, expand and collapse levels of data to focus your results, move rows to columns and vice versa (pivoting), and filter and sort the data. In order to create a Pivot Table, you must first have a source of data organized in List format. It is easy to create an Excel table in list format with column labels in the first row. Each cell in subsequent rows should contain data appropriate to its column heading. There should not be any blank rows or columns within the data of interest. Excel uses your column labels for the field names in the report. View a Pivot Table Report. Before you get into creating your own Pivot Table, use this link and download this Excel file that has a Pivot Table using an Excel table in list format. Pivot Table-Example.xlsx Watch the videos that show you how to do Pivot Table: https://support.office.com/en-us/article/Create-a-PivotTable-andanalyze-your-data-7810597d-0837-41f7-96995911aa282760?ui=en-US&rs=en-IN&ad=IN PRACTICE: Now create your own Pivot Table using this Excel file that has the data in an organized manner. • • • • • • Download this Excel file: Pivot Table-Practice Data.xlsx Organize the data into a data table in list format. Create the Pivot Table (and the Pivot Chart). Go to Quiz Tab and answer the questions. Check your Pivot Table and answers by looking at the Solution Tab in the spreadsheet. Then you should be ready to do the Case 3 Assignment. Multi-Attribute Decision Making (MADM) This decision method assumes certainty. In other words, there are no probabilities of future states to determine. And the data and costs are assumed to be known and accurate. The most common type of decision is a preference decision. The decision maker wants to determine which of several options is the best to achieve some set of goals or fulfill a set of criteria or attributes. Common examples include deciding which car to buy, which house to buy, which apartment to rent, where to go on vacation, which machine to buy for production, which supplier to use, and many more. The decision process consists of the Decision Maker (DM) identifying the need for some object (or person) or concept that he/she currently does not have. Or it could be to replace some object that has outlived its usefulness, such as replacing a copying machine. The decision consists of determining a set of criteria that the object must have or meet with some level of satisfaction. For example, when buying a car, the DM might consider its price, color, fuel efficiency, safety rating, warranty, comfort/ride, among other factors. This process is important because it provides and defines the performance and outputs that the user will expect. The step for this decision is to search for and find the choices (alternatives or options) to be considered. There may be one criteria that is used as a filter, such as price. In the car buying example, the DM may have a price range that fits into his/her budget. They may also have a preference of Make, such as Chevrolet or Ford. But this second preference may actually be a bias and could limit the choices and exclude some viable choices. The search for alternatives usually generates choices in a serial manner. Specific alternatives are identified one at a time. It is possible to find several choices at nearly the same time, for example, being shown several different makes and models of cars at one dealership during a single trip. The DM now has identified the choice options as well as the criteria to be fulfilled. Each alternative will fulfill each criterion at some level of value. The DM must collect this data and put it into a table for easy analysis. Here is an example of a decision table for purchasing a car. Price Fuel Effic. (MPG) Safety Rating Comfort/Ride Color Prexel $22,000 32 8.5 6.7 Red Criston $25,000 38 8.2 7.9 Black Thrush $27,000 35 9.6 9.2 Blue Note that the names are fictitious. The safety ratings and comfort/ride ratings could easily be obtained from a car buyer magazine. Price and MPG are from the dealerships. We are using only 3 options and 4 criteria for example purposes. The colors are those of cars that are in stock. You could order a car of your preferred color, but you do not want to wait 6 – 8 weeks for delivery. As you look at this table, you will see that each criterion is measured differently than the others. How do you compare price with MPG, with Safety rating, and with color? To do this, you need a common metric and one that each criterion’s value can be converted easily. This metric is Utility, which is scaled between 0 and 1. Utility of 0 has no or minimal value and utility of 1 is the maximum. Let’s take a minute to get rigorous in our model and use some shorthand notation. Let Ci be the ith criterion. We have five criteria and we number them from 1 to 5. So Price is C1 and Color is C5. Criterion Ci, i = 1 to n, and n =5. Let Aj be the jth alternative. We have three alternative and we number them from 1 to 3. A1 is the Prexel, A2 is the Criston, and A3 is the Thrush. Alternative Aj, j = 1 to m, and m = 3. In the table we have the values of each alternative j for each criteria i and we term this vij, the value of the ith criteria for the jth alternative. Value vij, i = 1 to n, and j = 1 to m. But we need to convert the values, vij to utilities, uij, so that they are all measured on the same metric. This will allow us to compare alternatives. To convert from raw values to utilities can be done in several different ways. The easiest way is to use a linear transformation. Take fuel efficiency. There is 38, 35, and 32. The maximum value is assigned a Utility value of 1 and the minimum value is assigned a Utility value of 0. The intermediate values are assigned a proportionate level using a linear translation. So Utility of 38, or U(38) = 1 and U(32) = 0. Or in general, U(Max value) = 1, and U(Min value) = 0. But what about U(35) = ?? To find U(35), use a translation formula: U(X) = (X – Min value) / (Max value – Min value) In our example, U(35) = (35 – 32) / (38 – 32) = 3 / 6 = 0.5 This formula for U(X) is for a criterion when More is Better. You can use this to convert the Safety Rating and the Comfort/Ride rating, because More is Better. But for a criterion where Less is Better, like Price, you need to use this formula: U(Max value) = 0, and U(Min value) = 1. (remember, Less is Better). U(X) = (Max value – X) / (Max value – Min Value). In our example, on the Price criterion: U(27000) = 0, U(22000) = 1, and U(25000) = (27 – 25) / (27 – 22) = 0.4 But what about criterion that are subjective or do not have any numeric raw values, like color? The utility scores are determined strictly by personal preference. Which of the three colors is most preferred and which is least preferred, and which are in the middle? In our example, let’s say that Red is most preferred and Black is least preferred, making Blue with a medium level preference. U(Red) = 1, U(Black) = 0, and U(Blue) = ?? Where does Blue fit on a scale of 0 to 1? This is a subjective rating. You can choose any score. In our example the DM prefers Blue to be 0.7, closer to Red than to Black. Now we have our utilities, uij. Here is the decision table with utility scores. Price Fuel Effic. (MPG) Safety Rating Comfort/Ride Color Prexel 1 0 0.21 0 1 Criston 0.40 1 0 0.48 0 Thrush 0 0.50 1 1 0.70 There is one more step. Each criterion must be weighted according to its relative importance to the decision or the overall performance or results. These weights are decimals or percent and must total to 1.0. We will let wi denote the numerical weight for each of the i criterion. And we use this formula to insure the correct amount of total weight. Sum(wi) = 1.0. How does the DM determine these weights? This is subjective as well. The first step is to rank order the criteria with 1 most important and N as the least important (here N = 5). In our example, the DM thinks that Fuel Efficiency and Safety Rating are the most important, but not sure which is first. Then Comfort/Ride. Finally, the DM thinks that maybe price and color are last. The rank ordering is: [C2 and C3], C4, [C1 and C5]. The DM decides to use the following weights, at least as a starting point. Then he/she can do some sensitivity analysis and adjust them a bit. C1 = 0.1 C2 = 0.3 C3 = 0.3 C4 = 0.2 C5 = 0.1 Note that these weights total to 1.0. The final step is to multiply the weights times each utility score for each alternative and sum these to get a total score for each alternative. Using our notation, the formula for Total Score, Tj , for the jthalternative is: Tj = Sum[(wi)(uij)], i = 1 to n (n = 5) For example, the total score for the Prexel is calculated as: (0.1)(1) + (0.3)(0) + (0.3)(0.21) + (0.2)(0) + (0.1)(1) = 0.263. This is the table with all alternatives and their Total Scores using the weights above. Weight 10% 30% 30% 20% 10% 100% Criterion Price Fuel Effic. (MPG) Safety Rating Comfort/Ride Color Total Score Prexel 1 0 0.21 0 1 0.263 Criston 0.4 1 0 0.48 0 0.436 Thrush 0 0.5 1 1 0.7 0.720 We should not be surprised that that Thrush is the preferred choice. It is scored the best on two of the criteria and 2nd best on two others. And is only scored the worst on one criteria which was given the lowest weight of 0.1 The Prexel scored best on two criteria, but these were the least important in weight and it also scored the worst on two criteria. The question now to consider, “do these weights reflect the true preference and importance to the DM? If they were adjusted up or down by some degree, would it change the overall total score and hence the decision?” This process is Sensitivity Analysis. For a general understanding of decision making and the process, watch the following videos: http://permalink.fliqz.com/aspx/permalink.aspx?at=d55a346d20aa46 6d84ffd99b15f7d128&a=5fae3cf0f1624f39b0341263a6541ea0 http://permalink.fliqz.com/aspx/permalink.aspx?at=2cc5262c22b543 ec928bec51be6c23f6&a=5fae3cf0f1624f39b0341263a6541ea0 Download this Excel file with the Car Decision Example: BUS520-Module 3 SLP Car Example.xlsx PRACTICE EXERCISE: Now that you have seen how to develop a Decision table, try this Practice Exercise. Practice Exercise Scenario: Hiring a key person. You are hiring a person for a top position in your company. You have narrowed the field down to the top four candidates. You want to use multi-attribute decision analysis. You have determined that there are four decision criteria that are most important. Four Attributes (criteria): Salary, experience, education, leadership personality. The salary number is the amount that the candidate said he/she needed to accept the job. Experience is based on number of years of direct experience, plus an add-on for other related experience that is equal to about half of the years. Education is the level of degree plus any other training or certifications. You have decided to use a scale of 1 to 5 to evaluate. 1=bachelor, 3=master, 4=PhD or other doctoral degree. Add-ons for certifications, i.e. CPA, Certified Coach, etc. and for second degrees can be applied from .5 points, or 1 point. The max score cannot exceed 5 points. Leadership Personality is based on your subjective evaluation including the opinion of your Supervisor who will be working with this person. This score is also a rating scale of 1 to 5. 1 = probably needs a lot of effort to be a leader, and 5 = probably will perform at top leadership capability. Here is the information on the four top candidates: Bob: Salary: $75,000, Experience: 22 years direct, 8 other related, Education: Bachelors plus certified coach and certified leadership graduate; Leadership personality: 4 Sam: Salary: $68,000, Experience: 18 years direct, 10 other related, Education: MBA; Leadership personality: 3.5 Mary: Salary: $69,000, Experience: 15 years direct, 4 other related, Education: Masters, plus certified HR Professional; Leadership personality: 4.3 Lisa: Salary: $62,000 Experience: 5 years direct, 6 other related, Education: Doctor of Business Administration (DBA); Leadership personality: 3.7 Create a multi-attribute decision analysis using Excel. What are the weights that you would assign to the four criteria? How do you convert the raw data into utility values? Which criteria are “Less is better” and which are “more is better”? When you have worked through this example, download this Excel file and check your work: BUS520-Module 3 SLP Practice.xlsx Optional Reading Check the following video on pivot table: http://permalink.fliqz.com/aspx/permalink.aspx?at=d4fd059eda8a46 a2b90041deffd6c061&a=5fae3cf0f1624f39b0341263a6541ea0 Check the Multi-Attribute Decision Making_BUS520 Module 3 Background PowerPoint presentation Assumed Certainty: Multi-Attribute Decision Making (MADM) Scenario: You are the Vice President of Franchise Services for the Lucky restaurant chain. You have been assigned the task of evaluating the best location for a new Lucky restaurant. The CFO has provided you with a template that includes 6 criteria (attributes) that you are required to use in your evaluation of 5 recommended locations. Following are the 6 criteria that you will use to evaluate this decision: Traffic counts (avg. thousands/day)—the more traffic, the more customers, and the greater the potential sales. Building lease and taxes (thousands $ per year)—the lower the building lease and taxes, the better. Size of building (square feet in thousands)—a larger building is more preferable. Parking spaces (max number of customers parking)—more customer parking is preferable. Insurance costs (thousands $ per year)—lower insurance costs are preferable. Ease of access (subjective evaluation from observation)— you will need to “code” the subjective data. Use Excellent = 4, Good = 3, Fair = 2, and Poor = 1. Now that you have collected the data from various sources (your CFO and COO, local real estate listings, personal observation, etc.), you have all the data you need to complete an analysis for choosing the best location. Download the raw data for the 5 locations in this Word document: BUS520 Module 3 SLP.docx Assignment Review the information and data regarding the different alternatives for a new restaurant location. Then do the following in Excel: Table 1: Develop an MADM table with the raw data. Table 2: Convert the raw data to utilities (scaled on 0 to 1). Show the utility weights in a second table. Table 3: Develop a third table with even weights (16.7%) for each variable. Evaluate Table 3 for the best alternative. Table 4: Complete a sensitivity analysis by assigning weights to each variable. In a Word document, do the following: • • • Discuss the process used to put together Tables 1–4 above. Provide the rationale you used for choosing for each of the weights you used in Table 4. Give your recommendation of which location the company should choose (based on results of Table 4). SLP Assignment Expectations Excel Analysis Complete Excel analysis using MADM (all four tables noted above must be included). Accurate Excel analysis (Excel file includes working formulas showing your calculations; all calculations and results must be accurate). Written Report • • • Length requirements: 2–3 pages minimum (not including Cover and Reference pages). NOTE: You must submit 2–3 pages of written discussion and analysis. This means that you should avoid use of tables and charts as “space fillers.” Provide a brief introduction to/background of the problem. Discuss the steps you used to compile the Excel analysis (i.e., the four tables). • • • • • • • Discuss the assumptions used to assign weights to each variable of your sensitivity analysis (Table 4). That is, provide the rationale for your choice of weights for each variable. Provide a complete and meaningful recommendation related to the location that should be chosen as a new site. Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12 pt. type size. Have an introduction at the beginning to introduce the topics and use keywords as headings to organize the report. Avoid redundancy and general statements such as "All organizations exist to make a profit." Make every sentence count. Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if absolutely necessary, should rarely exceed five words. Upload both your Excel file and written Word report to the SLP 3 Dropbox by the assignment due date.
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