Business Analytics Test 1: Dr. Forest Chapter 1

A supermarket has been experiencing long lines during peak periods of the day. The problem is noticeably worse on certain days of the​ week, and the peak periods sometimes differ according to the day of the week. There are usually enough workers on the job to open all cash registers. The problem the supermarket manager faces is knowing when to call some of the workers who are stocking shelves up to the front to work the checkout counters. Use this information to answer the given questions.

- A model might be developed to find the number of checkout counters to open to ensure a reasonable wait time or maximize customer throughput. - The manager can use analytics by examining historical data on customer flow during each hour of each day of the week and month. -Business analytics can help to predict customer demand at the checkout counters and determine the appropriate number to have open. -A model can be developed to forecast customer demand for a set amount of time in the future.

Analyzing the Problem

- Analysis involves some sort of experimentation or solution process, such as evaluating different scenarios, analyzing risks associated with various decision alternatives, finding a solution that meets certain goals, or determining an optimal solution. - Analytics plays a major role.

Where do we get data?

- Annual Reports - Accounting audits - Financial profitability analysis - Economic Trends - Marketing research - operations management performance - Human Resource measurements - web behavior: Page views, vistors county, time of view, orgin and desitnation paths etc.

What is the impact of Business analytics?

- Benefits = reduced costs, better risk management, faster decisions, better productivity and enhanced bottom line performance such as profitability and customer satisfaction - Challenges = Lack of understanding of how to use analytics competing business priorities, insufficient analytical skills, difficulty in getting good data and sharing information, and not understanding the benefits versus perceived costs of analytics studies

What tools are used in business analytics? (Foundation)

- Business intelligence - information systems statistics - operations research/management Science (OR/MS)

What is Business Analytics the use of?

- Data - Information Technology - Statistical analysis - Quantitative methods - Mathematical or computer based models

Modern Business Analytics

- Data Mining - Simulation and risk analysis - Decision Support Systems (DSS) - Visualization

Why Are we learning Business Analytics?

- Pricing -Customer Segmentation - Merchandising - Location - Supply chain design - Staffing - Health care

Predictive Model

- Using a graph to represent supply and demand of a product

Model

- an absorption or representation of a real system, idea, or object -- captures the most important features -- can be written or verbal description, visual representation a mathematical formula or a spreadsheet. --- almost like an expression or formula

Example Question: When to reduce prices and by how much to maximize revenue. Explain the ways to find the answer using Descriptive, predictive and prescriptive

-Descriptive examine historical data for similar products (Prices units sold and advertising) -Predictive: predict sales based on price -Prescriptive: find the best sets of pricing and advertising to maximize sales revenue

Commercial Software

-IBM Cognos Express -SAS Analytics - Tableau

Interpreting Results and Making a Decision

-Models cannot capture every detail of the real problem. -Managers must understand the limitations of models and their underlying assumptions and often incorporate judgment into making a decision.

Implementing the Solution

-Translate the results of the model back to the real world. -Requires providing adequate resources, motivating employees, eliminating resistance to change, modifying organizational policies, and developing trust.

Complexity increases when the following occur:

-large number of courses of action -the problem belongs to a group and not an individual -competing objectives -external groups are affected -problem owner and problem solver are not the same person -time limitations exist

Problem solving in Analytics Steps

1. Recognizing a problem 2. Defining the problem 3. Structuring the problem 4. Analyzing the problem 5. Interpreting results and making a decision 6. Implementing the solution

Structuring the Problem

1. Stating goals and objectives 2. Characterizing the possible decisions 3. Identifying any constraints or restrictions

Visual Model

A sketch of sales as an S-shaped curve over time

Difference between Validity and Reliable

A tire pressure gage that consistently reads several pounds of pressure below the true value is not reliable although it is valid because it measures the right information - The number of calls to a customer service desk might be counted correctly each day ( and thus is reliable) but not valid if it used to assess customer dissatisfaction, as many calls may be simple quarries - A survey question that asks customers to rate the quality of the food in a resteraunt may be neither reliable no valid since different customers may have conflicting impressions and not valid is measuring total customer satisfaction since that includes other aspects

Discuss how one might use business analytics in their personal life.

A. An automobile owner can predict when the next maintenance will be due by recording things such as the miles driven and gas mileage. This would help them budget accordingly. Your answer is correct. B. A store might keep track of inventory and use it to decide what items are in demand. Your answer is correct. C. A reporter could analyze social media posts to see what types of people are more likely to share unverified news stories. Your answer is correct. D. A golf player might use statistics to help to diagnose problems and improve their game.

What does the model for b greater than 1b>1 tell about the relationship between demand and marketing​ effort?

As marketing effort​ increases, demand increases at an increasing rate

What does the model for b=1 tell about the relationship between demand and marketing​ effort?

As marketing effort​ increases, demand increases linearly.

What does the model for 0 less than b less than 0<b<1 tell about the relationship between demand and marketing​ effort?

As marketing effort​ increases, demand increases with diminishing returns.

Model Assumption

Assumptions are made to -simplify a model and make it more tractable; that is, able to be easily analyzed or solved. -better characterize historical data or past observations.

Define range names for each month and type of expense. Which are reasonable range names to give each​ month? A. ​Apr_May_Jun, Jul_Aug_Sept,​ Oct_Nov_Dec B. ​Apr, May,​ Jun, Jul,​ Aug, Sept,​ Oct, Nov, Dec This is the correct answer. C. ​Cost, Advert,​ Salaries, Supplies, Misc Your answer is not correct. D. Cost of​ Goods, Advert,​ Salaries, Supplies, Misc

B

Decision Model (Imputs)

Data - assumed to be constant Uncontrollable inputs - quantities that can change but cannot be controlled Decision options - controllable and selected at the discretion of the decision maker

Reliability

Data are accurate and consistent

What data would be needed when developing the model to facilitate good​ decisions?

Day of the week,Time of day,Number of customers served per hour

What does the model for b=0 tell about the relationship between demand and marketing​ effort?

Demand does not depend on marketing effort.

A human resource manager needs to understand whether the​ company's current employee mix has the skills and capabilities needed to achieve the goals laid out by a new strategic plan. Which analytics tools would most likely be used for this​ scenario?

Descriptive

A logistics company wants to better understand the relative profitability of its numerous customers over the past three years. Which analytics tools would most likely be used for this​ scenario?

Descriptive

b=1

Diagonal straight

Model Assumption Example

Economic theory tells us that demand for a product is negatively related to its price. Thus, as prices increase, demand falls, and vice versa (modeled by price elasticity — the ratio of the percentage change in demand to the percentage change in price). - The key assumption in developing a model is the type of relationship between demand and price.

Predictive Models

Focus on what will happen in the future -Many predictive models are developed by analyzing historical data and assuming that the past is representative of the future.

Example of decision Model

Grocers often study the relationship of sales volume to these strategies by conducting controlled experiments to identify the relationship between them and sales volumes. That is, they implement different combinations of pricing, coupons, and advertising, observe the sales that result, and use analytics to develop a predictive model of sales as a function of these decision strategies. *There is also a chart*

b=0

Horizontal line

Nature of Decision Making

Imputs ->Decision Model -> Outputs

A financial advisor would like to develop the best mix of​ stocks, bonds, and other investments for a client to achieve a comfortable level of risk. Which analytics tools would most likely be used for this​ scenario?

Prescriptive

A large service firm wishes to determine how to invest the cash received from its financial product to achieve the best return. Which analytics tools would most likely be used for this​ scenario

Prescriptive

Recognize a Problem

Problems exist when there is a gap between what is happening and what we think should be happening. -For example, costs are too high compared with competitors.

Define Information:

Result of analyzing data: that is extracting meaning from the data to support evaluation and decision making -> what we get my analyzing data

Example of perceptive model

Sales= -2.9485*Price + 3240.9 Total Rev= Price * Sales = Price * (-2.9485 * Price + 3240.9)

Health Care

Scheduling operating rooms to improve utilization, improving patient flow and waiting times, purchasing supplies and predicting health risks

Pricing

Setting prices for consumer and industrial goods, government contracts, and maintenance

Validity

data correctly measures what it is supposed to measure

Merchandising

determining brands to buy quantities and allocation

Supply chain Design

determining the best sourcing and transportation options and finding the best delivery routes

Staffing

ensuring appropriate staffing levels and capabilities and hiring the right people

Descriptive models

explain behavior and allow users to evaluate potential decisions by asking "what-if?" questions.

location

finding the best location for bank branches and the ATMS, or where to service industrial equipment

Optimization

finding values of decision variables that minimize (or maximize) something such as cost (or profit)

b<0

going down and concave down

b>1

going up concave down

Prescriptive Models

help decision makers identify the best solution to a decision problem.

What does Business Analytics help us do?

help managers gain improved insight about their business operation and make better fact based decision

Prescriptive analytics

identify the best alternatives to minimize or maximize some objective

Risk

is associated with the consequences of what actually happens.

Uncertainty

is imperfect knowledge of what will happen in the future.

Predictive analytics

predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time

Objective function

the equation that minimizes (or maximizes) the quantity of interest

optimal Solution

values of the decision variables at the minimum (or maximum) point

Mathematical Model

where S is sales, t is time, e is the base of natural logarithms, and a, b and c are constants

What does the model for b less than b<0 tell about the relationship between demand and marketing​ effort?

As marketing effort​ increases, demand decreases with diminishing returns

Defining the Problem

Clearly defining the problem is not a trivial task.

How would one go about selecting the appropriate​ model?

Collect a large amount of data for marketing effort and demand and fit the data to a model

0<b<1

Concave up

Which are reasonable range names to give each type of​ expense? A. ​Apr_May_Jun, Jul_Aug_Sept,​ Oct_Nov_Dec B. ​Apr, May,​ Jun, Jul,​ Aug, Sept,​ Oct, Nov, Dec C. Cost of​ Goods, Advert,​ Salaries, Supplies, Misc D. ​Cost, Advert,​ Salaries, Supplies, Misc

D

What assumptions are​ implied? Are they​ reasonable?

Marketing effort and demand can both be clearly measured...There are no other factors that affect demand. This assumption is not reasonable

Big Data

Massive amounts of data ( kind of like when you search on google) - refers to massive amounts of data from a wide verity of sources, much of which is available in real time (velocity), and much of which is uncertain or unpredictable

Outputs

Measures of performance or behavior

Define Data

Numbers or textual data that are collected through some type of measurement process

Which is a reasonable range name to give​ post-MBA salary? A. ​post-MBA salary B. Post MBA C. ​post-MBA-salary D. ​Post_MBA

Post_MBA

Define range names for each of these ranges. Which is a reasonable range name to give​ pre-MBA salary? A. ​Pre_MBA Your answer is correct. B. ​pre-MBA-salary C. Pre MBA D. ​pre-MBA salar

Pre_MBA

An automobile company would like to determine the number of vehicles it could sell next year based on the proposed price. Which analytics tools would most likely be used for this​ scenario

Predictive

The chief financial officer for a small manufacturing firm would like to estimate the net profit that the firm could expect over the next three years. Which analytics tools would most likely be used for this​ scenario?

Predictive

A baseball team would like to set ticket prices for different sections in its stadium to attract the highest number of fans throughout the season. Which analytics tools would most likely be used for this​ scenario?

Prescriptive

A disaster relief agency needs to allocate its budget for the next year among various relief efforts and programs. Which analytics tools would most likely be used for this​ scenario?

Prescriptive

A bank developed a model for predicting the average checking and savings account balance as balanceequals=negative 15 comma 923−15,923plus+397397times×ageplus+1 comma 3801,380times×years educationplus+0.1270.127times×household wealth. b. Suppose that a customer is 2929 years​ old, is a college graduate​ (so that years educationequals=1616​), and has a household wealth of ​$130 comma 000130,000. What is the predicted bank​ balance? What Numbers have meaningful interpretation?

The number negative 15 comma 923−15,923 does not have a meaningful interpretation. The number 397397 means that the average account balance increases by $397 for each year increase in age. The number 1 comma 3801,380 means that the average account balance increases by $1,380 for each year increase in education. The number 0.1270.127 means that the average account balance increases by $0.127 for each $1 increase in household wealth.

Model-Verbal Descriptive

The rate of sales starts small as early adopters begin to evaluate a new product and then begins to grow at an increasing rate over time as positive customer feedback spreads. Eventually, the market begins to become saturated and the rate of sales begins to decrease

One of the earliest operations research groups during World War II was conducting a study on the optimum utilization of Spitfire and Hurricane aircraft during the Battle of Britain. Whenever one of these planes returned from​ battle, the locations of the bullet holes on it were carefully plotted. By repeatedly recording these data over​ time, and studying the clusters of​ data, the group was able to estimate the regions of the aircraft most likely to be hit by enemy​ gunfire, with the objective of reinforcing these regions with special armor. What difficulties are involved with this​ study

The study was only observing planes that returned safely. Planes that were shot down could not be analyzed.

Descriptive Analytics:

The use of data to understand past and current business performances and make informed decisions

Decision Model

a logical or mathematical representation of a problem or business situation that can be used to understand, analyze, or facilitate making a decision