MBA 669 / MIS 366 Business Intelligence Professor Jeff Hoffer, University of Dayton

The course has a mixture of junior and senior undergrads and MBA students. Some are part-time and a few full-time. The undergrads are all in the business school and are mainly MIS majors or MIS minors. For MIS majors there is a track on BI, so most of the majors are taking Professor Hoffer’s course as a requirement in that track. All students are in a residential program, but Professor Hoffer’s course is taught as an online, distance ed class (e.g., for this semester he was in Naples, FL for most of the time, and had guest speakers from Texas, Colorado, and California all via WebEx).

The course overviews the field of business intelligence (BI). This is not a technical or mathematically sophisticated course, although technology and analytical techniques for BI will be reviewed. Rather, this course is about developing a program for BI in an organization. Thus, the course covers the frameworks, concepts, methods, people skills, and technologies necessary for making effective decisions fast. The course addresses issues from the capture of facts to the delivery of information and decision support systems, including data quality, data warehousing, BI success factors and impact on organizations, business performance management (dashboards and scorecards), multi-dimensional data analysis and on-line analytic processing, data visualization, and applications of BI. Methods and tools for the design of BI systems will also be covered. The course includes some hands-on use of advanced BI tools to help the student ground concepts in practice. A term project assists students in understanding a real BI issue or application in an organization.

Below is the homework assignment for one of Dr. Hoffer’s students. The student was John Fleming. John, an Indiana University graduate, lives in Cincinnati and works for the Kroger Company as a Systems Management Engineer. By night, he is an MBA student at the University of Dayton. He says that he has a growing appreciation for decision support technology and found the Planners Lab to be full of promise for modeling business scenarios and experimenting with possibilities in an innovative way.

At the end of the assignment is John’s comparison of Microstrategy, Tableau and the Planners Lab.

Thanks John and thanks Jeff.

Using Planners Lab to Achieve Business Goals

Assignment

The purpose of this assignment is to develop some basic skills with The Planners Lab and from that experience create a general comparison of the value or appropriate application of The Planners Lab compared to two BI tools, MicroStrategy and Tableau. In the

Planners Lab™ Teaching Tutorial file there is a charge in the Introduction for you “to prepare a financial plan for the business and then take over the business to make it all happen.” That’s a large assignment. However, working towards this goal as well as covering the whole tutorial, are what prepares you for the assignment.

What you are to do is to develop a tutorial Word file, with screen shots, for your financial plan. I want the plan and tutorial to do the following:

Set some goals for the company (i.e., where you want to take the company with targets for goal and possibly what if variables) and show (i.e., justify) how you would achieve these goals (i.e., your plan for growing business, raising prices, whatever, in terms of manipulating independent variables)

Do this goal setting after making some of the what if variables stochastic (just make up what you think might be reasonable distributions for the variables)

Do a what if analysis on at least one variable you think you could manipulate to achieve your goal plan showing sensitivity to that variable (whether it be deterministic or stochastic) and what you would conclude from this analysis

Finally, I want you to write a brief overview of your experience using Planners Lab and observations you’d make in comparing it to MicroStrategy and Tableau, including for what decision circumstances you think each BI tool is best suited.

What I expect you will produce is a Word file that looks much like either the University of Nebraska or University of Georgia tutorial files, but also with a final section for the last bullet above.

Introduction

In this assignment, I will explore using Planners Lab with some sample data for a hypothetical company, Westlake Lawn and Garden. First, I am going to alter the model to use some probabilistic rather than fixed values for the what if variables. Then, I am going to set some goals for growing the company. I will then use Planners Lab to determine what needs to happen to achieve those goals, based on my model. I will then perform some what if analyses to show how possible variations in independent variables can influence the outcome of the goals I have set. Finally, I will compare and contrast Planners Lab to other BI tools we have used in our course, MicroStrategy and Tableau.

Modeling

To begin with, I have opened up the sample data file for Westlake Lawn and Garden, and clicked on Materials Expenses. I’ve decided to change the cost increase factors to stochastic variables, indicating a variability in them based on probability, rather than using a fixed assumption.

I am going to use the TRIRAND keyword to indicate worst, most probable, and best case scenarios for each cost increase factor variable, using the previous fixed value as the most probable value. My new equations for the cost increase factors are as follows:

Cost increase factor for Garden furniture = TRIRAND(1.0, 1.025, 1.05)Cost increae facator for Garden tools = TRIRAND(1.0, 1.01, 1.02)Cost increase factor for Trees = TRIRAND(1.0, 1.02, 1.04)Cost increase factor for Annual plants = TRIRAND(1.01,1.03, 1.04)Cost increase factor for Perrenial plants = TRIRAND(1.0,1.03, 1.035)Cost increase factor for landscaping manpower = TRIRAND(1.07,1.08,1.10)

In each case, I used the given fixed value as the most likely value, and came up with some worst and best case values somewhat arbitrarily, to add variation to these values. (I note here that there are several spelling errors in the variable names as given in the sample data. These could be corrected but it isn’t necessary for the purposes of this exercise to do so.) After making these changes, I press the Validate Model button to ensure the model is still valid and I haven’t made any syntax errors or other mistakes when modifying the equations.

Planners Lab reports that no errors are found, so we are ready to enter the Playground to play with the data. We do that by pressing OK, then pressing the Playground button.

First, I just want to look at my current profits for my various offerings. I drag a line chart down to the stage, then click Profit before G&A and drag all goal variables over to the line drawing. I remove the total profit by dragging it off the chart. Finally I resize the chart and get this:

The Landscaping Services unit seems to be the fastest expected grower. The other segments are expected to grow more modestly, except for Garden Furniture profit which is expected to decline. All of these expectations are based on the model and assumptions already set up.

Based on this information, my first goal is to get Garden Furniture back on track with increasing rather than declining profits. My goal is to increase Garden Furniture profits 3% each year over the forecasted period.

Before moving on, I double click on my layout name and name it “Profit by unit” and then click the plus sign to create a new layout, which I name Variable Tree. I drag a Variable Tree onto the stage and then add Garden Furniture profit to the tree, choose year 2008 (as my first forecast year) and expand all branches:

This shows me that Garden Furniture profit relies on the goal variables Garden Furniture price per unit, Garden Furniture sales volume, and Garden Furniture cost per unit. These goal variables in turn rely on the what if variables Price increase factor, Sales volume growth rate, and Cost increase factor.

First I am going to use Planners Lab’s Goal Seek chart to determine how I might reach my goal of 3% annual profit growth in Garden Furniture over the next three years. I create a new layout, name it Goal Seek, and drag a Goal Seek line chart to the stage as well as a regular line chart. I populate the Goal Seek chart with Garden Furniture profit and growth rate in Garden Furniture sales and the line chart with just growth rate in Garden Furniture sales:

Now I should be able to drag the goal seek line for Garden Furniture profit to indicate how the growth rate in Garden Furniture sales would need to change in order for me to reach my Garden Furniture profit goals. Since the percentage changes shown in the Goal Seek graph are relative to the current value of that point and not to the relation of that point to the start point or previous point, I independently calculated roughly what the values should be on the Goal Seek chart to indicate 3% growth rate each year. The resulting view is:

The needed growth rate would be 3% greater than current projected rate in 2008, 7% greater than current projected rate in 2009, and 10% greater than current projected rate in 2010. This gives us some idea that if we can grow sales modestly each year, we can achieve the desired profit growth. This seems like common sense, but maybe sales growth is not the easiest target for profit growth. Let’s use the Impact Analysis to see how What If variables contribute to the Garden Furniture profit.

In a new layout, I add an Impact Analysis chart and populate it with Garden Furniture profit and the What If variables that contribute to it:

This shows that growth rate in sales contributes only 10% (positively) to the Garden Furniture profit. Cost increase factors increase (negatively) 26.5%! Reducing cost increases would have a higher impact on profit than even increasing sales. But, the highest contributor is price increase factor. Of course, this doesn’t take into account the economic factors of how higher prices at some point will result in reduced sales volume.

Let’s see how high we’d have to raise prices to reach our goal.

It turns out our price growth rate for Garden Furniture can be a modest 1% higher than the current value in our model and we can achieve our desired 3% annual growth rate in Garden Furniture profits. Having learned that, I’ve decided to build this assumption into our model by pressing the Apply To Model button.

The ghosted line in the Goal Seek model now becomes solid. What had been my goal has now been built in to the model as the expected values. This might be what I would do after actually making and executing the decision to raise the price growth rate by 1% annually.

Now, since I haven’t used the What If feature independently of Goal Seek yet, I will use it to illustrate a new scenario. Let’s say I just heard perennial plant costs might be going up in the next few years and I want to see how that will affect my profits. Again, we’re

using the new model now with my Furniture price increases already built in. What will happen now if prices go up? I drop a What If chart and a line chart on a new layout and populate the What If chart with Perennial Plant prices and drop Total profit on the regular line chart:

Manipulating the cost increase factor for perennial plants will show the corresponding effect on the total company profit in the other chart. An increase across the board of 7% for perennial plant growth rates increases in only 1% or 2% lower profit rates in the future:

I will want to keep an eye on any cost increases and adjust my strategy accordingly. However, a cost growth increase of 7% in that area won’t devastate my business immediately.

Before I close, let’s look briefly at the Risk Analysis tool to see the stochastic variables I set up in action. Dropping a Risk Analysis chart on a new layout and dropping Cost increase factor (for Garden Furniture, since that’s what we’ve been working with) on it, and then running the simulation for 500 iterations, we get:

A similar triangle-shaped distribution is shown for the other three years by moving the slider. This is the expected distribution since I used TRIRAND to set up my stochastic variables. This chart shows the probability of this cost increase factor falling in the given ranges, with the highest probability that the value will fall in the middle of the ranges.

Conclusions

We have looked in depth at three data modeling and visualization BI tools (besides Excel) in this course: MicroStrategy, Tableau, and the Planners Lab. Each is an extremely powerful and versatile tool. They have their individual strengths and weaknesses, which I’d like to briefly examine.

MicroStrategy is a particularly full featured BI tool allowing in depth reporting, charting, and dashboards. It is deployable via web or thick client. Libraries of reports can be created and scheduled to be delivered in many ways. I would say MicroStrategy is most suited for providing ongoing reporting based on past and current data. It is also useful for creating dashboards that can give an overview of data and then be drilled into for more detail. MicroStrategy’s user interface, at least when delivered on the web, is not the most intuitive or simple to use.

Tableau, too, is useful at analyzing past data. Its strength is in its intuitive, fast interface, and its ability to guess the best way to visualize data as you manipulate it. The concept of shelves allows data to be sliced and diced by row, column, color, dimension, etc.

Whereas MicroStrategy is good at providing routine reporting or standardized dashboard views, I think Tableau’s strength is in finding new ways to look at data, and finding patterns that weren’t known before by creative manipulation of data. I would choose Tableau before MicroStrategy when I wanted to investigate data. If I found a visualization in Tableau that really stuck out as meaningful to me, maybe I would recreate it in MicroStrategy to run that report each week and e-mail it to me, or maybe I would add it to a dashboard I had already created in MicroStrategy.

Finally, the Planners Lab is a powerful new tool for modeling and forecasting data. It is clearly a powerful tool and has a lot of potential! Excel can be used to create similar models and do similar forecasting, but with the Planners Lab you can describe your models in English, and do very intuitive graph manipulation directly, and then see the results in the numbers if you want to, instead of having to manipulate the numbers and non-English equations in order to see the results in a graph as you would in Excel.

The strength of the Planners Lab is obviously not to analyze past data—past data is codified into the modeling stage of the tool. Its strength is in projecting future data, finding relationships between variables, and determining how different future scenarios may play out based on changing assumptions in the model. As a simulation and projection tool, it is amazing. I expect it to continue to evolve into something truly wonderful.