Case

INTRODUCTION

Organizations create and collect massive amounts of data as

a result of their day-to-day operations. Frequently referred

to as “Big Data,” it represents an important asset for the

organization. Big Data presents both opportunities and

challenges for accounting professionals, who are expected to

know how this data is created, collected, stored, and accessed.

As the custodians of the organization’s assets, accountants

are expected to understand and implement controls over the

storage and use of the organization’s data. Further, as business

professionals, accountants are expected to know how to use

this vast source of data to make better business decisions

and identify potential risks. Understanding how to use Big

Data to formulate and solve business problems provides

an opportunity for the accounting professional to become a

forward-thinking strategic partner in the organization. The

challenge for accountants is to develop the skill set needed to

extract value from Big Data through advanced analytics.

One skill that is becoming increasingly important for

analysis of large data sets is data visualization. Data visualization

is the process of displaying data to provide insights that will

support better decisions. Gartner’s 2017 “Magic Quadrant for

Business Intelligence and Analytics Platforms” states that “the

visual-based exploration paradigm has become mainstream.”1

Gartner identifies three platforms as leaders in visualization

software: Tableau, Microsoft, and Qlik. All three products

provide relatively easy-to-use data visualization tools.

HUSKIE MOTOR COMPANY: BACKGROUND

Huskie Motor Company (HMC) is an automobile

manufacturing company with production and sales

throughout the world. Automobile manufacturing and sales

is a complex and highly competitive business. Although

the automotive industry has a broad global reach, only 15

companies produce 88% of the world’s vehicles.2 HMC is a

new and a smaller player in the automotive manufacturing

market. If it is to survive, the company must fully understand

its markets, customer base, and costs to keep profit margins

positive. It has some very popular brands and high customer

satisfaction—both are critical assets at this stage of the game.

Miranda Albany was hired at HMC as a senior cost

analyst three years ago, when the company first began

operations after a spin-off from Blue Diamond Automotive,

a large auto manufacturing company. Recently promoted

to assistant controller, Miranda is anxious to make a good

impression on her boss. She has advocated a “data-driven”

strategy for decision making at the company by capturing

a vast number of product-specific details relevant for both

production and marketing. The problem is that the company

has grown so quickly that Miranda is having a difficult time

keeping up with the massive amounts of data that continue

to accumulate. To further complicate matters, there is a

growing need for reporting detail and in-depth analysis of

product lines given the availability of additional data.

IMA EDUCATIONAL CASE JOURNAL VOL. 11 , NO. 2 , ART. 3 , JUNE 20181

ISSN 1940-204X

Huskie Motor Corporation: Visualizing the Present and Predicting the Future

©2018 IMA

Ann C. Dzuranin Associate Professor Northern Illinois University

Johan Perols Associate Professor of Accountancy University of San Diego

Dana L. Hart Assistant Professor–Accounting and Finance University of North Florida

Although Miranda is sure that the data she has collected

can help her management team make better decisions, she

does not have the time, or expertise, to figure out how to

organize or use the data effectively. Miranda communicates

with HMC’s executive team on a weekly basis to convey

vital information regarding marketing strategies, sales targets,

and production needs. Yet she feels that her information is

often “lost in translation,” as the executive team struggles to

digest the numbers. Miranda believes that data visualization

may be a crucial component in helping her effectively

connect with HMC executives. To help her utilize the

massive amounts of data at her disposal, Miranda has

interviewed consulting firms that specialize in information

technology (IT) and data engineering. Ultimately, Miranda

chose D & A Consulting because of its automotive industry

expertise and its focus on data analytics and visualization.

Miranda asked Megan Martinez, a senior staff accountant

at HMC, and Adam Green, a staff accountant at HMC, to

work with D & A on the project. Megan has been with HMC

for two years and has recently relocated to the corporate

headquarters in Dearborn, Mich. Megan’s corporate

transition is almost complete, and she is anxious to move

forward in her current position. Adam is a young, aggressive

employee who began with the company eight months

ago, since he graduated college. He has a good sense of

judgment and is eager to make a good impression on upper

management. Miranda believes the two employees will

provide a good mix of experience, dedication, and teamwork.

D & A CONSULTING GROUP: BACKGROUND

D & A Consulting was started by Doug Chan and Arlo

Paxton five years ago. Doug and Arlo have been friends

since college, having graduated with accounting degrees

from the same university 15 years ago. Although they

initially went to work at different accounting firms, they

both followed similar career paths. After becoming managers

at their respective firms, Doug and Arlo decided that their

real passion was in teaching clients how to use data to

make better business decisions. They started their own

consulting firm with one primary focus: helping clients better

understand their businesses via the use of data analytics

and data visualization. Though data analytics is not a new

concept in the business world, the amount of data available

and the number of sources from which it can be captured has

skyrocketed. Driven by lower storage costs and more “user-

friendly” analysis, software businesses have vastly increased

the amount of data they collect and store. Yet finding the

talent needed to transform that data into useful insights is

what businesses find challenging. D & A Consulting helps

companies fill that void.

Doug and Arlo are excited about the opportunity to work

with HMC. They have assigned their automotive industry

expert, Kevin Lydon, as the project lead, along with a D & A

new hire, Jan Morrison. Kevin has been with D & A nearly

as long as the company has been in existence. He and Doug

had met each other on a consulting project where Kevin

was working as an IT engineer. As project lead, Kevin is

enthusiastic about mentoring Jan on her first assignment. He

is equally enthusiastic about the potential for improvement

at HMC. He reassures Jan that this client will be a great

opportunity for her to test her data analytics and data

visualization skills.

HUSKIE MOTOR CORPORATION: OPERATIONS HMC is currently selling in 15 countries in three regions:

North America, South America, and Europe. Table 1

provides a breakdown of the countries within each region.

Table 1. Huskie Motor Corporation’s Regions and Countries

Region Country

Europe France, Germany, Poland, Spain, Sweden, United Kingdom

North America Canada, Mexico, United States

South America Argentina, Bolivia, Brazil, Chile, Columbia, Venezuela

Automobile manufacturing and sales is a complex and highly

competitive business. Though the automotive industry has

a broad global reach, only 15 countries produce 88% of the

world’s vehicles.3 HMC is a fairly new and small player in the

automotive manufacturing market.

HMC currently offers three brands: Apechete, Jackson,

and Tatra. Each brand has several models as detailed in

Table 2. The models available fall within seven segments

of vehicle types: compact, sub-compact, full-size, mid-size,

luxury, minivan, and sports utility.

Table 2. Huskie Motor Corporation’s Brands and Models

Brand Models Available

Apechete Chare, Island, Pebble, Robin, Summet

Jackson Brutus, Crux, Fiddle, Rebel, Wood

Tatra Advantage, Bloom, Jespie, Mortimer, Rambler

HMC offers several series for each model for a total of 34

different series. A breakdown of the available series offered

by model is provided in Table 3. Each model is available

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in various body styles, engines, drive configurations,

transmissions, trim, color, and seat types. Since various

engine and transmission builds (see Table 4) come from

one division and finishing (see Table 5) is done in another

division, these options are described in different tables.

Table 3. Huskie Motor Corporation’s Series Offered By Model

Model Series Model Series

Advantage A1, A2, A3 Mortimer N1, N2

Brutus Bas 1, Bas 2 Pebble B1, B2

Bloom Tr1, Tr2 Robin R1, R2

Chare C1, Cx2, S1, S2 Rambler Ra1, Ra2

Crux Cr1, Cr2 Rebel Ro1, Ro2

Fiddle F1, F2 Summet P1, P2

Island M1, M2 Wood V1,V2

Jespie J1, J2

Table 4. Huskie Motor Corporation’s Engine and Transmission Builds

Body Style Engine and Type Transmission Drive Configuration

Coupe i5

Crossover Diesel v7 5 Speed Auto 4WD

Pickup Truck Gas v9 5 Speed Manual AWD

Sedan Supercharged v11 6 Speed Auto FWD

SUV Turbo v13 6 Speed Manual RWD

Van

Table 5. Huskie Motor Corporation’s Finishing Styles

Trim Color Seats

Black

Base Blue

Deluxe Brown Leather

Luxury Champagne Cloth

Special Edition Gray Vinyl

Silver

White

Like many automotive manufactures, HMC offers a variety

of packages and options that buyers can add to their vehicles.

Packages include a specific set of bundled options, or buyers

can choose individual options separately. Table 6 provides

a list of the six packages along with the detail of products

and services contained in each package. Table 7 provides

information regarding options that may be purchased ala

carte. At least one option, however, is contained in each

available package.

Table 6. Huskie Motor Corporation’s Package Breakdown

Body Style Transmission

Appearance Package Moon roof, moon roof spoiler, sunshade, and auto-dimming mirror, hybrid option

Exterior Protection Package

Clear coat add-on with peck/ scratch warranty, predictive traffic, remote start, lane change alert system, door strip dent protectors

Handling Package

Smart key system, keyless entry keypad, push-button start, automat- ic/manual transmission, anti-lock braking system, hands-free GPS and compass, emergency assist

Interior Package

Multimedia bundle with high- resolution touch screen, premium radio, six speakers, advanced voice recognition, hands-free phone capability, low fuel alert system

Parking Technology Package

Auto-dimming rearview mirror, power mirror, onboard backup camera, contact sensor alerts, auto-adjust sideview mirrors, stolen vehicle locator software

Technology Package

Precollision system, parking assist, lane-departure alert, automatic high beams and compass, miles-to- empty indicator, fuel efficient cruise control

Table 7. Huskie Motor Corporation’s Add-On Options

Keyless entry keypad

Moon roof

Parking assist

Power mirror

Premium radio

Remote start

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DATA DILEMMA

Prior to calling Miranda at HMC, Kevin meets with Jan to

go over some basics regarding the project. Kevin uses a five-

step model as a data analytics framework. As a first step, he

explains this model to Jan, because he believes it will help

her understand his process.

Kevin: “So, Jan, this should be an interesting client and project.

I know you are a bit nervous about your lack of experience

with data analytics design, but I have been studying this stuff

for a long time, both from an applied perspective as well as a

theoretical perspective. There is a great book on this subject,

Data Analytics, by Warren Stippich and Bradley Preber. In

this book, the authors describe a five-step approach to the

data analytics process: (1) Define the question, (2) obtain

the data, (3) clean and normalize the data, (4) analyze the

data and understand the results, and (5) communicate the

results. I think that understanding and following these steps

will help you tremendously as you find your footing on this

engagement. Our client, HMC, would like to have the project

finished within a four- to six-week time frame. How do you

feel about that?”

Jan: “That seems like a pretty short window, Kevin. Do you

think we can finish within that time frame?”

Kevin: “Well, I do not think we can set a realistic time frame

without knowing exactly what our challenges are and what

the data looks like. Remember, we have to be able to trust

our numbers. We do not know yet whether the data is clean.

We will have to do some validity testing first. Remember,

follow the steps, think it through, and keep a calm head.”

Jan: “What exactly is our goal for HMC?”

Kevin: “Basically, HMC captures very detailed transaction-

specific data. For example, HMC has extensive cost data,

marketing information, and plan data for each car sold. The

amount of data it has is overwhelming for both the HMC

management team and the executive board. Our job is to help

Miranda and her team figure out how to use the data to better

understand costs and profitability by vehicle model. They also

need detailed, relevant feedback regarding sales volume and

sales location for planning purposes. Miranda would like to

be able to predict sales at least three quarters out so that the

management team can better plan production schedules.

“An equally important goal is to help them understand

the benefits of data visualization and give them some ideas

about how to present the data to their executive board. As you

know, Jan, data visualization software, such as Tableau and

Qlik, allows us to turn large volumes of raw data from various

sources into easily comprehensible graphical representations

of information. Data can be accessed live or extracted from

some other source. Data can also be presented in summary

fashion while preserving the underlying detail, which can be

instantaneously viewed as desired. This type of technology

could be instrumental in helping HMC improve and maintain

its competitive advantage.”

Jan: “How does the management team store the data, and how

will we be able to integrate it with the visualization software?”

Kevin: “I believe that the plan and forecast data are on Excel

spreadsheets. The actual sales data will likely come from its

enterprise resource planning (ERP) system. We will need

to confirm that at our initial client meeting. We should be

able to get transaction-specific information at the vehicle

identification number (VIN) level, since the VIN is a unique

identifier. I am not sure how much information is being

collected at the individual vehicle level, but that could be

a rich data source for us if we can get it. We will, however,

need to resolve our questions during our introduction call.”

Jan: “Is the data they have ready to be used, or will we need

to verify and organize it?”

Kevin: “We will see just how clean it is once we start

pulling it in. We will initially build the analysis using

a representative sample of HMC’s data. The sample

represents about 25% of the total company data. The four-

to six-week time frame should be feasible if we receive

clean data. Our contact at HMC, Miranda, is assigning two

accountants from the controller’s department to pull the

sample and help clean the data. Once the client approves of

the proposed analytics and the dashboard, we can roll it out

using all the data.”

Jan: “Will using a sample be enough to convince the client of

the value of the analysis?”

Kevin: “Absolutely. The sample allows us to determine

which type of analyses we can provide, by looking not only

at the data, but at the format of the data as well. We can use

the sample data to create demonstration dashboards for the

client. The analyses we perform with the sample can then be

recreated for the full data set.”

Jan: “I am not sure I understand what you mean by a

‘dashboard.’”

Kevin: “Basically, a dashboard is a screen that consolidates

visualizations, graphs, charts, and so on to concisely display

the metrics and key performance indicators for a business.

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Summarized data can come from a variety of sources and can

even be presented in real time.”

Jan: “I cannot wait to get started! This is going to be a

tremendous learning opportunity for me.”

After reviewing the project proposal and meeting with Jan,

Kevin calls Miranda to introduce himself and confirm their

initial meeting the following week.

Kevin: “Hi, Miranda. This is Kevin from D & A Consulting.

I just wanted to touch base with you to set up our initial

meeting.”

Miranda: “Hi, Kevin. I am really looking forward to your team’s

visit. We need help as soon as possible. I have so much data

coming in that I barely have time to look at it, much less analyze

it. Also, our data is located in various databases, spreadsheets,

and our ERP system, making it difficult to integrate and fully

utilize. We have data visualization software available on our

server, but we have not really tapped in to its capabilities just

yet. I am told that we can easily pull together large amounts of

data from various sources with user-friendly, adaptable output

using dashboards. Is that something you can help us with?”

Kevin: “Absolutely. What kind of data visualization software

do you have?”

Miranda: “Tableau. It is supposed to be very user-friendly.

I am hoping you can get a dashboard up and running in a

couple weeks.”

Kevin: “Do you know what you want on the dashboard?”

Miranda: “I know that we want to be able to see profitability

by brand and model, since solid profit margins are crucial if

we want to stay in business. We also want to keep a pulse on

sales by country and region. Ultimately, we want to do a better

job of planning our production schedule, but as you know, this

requires up-to-date information on many moving parts.”

Kevin: “I cannot say whether a couple of weeks will be enough

time until we take a look at the data and map out the specific

decisions you hope the dashboard will enable you to make. I

will have a better idea after our initial meeting next week.”

Miranda: “Okay, fair enough. We’ll see you on Monday at 1 pm.”

The following week, Kevin and Jan meet with Miranda and

her team for their initial meeting.

Kevin: “So, let’s get down to business. Let’s talk about the

dashboard. Help me understand what questions you are

trying to answer? Or what story are you trying to tell? Who

are the users going to be? Will different users need different

dashboards?”

Miranda: “I have a list of questions that I would like to be

able to answer using our data.

“There are several areas where I believe we can gain

greater insight from looking at our data. Specifically, we

need overall performance analytics to tell us how we are

performing globally. We need to know which of our models is

profitable, where we are selling well, and, perhaps, how sales

channels are driving sales volume. It is also important that

we are able to see information from a top-level perspective

with the ability to drill down into the detail. The profitability

information is crucial for our executive team: chief executive

officer (CEO), chief financial officer (CFO), chief operations

officer (COO), and chief marketing officer (CMO). This

team is ultimately responsible for the direction of HMC and

profitability for shareholders.

“I would like for our management accounting group

and financial reporting group, including the CFO, to

have a financial analytics dashboard that would give them

information about contribution margins, total costs, and

sales volume. Both groups will also need to monitor changes

in costs and contribution margins for all of the models we

offer. These are the most important metrics and, therefore,

the ones we need to work on first. We will expand our

analysis for the management accounting group to include

other efficiency measures as soon as we can get these initial

dashboards up and running.

“Our CMO and the sales team will need operations

analytics to help them understand turnover and demand.

They will need to know which models are selling and how

long it takes to sell them. They will also need to understand

how packages and options impact sales. They will need to

have a handle on which of our package/option offerings are

popular and which ones are profitable.

“Finally, our budgeting and production teams will need

to utilize forecast analytics to predict sales and margins at

least three quarters in advance.

“Do you think it is possible to make the dashboards ‘real

time’?”

Kevin: “I think it is possible, but first we need to understand

the data that you are currently collecting. What can you tell

me about the extent and magnitude of your data?”

Miranda: “Well, we track details down to the VIN level in our

ERP system. So, we have a lot of data. For each vehicle sold, we

know the sales amount, marketing expense, and all variable and

fixed costs. We also track a lot of nonfinancial data.”

Kevin: “What kind of nonfinancial data?”

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Miranda: “Well, there is vehicle data such as brand, model,

model year, series, segment, body style, drive configuration,

engine type, and transmission type. We also have the detail

for each vehicle sold as far as color, trim, and so on, as well as

any package or options purchased.”

Jan: “Wow! I can see why the data set is so large!”

Miranda: “That is just the vehicle information. We also

capture the region and country of sale, the number of days

that any given car was on the lot prior to sale, and the type of

marketing campaign in place at the time of sale. In addition

to that, we track sales channel information.”

Jan: “Can you give me a little more detail about what you

mean by sales channel information?”

Megan: “Sure. We identify sales using three sales channel

dimensions. Sales channel 1 identifies whether the sale

was made through our dealers, fleet, or retail operations.

Sales channel 2 identifies the type of customer account.

We have commercial accounts, employee/partner programs,

government accounts, nonemployee accounts, and rental

accounts. The third sales channel identifies whether the sale

was cash, financing, or lease.”

Kevin: “Okay, so ideally we will want to use both the financial

and nonfinancial data in our analyses.”

Miranda: “Exactly! Other potentially informative data

sources that we have are social media platforms like

Facebook, Twitter, and Instagram. We have just started

collecting data from these sources about our vehicles, but we

are still trying to figure out the best way to analyze it.”

Kevin:“Eventually we can bring that data into the analysis

and dashboard as well, but for now let’s stick to the data we

already have.”

“So, the actual sales data is in your ERP system. Where is

the budget data?”

Adam: “I keep track of the budget data in an Excel

spreadsheet we call ‘plan data.’”

Kevin: “Okay, if you give Jan and me a sample, meaning at

least 25% of the most recent data, we will make sure that the

data is ‘clean’ by running some data validity tests, and then

we can start putting together some analytics. Once we have a

better feel for the data, we can give you a better estimate of

how long it will take to develop the dashboards.”

Miranda: “Great! Megan and Adam will make sure you get all

the data you need, and they will be available to help as well.”

ASSIGNMENT

Assume you are Jan Morrison. Kevin has asked you to do a

preliminary analysis of the data. Specifically, Kevin has asked

you to determine:

1. Overall performance analytics:

a. How is HMC performing globally?

b. How are various HMC brands performing?

c. How are the various sales channels performing?

d. What are the most and least profitable models?

2. Financial analytics:

a. What is the current contribution margin (CM) per

model?

b. What is the average variable cost per model, and how

has that changed over time?

c. Which model has the most variability in variable costs?

d. What is the current CM per channel?

3. Operations analytics:

a. What model options are the top and bottom sellers?

b. How many days are the various models on the lot prior

to sale?

4. Forecast:

a. Sales Volume – Four quarters in advance (Hint: use

Trendline option in Excel or Forecast option in Tableau)

b. Contribution Margin – Four quarters in advance (Hint:

use Trendline option in Excel or Forecast option in

Tableau)

Once you have prepared the analysis, Kevin would like

you to prepare an interactive dashboard for each of the four

areas described earlier. The client uses Tableau for data

visualization and would like you to create the dashboards

using that software. Kevin has also given you guidelines for

preparing a draft of the business report (see “Deliverable”)

that will be delivered to HMC along with the dashboards.

DELIVERABLE

You should prepare a business report formatted as follows:

I. Title page

II. Executive summary

a. Clear and concise information about the problem the

report is analyzing

b. Main points, conclusions, and recommendations (use

bullets or numbered lists to highlight important points)

III. Introduction

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IV. Main body

a. Follow the order of the questions in the assignment

b. A paragraph about the relevance of the findings can be

included in each section

c. Include a discussion of relevant descriptive statistics

V. Data tabulations/visualizations

a. These should support your findings in the main body

b. You may include these within the main body if you

prefer

VI. Recommendations

a. Based on your analysis, what should be HMC’s course

of action? Are there countries, brands, models, options,

and so on that the company should increase

or eliminate?

b. What are the potential implications of those actions?

Consider both financial and nonfinancial implications.

You should also submit a file with your analyses and

dashboards.

ENDNOTES

1 Rita L. Sallam, Cindi Howson, Carlie J. Idoine, Thomas

W. Oestreich, James LaurenceRichardson, and Joao

Tapadinhas; Magic Quadrant for Business Intelligence and

Analytics Platforms; Gartner Data Analytics; February 16,

2017; www.scribd.com/document/353095001/Gartner-Data-

Analytics.

2 Henry Kallstrom, Intense competition leads to low profit

margins for automakers, Market Realist, February 5, 2015,

https://marketrealist.com/2015/02/intense-competition-

leads-low-profit-margins-automakers.

3Ibid.

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