MARTEN BIG DATA

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Editor: Matthew T. Mullarkey
Volume 5, Case Number 9, 2016
10/19/2016
JESSICA DELEANIDES, KEVIN GEBHARDT, JUSTIN HURD, CLAYTON STANLEY
BIG DATA: CAN IT BE MANAGED?1
Justin Hurd was confident the answers were in the vast amount of data collected, but how could
he best use that data to meet his cost reduction and performance improvement mandate?
Justin Hurd, a regional manager for Marten Transport, Ltd. (Marten), hung up the phone feeling frustrated
following a call with his Division VP. The mandate he received was simple–improve operational efficiencies
and reduce operational costs. Justin saw his company do amazing things. He saw the company
thrive by delivering loads quickly, efficiently and accurately to its customers. They always maintained a
good work ethic and honest business practices, even in the face of high stress and publicity. He even saw
his company make leaps and bounds in how much technology they utilized, from GPS tracking to data
analytics.
However, as he considered his current challenges, he realized that finding and using the data to drive performance
improvements was not going to be easy. Justin considered Marten a unique trucking company
that stood apart from its competitors. But right now he needed to find the information on where Marten
could improve. He contacted Marten’s Vice President of IT, Randy Baier to see what performance data
was already collected. Randy informed him that by utilizing telematics to include ID Systems and StarTrak
devices, Marten could monitor reefer settings and performance as well as trailer positions to uncover
possible trailer abuse or operating inefficiencies. The data collected so far, identified clear inefficiencies,
and indicated possible abuse by warehouse operators at Marten’s expense.
The challenge any firm faced with a big data problem was determining how they would convert relevant
data into useful information. Because of the exponential rate at which data was produced, companies must
practice ingenuity by proposing specific operations, and focusing their data gathering efforts on what they
needed and what they could use.
Marten’s data gathering initiative had produced a wealth of information related to these issues, but how
could this data be transformed into useful, actionable metrics that could reduce these inefficiencies and
protect Marten’s bottom line? Was more data needed? If so, what kind of data and how should it be collected?
Were there other technological control measures that could be applied by Marten to mitigate the
impacts of their current issues? Surely other leading transport companies were facing similar issues. Perhaps
there were existing solutions being implemented, but would they work for Marten? Justin Hurd was
confident the answers were in the vast amount of data collected, but how could he best use that data to
meet his cost reduction and performance improvement mandate?
1 Copyright © 2016, Muma Case Review. This case has been reprinted from the Muma Case Review, Volume 1,
Number 12 and was prepared for the purpose of class discussion, and not to illustrate the effective or ineffective
handling of an administrative situation. Names and some information have been disguised. This case is published
under a Creative Commons BY-NC license. Permission is granted to copy and distribute this case for noncommercial
purposes, in both printed and electronic formats.
DELEANIDES, GEBHARDT, HURD, STANLEY
2 Marten
Industry Background
Origins
The trucking industry could be traced back to the early 1900s, as American industry expanded throughout
the nation. In a consumer-driven economy, the transportation of goods was critical in the supply chain.
From 1869 until the 1920s, the Transcontinental Railroad was the primary way businesses transported
their goods across the country. Moving into the early 1920s and 1930s, business owners began shipping
and delivering goods by truck (Tyler, 2014). In 1935, the Interstate Commerce Commission (ICC) began
regulating motor carrier rates, causing very little competition in the industry, thus leading to inefficiencies
(see Exhibit 1).
Deregulation and Competition
1980 was a benchmark year in the trucking industry. Jimmy Carter signed The Motor Carrier Act (see
Exhibit 1) that would eliminate restrictions on commodities that could be carried, routes that motor carriers
could use, and geographical regions that they could serve (Moore, 1993). As a result, many large
trucking companies failed because of revealed inefficiencies in their spending and operations in an unregulated
industry.
Companies that thrived were those that capitalized on saving delivery costs and strategically expanding
their territories. Truck drivers no longer had to seek authority to carry goods into a particular geographical
area. Before deregulation, truck drivers were required to seek authorization to carry a load, however, it
may not have been authorized to carry a different material on the return trip and therefore, would return
empty. When deregulation occurred, companies that could decrease these delivery costs became extremely
competitive. Cutting costs and increasing efficiencies continued to be the trend in trucking company
successes.
Operations
Trucking operations were categorized in a variety of ways. Companies were either private or public, also
called “for-hire.” A private fleet was owned by the company it serviced. Common examples of private
fleets would be grocery stores and retail chains (“Trucking Industry Overview,” 2014). These fleets contributed
to the companies’ overall costs and bottom line. For-hire companies provided transportation services
for others. For-hire companies would provide an array of services, and would be evaluated based on
capacity, operating areas, services, certifications and vertical specialties (see Exhibit 3). Their revenues
were increased by continued expansion and by growth of client bases. Profits were increased by increasing
efficiencies and cutting costs.
The national average fleet size (capacity) was 3,268 trucks, with 71% operating in all of North America,
84% of companies offering truck load (TL) services, and 85% of companies specializing in transporting
“freight of all kinds” (O’Reilly, 2014).
The most common trucking services were less than truckload (LTL) services, shipments of loose freight,
and truck load (TL) services via shipments in sealed containers. Seventy-one percent of companies operated
in all of North America, with 28% operating in the U.S alone and 38% operating globally. These two
factors played an especially large role in the for-hire company’s ability to generate revenue.
Marten Transport Background
Marten Transport was a leading transportation provider of time and temperature sensitive commodities
throughout North America. It had its beginnings in 1946, as founder Roger Marten at the age of 17 started
JITE: DISCUSSION CASES Volume 5, Case Number 9, 2016
Marten 3
the enterprise as a small milk and dairy product delivery service for Modena Co-Op Creamery in his
hometown of Modena, Wisconsin. Throughout the 50’s, 60’s, and 70’s Marten continued to slowly grow
and expand through acquisitions of other small carriers, while it purchased new regulated interstate lanes
to grow and service customers throughout the Midwest, West Coast, and Southeast, and developed and
built a home terminal in Mondovi, Wisconsin. In 1976, there was a major change when Marten purchased
Hiawatha Produce, which started a transition of the company into its current niche as a long-distance
hauler of perishable foods in refrigerated trailers.
Marten specialized in providing full truckload, irregular route carrier services across 48 states, Canada
and Mexico, as well as long haul and multi-faceted temperature control carrier services. Marten strived to
be the premium supplier of time and temperature-sensitive transportation services to customers nationwide,
serving customers with more demanding delivery deadlines, as well as those who shipped products
requiring modern temperature controlled trailers to protect goods. Critical to Marten’s business model
was quality of service, on-time delivery and quick-to-market operations.
In 1979, through the stroke of his pen, U.S. President Jimmy Carter changed the trucking industry forever
with the Motor Carrier Act of 1980. According to the President,
This is historic legislation. It will remove 45 years of excessive and inflationary Government restrictions
and red tape. It will have a powerful anti-inflationary effect, reducing consumer costs
by as much as $8 billion each year. By ending wasteful practices, it will conserve annually hundreds
of millions of gallons of precious fuel. All the citizens of our Nation will benefit from this
legislation. Consumers will benefit, because almost every product we purchase has been shipped
by truck, and outmoded regulations have inflated the prices that each one of us must pay. The
shippers who use trucking will benefit as new service and price options appear. Labor will benefit
from increased job opportunities. And the trucking industry itself will benefit from greater flexibility
and new opportunities for innovation. (White House Press Release).
By eliminating barriers to entry, the number of licensed carriers doubled to over 40,000 by 1990, which
greatly increased competition between firms. This change of regulation allowed Marten to rapidly expand
its customer base to include many Fortune 500 companies such as Anheuser-Busch, Coors, Proctor and
Gamble, Pillsbury, Kraft and 3M. With this national expansion in 1985, Marten added service centers in
Southern California, Oregon, and Georgia to perform maintenance on its equipment while out in the field.
The next few years brought continuous change at Marten Transport, as the company went public on the
NASDAQ in 1986, with an IPO of $13 per share. The same year, with Roger’s son Randy Marten as
President of the company, Marten posted gross revenues in excess of $50 million. In 1993, founder Roger
Marten passed away and Randy became CEO of the company.
Since 2000, Marten had continued to grow and diversify its business model. In 2004, the corporate office
received an 11,000 square foot addition, and a new service facility was opened in Indianapolis. A year
later in 2005, Marten opened two logistic services departments including its own brokerage firm and an
intermodal department, both of which helped to provide capacity to Marten’s existing customer base,
while at the same time maintained Marten’s competitive pricing. Through the 2008-2009 recession, Marten
continued to show a profit while many competitors posted losses, and others were forced to leave the
industry. At the same time, Marten continued to expand its footprint by opening two new service centers,
and beginning door to door service to customers that had business in Mexico. By 2010, Marten opened an
additional 5 service centers in Florida, Pennsylvania, Arizona, Kansas and Tennessee. These additional
service facilities helped decentralize Marten’s operations from a corporate hub model to a regional platform
that allowed each facility’s operations personnel to be in closer contact with drivers and customers.
DELEANIDES, GEBHARDT, HURD, STANLEY
4 Marten
Throughout its history, Marten Transport was an industry leader in profitability, growth and innovation.
By 2013, Marten had grown to over 3000 employees, and reported the highest net income for any year in
its history (see Exhibit 5). For the year ending December 31, 2013, gross income was $659.2 million,
while net income improved 10.6% to a record $30.1 million (“Marten Transport,” 2014).
Over this period, Marten was in the Forbes “200 Top Small Companies” list twice and named one of
Forbes “America’s 100 Most Trustworthy Companies” four of the last five years. Marten was also a partner
of the EPA’s Smartway Transport program due to its efforts to reduce emissions by installing auxiliary
power units on its tractors along with reducing its total hours of reefer usage. This was accomplished
by working with customers to raise the temperature on frozen loads from the industry standard of -10 to –
1 without compromising product integrity. And Marten was a certified “Top Pay Carrier” for drivers–
historically a big factor for driver retention and company growth.
Current Issues
Marten had done well with implementation of technology up to this point. They started with Qualcomm,
the most current technology of its time, and then switched to OmniTracs MCP200 after identifying competitive
advantages. They purchased StarTrak systems from OmniTracs, and saved millions by increasing
efficiencies of their refrigerated units. But Justin wondered how Marten could effectively utilize such a
large amount of data. Was the current data sufficient to address the issue of reefer cost reduction, or was
more needed?
Captured Data
The trucking industry as a whole had focused much of their technology on improving efficiencies and had
made no effort to hide it. When Kyle Mayo, Knight Transportation’s service center manager in Lakeland
FL, was asked what additional technologies he thought would enhance efficiency within the industry, his
response was, “Anything that can help the driver.”
Marten attempted to have its drivers deliver one loaded trailer at a customer location, pick up a preloaded
trailer, and then have that driver continue to the next location. The idea was to save payroll because the
wait time of the driver would be reduced. While improved routing and determining fuel stop points obviously
cut costs for one entity, it may have increased a cost elsewhere within Marten’s financials.
As Vice President of Information Systems, Randy Baier was very knowledgeable on the information that
OmniTracs MCP200 and StarTrak provided to the company. He believed that data mining and analytics
was the key to improving efficiencies within the industry. As he explained it, “We have tons of data within
our reach. We just need to use it.”
Data Collection Devices
Advancements in information technology were the next phase of increased competition within trucking.
As early as 1986, larger companies began installing two-way satellite communication and computer dispatching
systems in their operations. These technologies became affordable in the 1990s (Kettinger, Patel,
& Ryoo, 2012).
Trucks became equipped with Electronic Onboard Recording Devices (EORD) (see Exhibit 4). EORDs
recorded driving hours, monitored duty status, short idling time, long idling time, miles per gallon, excess
speed, and log in and log out times, among other parameters (Kettinger, et al., 2012). These data were
then transmitted to operation managers for data analysis. It was not until 2002 that mainstream trucking
companies began to invest in logistics software and technology as a way to use the data to reduce costs
(“Trucking Industry Overview,” 2014).
JITE: DISCUSSION CASES Volume 5, Case Number 9, 2016
Marten 5
A decade later, other technologies such as mobile communication and Wi-Fi, contributed to increased
efficiency in communication. No longer did drivers have to pull over to call operation centers to report a
breakdown or emergency, they would now be able to communicate from the road. Also, global positioning
systems (GPS) enhanced tracking of fleets. Route selection, scheduling and intermodal coordination
became more efficient, and “just-in-time” with the ability to track locations of trucks, traffic and other
modes of transport.
Continuous Data Collection through Mobile Tracking
In 1993, Marten began to implement a new tracking and communications device called Qualcomm in all
of its tractors. It was a simple satellite unit that, through GPS technology, provided real time position
tracking, communication of load information, and acted as a portal from the driver of the tractor back to
operations. This system, while basic when compared to today’s standards, was ahead of its time in a
communications era where cell phone ownership was not widespread, and as compared to prior methods
of daily scheduled check-in phone calls. As new software was introduced, Marten upgraded its Qualcomm
package to track engine performance, fuel mileage, driver idle time usage and critical event reporting
which recorded driving habits in order to help identify potential drivers at risk for future accidents.
At the time of the case, Marten used the OmniTracs MCP 200 platform. It had all the features of the older
style Qualcomms, but ran off terrestrial cell towers instead of satellites. The new unit was a color touch
screen, and included a company approved GPS routing system, web browser, a media player for company
safety messages, a scanner for the driver to turn in paperwork, and electronically managed the driver’s
DOT regulated logbook.
Since Marten specialized in refrigerated transportation, in 2008 it completed the installation of the StarTrak
devices on all of its trailers. These devices provided real time information to monitor temperature
settings, reefer performance, trailer location and fuel level monitoring. These systems were created to attempt
to eliminate costly claims related issues due to spoilage from inaccurate temperature settings, mechanical
breakdown on the reefer units, and running out of fuel while loaded. These devices have saved
Marten millions of dollars since installation, and were a critical part of daily operations.
Data Reporting
In the temperature-controlled freight industry, the end of the summer marked the beginning of “candy
season.” Many of the confectionary industry heavy-weights geared-up to ship thirty to forty percent of
their annual volume in a two-month period, in preparation for the Halloween holiday. This time frame
also marked many new-product launches within the candy industry, and it was not uncommon for many
marketing executives to become engaged with their supply chains. This level of demand made it very
challenging for any carrier to meet its capacity commitment to confectionary customers, while still satisfying
demand from its other customers.
August of 2013 marked another prelude to candy season, but the much-increased demand for capacity
throughout the year, along with newly-made commitments to M&M-Mars would change the way Marten
evaluated customer relationships, and the way it tracked trailer utilization. Newly added commitments
from a production facility in Joliet, IL shipping to a distribution center in Kennesaw, GA, along with
commitments from Cleveland, TN to a distribution center in Romeoville, IL would set the stage for a
showdown between the two companies. As the last week of August was coming to a close, recent activity
within Marten Transport revealed approximately 27 trailers stock-piled, and inbound loaded in Kennesaw,
with an additional 32 in Romeoville, IL. Upon further examination, with the use of telematics on Marten
Trailers, many of these trailers had been sitting for 5-6 days, running at 35 degrees, and burning up the 75
DELEANIDES, GEBHARDT, HURD, STANLEY
6 Marten
gallons of diesel fuel per trailer–only to be refilled by each distribution center, so they could continue to
run even longer.
In Kennesaw, Georgia, the Area Sales Director was asked to visit the site and evaluate the issue as to why
the receivers were not unloading the equipment. Kennesaw was a third-party warehouse facility, so the
managers on-site were not actual Mars employees, which made it very easy to assess the source of the
issues. The meeting revealed that there was sufficient labor to unload the trailers, and sufficient warehouse
capacity. Mars was simply not ordering the trailers to be unloaded due to cost. The contracted rate
of four days free, followed by a charge of $50 per trailer, per day excluding weekends, was far less than
warehousing labor and storage rates. After these discussions in Kennesaw, Marten Transport immediately
stopped accepting all load tenders from production facilities into Kennesaw, GA and Romeoville, IL. At
the same time, sales executives throughout Marten reached out to corresponding supply-chain managers
at Mars, but were not met with any real answers.
After a little over a week, significant supply-chain issues had arisen within Mars, and top Mars executives
now wanted to know why one of its major truckload carriers had decided to stop meeting its agreements
with the massive confectionary shipper. The pipeline for distribution within Mars had been seriously disrupted,
and Mars wanted answers. A conference call with executives between Mars and Marten transpired,
and at its conclusion, Mars executives stated: “We don’t care about your trailer issue. That is your
business; we are in the business of getting our candy to market.” Marten Transport’s response was simple:
“If you will continue to abuse our relationship, we don’t care about getting your candy to market.” This
call would terminate the relationship between the two companies. Additionally, a trailer utilization report
was established, recognizing which customers would not unload Marten equipment, and which were impacting
Marten’s profits through improper use and excessive consumption of diesel fuel in its reefer
tanks.
Marten would follow candy season 2013, and the dissolved relationship with Mars with two recordquarters
in terms of revenue and profit. Marten’s “Trailer Dwell Report” would be renamed, “The Mars
Report.”
The Future of IT in Trucking
The new advancements in trucking technology had attracted companies because of their proven inherent
ability to decrease costs. These costs were mostly associated with the trucks themselves- how long were
they on the road, how much fuel they were using, how long they remained idle at a stop, etc. (see Exhibit
2). EORDs delivered huge amounts of data to operation managers daily, only some of which was mined
to glean important information. Nonetheless, it had taken almost 20 years after the EORD became affordable
before business analysts began to leverage this data on a larger scale to make decisions and form projections.
As a company Marten thrived after the 1980 Motor Carrier Legislation, and after two decades of expansion
and growth, diversified its business model to include intermodal transportation. Marten had maintained
competitive pricing to its clients, provided top speed and efficiency in its delivery, and stayed on
the edge of innovation–making it a top transport company in the industry.
In a survey done on roughly 3,000 managers and analysts, MIT Sloan Management Review and IBM Institute
for Business Value discovered that the top performing firms used analytics five times more frequently
than the lower performers. Marten had an outstanding track record within the industry, but how did their
analytical skills compare to the industry? How could they improve?
JITE: DISCUSSION CASES Volume 5, Case Number 9, 2016
Marten 7
Marten had always maintained fair business practices, even after seeing their relationship abused by some
customers, such as Mars. This kind of philosophy never hindered its performance; in fact, it enhanced
their ability to thrive. Marten did not hesitate to embrace the Qualcomm tracking device as soon as it became
affordable, and upgraded at every opportunity. They received data on metrics such as idling, speed,
RPM, coasting out of gear, fuel consumption, engine time and distance traveled. They also received safety
reports on hard breaking deceleration rates, parking break status, trucker positions and locations. Most
recently and most notably, they developed and embraced trailer utilization reports. Dozens of metrics and
reports on hundreds of trucks appeared in Justin Hurd’s inbox every day.
Justin asked himself: How could he use the data generated to answer the questions he had and to identify
the true inefficiencies? Was all data good data? What data feeds would the reports generate? How massive
was this big data?
Unidentified Data
The technology within the industry allowed transportation companies to capture data points of trailers
including: whether trailers were loaded or unloaded, if they were running or not running, and what the
temperatures were in various areas of the trailer. These data points were available through Qualcomm;
however, they were not available through OmniTracs’ StarTrak.
Qualcomm would display a warning across the screen when the temperature fell out of the five-degree
standard. They may have also questioned the warehouse or customer as to why their reefers were running
while their trucks were not unloaded. Many warehouses did not store the contents of reefer trailers, and
left Marten’s trailers running outside. Because of this misuse, Marten had recognized increased fuel usage
during the summer months.
The trucking industry had always monitored weather as part of the decision making process. Most companies
within the industry based operational decisions on current and forecasted weather patterns.
Weather was often unpredictable and could not be controlled, but variables affecting costs and operations
that could be influenced were their customers and warehouses.
Just as transportation management systems (TMS) were utilized by Marten, Knight, and their competitors,
warehouse management systems (WMS) were utilized by many customers. Marten provided some
feedback from its TMS, but for the most part, they received very little feedback from the warehouses. If
Marten were able to increase communication with its customers, what information could be provided between
the two industries to help Marten with their IT issues, specifically keeping warehouses from abusing
their reefer trailers, and driving up Marten’s fuel costs?
Big Data
Marten had technology in place which provided crucial information that could lead to a reduction of inefficiencies.
However, the enormous volume of data available presented Marten with a big data dilemma.
Big data was defined as being “too voluminous or too unstructured to be managed and analyzed through
traditional means” (“How ‘Big Data’ is Different,” 2012). The idea that effective management of big data
could unlock new organizational competencies was a common belief in business that continued to build
momentum. While the data provided in years prior may have been manageable, Marten was at the point
of gathering too much data to be examined by old fashioned analytics. The sheer magnitude of big data
collected by Marten’s monitoring devices was staggering. For example, within a six week time period,
Marten gathered information on 13,403 loads, including the number of loads over 12 hours, average run
time, and the sum of those costs. Examples of how this data was captured and presented are illustrated in
Exhibit 6: Marten Data Collection.
DELEANIDES, GEBHARDT, HURD, STANLEY
8 Marten
Marten had been using information provided to them by their truck and trailer tracking systems, and had
already identified one Walmart distribution center as being a major offender of reefer run times compared
to others. Could appropriate analytics continue to improve their performance in the future? Justin thought
a closer look at reefer times, and costs to identify the smallest offending customers that Marten had, might
have been a start. With this data, Marten could identify peak seasons for those customers, and work on
securing more loads from those same customers.
Justin was not very knowledgeable of where in Marten’s operations current information systems were
being utilized, or where data was being stored. He just knew the reports he received–not the data, or data
collection processes behind them. He did know however, that the same reports he received each week
were identifying several choices he needed to make about where to affect change.
The Decision
Justin started to look at his fuel expense line for the last few years to identify patterns and trends. Tractor
fuel was mostly flat and based on truck counts. Reefer trailer fuel however, always spiked during the
months of summer because higher temperatures influenced the refrigeration energy expenditure, but this
did not account for all of the cost. Typically, the trailers were only turned on when loaded, but that was
only when drivers live-loaded and unloaded at a delivery stop, and used the same trailer for an entire trip.
Justin wondered if trailers were turned on in the same manner when Marten pre-positioned equipment at
customer locations, so the customers could pre-load trailers, preventing Marten drivers from having to
wait for live-loading. Pre-positioning saved drivers a lot of time, allowing them to haul more loads, and it
was good for business. But was that efficiency being countered by customer actions? When were the customers
turning on the reefers and for how long? Were customers running trailers excessively before the
pickup appointment? How much refer fuel was expended per hour on cooling a trailer in the middle of
summer?
The same excessive costs could be occurring when Marten drivers delivered loaded trailers to a customer,
and then left with an empty trailer without waiting for the trailer unload. How long did the trailer sit running
until the customer unloaded it? Once unloaded, was the customer shutting the trailer off? Justin
looked at the trailer pools at some of his larger customers, and noticed some trailers had not moved for a
couple of weeks. What was happening with those trailers? Justin began to think reefer fuel was a solid
cost reduction target to focus on, but how could he get the data he needed to verify his theory? And once
obtained, how could that data be used to effectively reduce costs for Marten?
Performance, efficiency and innovation carried Marten to the top, but with the inflow of massive amounts
of data–how could they sift through it all to gain the right analytics to stay on top?
Justin contemplated how to put together his plan of action to present to his Division VP. He had identified
reefer fuel costs as a target area for cost reduction. He also discovered that an enormous amount of information
had been collected by Marten’s existing technology, supporting his theory that there were cost
inefficiencies, and possible abuse by customers involving Marten’s reefer trailers. Justin knew the core
issue at hand was how Marten could transform this large amount of collected data into useful, actionable
metrics that could be used to reduce the identified cost inefficiencies and protect Marten’s bottom line.
Justin considered his options.
Existing Data
Marten could use the data already collected through current telematics systems to identify specific metrics
showing the exact points of reefer runtime that were unnecessary. The same data could also be used to
JITE: DISCUSSION CASES Volume 5, Case Number 9, 2016
Marten 9
identify runtimes initiated by customers outside of Marten’s knowledge. Marten could then approach offending
customers to reduce or stop that type of runtime, or if determined as necessary, adjust their contracts
with those offending customers to receive compensation. Existing data could also likely be analyzed
for other potential cost efficiencies beyond reefer runtimes. Justin thought it very possible that potential
cost reduction efficiencies were being overlooked because of a narrow focus on this single issue.
Expanding the analysis of existing data would certainly be a low-risk method from a cost perspective as
far as the data was concerned, but changing customer behavior might not be so easy.
New or Additional Data
A more effective action for Marten could be to focus on filling the current holes in the existing data available
by modifying the type of data collected, and the way it was collected via Marten’s telematics systems.
It was possible that Marten had not been utilizing its current technology in the best way to maximize
the use of the data they collected. If the possibility existed that Marten was not utilizing all of the existing
data to its maximum potential, it was also possible that Marten’s current technology was not being
used to its maximum potential to net new additional data. If the technology could be applied to identify
specific metrics for trailers while at customer warehouses, that could provide evidence and substantiation
for Marten’s claims of abuse. Maybe, there would be enough leverage to influence a change in customer
behavior. Justin thought an adjustment to the type of data Marten collected, as well as an adjustment to
the way it was collected using existing systems might be a low risk, low cost option for realizing cost savings
across the board.
New Technology
Justin considered the possibility that Marten’s current technology simply wasn’t suited for solving the
issue of cost reduction, specifically concerning reefer fuel usage and customer abuse. After speaking with
Randy, Marten’s VP of IT, there definitely seemed to be a focus on GPS technology and tracking Marten
driver actions more than the actions of Marten customers. Perhaps newer cutting-edge industry technology
was what Marten needed to achieve the desired cost reductions. Justin wasn’t sure what up-front costs
would be involved in acquiring and implementing new technology targeted at this specific issue, or if
Marten’s IT group had the knowledge and ability to implement the new technology if it was acquired.
But, if successful, the cost savings benefit of the new technology might make the up-front investment
well worth it.
Outside/Consultant Help
Another thought occurred to Justin. Maybe the bigger problem for Marten was not collecting more or different
data. It may be that Marten could benefit most from assistance in determining the root causes of
their issues through a detailed analysis. Justin knew from experience that it could often be difficult for
people to be objectively critical of their own work or processes. A review and analysis of their operating
processes and procedures, specifically regarding their use and the application of currently owned technology
might be of great benefit. Maybe if the review was conducted by an outside group of industry consultants,
they might be able to better isolate the root causes of the cost inefficiencies, and help with recommendations
for new technology, or better usage of existing technologies. This however, could carry an
estimated cost between $14,000 and $27,000. Would this up-front cost be more than Marten’s executive
leadership was willing to spend?
Do Nothing?
As Justin ran through these decision options, and the challenges associated with each, he began to wonder
if it was necessary to add anything new to what was already being done. Marten had already realized
some fairly significant cost savings from the data analysis conducted up to this point. It was probably a
DELEANIDES, GEBHARDT, HURD, STANLEY
10 Marten
safe assumption that a continued steady-state reduction in costs could be achieved through this previously
conducted analysis. Justin also thought he could find further efficiencies that realized cost savings by having
his drivers change their behaviors to provide him feedback on identified cost reduction areas after
each run. Justin was a people-person, and found it much easier to interact with his drivers to achieve results.
He was not overly confident in the cost reduction outcomes that could be achieved through data collection
and mining alone.
Justin had some decisions to make before he could make his recommendation to his Division VP. He was
certain he had all the pieces to the puzzle, now he just had to put them together.
References
Comprehensive truck size and weight (CTS&W) study. (2011). Retrieved from
https://www.fhwa.dot.gov/reports/tswstudy/Vol2-Chapter3.pdf
Davenport, T. H., Barth, P., & Bean, R. (2012). How big data is different. MIT Sloan Management Review,
54(1), 43.
Harps, L. H. (2004, September). The transformation of truck transportation. Inbound Logistics. Retrieved
from https://trid.trb.org/view.aspx?id=738735
Kettinger, W., Patel, J., & Ryoo, S. (2012, June 1). The role of information technology in causing and
reducing truck driver stress and its relationship to turnover. Retrieved from
http://www.memphis.edu/ifti/pdfs/cifts_truck_driver_stress.pdf
Moore, T. G. (1993). Trucking deregulation. The Concise Encyclopedia of Economics. Retrieved from
http://www.econlib.org/library/Enc1/TruckingDeregulation.html
O’Reilly, J. (2014, September). 2014 Trucking perspectives. Inbound Logistics. Retrieved from
http://www.inboundlogistics.com/cms/article/2014-trucking-perspectives/
Schultz, J. (2012, April 12). Top 50 trucking companies: Strongest get smarter. Retrieved from
http://www.logisticsmgmt.com/article/top_50_trucking_companies_strongest_get_smarter
Trucking industry overview – complete version. (2014, February 13). Retrieved from
http://www.irs.gov/Businesses/Trucking-Industry-Overview—Complete-Version#3
Tyler, G. (2014). History of freight transportation. Retrieved from
http://www.sourceconsulting.com/history-of-freight-transportation/
JITE: DISCUSSION CASES Volume 5, Case Number 9, 2016
Marten 11
Biographies
Jessica Deleanides is an MBA student at the University of South Florida in Tampa,
FL. She earned a Bachelor’s of Arts degree in International Studies from the University
of South Florida in 2009. She currently works in education and career development
while pursuing her MBA.
Justin Hurd is the Regional Operations Manager for Marten Transport. He regularly
identifies gaps in performance, operational compliance and adherence to established
protocols. He started his MBA program at the University of South Florida in
July of 2013 with an anticipated graduation in the fall of 2016.
Kevin Gebhardt is a Department Manager for Lowe’s Home Improvement. He
manages inventory and staffing in two departments, as well as controlling pricing
and running sales events. He received his Bachelors in Business Administration at
the State University of New York Institute of Technology in Rome, New York.
Kevin was previously a Sales Manager for Bridgestone tires.
Clay Stanley is Group Leader, Proposals for CAE USA, an Aerospace & Defense
Simulation company. He leads a team of proposal managers, cost & price estimators,
writers and editors in the planning, development, validation and delivery of
compliant bids and proposals. Clay is a combat veteran with over 10 years as an
officer in the US Army.
DELEANIDES, GEBHARDT, HURD, STANLEY
12 Marten
Exhibit 1: Early Timeline of Trucking Industry
Source: Trucking industry overview – complete version. (2014, February 13). Retrieved from
http://www.irs.gov/Businesses/Trucking-Industry-Overview—Complete-Version#3
JITE: DISCUSSION CASES Volume 5, Case Number 9, 2016
Marten 13
Exhibit 2: Greatest Challenges of Trucking Companies
Source: O’Reilly, J. (2014, September). 2014 Trucking perspectives. Inbound Logistics. Retrieved from
http://www.inboundlogistics.com/cms/article/2014-trucking-perspectives/
DELEANIDES, GEBHARDT, HURD, STANLEY
14 Marten
Exhibit 3: Overview of Trucking Specialties
Source: O’Reilly, J. (2014, September). 2014 Trucking perspectives. Inbound Logistics. Retrieved from
http://www.inboundlogistics.com/cms/article/2014-trucking-perspectives/
JITE: DISCUSSION CASES Volume 5, Case Number 9, 2016
Marten 15
Exhibit 4: Electronic Onboard Recording Devices (EORDs)
Source: Kettinger, W., Patel, J., & Ryoo, S. (2012, June 1). The role of information technology in causing
and reducing truck driver stress and its relationship to turnover. Retrieved from
http://www.memphis.edu/ifti/pdfs/cifts_truck_driver_stress.pdf
DELEANIDES, GEBHARDT, HURD, STANLEY
16 Marten
Exhibit 5: Marten’s Ratings
Source: Schultz, J. (2012, April 12). Top 50 trucking companies: Strongest get smarter. Retrieved from
http://www.logisticsmgmt.com/article/top_50_trucking_companies_strongest_get_smarter
JITE: DISCUSSION CASES Volume 5, Case Number 9, 2016
Marten 17
Exhibit 6: Marten Data Collection
DELEANIDES, GEBHARDT, HURD, STANLEY
18 Marten
JITE: DISCUSSION CASES Volume 5, Case Number 9, 2016
Marten 19
Source: Developed by case writer

  1. Individual Case Assignment

General
For the case assignment, you will investigate the firm’s problem(s) as they relate to Business Intelligence & Business Analytics (BIBA) and Information Systems (IS) and prepare a written paper for the case. The individual case must be completed by each student and submitted for grade by the due date listed in the course schedule.  The individual case assignments are noted on Blackboard under Upload Assignments.
The case analysis and discussion needs to address the main issues in the case related to BIBA and information systems.  Assume you are a consulting group and are giving advice to the CIO and other senior BIBA managers and they are the audience for your analysis report.  Read the rubric carefully and make sure you understand the requirements for “exemplary performance”.
Refer to the rubric and in addition the following instructions.  Points will be deducted if these instructions are not followed:
1) The report should be up to 5 pages using Times New Roman font size 12, double or 1.5 spaced in MS Word format.
2) Use appropriate 1-inch margins, headings and sub-headings to correspond to the sections mentioned below
3) DO NOT leave any blank lines between sections, paragraphs or headings
4) Use the following naming convention for your document: (Last name)(First initial)(IndCase) (Example: GudiAIndCase.docx where docx is the MS Word extension).
Executive summary – a couple of short paragraphs which summarize the remainder of the report
Background – use this section to lead in to your Problem Statement; identify symptoms, critical factors and the current state
Problem Statement – a succinct statement of the problem/dilemma/issue, preferably in a single declarative sentence; be careful to identify the real problem and not the symptoms of the problem
Analysis – apply models, course content, and outside research to support your position; logically discuss options, implications and tradeoffs
Recommendations and Conclusions – these should be your recommendations regarding how the organization should deal with the problem; they should be fully supported by the Analysis section
Appendices – References and Charts – does not count towards the 5 pages
 
Citations must be referenced according to APA style.
Appropriate references:  This is a library research paper and you must use at least 3 different sources, not including textbooks. These sources should be company websites, industry sources, journal articles, periodicals, such as the Wall Street Journal, Business Week, and so on, and governmental sources such as the SEC. Wikipedia and other similar sources are not to be used in this course.
 
The rubric to be used for grading all cases is shown below (refer to “Case Grading Rubric”).
CASE GRADING RUBRIC
 

  Earning maximum points in each box in ‘PROFICIENT’ column and / or points in columns to the right of ‘PROFICIENT’ meets standard.
  <<<<<<<<<< less quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . more quality >>>>>>>>>>
Performance Criteria Basic             (2 pt)* Developing (3.25 pts)* Proficient          (4.0 pts)* Accomplished   (4.5 pts)* Exemplary           (5 pts)* Score
Identifies and describes problem/s effectively Does not state problem(s) or identify symptoms, critical factors and current state in Background discussion. Somewhat states problem(s) in multiple sentences. May identify symptoms, critical factors and current state in Background discussion. States problem(s) in multiple sentences.  Identifies symptoms, critical factors and current state in Background discussion.  States problem(s) clearly in one sentence. Identifies symptoms, critical factors and current state in Background discussion. States problem(s) clearly and concisely in one sentence. Effectively and completely identifies symptoms, critical factors and current state in Background discussion.  
Applies Information Systems management models  Does not apply BIBA models, course content, and outside research to support position. Applies some BIBA models, course content, and outside research to support position. Applies BIBA models, course content, and outside research to support position. Applies most BIBA models, course content and outside research to support position. Completely and effectively applies BIBA models, course content, and outside research to support position.  
Analyzes case, and recommends actions  Does not discuss options and/or implications and tradeoffs. May not support position with research.  Somewhat discusses options, implications and tradeoffs logically. Some research supports position. Discusses options, implications and tradeoffs logically. Supports position with research. Flows smoothly into Recommendations Discusses most options, implications and tradeoffs logically. Position well- supported with research. Flows smoothly into Recommendations Completely and effectively discusses options, implications and tradeoffs logically. Fully supports position with research. Flows smoothly into Recommendations.  
Uses effective writing organization and format Does not communicate in clear, logical, and grammatically correct language. Does not use primary research sources and/or incorrect APA format. Communicates in ambiguous, and/or and grammatically incorrect language. Uses marginal primary research sources and/or partially correct APA format. Communicates in clear, logical, and grammatically correct language. Uses adequate primary research sources and correct APA format.  Communicates in exceptionally clear, logical, and grammatically correct language. Uses substantial research sources and correct APA format. Communicates in exceptionally clear, logical, and grammatically correct language. Uses significant primary research sources (at least 3 excluding textbook) and correct APA format.  
OVERALL GRADE (20 total possible points)*:  

 
 
 
Sample of Cover Page to be Used for All Assignments
 
Nova Southeastern University
Wayne Huizenga Graduate College
of Business & Entrepreneurship
 
 
 
Assignment for Course:             (Course number and title)
 
Submitted to:                            (Professor’s name)
 
Submitted by:                           (Student’s name)
(Student’s ID number)
(Address)
(Work phone number)
(Home phone number)
 
Date of Submission:
 
Title of Assignment:
 
CERTIFICATION OF AUTHORSHIP:          I certify that I am the author of this paper and that any assistance I received in its preparation is fully acknowledge and disclosed in the paper. I have also cited any sources from which I used data, ideas of words, whether quoted directly or paraphrased.  I also certify that this paper was prepared by me specifically for this course.
 
 
 
Student Signature:   ___________________________
 
*******************************************
 
Instructor’s Grade on Assignment:
Instructor’s Comments:
 

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