SherTrack Applies Predictive Analytics to Rail Car Management

1. Lean Rail Fleet
Control
Rail transportation is a key logistics component for large volume manufacturers. Rail transportation
has both unique advantages and challenges for organizations focused on operational excellence.
Major rail users now have new means to improve rail fleet management through the application of
Lean manufacturing processes that are enhanced with predictive analytics. By applying predictive
analytics used in next generation Sales & Operations Planning solutions, rail fleets can be “Lean
sized” and daily operating procedures augmented with valuable predictive alerts to reduce costs and
improve execution. For demand-driven operations, railcar usage can be synchronized with
production scheduling to drive even greater performance. Companies that successfully adopt these
new railcar fleet sizing and control methodologies can improve service and competitiveness while
reducing operating risks and costs.
Aspects of rail transportation
Rail line haul rates are approximately 3 times lower than the corresponding truck rates offering a
substantial logistics savings for large volume producers. Fewer shipments on more secure routes
reduce handling costs and product risk during transit. Furthermore, unlike trucks, railcars provide
flexible short term storage of inventory for both producing and consuming locations. Using railcars for
inventory storage reduces fixed storage requirements and provides flexibility to organizations dealing
with variation in supply and demand.
Railcar storage is more flexible than storage tanks, but creates higher costs and requires tight
logistical control to maximize the benefits. Imprecise control of railcars generates inefficiencies which
are difficult to track and harder to control. Typical multi-year lease structures limit the ability to quickly
reduce a railcar fleet in response to lower demand and many firms experienced this first hand during
the recession of 2008-2009.
Railcars are typically obtained on long-term leases as fixed cost financial obligations versus a more
variable cost structure typical of truck transportation. Railcar expenses tend to be sticky, with a
propensity to add to the railcar fleet over time. Lacking practical tools for sizing and controlling railcar
fleets, many companies find themselves with larger than necessary fleet sizes and no practical
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2. means of proactively identifying transient railcar shortages in time to conveniently arrange the
necessary remedial actions.
The 2008/2009 recession has punished those organizations with substantial fixed costs that were
unable to right size expenses as sales revenue plummeted. Intense pressure for cost control has
continued as organizations fight to preserve cash and improve operational capability and competitive
position. One practical means to improve financial performance and achieve operational excellence
is the application of business intelligence and predictive analytics to strategic process improvement
initiatives.
Achieving Operational Excellence
Operational excellence, stated simply, is minimizing waste while maximizing customer value.
Reducing the number of railcars in a fleet not only lowers fixed operating costs but also simplifies rail
yard operations. However, to maximize customer value requires more precise control of the railcar
fleet, an accurate and timely method to allocate railcars by demand, and more accurate fleet sizing
techniques.
Financial analysis reveals that
the optimal performance is
actually achieved with a 96% Logistics Costs versus # of BRCs
to 98% railcar delivery $8,000,000 100%
performance, not 100%. That $7,500,000 99%
is, a complementary mixed- $7,000,000
98%
mode logistics strategy where 97%
Service Level
$6,500,000
approximately 1 out of every
$ per Year
96%
30 shipments is sent by truck $6,000,000
95%
instead of railcar is the sweet $5,500,000
94%
spot for achieving operational $5,000,000
93%
excellence. This mixed mode
$4,500,000
approach allows organizations 92%
to have fewer railcars in their $4,000,000 91%
(fixed cost) fleet, while $3,500,000 90%
450 465 480 495 510 525 540 555 570 585 600
ensuring world-class
Number of Bulk Rail Cars
performance through the use
BRC cost Inv holding cost TL Frt differential Service Level
of strategically utilized truck
transport.
Less obvious, but equally important is the fact that more precise control of railcars will reduce
business risk by accurately predicting where and when you need railcars. Proactive control ensures
that potential problems with railcar availability are accurately predicted in advance, providing
adequate time to reallocate existing railcars, adjust production schedules or arrange for alternative
(truck) transportation as business needs dictate.
Lean pull processes are the most efficient basis for operational excellence, and predictive analytics
enables the adoption of Lean pull in complex manufacturing and transportation environments. Using
historical business intelligence, predictive analytics and computer modeling, it is now feasible to size,
allocate and control your rail fleet more competitively using practical, commercially available
solutions.
A Practical Approach to Achieving Operational Excellence
Companies pursuing operational excellence in their railcar operations will need enhanced
methodologies and tools to develop and sustain new high performance operating procedures. There
are three stages of maturity for achieving a high performance Lean rail fleet: Determining the Lean
size of the rail fleet, enhancing operations with predictive control and finally integrating and
synchronizing production and railcar deployment with demand-driven manufacturing.
©2010 SherTrack LLC -2- www.SherTrack.com

3. Lean Rail Fleet Sizing
Next generation Sales & Operations Planning techniques, predictive analytics and Lean demand pull
methodologies can be combined so that businesses can better understand their process capabilities
and more precisely assess their requirements for a Lean rail transportation infrastructure.
The rail fleet network is modeled in
SNAPPS using predictive analytics of Fleet Performance Curve
consumption patterns by customer and
transportation time probability profiles for 100%
each route. This model is then used with
next generation Sales and Operations 98%
LEAN Zone
Planning (S&OP) techniques for a robust for
96%
analysis of demand scenarios for the Operational
Service Level
Excellence
tactical planning horizon. Tactical
94%
operating plans, including fleet size Redundant
recommendations, are developed from Assets Mask
92% Inefficiencies
this extensive analysis. Furthermore,
operating conditions and demand 90%
assumptions are identified that would Undersized Fleet
trigger a review of the tactical plans and 88%
Undermines
Peformance
rail fleet requirements.
86%
Typical fleet sizing scenario curves map 450 500 550 600 650 700
the system capability of customer service Fleet Size (Rail Cars)
vs. fleet size for a transportation network
with specific customer demand, transit time variation and supply constraints. The curves help
companies understand past operating practices and rank the historical performance of specific
transportation routes against feasible operating capabilities. More importantly, the fleet sizing
scenarios promote understanding of the possible failure modes and the system performance at
different fleet sizes and sub fleet allocations.
Predictive Railcar Control
To achieve sustainable business success, organizations must strive for excellence in execution of
their day-to-day operations. Continuous improvement initiatives targeting infrastructure and enabling
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processes have been shown to produce long lasting returns on investment . Predictive analytics that
use business intelligence data to more completely understand, and accurately predict the behavior of
complex business systems offer practical new techniques to competitively improve operating
performance.
The rail fleet model developed for Lean Effect of Predictive Control
Sizing forms the basis of predictive rail
100%
fleet control. The model contains
consumption profiles by customer
location and route specific delivery time 98%
probability profiles for each railcar route. Predictive Control
Service Level
For predictive control, this model is Improves Fleet
connected to daily updates of customer 96% Effectiveness
consumption (i.e. new orders) and the
current location and route of each car. 94%
Performance Curve
From these up-to-date initial conditions, With Predictive Control
the predictive model generates operating
92%
alerts that proactively identify car and
location mismatches for the upcoming 14
to 28 days. Alerted to pending issues 90%
with plenty of lead time, the railcar 450 500 550 600 650 700
Fleet Size (Rail Cars)
planning team can evaluate remedial
action(s) to preserve on-time delivery performance, understand the future ramifications of each
choice and select the most appropriate option.
©2010 SherTrack LLC -3- www.SherTrack.com

4. This predictive railcar control model provides a key operations tool to identify rail transportation
issues with sufficient lead time to take corrective action. A further benefit is the on-going ability to
easily execute scenario analyses as significant business operations issues arise (e.g. hurricane risk
mitigation planning, business expansion or contraction, logistics planning, etc.).
Synchronized Demand-Driven Manufacturing
The Lean pull process is known to be the most efficient method for executing a firm’s order-to-
fulfillment process. However, manufacturers with complex product mixes are unable to use the
Kanban method that is used to implement the pull system in traditional Lean manufacturing (i.e. the
Toyota Way). However, demand pattern research has led to the development of innovative
predictive analytics that provides an effective demand signal for implementing the pull process in
even the most complex operations.
SherTrack’s demand-driven manufacturing solution (SNAPPS) provides an easy to use and
implement system for adopting the Lean pull process in complex facilities and complements existing
advanced planning and optimization technologies. Railcar resource management is an integral
component of SNAPPS’ overall architecture. SNAPPS effectively synchronizes production and
railcar utilization with true customer demand to truly achieve operational excellence. In this demand-
driven environment, customer orders trigger the optimal production and transportation response to
maximize business performance.
This demand-driven process reduces conversion costs through fewer and more efficient production
transitions (or setups), increases effective capacity (OEE%) and improves on-time delivery (OTD%).
It also enhances return on net assets (RONA) by paring excessive railcars and product inventory.
Summary
For businesses committed to operational excellence, advances in predictive analytics have enabled
new Lean based, demand-driven operations. The synchronization of true customer demand with
supply and transportation assets drives a new level of superior performance and reduces operational
risk.
More Information
For more information, call SherTrack at (248) 383-5620 or visit us at www.SherTrack.com
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©2010 SherTrack LLC -4- www.SherTrack.com