Rail Fleet Optimization

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SherTrack Applies Predictive Analytics to Rail Car Management

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Rail Fleet Optimization

  1. 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 1 www.SherTrack.com ©2010, SNAPPS and SherTrack are trademarks of SherTrack LLC, other product or service names mentioned herein are the trademarks of their respective owners.
  2. 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. 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 i 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. 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 i http://www.busmanagement.com/article/Complex-Manufacturing-Process-Innovation-for-Survival/ ©2010 SherTrack LLC -4- www.SherTrack.com

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