The current outlook for the rail freight industry is, at best, mixed. Where rail freight once was the price-competitive transport mode of choice, low fuel prices and increased availability of trucks in the spot market have resulted in low freight rates that are expected to remain low¹. It is becoming increasingly difficult under current circumstances for rail freight operators to earn sustainable margins and create returns for shareholders. In the US, rail operators have seen an accelerating decline in high margin coal business and McKinsey reports that none of the larger European carriers have achieved average margins of 3% in earnings in recent decades, and at least 7% is needed to cover their costs of capital².
To counter this enduring situation of low profits, some operators focus on reducing operational costs, cutting personnel, and expanding to other modes of transport. In the long run, however, these measures are unsustainable. Financial benefits are shrinking with each round of cuts that are made and expansion exercises require massive investments. Furthermore, personnel cuts are a highly unpopular move.
These industry developments are beyond any rail freight operator’s control. However, there is one that isn’t ― pricing.
Developing an effective pricing strategy and executing it is not a trivial challenge. Rate structures in rail freight are complex. Costly pricing errors and inconsistencies are commonplace when quotations are made with too little data, outdated systems and spreadsheets. Reliance on traditional cost-plus pricing as a default position rather than a revenue maximizing strategy is widespread. Lack of visibility into rolling stock utilization at the point of transaction and an inability to accurately predict future usage further contributes to the challenges.
When done right and enabled by predictive analytics however, pricing can be the most powerful profit lever available. For decades, fixed pricing strategies have rendered operators unable to adjust their rates in response to market fluctuations, innovate their portfolio of services or capitalize on market opportunities. Today, these operators are starting to see pricing as an opportunity to defend and expand their share of the market and run profitably in an industry that’s predicted to remain largely flat in the coming years.
Research shows that effective price management can increase margins by 2 to 7% within 12 months, and yield a 200 to 350% return on investment.³
By tapping into the power of pricing analytics, rail freight operators will stand not only to reap financial gains fast, but can secure a strong position to compete ― and win ― in the future. It’s time to rethink pricing processes and technologies to maximize revenue, increase margins and return to profit. In order to compete with other modes of transportation, rail freight operators need an innovative pricing approach that takes into account historical pricing patterns, fluctuating market forces, and individual customer behavior. In-house analysts also need the ability to respond quickly and optimally to changing market forces – whether to capitalize on an opportunity or minimize the effects of a disruption. Reducing under- and over-pricing can mean the difference between profit and loss.
How effective pricing can raise profitability
In a study by McKinsey that surveyed of over 1,000 pricing initiatives in B2B industries, it was found that the initiatives resulted in a 2 to 7% increase in return on sales 4. The key enabler of an effective pricing initiative is a price optimization solution that is fully integrated with existing planning systems. It should provide end-to-end visibility, take into account all relevant pricing and utilization data, customer behavior, rules, constraints and market factors, and use all of this information to provide pricing guidance by prescribing prices that optimize overall company profitability.
A price optimization solution reduces manual effort, along with all the pricing anomalies and costly errors that come with it, and helps analysts to consistently quote prices that maximize overall profitability. Analysts can then focus on other value-added activities such as reviewing pricing strategies, managing pricing campaigns and evaluating tender bidding processes. Being based on historical data, the consistency and accuracy of a price optimization solution is assured. This ensures that arbitrary and costly pricing choices which can erode a customer’s trust are avoided.
Support for what-if scenarios helps analysts to see the consequences of decisions before making them ― for instance, the operational feasibility of a train movement or the financial viability of a customer consignment, train service or market service. Decision support systems need to assist analysts in looking beyond the profitability of a single transaction and in evaluating and understanding the impact on the business as a whole and its overall profitability. This functionality increases the analyst’s confidence in making optimized pricing decisions.
A pricing optimization solution can offer profitability analysis of both operating costs and revenue at a granular level (e.g. by train service, market service or customer). It can also be used to ensure that resultant back-haul movements are factored into the sell price. The ideal strategy is to take expected revenue opportunities into account, and use these as the basis for defining balancing requirements.
Lastly, the solution should be able to take into account the expected utilization. Integrated Demand Planning predicts how full the rail cars will be by the time they leave the yard. This is key in maximizing revenue. When the expected utilization of a train is low, demand can be increased considerably by marginally reducing the price so that more revenue is generated from the same train. If on the other hand capacity utilization is high, prices can be increased up to, but not beyond, the point that a particular customer is willing to pay. By dynamically choosing prices based on the expected remaining capacity and price sensitivity, the available capacity is used such that revenue is maximized.
A commitment to pricing improvement is easy to make but challenging to execute. Business change, particularly one that involves technology, is rarely successful with a ‘big bang’ approach. Instead, a prudent plan that gets you from where you are to where you want to be, at a comfortable yet consistent pace, is more likely to be successful.
¹Trucking, Rail Outlook Poor in 2016, Analysts Say. The Wall Street Journal, Erica E. Phillips, Jan. 14, 2016
²Daniel Girardet, Jurgen Muller, and Anselm Ott; Getting freight back on track (McKinsey, 2014)
³Larry Montan, Terry Kuester, and Julie Meehan; Getting pricing right (Deloitte University Press, 2008)
4Jay Jubas, Dieter Kiewell, and Georg Winkler; Turning pricing power into profit (McKinsey, 2015)
Dr. Edwin de Jong is the Head of Predictive Analytics Technology at Quintiq, a global leader in supply chain planning and optimization (SCP&O). He has a background in machine learning research, and published over 60 technical articles in his field. To bring ideas from research into practice, he co-founded data mining company Adapticon, with a focus on demand forecasting, predictive analytics, and traffic prediction. Edwin has been with Quintiq since 2012, and led the development of Quintiq’s Demand Planner solution.