The Time Gap
Within the realm of supply chain there has always been an inherent separation between the notion of planning and that of execution. Historically, this separation was easily attributed to the time required to plan, as well as the gap in time between planning and when orders begin to be placed. In some aspects of supply chain planning, this time gap can be measured in months. As supply chain planning technology improves, the gap shrinks.
In the realm of transportation management, however, this is not the case. As transportation is an intrinsically execution-level activity, the gap between planning and execution is naturally limited, and in some cases, non-existent. The consequence is that plans resulting from transportation optimization technology must be operationally executable from the start. If not, planners are forced to react — sometimes to significantly less than optimal or negative results.
To combat this challenge, leading transportation solutions focus on improving the processes on either end of the spectrum: the ability to plan effectively and create a better, more actionable result, or to create more nimble execution processes allowing users to more efficiently react to imperfect plans or network fluctuations. With the former approach, technology providers focus on creating more comprehensive models that factor in as many network variables as feasibly possible while still allowing for reasonable solving times, resulting in a point-in-time approach that does not allow for the inevitable variability that occurs during execution.
Similarly, the latter approach — while providing a semblance of agility — puts all the onus of enablement at time of execution and consequently invalidates much of what any sophisticated planning engine was trying to achieve in the first place. This causes significant value leakage.
A Different Approach
A third option exists, however, in the form of creating an active interoperability between the planning activity and the ever-changing dynamics of the transportation network. This approach requires viewing transportation optimization in a different light, creating an environment where the notions of planning and execution become an intertwined, iterative set of inputs and processes versus the traditional approach of two distinctive and disparate independent activities. To effectively enable this approach, three distinct concepts must be actively invoked.
The first of these concepts is an iterative and incremental (instead of point-in-time) approach to transportation optimization. The solution must be able to take an initial plan and incrementally improve it based upon new information. An iterative and incremental approach to planning changes the paradigm to one where planning becomes an ongoing function that does not really end but instead continuously functions, ever improving on itself and releasing moves just in time to be executed. Additionally, in this model, the concepts of orders and constraints become organic and are carried across subsequent optimization runs so that each round of planning understands what happened before it and consequently understands the current operating environment.
Comprehensive and Dynamic Constraints
The second of these concepts is the inclusion of more discrete network constraints and subsequently the ability to consider those constraints dynamically over time. Transportation planning engines have become more sophisticated not only in their ability to consider complex routing and consolidation scenarios, but also in the expansion of the constraints that they can consider. To extend this further, the deeper those constraints can represent execution-level processes, the greater the interoperability. Yet expanding consideration of these constraints is not enough. Solutions must be able to dynamically consider how those constraints change over time, as well as layer in the previously stated fundamental of an iterative and incremental approach to optimization.
Examples of this type of execution-level constraint are the representation of available dock capacity and throughput and the availability of network asset capacity. In both cases, not only is it critical to have a representation of these constraints within the planning paradigm, but there also needs to be an ability to understand and process ongoing network changes.
The third concept in enabling effective interoperability between planning and execution — related to the consideration of comprehensive and dynamic constraints — is the inclusion of network constituents in the collection of actionable information. Many of the execution-level constraints that an effective solution should consider, such as dock availability and asset capacity, are dynamic based on the inputs of external parties, with carriers being the most prominent. It is critical that any solution have an established method or set of methods to capture this information in real time for consideration in any subsequent optimization solves.
A critical value driver in any transportation network is the ability to keep assets moving. Trying to create these opportunities can be challenging, particularly in a static planning environment where variability can occur between when an extended movement is planned and when the move has to actually occur. Many solutions leave this as an execution-level function but lose out on the impact that potential moves may have on actual upstream load building and carrier selection decisions.
The ideal solution can incrementally build backhaul or continuous move opportunities in the current planning cycle while leveraging moves that are already in transit, maximizing not only the value but the likelihood of successful execution.
Transportation management is an execution-level function, with little room for error between effective planning and flawless execution. The ideal solution combines these two traditionally separate domains into an actively interoperable and interactive cycle maximizing value creation and minimizing disruption.
Fabrizio (Fab) Brasca is vice president, global logistics at JDA Software. In this global role, he develops innovative transportation and logistics strategies across all industry verticals, strengthens executive-level relationships with JDA’s key customers and prospects and advises companies on best practices. Brasca joined JDA as part of the i2 Technologies acquisition in January 2010 after spending more than 10 years at i2 overseeing the company’s global transportation practice. His previous employers include InterTrans Logistics Solutions, IBM, Tandem Computers and ACNielsen. He holds an Honours Bachelor of Mathematics, Co-op degree with a specialization in business and information systems from the University of Waterloo, Ontario.