execution Archives

Guest Commentary: The Tougher Challenge for a TMS is Execution

Guest Commentary | By Mitch Weseley • 10/24/2013

As a follow-up to my previous commentary, which focused on setting the playing field for transportation routing algorithms and the fact that these are not really “optimizations” but heuristic algorithms, as well as the fact that modeling the constraints is at least as important as getting a lower cost, I want to now shift the focus to the tougher challenge for a transportation management system (TMS) – execution of the recommendations.

The tougher challenge that we face in this industry is trying to route (via algorithms) freight in an environment where the data are constantly changing. This is particularly important for companies that are building multi-stop loads and using pool distribution. These routings create “dependencies” where the routing of one order is dependent on many others.

For example, consider a shipper with a warehouse in Memphis shipping to the northeast. He might have one large order that’s half a truck going to upstate NY along with a dozen small orders going to a pool point in PA. Just as the algorithm recommends this route the big customer in NY calls and adds several items to the order. Suddenly most of the freight going to PA won’t fit. Their routings will have to change even though nothing about the orders has changed. This is dependent routing. If all of the orders were shipping as LTLs or single stop truck loads this would be a minor issue, but in our case, life just got a lot tougher.

The situation described above is exacerbated by the fundamental challenge that algorithms are basically batch processes and essentially run on static data. However, in our industry, the data are not stagnant — they are changing throughout the process. Additionally, much of the information can’t be known until after the algorithm makes its recommendation and the process of load tendering begins.

There are too many dynamic components in this process to list but below are several of the more important ones that are worth thinking about throughout this discussion.

Order Changes – Most companies strive to allow order changes into the process as late as possible. These changes typically come from an ERP and can occur after an order is picked or even staged.
Warehouse Changes – The WMS/TMS integration is particularly challenging. There can be stock outs and in some environments orders can fail QA. In other situations (particularly with mixed product pallets or cartons) the cube and/or pallet count from the ERP might be incorrect. In other cases weight could possibly change. Therein lies the proverbial chicken and egg scenario. The warehouse really can’t pick an order until they know when it will be shipped. On the other hand, order quantities aren’t solid until after it is picked so the algorithm is using data that may not be accurate.
Carrier Availability – Until a load is tendered and a carrier accepts it the rate that the load will run under is uncertain. Therefore one of the most important data elements driving the algorithm can’t be known until well after the algorithm is completed. There are related issues around the availability of specific equipment in some situations and in others there may be a problem getting any equipment given today’s driver shortages. Again, all of these key inputs to the algorithm aren’t known until well after the algorithm is finished.
Appointment Scheduling – This is another chicken and egg situation. If we go back to our Memphis shipper, he might have two orders going to the northeast. Both orders have delivery windows set for only Thursday. However, when the destinations are contacted for appointments the first stop is booked up for Thursday and wants a Friday delivery but the second stop still needs to be delivered on Thursday. Again, this load can’t run as created by the algorithm.
Need to Tender Some Loads Before all Orders are in – Most shippers face a driver and general truck load shortage. Therefore, many shippers want to start tendering loads as early as possible. To reference the Memphis shipper again, he might have an order that is almost a full truck placed late Monday, to ship on Tuesday. He could tender this load as soon as he sees it to get a good carrier and rate. However, if he waits until late Tuesday morning he might find a great order to throw in the back of the truck and have it ride for free.
Real Time Flow – I like to view this process as one where orders are flowing like objects on a river. The orders get created, they go through a process and then they ship. This process sometimes feels like the orders are being shot at with changes such as those described above. Those shots may change the course, but somehow they still need to come out in one piece and routed correctly.

It’s clear that a TMS should not simply send a lot of data to an external algorithm a few times a day and expect to create much value for the customer. In fact, a TMS needs to be built as an execution system to manage the process described above that accesses different routing algorithms, at different points in the process to help make decisions that are too large and too complex to be made manually. The very idea of using a third party algorithm or having an algorithm as a separate module where data is just thrown to it en masse is fundamentally flawed and fated for failure. History shows many companies that have taken this approach and not one has created the quality products that this market needs.

Given that these issues are complex, hard to solve, and expensive when done wrong, one might ask, Is the view worth the climb? Is it worth dealing with all of these challenges when one can just ship the freight direct on single stop truck loads and LTL?

First of all, the potential savings are enormous. I’ve worked with clients that have cut 10-20 percent off of their freight spend. Most companies are willing to put up with a lot to save that much money. Secondly, these issues don’t occur very often so they can be dealt with fairly easily if one has the right tools. However, if these issues are not dealt with correctly all of the savings can disappear or freight could be delivered late or broken. Third, multi-stop routing should improve customer service with faster and more reliable deliveries and less OS&D. So the answer is yes, it is most definitely worth the climb if one has the right tools.

This is an extraordinarily difficult problem to try and solve, and the problems will continue to get more interesting over time as we are more creative with the options we have to ship freight and the requirements are more stringent on how customers want orders delivered. The industry is in need of a solution that will focus on and address issues such as:

Heuristic algorithms to solve problems that can’t be truly “optimized.”
Solving these issues subject to countless constraints that are very hard to model and can’t be violated without serious repercussions.
The realization that algorithms must run on data that is constantly changing.
There are several “chicken and egg” scenarios where decisions need to be tweaked or changed dramatically because of information that could not be known when the algorithms were initially run.
Recommendations must be acted on immediately as orders must be picked (sometimes within minutes) and trucks need to be loaded immediately thereafter.

We have better connectivity tools to provide better quality data faster and we have more powerful hardware to write more complicated solutions. Therefore, there are significant opportunities to revisit how we are currently solving today’s problems – creating brand new, next generation solutions that can change our industry entirely.

Mitch Weseley is the CEO of 3Gtms. With 30 years in the industry, Mitch is widely regarded as the “father of the TMS industry” having created six successful companies in the technology and logistics industry, including Weseley Software and G-Log.

Guest Commentary: Next Generation of Visualization and User Interaction

Logistics Software Vendors | By Fabrizio Brasca • 09/06/2012

I was recently invited to participate in the Massachusetts Institute of Technology (MIT) Center for Transportation & Logistics’ roundtable focused on the trade-off between planning and execution. The event spanned two days and presented an agenda of discussion topics across the multiple time horizons of planning (strategic, tactical and operational) as well as views on process and technology. The audience was comprised of an excellent collection of experts, including practitioners from a variety of industries, academics and a few of us from the software solution industry. The format of the roundtable entailed a number of session topics complemented by a series of interactive polls designed to spark and stimulate active discussions among the group, which was expertly facilitated by Dr. Chris Caplice, Executive Director of MIT’s Center for Transportation & Logistics.

Of particular interest to readers of this blog was the final session entitled “Software Provider Perspectives.” In this session, my industry peers and I were asked to give a brief presentation on some of the underlying concepts and approaches to our respective solutions, as well as give a view into what we see as the future of the planning and optimization space. This was followed by a lively and very interactive group discussion on the challenges, limitations and opportunities of technology in this domain. In particular, my peers and I were keen on listening to what the users of our capabilities had to say.

The discussion was lively and easily the most interactive of all the sessions, and while there were a number of concepts bandied about from buying/selling behavior to optimization approach, there was one particular view that emerged consistently at the forefront of everyone’s mind. The issue was visualization and usability.

As views were shared and opinions presented, the topic of usability, information (not data) presentation and overall visualization appeared to be the hot topic. In particular, the need for more intuitive interfaces that mask complexity, define metrics, and aggregate views designed to synthesize the abundance of available data into something actually digestible for both the day-to-day user as well as the supply chain senior executive. I cannot say this was surprising as the industry as a whole has been so focused on making more sophisticated solutions that it is easy to see how the solving technology has outpaced the focus on user interaction. That being said, however, a key salient point that emerged was that despite the desire for better interaction, it was clear that better interaction did not mean simplifying the underlying engines. There was no complaint about sophistication or approach and that a “simple” user interface did not diminish the need to be able to solve complex problems. The ideal was the merging of these two paradigms into one.

As the discussion continued, we began to engage on where we thought this might go, and in particular, the role newer user interface and visualization technology might have in the world of sophisticated supply chain planning. No one had a definite answer but here are a few directional thoughts that, in my view, are critical when thinking about the next generation of user interaction:

A picture is worth a thousand words – As a rule, people understand and react to images better than they do streams of data and tables. Metric-enabled graphics, intuitive and directional alerts and representative maps can all be effective tools to communicate information to a user.
What I want, when I want it – Configurability is becoming more and more important as an enabling factor to user productivity. The ability to not only decide what I want to see but also the sequence and importance can dramatically increase ease of adoption.
More than one way – Mobility has become a fact of life for the average consumer, let alone the supply chain practitioner. Enabling views on multiple formats for different levels of the organization without having to be a slave to a desktop creates organizational synergy.
Enable change – I have long been a proponent of the interoperability between planning and execution in the transportation space. Along those lines, interfaces should provide effective ways for users to digest and act on rapid changes that occur within their domain through a combination of sensitivity-based alerts, embedded actionable analytics and powerful underlying engines that can weed through available “big data” and suggest courses of action.

It was an exciting and rewarding experience sharing and interacting with my peers and customers in an open forum. I listened and learned and I particularly enjoyed discussing and speculating on where we could go. It will interesting to see the fruits of our debates unfold.

Fabrizio Brasca is vice president, global logistics, JDA Software. He is responsible for developing innovative transportation and logistics strategies across all industry verticals.

Guest Commentary: Effective Interoperability between Transportation Planning and Execution

Guest Commentary | By Fabrizio Brasca • 06/30/2011

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.

Iterative Optimization
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.

Network Collaboration
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.

Conclusion
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.

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