I recently watched an interesting YouTube video that basically mapped out the history of the universe using a simple chronological chart with milestones ranging from the birth of the Milky Way all the way to the existence of life on earth. Based on a 24-hour clock, this translated to life existing on earth during just the last few seconds of the very last minute. While watching the video, I couldn’t help but compare the history of the universe to the ongoing digital revolution. I remember writing my first computer program using punch cards on a mainframe, and my first personal computer had only a few kilobytes of memory — just a few decades ago. It wasn’t long ago that the lack of data and computing power were real constraints for those that desired robust analytics to better manage their business. Today, we now have extraordinary amounts of data and computing power; however, we are only just starting to fully leverage the immense insight and value of all this data. This begs the question: “Do you know what to do with your parcel data?” I can assure you that companies wanting to become best-in-class are thinking hard about this question.
To answer fundamental questions about your parcel spend and formulate strategies for optimization, you need to know and understand your current state. A common method for defining your current state is understanding your parcel shipping profile. Obviously, top-level package counts and total spend is important, but it isn’t really helpful in answering more complex questions about the carriers you use, optimal origins, or contractual rate discounts and their alignment with shipping patterns. To answer these sorts of questions, you really need to fully understand your shipping profile. To get to the fun stuff like saving money based on executing optimization strategies, you can’t get there without a complete understanding of your “as-is” state. So, let’s briefly refresh our memories on how to define your shipping profile.
A parcel shipping profile generally consists of two distinct types of data element sets or descriptors. The first set consists of all the components that support how much you pay to ship a package such as service, zone, weight, volume, package type, or residential destination. The second element set allows for the sub-categorization into business units, channels of delivery, or any component that describes your logistics flow from supplier to customer. Combining these data sets with transportation cost information (base rate, fuel, and accessorial charges), time in transit information, and other aggregate elements completes the overall profile definition. Collecting this comprehensive set of data over a long period of time and adding a robust analytical visualization tool enables you to capture and fully understand your current state. Additionally, by refreshing this data regularly, you can begin to effectively measure certain operational key performance indicators (KPIs), such as transit time or delivery status, to monitor and reduce the risk of disruption during peak order cycles.
With a firm foundation and understanding of your current state, you can now quickly answer questions like “What did I spend?”, “How did my carriers perform?”, or “Is my contract aligned with my shipping profile?”. Now it’s time to leverage the value of this insight and develop effective optimization strategies that can create significant value for your business. These strategies can range from going to market via RFP for your parcel requirements, aligning your distribution network to reduce distance and transit time, avoiding unnecessary accessorial charges, leveraging regional carriers for last-mile delivery, reducing the use of premium services, or changing your packaging to reduce dimensional weight. To fully understand which of the strategies may be appropriate and reasonable, a different level of analysis is required.
The Power of Predictive Analytics
Predictive analytics provide a powerful capability to actually model potential changes to your “as-is” state and essentially predict what the potential outcomes could be. It is a vital tool and can offer key insight and validation of your strategic initiatives. All predictive models require good (and very clean) data and typically start with a historical perspective, which acts as a baseline. Identifying and understanding any variance to baseline (current course) often reveals the best option to follow and establishes a prioritization of your overall optimization program. Whether you are targeting cost reduction, attempting to reduce transit time, or find a balance between the cost to serve and service, predictive tools can be successful at maximizing the value of your parcel data.
How many times have you set out to achieve something only to find that the actual execution of your plan is hamstrung by unexpected roadblocks? Measurement of how well your strategic plans are meeting expectations and having answers as to how to improve or make corrections is as important as the plan itself. To maximize the savings associated with a plan or strategy, getting as close as possible to optimal execution is paramount. This is where your shipping profile’s value is really highlighted, as it enables you to compare your historical profile (or planned profile) with actual execution results and monitoring any variance to your plan.
Ultimately, being able to answer questions such as “Is my average transit time reduced?”, “Is my average cost per shipment lower?”, or “Are accessorial charges being reduced relative to freight?” is the name of the game. More importantly, being able to determine what actions you can take to make changes in your program or change those outcomes is critical.
In the end, maximizing the value of your parcel data requires a best-practice approach to describe, predict, and monitor actions to stay on course. While it may seem daunting, establishing a disciplined approach to capturing and normalizing data along with an adoption of the proper analytical tools will enable you to see, plan, control, and ultimately save money, which answers the fundamental question of how to maximize ROI and “What do I do with my parcel data?”
Steve Beda is EVP, Customer Solutions, Trax. He has spent the last 25 years working with companies on supply chain automation, execution strategies, compliance strategies, and more recently, spend management strategies. Over the past ten years, he has assisted dozens of clients with contract optimization strategies through coordination of transportation RFPs as well as measurements of savings post-RFP. He has blended the effective use of predictive modeling, modal expertise, and an optimized process for all modes of transportation.
This article originally appeared in the January/February issue of PARCEL.
