Reduce LTL Costs Through Big Data Optimization
The real estate phrase location, location and location is used to point out to buyers a home can increase or decrease in value due to its location. The reason for repeating the location three times is because location cannot be emphasized enough in real estate decisions.
The reason for bringing the location phrase to and LTL blog is data, data and data cannot be emphasized with LTL studies. There is nothing more critical in in developing a winning LTL shipping strategy than having the data to analyze the shipper's network for freight savings and KPI opportunities.
While data is the driving factor in LTL decisions, other challenges come into focus when shippers try to confront their LTL analysis, which are outlined below:
- All the data required is not readily available or there are mountains of historical, forecasts, budgets and KPI's.
- Either is a challenge.
- Internal systems are not robust enough to crunch through the data to efficiently sift through and prioritize the LTL opportunities.
- Lack of internal resources to work through the details.
- Lacking the knowledge to understand the characteristics that bring the opportunities.
- Unable to translate their data into the storyline to gain further funding to execute the results.
- Not enough resources to implement and execute the opportunities to deliver the desired results and to monitor for continuous improvement.
Because of the above challenges, managers often feel they are forced to table the analysis in favor of executing the daily fires and miss the opportunities for pushing their organizations to the next level in service and cost improvement. We are here to say this does not have to be your option. There are numerous supply chain coaches, consultants and third-party logistics companies that are in the market to help by leading the charge in data collection, analysis and ultimately a solution the logistics team can execute and monitor for future success, so read on for more information.
The following is the list of data elements required for an LTL analysis: (preferably 12 months)
- Origin city, state, and zip code
- Destination city, state, and zip code
- Transit requirements
- Freight class
- Costs broken down by line haul, fuel and accessorials
- OS&D Claims
Not all organization have the above, but there are strategies described below that will get a data challenged shipper past the data element issue.
Data Analysis Process
Typically, the best places to acquire the date is through a transportation management system (TMS) or freight payment system or provider, but when these options are not always available LTL data is compiled from a variety of systems that include: shipping / WMS system, accounting records, sku databases, carrier invoices, carrier tariffs, etc.
Once the data is compiled, it is reviewed for completeness. Often assumptions will be needed to fill data voids, so the analysis can be run. Tribal knowledge and piecing other details together allow for well educated guesses to fill in the gaps.
After the data is reviewed for completeness, technology is brought to the equation to transform the reems of data into real-life solutions, with the key being real-life.
The first run through the analysis establishes and validates the baseline data. This validation is done by running an analysis of all the data elements and assumptions against the actual billing to ensure it closely matches the actual spend. This process is critical and often takes several passes before the data aligns to actuals. The reasons for the misalignment stems from incorrect assumptions used in filling the data voids and / or actual invoices were improperly recorded and paid. Both problems are correctable, but critical and may take several passes of refinement.
The next step in leveraging the technology is to optimize the possibilities found in consolidations, pooling, multi-stop runs and optimal tariff selection. As indicated earlier, it is critical to ensure the analysis is generating real-life solutions. What we mean by real-life solutions is be aware that not optimization tools are created equal. To transform data into real-life solutions it needs to account for LTL and truckload shipping practices, dock performance and characteristics, hours of service, etc. If the analysis does not incorporate these topics., along with industry knowledge, the analysis will be nothing more than a spreadsheet exercise that will never meet expectations.
While not incorporating all the details required may sound like consulting group crying wolf to drive fear and sales for itself, it happens more than one would expect. We have seen shippers get sold the goods and then been further trapped in the solution provided because the group that analyzed their data also sold them the solution that required the shipper to integrate with their TMS to execute. All this can be avoided by going into the engagement with open eyes and mind through well thought out questions on how the analysis will be performed and taking a critical and skeptical deeper dive into the details once the analysis is presented.
The following is an example to put an explanation point to the theoretical analysis that missed real-life situations. In this example a shipper received what we often call a "spreadsheet analysis" on their shipments with multiple 3-stop and greater shipments into grocery warehouse locations. Experience would say that one of three things are incorrect with such an analysis and should have thrown up the red flags:
- Grocery warehouses are well known to have dock challenges that slow deliveries and pick-ups.
- Many trucking companies do not want their assets at grocery locations.
- If the carrier will allow its assets to be directed into a grocery location, they will not guarantee multiple stops of 3 or more or they escalate their stop-off fees to make the costs uncompetitive against the baseline.
The shipper in this example instituted the TMS of the analyzing company and never obtained the promised savings and improved KPI's.
(We apologize for the detour on this article, but have seen this situations too many times not to point it out.)
Getting back to the analysis itself, the optimization process will be run multiple times to find the consolidation, pooling, multi-stop and rate optimization. Each run will include various assumption changes, with some examples of changes provided below:
- Incorporating other LTL provider tariffs that may be a better fit for the shipper.
- New rate structures for current LTL providers brought into the analysis.
- Incorporate various truckload carriers for the consols and incorporate domestic intermodal when possible.
- Change pool points to find optimal pool locations.
Once the analysis and decisions are completed and signed off by management, it is time to execute and monitor against the objectives, which is a continuous process.
There are multiple third-party logistics companies and freight consulting providers that are in the market to help with these analysis, but as mentioned earlier they are not all created equal so find the one that is the best fit for you.
Learn more about optimizing your company's LTL shipping.