Logistics Analytics – Beyond KPIs on Tender Acceptance and Carrier Performance

Talk to a transportation manager about data analytics and they might tell you they’re already harnessing data collected by their TMS and other logistics IT tools to measure key performance indicators such as tender acceptance by lane, carrier check call compliance and other commonly examined metrics.  But how many are actually using Big Data analytics in ways that drive efficiency savings beyond those controlled by transportation into areas like sales and finance?  Yet, the benefits of thinking more broadly are sizeable and the Big Data technology exists to capture the data and perform the analyses.  How can shippers make better use of Big Data and to what end?

It used to be easier to drive efficiencies in transportation spend as a shipper of goods/products.  Shippers could persuade their customers to simply order more productless frequently – and they’d ship nice full truckloads.  One pick, one drop, one invoice and no messy allocations to consider.  Today, market forces, including just-in-time manufacturing and the “Amazon-ization” of commerce, drive customers to order less product, more frequently.  This results in higher levels of LTL and consolidated freight and the corresponding complexity in terms of allocating shipping costs across numerous customers.

This is where Big Data analysis can help.  There is a wealth of data captured in a TMS system and providers are racing to deliver Big Data-based logistics analytics tools to help shippers unwind some of the complex challenges they face as they attempt to keep transportation costs under control.  Applying data analysis to the complex challenges of cost allocation across the increasing numbers of LTL and consolidated shipments being moved today.


Depending on the nature of the network, the visualized data can be manipulated to view allocation models by weight, pallet, mile, ton miles, direct miles and others.  Selecting the most appropriate allocation model helps shippers get a bead on where their sales density may not be supporting the shipment costs per order. 


Looking for trends and patterns within multi-stop shipments using spreadsheets can be overwhelming.  However, a data analytics tool in the hands of an analyst with solid skills can transform the rows and columns into easy-to-absorb visualizations.  Seeing a population of actual shipment data from the TMS brought to life in color coded charts and graphs makes it easier for a shipper to determine which allocation method is most effective for their operation.

Depending on the nature of the network, the visualized data can be manipulated to view allocation models by weight, pallet, mile, ton miles, direct miles and others.  Selecting the most appropriate allocation model helps shippers get a bead on where their sales density may not be supporting the shipment costs per order.

Over time, left unanalyzed, poorly allocated shipments can impact negatively on a shipper’s profitability.  Whereas, analytics-derived allocation decisions can ensure shippers are properly pricing goods and charging appropriately for freight.

Freight cost allocation is but one of the many complex logistics management challenges clarified by the application of Big Data practices and logistics analytics.  There are many other areas where logistics analytics can help a shipper improve their understanding, drive increased efficiency and capture savings.