Five Obstacles 3PLs Need to Overcome Before Effectively Utilizing “Big Data.”


It’s no surprise that data driven decisions are continually being incorporated into decision making and management of processes throughout the supply chain.  

Big data will allow companies (shippers) to optimize the supply chain, increase visibility across organizations, and drive efficiency throughout the supply chain.

Large logistics providers understand this and have more capacity to help shippers drive big data initiatives. However, mid-level 3PLs seem to be laggards in adopting processes for disseminating big data, but it’s not all their fault.

Big data sounds sexy and easy, but it comes with some inherent problems that make it costly to use in an effective manner.

Mid-level 3PLs that can figure out how to utilize and incorporate big data are in a prime position to differentiate themselves from other providers.  Only “35% of shippers say 3PLs can support their big data initiatives.”

Closing the gap on utilizing big data will become a competitive advantage for 3PLs trying to build stronger ties with shippers looking to outsource supply chain processes.

Benefits of using big data in the supply chain

“81% of shippers said the effective use of big data will become a core competency of their supply chain organizations.”

The following list represents the most important attributes of utilizing big data from the shipper’s perspective:

  1. Integration across the supply chain
  2. Improving data quality
  3. Improving process quality and performance
  4. Increasing levels of data transparency

I like the way Supply Chain Brief put it:

Think about it like this: if a company examines relevant, structured data and learns that specific delivery locations are not profitable, it can create a strategy to serve popular locations more frequently, like introducing a new distribution center to the area or increasing the transportation schedule to the closest DC. The company can also reevaluate its need of DCs in the less-profitable locations or have shipments and deliveries to that area less often. 

Digital wi fi signals filling a downtown business area.

The effectiveness of data is directly tied to the alliance between organizations, relationships within the supply chain and IT capacity.

The idea that 3PLS will start using big data for themselves and their customers can simply “plug in” is just not realistic. There are more than a few problems with that theory as I’ve outlined below.

Obstacles that come with big data initiatives

  1. Data is not uniform:  80% of relevant data is unstructured, costly to scrub and organize so it can be property analyzed.
  2. Transparency:  As data is shared between logistics providers and their customers, it will be critical for 3PLs to be more transparent with processes, billing and technology integration, which may prove challenging for some groups.
  3. Manual vs. Autonomous Data Points:  Data entry points are a mix of manual entry and autonomous entry, which can be confusing and inconsistent.  Inconsistencies in data entry exacerbate the already poor quality of data being collected.  This problem has become much less significant in the last few years, but has not been eliminated yet.  As companies become more efficient digitizing data, this will likely begin to resolve itself.
  4. Identifying the most important data:  With so much data available to shippers and 3PLs, finding the signal in the noise is often challenging.  Getting permission from the customer/shipper to use and/or have access to data is also an issue for some 3PLs.
  5. Real-time data is here, but not here:  71% of shippers want real-time analytics from 3PLs to better understand and utilize alternative shipping methods.  That said, most mid-level 3PLs cannot provide the on-demand data shippers are looking for. This is in part due to the time and costs required to scrub data and ensure accuracy.

These five obstacles do not take into account the fact that some larger manufacturers use multiple 3PL providers in multiple markets, and many of these manufacturers have multiple divisions adding to the complications of data transparency and data collection.

If 3PLs and shippers expect big data usage to become a core competency, then just tracking data will not be enough.

Both sides will need take a more practical and systematic approach by using key data points that will allow for a continuous flex in processes and transactions on the fly.

Many 3PLs require “flex” or seasonal flux space in their lease documents, and I expect that controlling a dynamic real estate footprint will become more and more important to accommodate shipper requirements for dynamic customer requirements.  This will in turn begin changing the way buildings are designed, developed and marketed in markets with heavy logistics activity.

Thanks for reading!


Resources:

  1. https://www.flatworldsolutions.com/logistics/articles/3pl-distribution-challenges-trends.php
  2. https://logisticsviewpoints.com/2012/12/05/the-problem-with-big-data/
  3. http://www.supplychainbrief.com/2017/3pl/big-data/?open-article-id=6191749&article-title=data-driven-decisions-rule-the-modern-supply-chain&blog-domain=plslogistics.com&blog-title=pls-logistics
  4. 2017 21st Annual Third-Party Logistics Study:  The State of Logistics Outsourcing” – Capgemini and Dr. C. John Langley, 2017 http://www.3plstudy.com/media/downloads/2016/10/2017-report_new.pdf

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