TMS Software: Effective Stand In for Big Data Scientist?

Big Data has captivated the minds of supply chain practitioners.  Look no further than Supply Chain Quarterly’s current cover story, “Lifting the Fog: Three Steps to Supply Chain Visibility”.  This issue also features an editorial perspective penned by Editor James Cooke lamenting the need for data scientists in supply chain management roles.  As a repository of transportation data, a TMS software platform surely generates intelligence to be leveraged towards greater supply chain efficiency.  But, do you need a data scientist to perform data analysis or do you just need to know what questions to ask?

While it would be great to have a data scientist on staff to really dig into the data, this is a luxury all but the largest organizations can ill afford in the current economic climate.  However, as Supply Chain Quarterly notes, there are three easy steps to visibility and, with that visibility it is possible for supply chain practitioners in transportation to harness their data, if they know what questions to ask.

The three steps according to the Supply Chain Quarterly article are:

  1. Set goals and explore visibility needs company-wide (or for our purposes, across the supply chain);
  2. Match data collection to visibility needs;
  3. Distribute data across the company (or supply chain department) and re-evaluate processes.

Following the above guidelines from a transportation perspective means first exploring visibility into cost metrics in the following areas:

  • Total Spend
    –  Line haul expenses
    –  Fuel costs
    –  Assessorial charges
  • Driver Performance
  • Pickup & Delivery Arrival and Departure Times
  • Mileage

Next, it means matching data collection in each of these areas to correlate with the relevant stakeholders involved in transportation management and broader supply chain management.  For example, one might segment the data to look individually at:

  • Financial information
  • Performance metrics
  • Root causes that drive performance (both good and bad)
  • Compliance/user effectiveness

Lastly, once this data has been identified and collected, it is distributed amongst all stakeholders in supply chain management roles.  By providing visibility into these key data points, management can begin the process of collaboration between overlapping links in the chain.  Data driven dialogue between route planners and carriers, fleet managers and yard managers, procurement and warehouse managers (and all other possible combinations) enables the kind of cohesion that is characteristic of best-in-class supply chains.   By taking these simple steps – and even without a data scientist – a organization can yield significant benefits from leveraging “Big Data”.

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