Get Your Free Data Quality Analysis

Insights in less than 30 days. Risk-free. No IT required. 

Our free analysis is designed to help you eliminate errors, omissions, and exceptions in your freight tracking & payment data causing non-value added manual work. 

Join other high-performing supply chains who benefit from our data quality tools.

start your aNALYSIS:

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$ 0
Annual Cost of Bad Data in the U.S.
Amount of time knowledge workers waste in hidden data factories.*
Wasted Time 50%
Fraction of time data scientists spend cleansing and organizing data.*
Time Cleansing and Organizing 60%
Total cost associated with hidden data factories in simple operations.*
Fraction of Total Cost 75%
*All figures as reported by Harvard Business Review.

How is data quality affecting your logistics decisions?

Get insights to help you discover opportunities to drive savings. Additionally, your results will help guide  improvements in customer service, inventory, operations, and risk management.

Leverage our Data-Quality Engine to:

  • Highlight invoice inaccuracies and identify track and trace issues.
  • Guide operational improvements and risk management strategies.
  • Use rate modeling to assess financial impacts and improvements.

Use Cases

Invoicing

0 :1

Sustainable ROI

Problem:
  • Customer had a team of people manually managing the invoice process and resolving exceptions. The business introduced a rate-tolerance to prevent adding more people to the team.
Solution:
  • Eliminated rate tolerance delivers sustainable ROI 20:1.
  • Customer was able to accelerate payment process and improve carrier relationships.
  • Enabled the team to focus on value-added work.

Tracking

ETA Visibility

Problem:
  • The business was inaccurately estimating delivery times from their vendors, causing staffing imbalances and operational inefficiencies.
Solution:
  • Properly sequenced tracking information was delivered to TMS to provide accurate arrival information.
  • Business improved operational efficiencies by proactively optimizing staffing levels.
  • Eliminated inventory blind spots and reduced safety stock by improving end-to-end visibility.

Rate Modeling

0 %

Sustainable Savings

Problem:
  • The business was unable to drive year-over-year improvements in freight spend.
Solution:
  • Data-driven rate modeling helped align spend with the right carrier in their preferred lanes.
  • Measure and monitor successful implementation by highlighting lost savings in real-time.
  • Collaborated approach created sustainable savings of 16%.

“Most companies won’t be able to understand the bigger patterns driving freight spend if they don’t have a partner that produces really clean data and analytics.”

VP of Logistics
Industrial and Healthcare Supplier