Get Your Free Logistics
Cost-Savings 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 cost-savings initiatives?

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