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.
$
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
