Only half of the respondents to a survey conducted by JBF Consulting, in collaboration with Adelante SCM, are satisfied with their transportation management system (TMS). Just 8% reported they are "very satisfied."
"The road to TMS satisfaction is fraught with integration challenges and unmet expectations," says Brad Forester, CEO at JBF Consulting, in a statement.
Despite high expectations, 72% of users feel neutral about the AI advancements in TMS, indicating a substantial gap between expectations and actual deliverables.
"Overall, the survey results underscore what's been true for a long time: conducting a thorough due diligence process upfront is critical for achieving sustained satisfaction with the TMS you implement," says Adrian Gonzalez of Adelante SCM. "Determining which TMS solution is right for you begins by thoroughly understanding your current processes and defining your desired future state. The survey results also suggest that it would be worthwhile for companies to come up with a "TMS satisfaction" metric -- i.e., define upfront how they will measure/quantify their satisfaction with the solution.
Key findings of the survey:
● Integration Planning is Key: Effective integration planning during the design
phase is essential to prevent functionality issues.
● Vendor Support Varies: The level of vendor support from sales to implementation
greatly impacts user satisfaction, particularly evident in DIFOT performance
metrics.
● A Measurement Gap Exists: The absence of formal success metrics suggests an
opportunity for establishing new standards, including SMART objectives for
solution satisfaction.
According to Mike Mulqueen, executive principal, Strategy & Innovation at JBF
Consulting, "Understanding what makes a TMS solution effective goes beyond just
technology—it’s about ensuring the system and accompanying processes, address the
critical needs of the business."
AI and Machine Learning Continues to Evolve
While many advancements have been made in AI and machine learning, deliverables
have yet to meet expectations. Further research is needed to understand why, but it is
possible that users do not fully understand how AI is embedded into their system or the
value those capabilities offer. Another possibility is that vendors are overselling what AI
and machine learning are currently capable of.
Ultimately, users care more about the end results than whether or not those results
were achieved using AI. With that said, as AI continues to evolve within TMS,
companies will need to stay informed on the latest developments and deliberately
integrate these technologies to reap maximum benefits.
Notable comments from respondents:
- "We do not track satisfaction, but we manage annual upkeep costs and savings. Neither
have been delivering on their promise despite using a Top 3 brokerage TMS system in
the United States." - Supply Chain Executive - "Biggest factor towards dissatisfaction would be [lack of] system reliability/availability;
our expectation is that the system will always be available except for planned
downtime." - Operations Director - "We implemented [a TMS solution] last year and the results promised were 95% DIFOT
[Delivered In Full, On Time] but we finished last year at 82%." – Sr. Logistics Manager
