The State of Observability in Manufacturing
We surveyed ITOps and engineering professionals worldwide to learn how organizations are building leading observability practices.
Here, we highlight key findings from respondents in the manufacturing industry.
The view is worth the climb
Manufacturing is witnessing exponential data growth as digitalization advances. So, it’s no surprise that the top data-related challenge for manufacturing is that the amount of data they collect exceeds the human capacity to digest it.
Alert fatigue associated with observability tools is the second biggest trouble spot. Over half (53%) call it somewhat or very problematic.
But organizations can rise above these difficulties by embracing observability as the path forward, gaining value in the process.
Leading observability practices aren’t born — they’re built
Being an observability leader pays off in a big way. But our research found that just 14% of manufacturers have reached that level. Nearly half (45%) of respondents are still in the beginning stages.
One potential roadblock to observability maturity may be lack of investment. Manufacturers spend an average of $1.06 million yearly on observability — the least of all industries.
Still, there’s a silver lining: observability investments yield substantial ROI of 2.6x annually.
Manufacturers are creating a flexible future with telemetry data
Manufacturing teams often feel overwhelmed by more and more telemetry data that makes it hard to take action. But many are starting to bring order to the chaos with OpenTelemetry (OTel).
In fact, 57% of manufacturing organizations report their primary observability tool leverages OTel. With OTel, they gain more standardization across teams and tools and can explore new prevention and remediation strategies.
Manufacturing organizations see the advantages of OpenTelemetry
52% claim it offers better control and ownership over data
50% like the access to a broader ecosystem of technologies
The platform engineering revolution is here
Our research found that 69% of respondents already practice platform engineering. Another 24% say they’ll implement it over the next year — the most of any industry.
Adopters are seeing the benefits:
- 58% cited increased IT operations efficiency
- 47% have improved application reliability and performance
- 40% report increased developer productivity/efficiency
AI in observability is starting to take shape
At this point, using AI and ML within observability tools has become table stakes.
Just over half (52%) of manufacturing respondents find AIOps most useful for detecting anomalies, determining root causes, and remediating incidents with greater intelligence.
Another positive sign: 60% of respondents say ROI from AIOps tools has exceeded expectations, compared to 49% in 2023.
96% of manufacturers are exploring generative AI in observability
66% have tried generative AI for data analysis
63% have experimented with generative AI recommended actions to resolve issues
11%
have fully adopted these capabilities
