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8 Takeaways from the State of Observability for Industrials, Materials and Manufacturing Report

Nic Benders
New Relic

New Relic's 2024 State of Observability for Industrials, Materials, and Manufacturing report outlines the adoption and business value of observability for the industrials, materials, and manufacturing industries.

Released in August, the report is based on insights from 285 technology professionals and was developed in association with the 2023 Observability Forecast. It reveals that manufacturers invest in observability to optimize uptime, improve productivity, and enable cross-team collaboration and strategic decision-making.

Here are 8 key takeaways from the report:

1. The Fifth Industrial Revolution is in effect

The manufacturing sector's shift into the Fifth Industrial Revolution, known as Industry 5.0, is defined by artificial intelligence (AI), sustainable product development, human and AI collaboration, and lean production practices. Usurping Industry 4.0's focus on the Industrial Internet of Things (IIoT), robotics, 3D printing (additive manufacturing), autonomous vehicles, digital twin simulation, touch interfaces, and virtual reality systems.

2. Observability is vital to reducing outages for manufacturers

Despite spending less on observability annually than most other industries, they had a higher proportion of observability capabilities currently deployed and more achieved full-stack observability than average. As a result, these industries experience outages less frequently, enjoy less annual downtime, and have lower outage costs than average. 30% reported outages at least once a week, compared to the average of 32%. Manufacturing organizations that had achieved full-stack observability saw a substantial improvement in their mean-time-to-respond (or MTTR), with 34% reporting an improvement of 25% or more since adopting observability. Additionally, just 12% of respondents estimated outages cost their organizations more than $1 million per hour compared to 21% across all industries.

3. Security is the biggest driver of observability adoption

The manufacturing industry must adhere to security, safety, and compliance requirements, standards, and guidelines set by governments and local and international organizations, such as the International Organization for Standardization (ISO), Society of Automotive Engineers (SAE), Defense Information Systems Agency (DISA), and Food and Drug Administration (FDA). As a result, the top technology strategy or trend driving the need for observability among industrials, materials, and manufacturing organizations was an increased focus on security, governance, risk, and compliance (50%). Regarding actual observability deployments, security monitoring was the most widely deployed capability and a higher proportion than average (78% in manufacturing compared to 75% for all industries).

4. AI is a core driver for observability

As a core pillar of Industry 5.0 in the manufacturing sector, organizations have already added AI solutions and capabilities to their tech stacks, a trend that will continue in the coming years.

The powerful combination of technologies like observability and AI creates more significant insights into telemetry data and is crucial to addressing the surmounting complexities of growing data sets. Observability is critical to the success of AI since it helps teams understand their telemetry data and how to improve MTTR and enables developers to easily apply fixes to code-level errors in their integrated development environment (IDE). It also increases automation for rapid alerts while improving incident detection and resolution. As a result, according to this year's report, 44% indicated that the adoption of new AI technologies in the manufacturing sector drove their need to onboard observability solutions. Just behind AI, 43% said IoT technologies contributed to the need for observability adoption.

5. They are moving toward tool consolidation

Industrials, materials, and manufacturing organizations continue to move toward tool consolidation to understand the different aspects of their business and avoid costly outages. This sector's low overall observability spending is likely due to the lack of tools. Industrials, materials, and manufacturing organizations were less likely than average to use multiple monitoring tools for the 17 observability capabilities included in this report. Three-fifths (61%) used four or more tools for observability compared to 63% overall. And 16% used eight or more tools compared to 19% overall.

The proportion of respondents using a single tool has increased since last year, growing from 3% to 4%. Additionally, the average number of tools has gone down by almost one tool, from an average of six tools in 2022 to five tools in 2023.

These figures indicate that industrial, materials, and manufacturing organizations are moving toward tool consolidation to understand the different aspects of their business and avoid costly outages.

6. Observability can prevent global supply chain disruptions

Critical outages can be particularly disruptive to carefully calibrated, intricate, and tightly scheduled global supply chains, putting business-to-business relationships and crucial revenue at risk. Yet, manufacturers are seeing improvements in their incident response timing, with 65% indicating their MTTR has improved since adopting an observability solution. Further, in the coming year, industrial, materials, and manufacturing organizations are expected to face an increase in supply chain disruptions, indicating that the time to tighten up and adjust their tech stacks to address vulnerabilities or make further deployments is now.

7. Observability improved job roles, performance, productivity, and team collaboration

Nearly half (43%) of IT decision-makers in the manufacturing sector said that observability made their jobs easier. Among IT practitioners, 47% said that it increases productivity since it allows them to find and resolve issues faster, and 31% said it enables less guesswork when managing complicated and distributed tech stacks. 45% indicated that observability improves collaboration across teams to make decisions related to the software stack — which was second highest across all other industries and 10% more overall.

8. Observability adoption in manufacturing will continue

Given their strong interest in deploying more capabilities in the next few years and a desire for a single platform for observability, this sector will likely continue to move from point solutions to robust observability platforms that provide end-to-end visibility. As technology transforms the industrial, materials, and manufacturing sectors to be ever more reliant on software and data, the need for observability will continue to grow.

Industrials, materials, and manufacturing organizations had ambitious observability deployment plans for the next one to three years. For example, by mid-2026, most are expected to have deployed security monitoring (96%), network monitoring (95%), and alerts (94%).

Nic Benders is Chief Technical Strategist at New Relic

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8 Takeaways from the State of Observability for Industrials, Materials and Manufacturing Report

Nic Benders
New Relic

New Relic's 2024 State of Observability for Industrials, Materials, and Manufacturing report outlines the adoption and business value of observability for the industrials, materials, and manufacturing industries.

Released in August, the report is based on insights from 285 technology professionals and was developed in association with the 2023 Observability Forecast. It reveals that manufacturers invest in observability to optimize uptime, improve productivity, and enable cross-team collaboration and strategic decision-making.

Here are 8 key takeaways from the report:

1. The Fifth Industrial Revolution is in effect

The manufacturing sector's shift into the Fifth Industrial Revolution, known as Industry 5.0, is defined by artificial intelligence (AI), sustainable product development, human and AI collaboration, and lean production practices. Usurping Industry 4.0's focus on the Industrial Internet of Things (IIoT), robotics, 3D printing (additive manufacturing), autonomous vehicles, digital twin simulation, touch interfaces, and virtual reality systems.

2. Observability is vital to reducing outages for manufacturers

Despite spending less on observability annually than most other industries, they had a higher proportion of observability capabilities currently deployed and more achieved full-stack observability than average. As a result, these industries experience outages less frequently, enjoy less annual downtime, and have lower outage costs than average. 30% reported outages at least once a week, compared to the average of 32%. Manufacturing organizations that had achieved full-stack observability saw a substantial improvement in their mean-time-to-respond (or MTTR), with 34% reporting an improvement of 25% or more since adopting observability. Additionally, just 12% of respondents estimated outages cost their organizations more than $1 million per hour compared to 21% across all industries.

3. Security is the biggest driver of observability adoption

The manufacturing industry must adhere to security, safety, and compliance requirements, standards, and guidelines set by governments and local and international organizations, such as the International Organization for Standardization (ISO), Society of Automotive Engineers (SAE), Defense Information Systems Agency (DISA), and Food and Drug Administration (FDA). As a result, the top technology strategy or trend driving the need for observability among industrials, materials, and manufacturing organizations was an increased focus on security, governance, risk, and compliance (50%). Regarding actual observability deployments, security monitoring was the most widely deployed capability and a higher proportion than average (78% in manufacturing compared to 75% for all industries).

4. AI is a core driver for observability

As a core pillar of Industry 5.0 in the manufacturing sector, organizations have already added AI solutions and capabilities to their tech stacks, a trend that will continue in the coming years.

The powerful combination of technologies like observability and AI creates more significant insights into telemetry data and is crucial to addressing the surmounting complexities of growing data sets. Observability is critical to the success of AI since it helps teams understand their telemetry data and how to improve MTTR and enables developers to easily apply fixes to code-level errors in their integrated development environment (IDE). It also increases automation for rapid alerts while improving incident detection and resolution. As a result, according to this year's report, 44% indicated that the adoption of new AI technologies in the manufacturing sector drove their need to onboard observability solutions. Just behind AI, 43% said IoT technologies contributed to the need for observability adoption.

5. They are moving toward tool consolidation

Industrials, materials, and manufacturing organizations continue to move toward tool consolidation to understand the different aspects of their business and avoid costly outages. This sector's low overall observability spending is likely due to the lack of tools. Industrials, materials, and manufacturing organizations were less likely than average to use multiple monitoring tools for the 17 observability capabilities included in this report. Three-fifths (61%) used four or more tools for observability compared to 63% overall. And 16% used eight or more tools compared to 19% overall.

The proportion of respondents using a single tool has increased since last year, growing from 3% to 4%. Additionally, the average number of tools has gone down by almost one tool, from an average of six tools in 2022 to five tools in 2023.

These figures indicate that industrial, materials, and manufacturing organizations are moving toward tool consolidation to understand the different aspects of their business and avoid costly outages.

6. Observability can prevent global supply chain disruptions

Critical outages can be particularly disruptive to carefully calibrated, intricate, and tightly scheduled global supply chains, putting business-to-business relationships and crucial revenue at risk. Yet, manufacturers are seeing improvements in their incident response timing, with 65% indicating their MTTR has improved since adopting an observability solution. Further, in the coming year, industrial, materials, and manufacturing organizations are expected to face an increase in supply chain disruptions, indicating that the time to tighten up and adjust their tech stacks to address vulnerabilities or make further deployments is now.

7. Observability improved job roles, performance, productivity, and team collaboration

Nearly half (43%) of IT decision-makers in the manufacturing sector said that observability made their jobs easier. Among IT practitioners, 47% said that it increases productivity since it allows them to find and resolve issues faster, and 31% said it enables less guesswork when managing complicated and distributed tech stacks. 45% indicated that observability improves collaboration across teams to make decisions related to the software stack — which was second highest across all other industries and 10% more overall.

8. Observability adoption in manufacturing will continue

Given their strong interest in deploying more capabilities in the next few years and a desire for a single platform for observability, this sector will likely continue to move from point solutions to robust observability platforms that provide end-to-end visibility. As technology transforms the industrial, materials, and manufacturing sectors to be ever more reliant on software and data, the need for observability will continue to grow.

Industrials, materials, and manufacturing organizations had ambitious observability deployment plans for the next one to three years. For example, by mid-2026, most are expected to have deployed security monitoring (96%), network monitoring (95%), and alerts (94%).

Nic Benders is Chief Technical Strategist at New Relic

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If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...