The Challenge

NetApp sells intelligent data infrastructure—combining storage systems, data management software, and cloud services—to help customers run, protect, and move data seamlessly across onpremises and cloud environments used by organisations worldwide. Unlike a consumer product with a fixed specification, NetApp designs equipment with customer needs at the forefront. NetApp’s hardware is inherently configurable, with each customer deployment—and even within the server rack—customized based on regional requirements, customer use cases, and evolving product generations. This variability limits the effectiveness of static or longcycle carbon footprinting and necessitatesnear realtime decarbonisation signals to inform product and configuration decisions in order to achieve NetApp’s 2030 Science Based Targets.

NetApp’s customers are signalling the need to provide credible product carbon footprints (PCFs) in a market where competitors already had ISO-compliant life-cycle assessments (LCAs) for their products. Producing a comparable PCF for a single representative configuration was achievable, but that approach carried fundamental weaknesses: a PCF for one build of one server is not representative of the hundreds of configurations that NetApp actually ships; and conducting their own life-cycle assessment each time to stay competitive would take too long and would not reflect the variability of their portfolio. NetApp needed a tool that could provide credibility and steer quantifiable actions at speed to actually drive decarbonisation at scale.

The Process

The deeper value emerged from how Neutreeno modelled the product portfolio as a whole. Rather than treating each server configuration as an independent analysis requiring its own data set, the system used the initial bill of materials to build a set of modular building blocks: the core components and sub-assemblies that recur across configurations, each carrying its own uncertainty-bounded PCF. From this foundation, new configurations can be assembled rapidly, drawing on existing modelling work rather than starting from scratch.

The deeper value emerged from how Neutreeno modelled the product portfolio as a whole. Rather than treating each server configuration as an independent analysis requiring its own data set, the system used the initial bill of materials to build a set of modular building blocks: the core components and sub-assemblies that recur across configurations, each carrying its own uncertainty-bounded PCF. From this foundation, new configurations can be assembled rapidly, drawing on existing modelling work rather than starting from scratch.

The deeper value emerged from how Neutreeno modelled the product portfolio as a whole. Rather than treating each server configuration as an independent analysis requiring its own data set, the system used the initial bill of materials to build a set of modular building blocks: the core components and sub-assemblies that recur across configurations, each carrying its own uncertainty-bounded PCF. From this foundation, new configurations can be assembled rapidly, drawing on existing modelling work rather than starting from scratch.

The deeper value emerged from how Neutreeno modelled the product portfolio as a whole. Rather than treating each server configuration as an independent analysis requiring its own data set, the system used the initial bill of materials to build a set of modular building blocks: the core components and sub-assemblies that recur across configurations, each carrying its own uncertainty-bounded PCF. From this foundation, new configurations can be assembled rapidly, drawing on existing modelling work rather than starting from scratch.

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How Neutreeno Helped

Neutreeno's engagement with NetApp demonstrated a principle that sits at the heart of how the system is designed: you do not need perfect data to start. From the moment NetApp submitted their first data, the clock started. Within 9 days, Neutreeno had returned a first-pass PCF with clearly quantified uncertainty bounds, accompanied by specific actions the team could take immediately based on what the analysis already showed.

The Neutreeno system ingested a bill of materials that NetApp had on hand. It focused on the important data, highlighting structured assumptions and quantifying the resulting uncertainty for every component in the analysis. This produced a meaningful range of emissions values. Crucially, it also explicitly flagged where the largest uncertainties were, so the team knew precisely where additional data collection would have the greatest impact on result quality. There is always a clear next step. All this was done without needing to do an LCA that forensically pursued every single data point: rapid results built on scientific rigour.

From this, Neutreeno identified three additional data points on the SSD component that reduced overall uncertainty in the results by 650%.

The deeper value emerged from how Neutreeno modelled the product portfolio as a whole. Rather than treating each server configuration as an independent analysis requiring its own data set, the system used the initial bill of materials to build a set of modular building blocks: the core components and sub-assemblies that recur across configurations, each carrying its own uncertainty-bounded PCF. From this foundation, new configurations can be assembled rapidly, drawing on existing modelling work rather than starting from scratch.

For NetApp, customer requests for PCFs at a regionalconfiguration level were historically out of reach, and when possible, required months of intensive analysis. The building blocks to support these request are already in the system and can be generated at will by sustainability teams. This is critical given how many requests teams are getting. The assumptions have already been validated. What would otherwise require a dedicated LCA practitioner for months, with Neutreeno, was turned around 60 times faster.