Laying the Groundwork for Automated Storage Success: How to Prepare Your Inventory for a Seamless Transition

Biopharmaceutical organizations need to store and manage higher sample numbers than ever before, driven by an increasing number of clinical trials (Figure 1),¹ and the growing prevalence of temperature-sensitive biological therapies.² With higher numbers of biopharmaceuticals also come stricter cold chain requirements, as materials have far less tolerance for temperature fluctuations during handling.

While traditionally organizations have expanded capacity by adding more ultra-low temperature (ULT) freezers, this can be inefficient for storing large numbers of samples, and it risks repeated transient warming as samples are placed into and removed from storage. As a result, labs are increasingly turning to automated cold storage, drawn by its potential to improve space efficiency, sample tracking, and the protection of temperature-sensitive materials.

However, realizing the full potential of automation depends on how well the underlying sample inventory is prepared before migration. Without proper preparation, existing inefficiencies can be carried into the new system, limiting utilization and return on investment (ROI).

Inventory organization is, therefore, a critical first step in any automation journey, but it can be complex. Here, we explore the value of automated storage, and the key preparatory actions organizations can take to ensure their sample inventories are automation-ready and positioned for long-term success.

The value of automated sample storage

The continued rise in sample volumes and biological products has exposed the limitations of manual cold storage systems. High numbers of ULTs become increasingly difficult to manage efficiently as inventories scale, introducing operational, spatial, and energy-related challenges.

Common limitations of manual freezers for large-scale storage include

• Inefficient tracking and retrieval: When materials are spread across multiple freezers, it makes it time-consuming to locate and access them.
• Higher risk of error or loss: Manual handling increases the likelihood of misplaced, duplicated, or poorly documented samples.
• Poor space utilization: As materials are “cherry-picked” out of units over time, partially filled racks and underused capacity become common.
• High energy consumption: Large numbers of individual freezers require significant power and contribute to rising operational costs and sustainability concerns.

Automated cold storage systems are designed to address these challenges by centralizing samples in a digitally managed environment, offering multiple benefits:

• Improved tracking and retrieval: Digital inventories provide unified visibility of stored samples, reducing time spent searching for materials and supporting consistent workflows.
• Enhanced sample protection: Retrieval without door openings eliminates routine temperature fluctuations, while built-in triple redundancy helps safeguard valuable materials.
• Greater space efficiency: High-density, vertical storage dramatically improves sample-to-footprint ratios, with a single automated unit capable of replacing up to 160 manual freezers.³
• Improved energy efficiency: Consolidation and optimized cooling systems reduce overall power consumption compared with extensive “freezer farms.”

Why inventory readiness determines automation success

Automated cold storage offers clear advantages, but the benefits can only be fully realized if stored materials and their associated data are well organized. When inventories are poorly prepared, several issues commonly arise:

• Incomplete or unreliable digital records, which can make materials difficult to locate both digitally and physically.
• Unclear fitness-for-purpose, where missing metadata prevents users from determining whether a material is suitable for its intended scientific or clinical use, or whether it meets regulatory/compliance requirements.
• Poor visibility and searchability, limiting researchers’ ability to discover and request relevant materials, causing stored samples to be underutilized.
• Delays during implementation, as data issues must be resolved during migration rather than in advance.
• Reduced ROI, driven by operational inefficiencies and continued reliance on manual freezers (including higher energy costs).

To unlock the full potential of automation, organizations must first develop a clear understanding of the inventory they already have and arrange it into a consistent, structured state. In practice, this involves five key steps.

5 steps to prepare your inventory for automated storage

Step 1: Assess your starting point

Successful organization of inventory begins with having a complete understanding of your current storage situation. Before materials can be standardized or migrated, organizations need an accurate picture of what samples exist, where they are stored, and what information is associated with them.

By establishing this baseline, teams can identify any gaps or inconsistencies and get an idea of the scope of work needed to move over to the automated system. Key actions at this stage include:

• Comprehensively cataloguing all materials: Conduct a systematic audit of samples across sites, departments, and storage types to ensure nothing is overlooked. Capture consistent, core details for each material, such as sample type, storage location, and ownership.
• Mapping how samples are used: Understanding how often materials are accessed, by whom, and for what purpose helps distinguish active samples from those better suited for long-term storage or retirement, informing later decisions about prioritization and migration.

Step 2: Assess and prioritize your clinical research samples

After mapping your inventory, the next step is to decide what should move forward. Organizations that have relied on large numbers of manual freezers often accumulate redundant, duplicate, or poorly documented samples over time.

Shifts in research priorities, completed studies, and evolving regulatory requirements can further contribute to the buildup of materials that are unlikely to be used. In many cases, researchers find that over 50% of items in their lab’s cold storage are considered “unusable” according to findings recently presented by GSK at SLAS and Azenta’s own experience with customers.

By systematically evaluating each sample’s value and relevance, you can determine whether it should be moved to automated storage, stored elsewhere, or discarded, avoiding the transfer of unnecessary materials into the new system.

To do so, organizations can apply structured decision criteria, such as the decision tree below (Figure 2).

Step 3: Standardize storage formats

Once you’ve identified which samples will move forward, the next step is to determine the automated storage format. Samples stored in standardized, automation-friendly labware allow the greatest efficiency, as materials can be accessed at the level of individual vials, tubes, bottles, or syringes.

Materials that are not in automation-compatible formats can still be incorporated, but typically at a secondary container level (by grouping non-standard items into boxes that can be automatically retrieved). While this still offers benefits over manual storage, it is generally less efficient than standardized primary containers.

Key actions at this stage include:

• Transitioning to automation-compatible labware: Move to standardized tube, rack, and vial sizes. Centralizing purchasing can help limit the introduction of incompatible labware and encourage long-term adoption of standard formats.
• Establishing data standards: To avoid confusion, align naming conventions and identifiers across teams to replace informal or legacy systems that vary between departments. Ensure key metadata, such as sample type, origin, collection date, and ownership, follow uniform formats.
• Incorporating barcoded labware: Barcodes ensure consistent sample identification and seamless linkages between physical samples and digital records. Where possible, using labware with barcodes on the base enables entire racks or trays to be scanned at once, improving automation throughput.

Step 4: Build robust metadata and data integrity

Automation can only manage what it can accurately identify, locate, and interpret, so ensuring metadata is complete, consistent, and validated is essential to its reliability.

Key actions at this stage include:

• Validating and completing metadata: Confirm that each retained material has all required information, recorded in a uniform format (e.g., unique ID, project, key dates, and owner). Existing digital records should be verified against physical samples and containers to capture any missing details and ensure consistent labeling.
• Aligning with automated and informatics systems: Test that metadata field structures, naming conventions, and formats are compatible with automated storage platforms and any connected systems, such as LIMS. Addressing alignment issues early helps avoid delays and rework during implementation.

Step 5: Pilot and embed change

The final step is about putting the newly organized and standardized inventory into practice. Testing and refining these processes ensure that the system functions smoothly once automation goes live and helps build user trust by demonstrating that materials can be managed and retrieved reliably in the new environment.

Key actions at this stage include:

• Plan the transition: Develop a clear migration plan detailing how standardized materials and validated data will be loaded into the automated system.
• Piloting and refining workflows: Test end-to-end processes, such as sample registration, retrieval, and re-storage. Identify and resolve issues related to labware compatibility, metadata completeness, or access protocols. Based on the pilot feedback, refine SOPs and training materials accordingly.
• Building confidence in shared systems: Establish clear access rules so users know exactly how to submit materials to, or request their retrieval from, the centralized environment. Reinforce new practices through training, visual guides, and ongoing communication.
• Monitoring and sustaining accuracy: Track indicators such as data-error rates, retrieval efficiency, and adherence to new processes to ensure standards are maintained, and processes can continue to be refined.

Reliable automated storage starts with strong inventory organization

The value of automated cold storage lies not only in the technology itself, but in the quality and readiness of the inventory it is designed to manage. When samples are poorly documented, inconsistently formatted, or misaligned with current scientific needs, automation cannot deliver its full potential.

By taking the time to understand what materials are held, assess their relevance, standardize formats, strengthen metadata, and pilot new workflows, organizations create a foundation that allows automation to function as intended. This preparation reduces implementation risk, supports regulatory confidence, and ensures valuable storage capacity is reserved for materials that actively support research and development.

As sample volumes continue to grow and cold-chain requirements become more stringent, the need for scalable, reliable storage solutions will only increase. Organizations that approach automation as both a technical and an inventory readiness challenge are best positioned to realize lasting operational benefits.

References

1. ClinicalTrials.gov. Trends, charts, and maps: registered studies over time. ClinicalTrials.gov website. Accessed February 13, 2026. https://clinicaltrials.gov/about-site/trends-charts#registeredStudiesOverTime

2. Pharmaceutical cold storage market to reach $34.5 billion by 2035, growing at a CAGR of 6.9% from 2025, says Meticulous Research®. PR Newswire. Published June 26, 2025. Accessed February 13, 2026. https://www.prnewswire.com/news-releases/pharmaceutical-cold-storage-market-to-reach-34-5-billion-by-2035--growing-at-a-cagr-of-6-9-from-2025--says-meticulous-research-302492625.html

3. Azenta. BioARC Ultra-High Density -80 °C Automated Sample Storage System. Azenta website. Published 2026. Accessed February 13, 2026. https://www.azenta.com/products/bioarc-ultra-high-density-80-c-automated-sample-storage-system

About the Author

Katheryn Shea is Chief Client Solution Officer, Sample Management Solutions at Azenta Life Sciences.