The average estimated research and development cost of bringing a new drug to market is more than $1.3 billion with a median cost of over $985 million. In addition, first movers can have a market share six percentage points higher in the first 10 years after launch. Keeping pace with the demands of developing innovative new therapies means scientists must spend more of their time performing the highly skilled work that advances discovery and expedites delivery.
The responsibility for managing efficient lab operations is often tasked to the scientists themselves. Lab management responsibilities may be divided among several researchers assigned to tasks like managing inventory. While inventory management is a task vital to therapy development, it isn’t widely considered a value-added activity.
Scientists, who are not typically trained to manage inventory, must often perform these “non-core” tasks relying on manual processes, such as spreadsheet input, adding time to research workflows and raising the risk of data entry errors. Researchers must do all of this while navigating a host of additional challenges, including:
- Lack of visibility: Fragmented processes across multiple sites or manually-based processes prevent easy access to inventory information, requiring researchers to spend more time figuring out where supplies are rather than working on the next breakthrough.
- Supply chain unpredictability: From the COVID-19 pandemic to political unrest, the supply chain has been hampered by backlogs and shortages that can delay experiments. As a result, labs might run out of needed supplies—or they may overstock supplies, which raises inventory costs and takes up valuable research space.
- Drive for sustainability: Labs are increasingly challenged to move beyond recycling and reuse programs to develop comprehensive, integrated sustainability programs grounded in data. Therefore, researchers tasked with inventory management must spend time identifying and eliminating wasteful, sometimes manual, inventory practices rather than focusing on scientific output.
Five inventory management best practices
Taking a comprehensive approach to inventory management allows researchers to spend more time on high-value work that gets treatments to market faster and better positions pharmaceutical companies for success. Consider these best inventory management practices:
1. Develop established workflows: The foundation for effective inventory management starts with well-documented workflows that meticulously outline what needs to happen, when it needs to happen, and which researchers are involved. For larger companies, workflow documentation can be invaluable for creating consistency across sites or regions. For smaller startups, taking time to establish workflows helps leaner operations run more efficiently. This allows labs to have visibility into what’s needed where so consumables, samples, or equipment can be managed properly.
In addition, established workflows allow labs to scale up more quickly when they invest in additional processes, technology, or personnel to improve inventory management efficiency.
2. Adopt automation: Remove manual processes—like spreadsheets—wherever possible and implement efficient digitalization of inventory management that goes beyond simply answering the question: Where’s my stuff? Interconnectivity that facilitates automated data transfer improves accuracy, reduces data loss, and increases productivity. For example, technology like smart shelves, radio-frequency identification (RFID), and other hardware connected to inventory management systems—or even integrated with suppliers—can provide real-time information around critical supplies for easier access and more informed decision-making.
When automated technologies are combined with artificial intelligence (AI) and machine learning (ML), even decision-making can be automated. These tools can examine historical data, such as order history and consumables consumption, and combine it with data analysis on what protocols scientists are working on. They can then generate real-time predictive analytics to help reduce the amount of time researchers spend identifying inventory trends and planning purchases accordingly. While human decision-making around inventory will always be needed on some level, these technologies make inventory management tasks less of a burden on researchers’ time.
As lab of the future technology evolves, inventory management technology will move toward prescriptive analytics. In this approach, data around past trends is combined with larger factors, such as supply chain disruptions or market trends, to determine what actions need to be taken now to mitigate risk in the future—just like leading e-commerce sites can predict what a consumer needs before the consumer realizes they need it.
3. Implement community visibility and control: Fragmented information across multiple departments or sites hampers inventory visibility and creates costly inefficiencies. As you implement technology, look for tools that provide visibility into spend and consumption at every level, including site, function, lab point of use (POU) and even end user.
The digitalization of requests and approvals can provide additional control that helps manage spending and mitigates risk. For example, an approval process might include sending a designated team member an email notification for a purchase request to a vendor. Inventory management software typically provides out-of-the-box approval processes; however, you may need the flexibility of software with customizable approvals, such as authority limits with multiple approval levels to comply with internal policies or delegation of authority .
4. Use technology that fosters an integrated lab ecosystem: Connecting operational efficiency (workflow optimization) with scientific efficiency (protocol execution) is at the core of an intelligent integrated approach. Open, vendor-agnostic inventory management tools and systems can harmonize information across the lab ecosystem. With fully integrated inventory visibility, scientists will have the supplies they need to do experiments in the necessary time.
In addition, proprietary systems can create data siloes when it’s time to integrate with future technology. Those siloes often create extra steps, like manual data transfers, that take scientists away from the bench. In contrast, open-architecture systems will better integrate with tomorrow’s technology, allowing researchers to focus on science.
5. Choose partners with in-depth expertise: Integration efforts are only as good as the partners you rely on. Providers with expertise in scientific workflows, digitization, and integration are well suited to help labs reduce time researchers spend on inventory management.
For example, if you’re sourcing inventory management software but don’t yet have consistent, well-documented workflows, look for a vendor with an experienced implementation team able to support that documentation and help transition it into the software tool. This type of expertise can help labs more rapidly adopt change at scale.
Additionally, seek partners with an innovation mindset that drives them to develop solutions today for the laboratory challenges of tomorrow. Partners with a robust innovation pipeline are better positioned to ensure you have access to flexible, scalable solutions that allow researchers to spend more time on science.
Each minute a researcher spends managing inventory levels is a minute they are not spending on development of the next breakthrough treatment. Reducing scientists’ inventory management burden through a comprehensive approach can be an important step toward getting innovative therapies to market faster.