Beyond the Proof of Concept: Why Scale Matters in Space Planning POCs

A Proof of Concept (POC) serves as a crucial tool for validating ideas before full-scale implementation. For tools with deployment processes or manual installations, they can help teams recognize the value of the tool and the ease of deployment. This is essential when Real Estate managers at corporations and Institutions of Higher Education are managing millions of square footages, often landlocked or up against rising real estate costs.

However, not all technologies are capable of scale at the same level. As you roll out hardware across enterprise portfolios, complications can arise. Additionally, once hardware is installed, regular maintenance work will be required of your IT or Facilities Teams. The smaller the POC, the easier it is to overlook the compounding time and resources it takes to install or deploy complicated hardware or software.

Engaging with a larger POC, specifically one that covers more space, when selecting an occupancy analytics software gives you a clearer picture of what to expect should you roll out the solution to your entire organization.

The Drawbacks of a Small POC

Small-scale POCs, while cost-effective and manageable, often come with limitations that can skew results and mislead stakeholders. Here are a few key drawbacks:

1\. Limited Scope: A small POC typically tests only a fraction of the proposed solution’s functionality. This restricted scope can overlook critical interactions and dependencies that may arise in a larger implementation. For instance, a POC that showcases how one floor or department of your organization uses the office could mislead you to assume other departments behave the same way. As most workspace planners know, each department’s culture and tasks require different space for working their best. Patterns of utilization are unique to the individuals using the spaces and fluctuate over time, particularly in Higher Education where semesters drive campus life.

2\. Inaccurate Data Representation: Small POCs may not accurately reflect the challenges and requirements of a full-scale rollout. The issues that arise in a larger environment—such as logistical bottlenecks, supply chains and shipping for physical occupancy sensors, or space utilization inefficiencies—might not be evident in a confined test scenario. This can lead to a false sense of confidence or a misguided evaluation of the solution’s effectiveness.

Some examples are:

• Time to install at Scale – A sensor deployment, for example, requires a manual installation of each sensor, which over a million square feet becomes a significant undertaking.
• Time to generate results – Once deployed, sensors require 30-60 days to generate enough data to begin to see trends in the data. If sensors need to be redeployed or moved, they will need additional time to generate new data.
• Blind spots – Sensors, Surveys, and Badge data each have blind spots, areas where people are not detected. Some are bigger than others, for example badge data only shows entrances, not where people go after they enter the main lobby. For sensors to be accurate, they must be deployed consistently across the space. This will require redeployment and adjusting over time, starting fresh with data from the moment of change.

3\. Overlooked Scalability Issues: What works well in a small POC may fail to scale effectively. Factors like infrastructure limitations, increased complexity, and higher user volumes can introduce new problems. A small POC might not reveal these issues, leading to a potential mismatch between the prototype and real-world application.

The Value of Scaling Across a Larger Portfolio

Scaling your POC to reflect the broader scope of your portfolio can provide valuable insights and drive more informed decision-making. Here’s why scaling up matters:

1\. Holistic Insights: Testing on a larger scale allows you to observe how the solution interacts with different elements within your space. This can include varying user behaviors, diverse space layouts, and different operational needs. By understanding these dynamics, you can refine your approach to better meet the overall requirements of your portfolio.

2\. Comprehensive Validation: A scaled-up POC provides a more accurate validation of the solution’s effectiveness across various scenarios. It enables you to test the solution’s performance, adaptability, and integration on a broader scale, which helps in identifying potential issues before full deployment. This comprehensive validation is crucial for mitigating risks and ensuring a smoother transition to the final implementation.

3\. Optimized Resource Allocation: By scaling your POC, you can better estimate resource requirements, budget implications, and potential return on investment. This helps in planning for future expansions and adjustments, ensuring that resources are allocated efficiently, and that the solution delivers value across your entire portfolio.

Speed of Deployment and Analytics: The Lambent Spaces Advantage

When considering space planning, speed and efficiency in deployment and analytics are critical. Deploying a POC at a floor level allows for swift implementation and rapid insights. With Lambent Spaces, the deployment process can be accelerated, providing you with actionable analytics faster than traditional methods.

Missing Metrics of Small-Scale Deployment

Small-scale deployments often miss critical metrics that are essential for a successful full-scale implementation. These missing metrics can include:

1\. Usage Patterns: Small POCs might not capture the variability in user behavior and space usage that occurs in a larger setting. Understanding these patterns is crucial for designing spaces that are both functional and efficient.

For example, understanding how people traverse across your campus can reveal that a population of your constituents are traveling across campus or from floor-to-floor multiple times a day. A POC that only covers one building wouldn’t surface accurate information about how long the average person stays on campus, only how long they stay in that one building.

2\. Operational Efficiency: Metrics related to operational efficiency—such as the flow of people and resources, maintenance needs, and energy consumption—can be overlooked in a small POC. These factors can significantly impact the overall effectiveness and cost of the solution. A customer used occupancy data to find an opportunity to improve HVAC operational efficiency and reduce run time by 21% over four buildings. This saves on costs and maintenance.

• Impact on Overall Space: A small POC might not account for how the new solution impacts other areas within your space portfolio. For instance, changes in one area might create ripple effects that influence the overall design and functionality of adjacent spaces. Restacking due to operational changes, fluctuations in department growths, and mothballing efforts can impact utilization across remaining buildings, stressing resources.

While small-scale POCs are valuable for initial testing and concept validation, understanding and planning for scale is essential to achieving meaningful results. By scaling your POC, you can gain a more accurate understanding of how the solution will perform across your entire portfolio, optimize resource allocation, and avoid potential pitfalls. Leveraging tools like Lambent Spaces for fast deployment and in-depth analytics further enhances the effectiveness of your POC, ensuring that you are well-prepared for a successful full-scale implementation.

To learn more about Lambent Space POCs, please contact our sales team at sales@lambentspaces.com