Case Study: Integrating FDD into Service Operations with LONG Building Technologies

Welcome to our Case Study series, where we dive into case studies of real-life, large-scale deployments of smart building technologies, supported by our Marketplace.

I emphasize “real life” because this isn’t a marketing fluff story. We're here to share real lessons from leaders that have done the work to integrate smart building technology into their operations. I also emphasize “large scale” because we're not here to talk about pilot projects. We're here to talk about deeper commitments to changing how buildings are operated.

The first edition was a case study of the University of Iowa’s FDD-driven maintenance program. This second edition is the story of a 5-year effort at LONG Building Technologies to integrate fault detection and diagnostics, or FDD, into their service operations. This is also part of our ongoing Insights to Operations column and is supported by our Partner Program. 

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Case Study Data: 

• Technology Categories Mentioned: FDD, Asset Management 
• Software Vendor: Clockworks Analytics
• Number of Buildings: 62 active
• Total square footage: 6,700,000
• Project Has Been Active Since: 2020
• Results: 661 tasks closed out, $550k/year in verified utility/energy cost savings

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Case Study Outline: 

• Introduction and story of the program
• Lesson #1: Dedicated resources on a new category of service   
• Lesson #2: Change management with technicians: “it’s a continuous iteration”  
• Lesson #3: Determine the “in scope” items 
• Lesson #4: Measure your performance 
• Action Items 
• Members only: Nexus Labs’ Takeaways

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LONG Building Technologies is a mechanical, controls, and security system contractor serving Alaska, Colorado, Montana, Oregon, Utah, Washington, and Wyoming. In 2018, LONG set out to transform their mechanical and building automation service contracts using fault detection and diagnostics (FDD) software. 

LONG’s main goal for their clients was to provide increased value by offering proactive, customized services that enhance various aspects of their clients' systems, such as efficiency, comfort, and maintenance. They aimed to move away from reactive checklists and provide in-depth, tailored solutions that contribute to overall system health and wellness. This approach leads to potentially delivering more for clients within the same timeframe or cost of service. 

Using FDD for maintenance leads to improved follow-up and issue resolution compared to traditional checklists. FDD enables technicians to actively track and assess the effectiveness of their solutions, ensuring that problems are genuinely addressed. This process involves ongoing verification and analytics to confirm whether the intended outcomes were achieved.

Unlike the conventional approach where tasks are often completed and then moved on from, FDD emphasizes continuous follow-up on the addressed issues. Technicians can delve into the data, examining score changes after implementation to ascertain if the desired improvements have occurred. FDD's analytical capabilities empower technicians to gain a deeper understanding of the system hierarchy and operational dynamics, enabling them to make more informed decisions.

The focus shifts from mere box-checking to data-driven results. Technicians have a tool that provides comprehensive insights into the magnitude of changes and faults, allowing for a precise evaluation of the effectiveness of their interventions. This data-driven approach fosters accountability and collaboration, as technicians can pinpoint areas where assistance is needed and engage in more informed discussions about solutions.

FDD transforms the maintenance process from a checkbox mentality to a dynamic cycle of analysis, action, and validation. Technicians use data to refine their approaches and ensure issues are thoroughly resolved, ultimately leading to more effective maintenance practices and higher levels of customer satisfaction.

From a business perspective, LONG also wanted to enables the business to handle a larger client base, expand, and grow without solely relying on increasing staff or selling hours of labor. By offering valuable, proactive services, the company not only increases client satisfaction but also enhances their own reputation and potential for financial gains through energy savings, equipment longevity, and streamlined maintenance processes. 

This analytics-driven approach also provides measurable data points that are closely monitored by management. It allows for better customer analysis and increased pull-through work, while establishing longer-term relationships with customers and extended contract durations compared to traditional services. 

As they’ve scaled the program up past 6 million square feet, it hasn’t always been smooth sailing. Their learnings, iterations, and pivot points can help inform service providers that want to follow in their footsteps. 

This case study is designed to share that playbook. We’ll walk through what LONG has learned and what others can take away from their experience. 

LONG’s FDD program began in 2018 and has gone through iterations as they’ve learned. They summarized the phases of the program as follows: 

• Phase 1: Tried a 3 building pilot using an unnamed provider that didn’t work out 
• Phase 2: Interviewed over 20 FDD providers, selected Clockworks Analytics, and deployed.
• Phase 3: Hit roadblocks with getting field technicians to use the software and sales people to sell the new service. Realized they needed dedicated resources. Hired an energy engineer to lead a new type of service offering.
• Phase 4: Built an analytics team. Technicians were slow to adopt the software and integrate into their workflows. The program only took off when they built the team up to 4 energy engineers who support field technicians.
• Phase 5 (currently): Empowering technicians to become more active users of the software.

As part of our Partner Program, we interviewed the program’s key stakeholders (podcast), including: 

• Leslie Beu, Analytics & Energy Manager
• Lawrence Trifiletti, Senior Operations Manager 

These experts shared several insights that we’ve packaged into a playbook for putting together an FDD-driven service program from scratch.

Enjoy!

Lesson #1: Dedicated resources on a new category of service

Implementing Fault Detection and Diagnostics (FDD) can’t be an add on. It needs to be a distinct service offering and requires dedicated resources and a full-time commitment. This involves designing, aiding sales efforts, and overseeing growth. A dedicated person is crucial to develop content, support sales, and ensure effective demonstrations for successful integration.

Simplifying FDD for salespeople is vital, making it relatable to their product-oriented mindset. Technical expertise, especially among energy engineers, bridges roles and facilitates effective client communication and solution delivery.

The dedicated team's value is evident in successful sales and satisfied clients, despite occasional challenges. The importance of allocating dedicated resources is emphasized, contributing to accomplishments and aligning with leadership foresight.

Lesson #2: Change management with technicians: “it’s a continuous iteration”  

To integrate FDD into operational processes, effective change management is vital. Initial efforts involved onboarding clients and supporting technicians in system usage and diagnostics understanding. This transition required shifting from time-based plans to fault-focused consultations, enabling more impactful solutions.

However, challenges emerged as technicians adjusted. Some embraced FDD's role as the "center of the universe," a primary information source, while others struggled due to their accustomed checklist approach. The transformation wasn't just a tool, but a fundamental shift in approach.

Technicians' comfort with routines led to resistance. They sought support and clearer instructions, which prompted staffing adjustments. Roles became defined: straightforward issues were handled by technicians, more complex ones by energy engineers.

Continual iteration and persistence were key. Adjustments were made based on data, technician feedback, and client results. Understanding the ups and downs, embracing change, and maintaining a dedicated team were critical. Success hinged on persistent commitment to a paradigm shift, not half measures.

Lesson #3: Determine the “in scope” items 

The resolution workflows for different types of faults involve two distinct paths to action:

1. Simple Faults (e.g., sensors out of calibration, low-cost troubleshooting): For straightforward issues, such as sensors being out of calibration or minor troubleshooting, the customer or the technician can address these problems directly. This approach allows for quick and efficient solutions to low-cost or no-cost troubleshooting, optimizing settings, and minor adjustments. These simple faults typically fall within the scope of the agreement and can be promptly resolved.

2. Complex Faults (e.g., larger troubleshooting concepts, out-of-scope issues): For more intricate issues or troubleshooting needs that fall outside the scope of the agreement, a collaborative approach is taken. A team of energy engineers becomes involved to analyze and identify the best course of action to solve the problem. This process involves close consultation with the client to discuss findings, implications, and recommended solutions. 

The workflow begins with identifying whether faults are in scope or out of scope, focusing on both simple and complex issues. For simple faults, either the customer or the technician can directly address the problem within the defined agreement parameters. On the other hand, more complex faults, acknowledged as being outside the original scope, involve the collaboration of energy engineers who work closely with the client to develop tailored solutions. The fault detection and diagnostics platform plays a central role in visualizing faults and opportunities, facilitating informed discussions and actionable insights.

Lesson #4: Measure your performance 

The LONG team offered unique insights and leadership on why service companies should measure maintenance performance using FDD. This enables the identification of improved scores, full fault resolution, or any potential trade-offs between issues. It also allows them to quantify the value of offering comprehensive service support rather than treating the tool as a standalone product.

The data demonstrates that clients receiving from dedicated service support experienced a substantial 76% improvement rate in task resolutions. On the other hand, those using the tool without comprehensive support saw only a 27% improvement rate, along with cases of stagnation or deterioration.

This recommendation underscores the significance of pairing the technology with the right humans. By continuously monitoring and analyzing data, service providers can fine-tune their interventions, prevent potential drawbacks, and engage in informed discussions with clients. This approach ensures that service contracts truly deliver the intended value and effectively address clients' needs while minimizing any unintended consequences.

Action Items 

• Service providers: check out Clockworks Analytics in our Partner Hub to watch a demo and/or learn more about how to implement your own FDD program using their tool. 
• Building owners: make sure all of your mechanical and controls service providers are integrating FDD into the service contract. 
• Become a Nexus Pro member to get access to the rest of our analysis below, where we provide the Nexus Labs takeaways for FDD product developers and specifiers 
• Give us your feedback on this piece so we can improve!

Members only: Nexus Labs’ Takeaways

As shown in this case study, it’s a lot of work to support FDD programs. LONG has 4 energy engineers across 60+ buildings. We think one of the reasons service providers love selecting Clockworks is because Clockworks doesn't overburden those energy engineers with also needing to be experts at ontologies, writing rules, and everything else required to crunch numbers and find faults. 

We think FDD products need to be created in a way that allows the crowdsourcing of analysis and diagnosis and prioritization methods. Unfortunately, most FDD products aren’t built that way. 

This makes the product selection process vital. The less time the end user spends on writing rules or setting up analytics, the more time they can spend on driving action and producing results. 

Our whitepaper on selecting FDD software explains this in more detail.