Facility managers deal with unpredictable equipment failures and rising repair costs. Predictive maintenance fixes this by tracking equipment health data and flagging problems before they cause a breakdown.
But many facilities lack the staff or technology to run these programs in-house. Instead, they turn to business process outsourcing companies that specialize in asset monitoring and analytics.
This article covers how BPO solutions support predictive maintenance programs, the technology involved, the advantages and challenges of outsourcing, and what to look for in a provider. By the end, you will get a clear picture of when outsourcing makes sense for their facility.
Why BPO solutions make sense for predictive maintenance
Siemens’s 2024 True Cost of Downtime report found that unplanned downtime costs the world’s 500 largest companies $1.4 trillion a year, equal to 11% of their combined revenue. A solution to avoid this expensive problem is predictive maintenance.
Predictive maintenance programs help determine the condition of assets and equipment to forecast when maintenance is necessary. But building this in-house means hiring and training data analysts to interpret the output and buying software. Most facilities don’t have that staff or budget sitting idle.
Another option is through BPO. So what is BPO? It is delegating non-core functions, including predictive maintenance, to external providers to allow your team to focus on core activities.
In this context, BPO solutions use different technologies, including predictive analytics, artificial intelligence (AI), and the Internet of Things (IoT), to perform the following:
- Monitoring a network of connected assets
- Automating parts of maintenance activities
- Integrating maintenance management tools
Maintenance management tools include a computerized maintenance management system (CMMS), enterprise resource planning (ERP), and manufacturing execution systems (MES).
BPO solutions for predictive maintenance
Outsourcing predictive maintenance tasks and asset optimization can improve the reliability, efficiency, and longevity of your equipment and infrastructure. The following table expands on their roles:
| Outsourced Process | Functions |
|---|---|
| Data collection and monitoring |
|
| Data analysis and predictive modeling |
|
| Condition monitoring and assessment |
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| Maintenance planning and scheduling |
|
| Inventory management |
|
| Asset performance metrics |
|
| Root-cause analysis |
|
| Vendor and contractor management |
|
| Compliance and regulations |
|
| Cost control and budgeting |
|
| Technology updates |
|
| Training and skill development |
|
| Risk management |
|
| Reporting and communication |
|
| Benchmarking and best practices |
|
| Emergency response and crisis management |
|
Benefits of outsourcing predictive maintenance
Outsourcing maintenance activities addresses three recurring gaps: skill shortages, staff bandwidth, and cost. It also delivers savings in two ways. Facilities avoid the upfront costs of hiring and training full-time technical staff and cut long-term overhead through a leaner internal maintenance structure.
Primary advantages of partnering with BPO solutions for asset optimization include:
- Immediate cost savings compared to building an in-house data analytics team from scratch
- Real-time visibility into asset health through the provider’s monitoring systems and analytics algorithms
- Access to specialized technical expertise without adding headcount or training budgets
- Faster deployment of predictive maintenance programs, since providers bring existing tools and processes instead of building them internally
- Scalable support that adjusts to facility size or asset count without renegotiating staffing
- A structured escalation path for equipment issues, backed by the provider’s SLA and accountability structure
By partnering with BPO solutions, real-time monitoring replaces manual inspection, and analytics algorithms replace guesswork. Most of all, a provider’s existing operational structure replaces the cost of building one in-house.
Potential challenges of predictive maintenance outsourcing
While BPO opportunities for predictive maintenance are numerous, facility managers might still encounter challenges when implementing BPO solutions.
The process still requires significant investment. Facilities pay for sensor installation and system integration before the provider’s ongoing service fees start generating net savings.
Other potential challenges include:
- The BPO team can misinterpret data, resulting in false maintenance requests.
- Timelines can trigger preventive maintenance activities rather than genuine asset and machine conditions.
- The third-party team’s predictive maintenance approach might not account for contextual factors such as weather and asset age.
- The internal team might be discouraged from conducting proactive asset maintenance and physical inspections.
Clear data-sharing protocols and defined escalation criteria reduce false requests and misread signals early on. Regular check-ins between internal and third-party teams keep the internal team engaged in asset oversight instead of stepping back entirely.
Considerations when choosing BPO solutions for predictive maintenance
When leveraging BPO solutions for predictive maintenance and asset optimization, selecting a reliable service provider with a strong track record of implementing similar programs is crucial. Clear communication and a well-defined SLA ensure the outsourced provider aligns with your organization’s goals and expectations.
In addition, a reliable predictive maintenance outsourcing vendor follows best practices such as:
- Using essential technologies such as machine learning (ML), sentiment analysis, big data, and analytics
- Examining historical data to assist clients in identifying crucial maintenance issues within established patterns
- Predicting challenges based on identified patterns and developing plans to either prevent or address them if prevention is no longer possible
- Employing ML algorithms and regression techniques extensively to enhance the accuracy of all predictions
- Monitoring maintenance and asset data along with log files to obtain the latest insights for informed decision-making
Facilities that verify a vendor’s track record and lock in a well-defined SLA upfront avoid the misaligned expectations that undermine most outsourcing arrangements.
Real-life example of successful predictive maintenance outsourcing
To illustrate the role of BPO solutions in predictive maintenance, consider this composite example:
A mid-sized meat processing facility operated four refrigeration compressors to cool its blast chillers, with one held in reserve as a backup. When a compressor failed, and the replacement part faced a two-month lead time, the remaining three ran continuously, with no margin for another failure.
The facility had already installed vibration sensors on the compressor heads and drive motors through its equipment vendor. What it lacked was staff to watch the data around the clock. Its BPO provider supplied a dedicated team to:
- Monitor the sensor feeds in real time.
- Flag anomalies against defined thresholds.
- Escalate findings to the facility’s engineering team.
Within weeks, the team detected an abnormal vibration pattern in one motor and alerted the facility before it failed. The compressor hadn’t broken down yet, but the alert gave the internal team enough lead time to schedule a controlled shutdown and replacement, rather than risk an unplanned failure with no backup capacity.
The facility’s engineering team used that lead time to order the replacement part early and schedule the shutdown for a low-production window instead of an emergency stoppage during peak output.
The BPO team monitored the compressor’s vibration levels for further degradation until the new unit arrived, providing the facility with a continuous read on how much longer the motor could safely run.
The result: no unplanned downtime, no rushed part orders at premium cost, and a chiller system that stayed at full backup capacity throughout the process.
