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5 challenges of predictive maintenance
Overcoming predictive maintenance challenges requires a proactive approach by C-suite leadership. Learn some of the most common issues so you can help prevent them.
Predictive maintenance can benefit companies in various ways, but C-suite leaders should be aware of potential roadblocks that could erode predictive maintenance’s ROI or derail the initiative altogether.
The most common hurdles are costs, poor data quality, integration problems, lack of employee skills and organizational resistance. However, CSCOs, CIOs and CFOs who proactively address these challenges can turn predictive maintenance into a competitive advantage.
1. Expenses
Implementing a predictive maintenance program requires an upfront investment in IoT sensors, edge computing devices and analytics as well as system installation and calibration. For optimum results, companies must also integrate predictive maintenance technology with existing enterprise asset management (EAM) and/or ERP systems to enable the automation of work orders, purchasing and other functions, which also demands a significant upfront investment.
Depending on the size and complexity of an organization, total project costs can reach as high as seven figures. That cost can create short-term pressure on a company’s cash, capital budgets and quarterly earnings targets.
To mitigate these risks, CFOs and CSCOs should consider pursuing a phased program rollout approach. Start with a narrowly defined pilot that focuses on high-value, frequently used assets and establish clear KPIs.
Lessons learned from initial pilot programs can also help teams as rollout continues.
2. Data issues
Predictive models are only as accurate as the data that feeds their algorithms, and many manufacturing plants still rely on incomplete, inconsistent or low-quality sensor readings and historical maintenance logs.
CIOs must treat data quality and availability as non-negotiable, and IT and data stewardship teams should review existing EAM and ERP data prior to any modeling. Those teams should also implement automated data cleansing and standardization as part of a broader data governance discipline.
The budget for this work should come from CSCOs. Carrying it out will increase upfront costs and may slightly delay the initial rollout period, but taking care of data needs during this critical phase can prevent significant predictive maintenance challenges later.
3. Integration problems
Integration is an important part of predictive maintenance, as it can help increase its ROI. Lack of integration leads to predictive alerts being confined to isolated dashboards and employees needing to manually create work orders and purchase orders, which could create delays and increases the likelihood of human error.
However, most legacy EAM and ERP platforms are unable to support real-time data flows to and from IoT sensors.
C-level leaders should instruct cross-functional teams to map business processes and data flows and evaluate integration capabilities when considering potential predictive maintenance technology. They should look for APIs, middleware or pre-built connectors that enable automatic work order generation and inventory updates.
This level of integration can potentially increase upfront project costs, but a lack of integration will likely lead to higher long-term operating expenses.
4. Employee skills gaps
Succeeding with predictive maintenance requires employees to possess expertise in areas including equipment maintenance, data science, IoT engineering and AI/ML. Lack of employee skills could lead to predictive models underperforming because of poorly maintained data and lack of attention to key alerts.
CSCOs should first identify the key players in the organization whose knowledge spans both MRO (maintenance, repair and operations) and IT. If employees with both skill sets are rare, consider working with CIOs and HR leaders to pursue a dual-track strategy. The company can partner with specialized vendors or consultants to produce immediate predictive maintenance results while the organization develops internal programs to develop and nurture the necessary skills in-house, including investing in training for maintenance teams so they know how to interpret model outputs and contribute to continuous improvement.
Treating talent development as a core part of the predictive maintenance initiative will help reduce ongoing consulting costs.
5. Organizational resistance
Shifting from familiar reactive or calendar-based maintenance to a data-driven culture may lead to employee resistance, especially from longtime workers. Some employees may distrust algorithms, while others may fear job displacement or resent the additional responsibilities that come with the rollout of new technology. If these concerns are not addressed, organizational friction could hurt the program.
CSCOs and COOs must understand the importance of change management and involve frontline staff early. Leaders should communicate transparently and share how predictive tools could make employees’ jobs safer and less reactive. HR staff can offer change management advice if needed.
Overcoming these predictive maintenance challenges requires a proactive approach by C-suite leadership. CSCOs, CIOs, and CFOs who address potential issues with costs, data quality, integration, skills gaps and employee resistance will help minimize potential company risks and accelerate ROI.
James Kofalt spent 16 years at SAP working with SME business applications and was a product manager for integration technology at Microsoft's Business Solutions division. He is currently the president of DX4 Research, a technology advisory practice specializing in ERP and digital transformation.
