More firms in industrial sectors lately appear to close in on a long-sought objective: upgrading to predictive maintenance in a determined effort to prevent critical parts breakdown and suppress reactivity to equipment failure.
This transition has never been purely about a shift in maintenance behaviors. Plenty of focus principally falls upon how spare parts managers respond to wider changes in operational requirements to support maintenance activities.
Firms pressing ahead with implementation plans for predictive maintenance are getting swept up by the appeal of maximizing the useful life of machinery through scheduled interventions. Under a predictive maintenance framework, it is possible to lay the groundwork for increased reliability as firms:
- Monitor the performance and condition of equipment and components in real time
- Assess the health and performance of equipment to detect potential abnormalities or failure patterns and determine when to carry out planned maintenance interventions
- Form predictions about the probability of equipment and component malfunctions based on historical and real-time data, and estimate the remaining useful life of machinery
The interest in predictive maintenance is not likely to ebb soon as it is becoming more obvious that industrial firms cannot afford the costs of waiting until equipment fails. It is much more convenient, not to say less expensive, to prevent a breakdown than to fix broken machinery. But firms must not fail to recognize that predictive maintenance leads to an extensive change in operational requirements for spare parts managers. The criticality of spare parts in predictive maintenance is distinctly high. For that matter, it is better not to rush to invest impulsively in advanced maintenance techniques and instead take a step back to firstly reflect on how to reorganize Spare Parts Operations for improved maintenance efficiency. Here are four of the most important responsibilities required of Spare Parts Operations to support predictive maintenance efforts.
Managing the Supply Chain
Predictive maintenance is an intervention scheduled in anticipation of potential machinery failure. Moving forward with planned maintenance operations is possible if spare parts are readily available for this procedure. Only now, parts availability is becoming an issue as pandemic-hit supply chains are still struggling to regain lost ground. This makes industry players likelier to confront difficulties in obtaining replacements, and operations managers must devise new spare parts management plans in response to this growing problem. A good tactical maneuver is to assess the bottlenecks perturbing supply chains and prioritize efforts to:
- Negotiate better deals with suppliers globally to diversify sourcing and counter possible supply chain delays
- Coordinate with regional and global parts distribution centers for the accurate and on-time supply of spare parts
- Deploy real-time tracking technology to gain advanced in-transit shipment visibility and optimize the movement of parts
- Invest in a digital spare parts supply chain and utilize additive manufacturing as a viable sourcing alternative for replacement parts
- Redesign logistics networks to minimize inventory carrying costs
Industry-leading companies have been recently signaling intentions to arrange investments in supply chains fully synchronized from parts manufacturing to delivery in attempts to enhance operational efficiency.
Optimizing Spare Parts Inventory
Operations managers have to improve the efficiency and accuracy of inventory management to transition profitably to predictive maintenance. Holding a large safety stock of spares is not feasible if inventory space is limited or substitute parts are too expensive. At the same time, carrying inventory for too long puts spare parts at risk of becoming obsolete—and hence firms must stock only as many replacements as required to perform planned maintenance.
Those seeking to maintain optimum stocks of spare parts in inventory may find it necessary to firm up plans for:
- Assessing current stock to remove excess and obsolete inventory
- Categorizing high-value and critical spare parts reserved for maintenance activities
- Implementing a stock control policy to determine minimum stock levels and perform regular inventory checks
Making use of inventory planning and optimization technology is a great way to automate the control of parts inventory levels for maintenance. Automated systems allow inventory-carrying firms to keep track of stock movements across different locations and utilize data-driven stock optimization plans to maintain an appropriate quantity of replacements at the ready.
Forecasting Spare Parts Demand
There is a dire need to forecast spare parts demand for the accurate planning of scheduled maintenance shutdowns. Forecasts also promise reductions in excess inventory and improvements in supply chain efficiency. But further adjustments to forecast tactics might be needed. Demand for spare parts is lumpy and, at times, extremely inconsistent—deeming conventional forecasting methods highly impractical for making demand predictions.
Some experts familiar with the matter claim that the expected demand is possible to estimate when firms stop depending on historical demand and sales data and pay attention to:
- The failure rate of a spare part
- The reliability parameters of a system’s components
Forecasting spare parts demand with installed base information, which involves the usage of data from service operations, is another widely employed method to ensure parts availability accurately and more efficiently.
Coordinating New Staffing Plans
Highly advanced technologies like IIoT sensors and artificial intelligence enable predictive maintenance, but they improve fast. As predictive maintenance systems grow more complex, industry players may need to train staff on how to run the newest technologies more efficiently and replace outdated skills. But bringing in highly skilled technicians to set up scheduling frequencies for maintenance or diagnose problems might also be necessary.
Operations managers have to coordinate proper staffing procedures to reduce skills gaps. Yet staffing experienced professionals is only a sliver of the journey. Managers must also supervise and evaluate the team’s performance to develop their expertise in data science, analytics, and other skills when required.
The Gap Between Spare Parts Operations and Maintenance Is Closing
When Operations work in isolation from Maintenance, firms are stuck in a highly reactive posture and often fail to limit otherwise preventable equipment malfunctions. Departmental silos impede information sharing, negatively affecting a firm’s ability to make better-informed decisions that drive increased efficiency.
Building a collaborative partnership between Spare Parts Operations and Maintenance should take priority as it helps industry players deliver firmer reliability. Data derived from operational and maintenance activities allows departments to jointly investigate degradation mechanisms in industrial equipment and maintain integrity with accurate predictions.