The success of any manufacturing business hinges greatly on its operational performance and its ability to manufacture quality products for customers in the most efficient manner possible. This means preventing unplanned asset downtime, optimizing maintenance processes, boosting equipment reliability, and addressing issues proactively rather than reactively.
To achieve this, asset-intensive organizations need both visibility and control of plant operations, which can be attained through a transition toward smart manufacturing.
In essence, a smart factory is a connected environment where assets and equipment can improve production efficiency through automation and self-optimization. Its benefits extend beyond just the physical manufacturing process and include value chain areas like planning, supply chain logistics, and even product development. The structure of a smart factory includes a combination of production, information and communication technologies integrated across the value chain. The end goal is for the organization to be proactive in tackling predicted challenges rather than being reactive.
The backbone of a smart factory is served by enterprise asset management (EAM), which is a combination of software, systems and services used to maintain and control operational assets, with the objective of optimizing the quality and utilization of assets throughout their life cycle. This can reduce unplanned downtime, and ultimately lower operating costs. EAM enables digital workflows through functionalities that support work management, asset maintenance, planning and scheduling, supply chain management and environmental, health and safety (EHS) initiatives.
To achieve visibility, plant managers need to understand the hidden costs associated with executing a poorly planned and completed maintenance strategy.
The Hidden Costs of a Poor Maintenance Plan
The business case for investing in intelligent predictive maintenance tools may not always be apparent—especially when faced with implementing a significant up-front investment before equipment fails or needs replacement. However, in the long run—and even with design redundancies—a reactive maintenance approach that involves waiting until an asset breaks down before replacing it may increase your maintenance costs.
While reactive maintenance requires no up-front investment and may yield short-term savings, this strategy can ultimately be expensive for asset owners.
Let’s take a look at a few examples of hidden costs and their potential consequences.
Increased Repair Costs
If plant assets are being run close to the point of failure, you could be putting your equipment at risk for substantially incremental amounts of damage. In complex types of machinery, a single component degradation can lead to a domino effect of failures that could debilitate the asset as a whole. For example, a worn impeller on an air compressor could force the entire piece of equipment to seize up. Likewise, a degraded motor-cooling fan could lead to overheating that compromises the operability of an entire pump.
When relying entirely on reactive maintenance, it can be extremely challenging to spot these trends, and plant assets would sustain more severe damage than would be the case if the issues had been identified at an earlier stage. Naturally, this leads to increased costs because of the unplanned downtime it creates, premium costs required for the expedited delivery of parts, and increased labor costs for any incurred overtime.
Shortened Asset Life
While there are a multitude of factors that impact the overall service life of equipment in a particular application, one of the most important ones is how well the asset has been maintained through the course of its operating life. Without a correct maintenance plan, the service life of practically any asset will be shortened.
For example, consider building HVAC or domestic water pumping systems, which are often treated as run-to-failure maintenance or reactive maintenance systems. A typical centrifugal pump in such applications requires preventive maintenance such as lubrication and seal/gasket inspection at a minimum, and perhaps predictive maintenance as well, such as vibration monitoring and alignment checks based on OEM recommendation. A well-maintained centrifugal pump with a robust preventive and predictive maintenance strategy can usually operate in the range of 20 to 30 years. However, that same pump operated under a run-to-failure maintenance strategy can have its service life reduced to half that time.
Regulatory Noncompliance
Failure to comply with safety, quality and environmental standards or regulations can have an adverse impact on a company’s financial results and public image, depending on the severity of the transgressions. For example, an asset performing at a suboptimal level could negatively impact the quality of the product being manufactured, which could lead to hefty fines in highly regulated industries such as pharmaceuticals or aerospace manufacturing, along with lasting damage to a company’s reputation. In more severe cases, noncompliance can lead to mandated shutdowns of a facility until the issues have been addressed.
EHS Risks
Deferring or practicing inadequate maintenance on equipment can compromise worker safety as well. For example, equipment that has excessive vibrations may lead to musculoskeletal injuries for its operators, and a machine that overheats due to inadequate maintenance can cause burns or physical injury. Failure to respond until the asset reaches a breaking point may result in legal and regulatory compliance, or worse, a lost time injury.
Operational Inefficiencies
Equipment inefficiencies can lead to increased energy consumption or inefficient operation. For example, increased vibration caused by shaft misalignment in rotating equipment can cause defects on bearings, couplings and gaskets that can eventually lead to increased energy consumption. This is similar to increased energy costs due to aggregation leaks in compressed air systems.
Cost Reduction Strategies in Factory Operations
The end goal of any organization is to maximize the total lifetime value of its physical assets. This means that every part of an asset’s life cycle, from design and operation to maintenance and disposal, must be done with the goal of extracting the most value from the asset.
A predictive or condition-based maintenance strategy is instrumental to achieving this goal as it allows asset owners to perform maintenance when the opportunity cost may be the lowest. Performing complex preventative maintenance based on a fixed schedule and not on the asset's current health will result in labor and material costs that may have been avoided. In contrast, letting the asset fail completely and then performing reactive maintenance comes with some steep costs as well. It all comes down to finding a balance between the two.
Intelligence-Based Maintenance
Asset life cycles can be extended through more informed maintenance strategies and embedding graded risk management into equipment reliability programs to improve return on investment. In today’s smart factories, business owners are beginning to incorporate advanced analytics, the Internet of Things (IoT) and artificial intelligence (AI) into EAM.
IoT, AI, analytics capabilities, and the aggregation of data across departments and information silos help enhance equipment maintenance practices by delivering actionable insight into the current and predicted health of assets. The resulting insights can help maintenance teams make intelligent value-based maintenance decisions and maximize the return on investment on critical plant assets.
In fact, the most exciting aspect of the maintenance spectrum is one that combines condition monitoring of equipment and processes with predictive algorithms to forecast equipment failures before they occur. Hence, data gathered from instrumented assets can be analyzed using predictive analytics to trigger condition-based maintenance. For example, this could involve predicting shaft wear on rotating equipment like pumps and compressors through vibration monitoring to initiate an overhaul, or monitoring pressure drop across filters to trigger filter replacement.
Transparent Maintenance Costing
Maintaining a clear vision of the profitability of maintenance processes can be challenging without the right tools. In the end, the goal is to improve the bottom line year after year. To achieve that, smart factories depend on solutions that optimize profitability throughout the value chain.
Oftentimes, manufacturing companies face cost fluctuations due to many variables involved in the process—labor, raw materials, energy, certification and testing, and overhead. Hence, a breakdown of maintenance costs at the component and workgroup level can help optimize the operational performance and monitor profitability to help make value-based business decisions.
A plant EAM system offers in-depth analytics features that consolidate real-time data as reports. These reports can then be used to forecast future expenses based on market demand trends and the current cost fluctuations of raw materials. A reliable data processing tool makes it easier to track and ultimately optimize costs to improve productivity. For example, contract owners can closely monitor raw material price trends by tracking purchase orders, maintenance managers can oversee work order costs and team efficiency, and supply chain teams can assess lead times and expediting fees.
Production Performance Monitoring
All plant assets are tied in some way to meeting both business production targets and customer demands. To make informed decisions about maintenance and operations, decision-makers need access to live production metrics. Without it, optimizing maintenance costs is nearly impossible.
An EAM solution makes key performance metrics instantly available. This helps provide transparency within a plant as well as across the whole fleet. With the help of business intelligence (BI) tools for quick and easy analysis, plant managers can conveniently see an overview of the production statistics and metrics like overall equipment efficiency (OEE) and total cycle time, as well as drill down into the details. With key data points at their fingertips, managers will find it much easier to make value-based decisions more decisively.
Conclusion
Without a sense of broad contextual visibility, one seemingly minor issue on the plant floor can quickly compound into a much more complex problem, eventually requiring costly remediation. The entire plant operation could be halted due to a maintenance problem with one piece of machinery, resulting in large expenses. When applied to manufacturing, EAM digital workflows can bring much-needed efficiency to any process that suffers from manual paper-pushing, spreadsheet tracking, broken channels of communication across workgroups, and a lack of actionable data.