Imagine if you could have a granular view of the maintainable assets; wouldn’t it increase your efficiency? Or, if you have a 24/7 view of your equipment, wouldn’t it augment your preventive maintenance strategies to the next level?

Digital twin technology creates a digital simulation of a physical object. This technology can replicate your physical devices, infrastructures, or assets to monitor their operation. The technology additionally helps you make more informed decisions on when to replace or upgrade parts and reduce downtime by providing notifications for preventative maintenance, thus helping you in more efficient maintenance budget planning.

Digital twin technology can potentially disrupt your management strategies in a better way. How? Let us understand in this article.

Digital Twins Magnifies Predictive Maintenance Efforts

Digital twins help you create virtual replicas of your physical assets and continuously gather real-time data from the embedded sensors. The predictive capability helps schedule maintenance tasks more efficiently, minimizing costs and maximizing asset lifespan. You can utilize this data to have a comprehensive view of your asset’s current condition and also identify anomalies or potential issues.

One of the important technologies enabling a shift from traditional scheduled maintenance to condition-based maintenance. Instead of following pre-fixed schedules, your maintenance activities can be triggered automatically based on your asset’s actual condition and performance; hence, you can have a more optimized resource allocation strategy. You will also be able to minimize unnecessary maintenance.

Predictive analytics implies the usage of advanced analytics and machine learning algorithms. When this combination is applied to the data generated by digital twins, it can forecast potential failures more accurately. Hence, your maintenance teams can take proactive measures, such as replacing components or scheduling repairs, before a breakdown occurs.

Predictive maintenance further enables prioritization of tasks based on criticality, resource availability, and urgency of the detected issues, resulting in more efficient use of time and resources. Additionally, addressing issues proactively tends to be less resource-intensive and less expensive than emergency repairs conducted after a breakdown.

With the augmented power of predictive analytics, you can analyze the data collected from the digital twin and detect patterns and trends that indicate when maintenance might be required. Based on the collected insights, the maintenance teams can figure out and address potential problems before they escalate, reducing the likelihood of unplanned downtime.

Digital twins facilitate the collection of historical maintenance data and performance metrics over time. Analyzing this data with the combined assistance of a comprehensive maintenance management software allows organizations to refine predictive models, improving their accuracy and enhancing their overall maintenance strategies.

Leverage Digital Twin to Monitor and Diagnose Assets Remotely

With the assistance of digital twin technology, your maintenance teams can remotely monitor assets’ performance and diagnose issues without physically inspecting them. Digital twinning technology as they create virtual replicas of your assets, can continuously collect and transmit real-time data through the sensors and IoT (Internet of Things) devices installed in the equipment.

The gathered data can be used to gain a comprehensive view of an asset’s performance and condition, further allowing your teams to monitor it from anywhere in the world. The maintenance team can also analyze the real-time data and compare it against established benchmarks or models; experts can identify potential problems, deviations, or anomalies. With this technology in place, your business will save time and resources by allowing experts to analyze data, identify problems, and even simulate solutions without being on-site.

Remote monitoring, coupled with predictive analytics, enables the identification of patterns or trends that might indicate future issues. Imbibing this proactive approach in your business will enable the responsible teams to anticipate maintenance needs in advance, assess risks, and take preventive actions before problems escalate.

With the capability of conducting remote diagnostics experts can analyze data and provide guidance to on-site personnel, optimizing the allocation of resources. Hence, teams can focus on critical tasks based on the severity and urgency of issues identified through remote monitoring. This not only saves time but also reduces travel costs and minimizes downtime by enabling faster identification and resolution of issues.

You can also use digital twin technology to facilitate collaboration among experts regardless of their physical location. Multiple stakeholders can collaborate, share insights, and collectively work on diagnosing and solving problems through shared access to the virtual replica and real-time data.

Furthermore, the data collected from remote monitoring and diagnostics contributes to ongoing improvements in predictive models and maintenance strategies. Organizations can improve their ability to predict, diagnose, and solve issues effectively by analyzing past data and outcomes. This helps them refine their approach and work more efficiently.

Digital Twin Solution Optimized Performance of Assets:

Organizations can optimize the performance of their assets by analyzing data gathered from the digital twin of the original asset. For example, can simulate different scenarios, tweak parameters, and test changes in the virtual environment before implementing them in the real world. Let us explain it in detail; Digital twins allow for the simulation and testing of different scenarios in a virtual environment.

Maintenance teams can, hence, experiment with adjustments, upgrades, or changes to operational parameters without affecting the physical asset. With this capability, your team can assess the potential impact of modifications on performance before implementing them in reality.

Real-time data collected by digital twins provides comprehensive insights into asset performance. By analyzing this data, maintenance teams can identify inefficiencies, areas for improvement, or deviations from optimal performance. They can then make informed decisions to fine-tune operations and improve overall efficiency.

Advanced algorithms applied to the data from digital twins can optimize asset performance by continuously adjusting operational parameters based on real-time data. These algorithms can fine-tune processes, such as production schedules or energy usage, to maximize efficiency while meeting operational objectives.

Some digital twin systems can employ adaptive control mechanisms. These systems adjust operations in real-time based on changing conditions or demands, ensuring that assets operate at peak efficiency under varying circumstances. Digital twins not only predict maintenance needs but also predict performance trends. By forecasting potential performance issues or bottlenecks, maintenance strategies can proactively address these concerns before they impact operations, ensuring continuous optimization.

Additionally, this technology facilitates a continuous iterative cycle. By constantly analyzing performance data and outcomes, organizations can refine their models, algorithms, and operational strategies to achieve higher performance and efficiency over time.

As facilitated by digital twins, optimized performance leads to better resource utilization. It helps in reducing waste, optimizing energy consumption, improving production output, and maximizing the utilization of assets, ultimately leading to cost savings and improved sustainability.

Streamline Lifecycle Management of Assets with Digital Twin Technology

Digital twins cover the entire lifecycle of an asset, from design and production to operation and maintenance. The technology can have a tremendous influence early in your asset’s lifecycle by assisting in the design and development phases. They virtually enable simulation and testing of various design iterations, which helps create more robust and efficient assets from the outset.

For example, during the production phase, digital twins facilitate real-time monitoring and optimization of manufacturing processes. They ensure that the production aligns with the initially designed parameters, leading to higher-quality products and reducing defects.

Digital twins continue to provide value during the operational phase. By creating virtual replicas of physical assets, they enable real-time monitoring of performance, predictive maintenance, and remote diagnostics, as discussed earlier. This prolongs asset lifespan and ensures optimal operational efficiency.

This technology also provides value during the operational phase. This prolongs asset lifespan and ensures optimal operational efficiency. Throughout the asset’s life cycle, digital twins help in assessing and mitigating risks. Organizations can proactively implement strategies to reduce risks and enhance asset reliability and safety by simulating scenarios and predicting potential failures or risks.

The data collected and analyzed by digital twins contribute to informed decision-making at every stage of the asset’s life cycle. It allows for data-driven strategies and adjustments to optimize performance, reduce costs, and ensure compliance with regulations. Digital twins support continuous improvement by providing a feedback loop of data and insights. Organizations can use this information to innovate, refine processes, and develop better-performing assets for future life cycles.

Conclusion

According to a study by IBM in 2022, the global digital twin market is expected to grow rapidly and reach a staggering value of USD 73.5 billion by 2027. Hence, we can anticipate that digital twins are already widely used in various industries, and their demand will likely continue increasing in the foreseeable future. As a result, the technology is expected to revolutionize maintenance management strategies in several ways.

Digital technology has prompted maintenance professionals to consider adopting digital transformation in their existing structure. Creating a digital twin model based on digital information can help you make smarter decisions at the right time, increase efficiency, and achieve a high return on investment.

FieldCircle can help you augment your existing maintenance infrastructure with its experienced teams and innovative technology. To know more, get in touch with us today!

The post How Digital Twin are Reshaping Maintenance Strategies? appeared first on FieldCircle.

Despite their separate relevance they are related, with maintenance dictating the terms of quality assurance, though implicitly. Maintenance and quality management along with safety achieves a cohesive and effective operational environment. Organizations that consider them as two components on the same plane, always get the upper hand. We move and analyze this relation more closely.

Quality Assurance: Tightening the Bolts for Business Success

Quality assurance (QA) is a key process in every industry. Since it directly affects the business success, businesses cannot approach it with a lackadaisical attitude. They must concentrate resources and inspect all processes that lead to QA to achieve the quality outcomes.

Technically quality assurance is the systematic process of checking and evaluating products, services, or processes to ensure that they meet predefined standards and specifications. The primary goal of quality assurance is to identify and rectify defects or deviations from the established criteria, ultimately ensuring that the final output meets the desired level of quality.

What does Quality Assurance involve?

Quality assurance process will vary from industry to industry. Typically, here are the steps that go into building a QA process:

1. Establishing Standards and Specifications: You define the criteria and requirements that a product or service must meet. This process sets the benchmarks for quality, ensuring that the end result aligns with predefined expectations. Standards and specifications serve as the foundation for subsequent quality assurance activities and evaluations.
2. Sampling and Testing: Quality management team examines a representative subset of a larger batch. Testing methods may include visual assurance, measurements, material testing, and more, depending on the nature of the product or service.
3. Documentation: This includes recording standards, assurance results, corrective actions taken, and any other relevant information. Documentation provides a basis for traceability and continuous improvement.
4. Application of Statistical Methods: Statistical methods such as statistical process control (SPC) are employed to monitor and control the production process. These methods help identify trends, patterns, and variations that could impact product quality.
5. Root Cause Analysis: In the event of defects or deviations, quality inspectors often conduct root cause analysis to determine the underlying reasons for the issues. Addressing the root cause is crucial to preventing future occurrences.

As quality management is an industry-specific process, let’s see how it is carried out in various industries.

Maintenance: A Precursor to Quality Assurance

“Be a yardstick of quality. Some people aren’t used to an environment where excellence is expected,” once remarked Steve Jobs. In the industries we looked at in the preceding part, maintenance is a key process to build an environment of excellence.

So, quality assurance is directly proportional to maintenance, as the effectiveness of the latter directly influences the outcomes of the former. Poor maintenance operations will lead to suboptimal product quality. Conversely, efficient maintenance practices will enhance the accuracy and reliability of quality assurance.

Maintenance ensures that equipment and machinery are in optimal condition, minimizing the likelihood of defects in the manufacturing process. Well-maintained machinery is less prone to breakdowns and malfunctions, leading to a more consistent production process. This, in turn, contributes to the production of higher-quality products.

Analyzing the Correlation

Regular maintenance prevents wear and tear on equipment, reducing the chances of deviations from product specifications.

In manufacturing, for instance, precision machinery and production lines are the backbone of the production process. Regular maintenance of these machines ensures that they operate within specified tolerances, minimizing the risk of unexpected breakdowns.

The correlation between maintenance and quality assurance becomes evident when considering the impact of equipment failure on product quality. If a key manufacturing component fails due to inadequate maintenance, it can introduce defects into the production process, compromising the quality of the final product.

In the oil and gas industry, where the stakes are high due to the potential environmental and safety risks, the correlation between maintenance and quality assurance is even more pronounced. Pipelines, storage tanks, and drilling equipment are subjected to harsh conditions and must be maintained meticulously to prevent leaks and spills. Inefficient maintenance can lead to inaccurate readings, making it challenging to assess the structural integrity of pipelines or the condition of storage tanks accurately. We have experiences such as the Deepwater Horizon oil spill, where an equipment malfunctioning was one reason for the catastrophic failure that resulted in severe environmental consequences.

In aeronautics, precision instruments, avionics, and structural components must undergo routine maintenance to ensure they meet stringent safety standards. Quality assurance processes, such as Non-Destructive Testing (NDT) methods, rely on the assumption that the equipment being tested is in optimal condition. Any oversight in maintenance could compromise the accuracy of these assurances, potentially leading to undetected flaws in critical aircraft components.

Regulatory norms bridging the gap

Adherence to standards and norms builds a link between maintenance and QA. So, in spite of working as two disconnected processes, they build one single process aimed to achieve consistent quality output. For instance, in

• Manufacturing adherence to quality standards such as ISO 9001 requires a systematic approach to maintenance to ensure that equipment is capable of consistently producing conforming products.
• Oil and gas regulatory frameworks such as API standards prescribe maintenance practices to guarantee the integrity of infrastructure.
• Aeronautics, organizations like the Federal Aviation Administration (FAA), enforces rigorous maintenance and assurance protocols outlined in documents like the Aircraft Maintenance Manual (AMM) to ensure airworthiness.

How businesses have reaped results

As we stressed about the inseparability of maintenance and quality assurance, we elaborate more with results achieved by recognized names. For instance, Delta Air Lines implemented a proactive maintenance strategy using advanced analytics and real-time monitoring of aircraft components. They began regularly inspecting and maintaining their fleet, ascertaining optimal condition of the aircraft during scheduled checks. The outcome was fewer in-flight issues and higher level of safety.

Deutsche Bahn has implemented a condition-based maintenance system for its train fleet. By monitoring the condition of train components, such as wheels, brakes, and engines, in real-time, they can schedule maintenance activities more efficiently. Likewise, Shell has been using predictive maintenance technologies, such as sensors and data analytics, to monitor the condition of equipment in real-time. By minimizing downtime and reducing the risk of equipment failures, they have enhanced the overall quality of operations.

Maintenance for Quality: Some Steps to Initiate

Across industries, here are some actions in maintenance that can be taken to witness direct positive impact of maintenance on quality.

The Role of Metrics

Here are various quality assurance metrics that businesses must monitor to get an idea about the efficiency of maintenance. By monitoring these metrics and understanding their relationship with maintenance efficiency, businesses can proactively address maintenance issues.

How a CMMS can enhance quality inspections through improvement in maintenance

One key element in achieving and maintaining high-quality standards is an efficient and effective maintenance strategy. A Computerized Maintenance Management System (CMMS) emerges as a crucial tool that not only streamlines maintenance processes but significantly contributes to enhancing quality inspections. From maximizing asset utilization for downtime reduction to maximizing warranty coverage, it offers manifold benefits.

With its various features, a CMMS aids in scheduled maintenance, keeping equipment and processes ever efficient. So, the maintenance software offers you a proactive approach that diminishes the likelihood of unexpected breakdowns, eliminating scope for detrimental effects on product quality.

We recommend you to connect with our experts to align FieldCircle CMMS with your quality assurance process and realize significant improvements.

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Robotics: The Evolution

In the 1990s, pioneers like Stefano Nolfi employed genetic algorithms for autonomous refinement of physical structure and control strategies. Then Sergey Levine’s reinforcement learning studies showcased robots learning tasks through trial and error, adapting based on rewards. In industrial robotics, Peter Kopacek’s work on self-optimizing control strategies enhanced productivity and reduced energy consumption. Further, Marco Dorigo’s swarm robotics experiments highlighted collective adaptive behaviors. All these efforts culminated in the evolution of modern robotics that began surging, along with other many areas, in maintenance operations too.

Maintenance in new age: The move towards Robotics

The arrival of Artificial Intelligence (AI) into maintenance practices has given rise to a new era of predictive maintenance. This proactive maintenance strategy uses AI algorithms for analyzing vast datasets and offers excellent diagnostic measures, allowing for timely intervention.

Connected with these developments are Augmented Reality (AR) and Virtual Reality (VR). Enterprises are mulling the implementation of these technologies to obtain precise insights into the performance of their maintenance operations.

The evolution of robotic maintenance practices is linked with these advancements that are taking place rapidly. Currently, robotics in maintenance is not merely a futuristic concept but one of the key parts of the present. Its global market was valued at $4.23 billion in 2021. Remarkably, this figure is projected to surge to $10.05 billion by 2030 at the CAGR of 10.1% from 2022-2030. With 2.7 million robots in factories at present, the present state of robotics in maintenance mirrors an impressive deployment of robots.

Robotics: The promise

Understanding what robotics has to offer in the future is not just a matter of foresight but a necessity for maintenance professionals. The shift towards automation maintenance is more than a trend; it is a strategic move towards a future where maintenance is not just about fixing issues but optimizing maintenance activities with the assistance of cutting-edge robotic technologies. It is clear that robotics is steering maintenance into an era of unprecedented efficiency and will play an important role in the future.

Robotics Trends that will share the future of Maintenance and Repair

Emerging developments in robotics will allow robots to adapt to diverse tasks, diagnose issues autonomously, and optimize maintenance schedules. These cutting-edge robotic technologies that are evolving in form of:

Collaborative Robots (Cobots)

Collaborative robots, or cobots, will continue to gain popularity. These robots can work alongside human technicians, enhancing efficiency and safety. Cobots are designed to collaborate with humans in shared workspaces, helping in tasks that require precision or strength.

The dexterity and precision that cobots offer will make them invaluable for tasks such as fine-tuning delicate components or handling small parts that are challenging for human hands.

Cobots will continuously monitor equipment through sensors, detecting anomalies, and predicting potential failures with remarkable accuracy, thereby bringing transformation in predictive maintenance.

Additionally, as cobots can work in hazardous environments, from high temperatures to toxic substances, they will ensure safer maintenance practices. Cobots will also reduce downtime through swift and autonomous execution of routine tasks.

Swarm Robotics

Swarm robots excel in inspecting expansive areas and can perform repairs in complex cases, surpassing the efficiency of individual units. A concept currently under development, its implementation will improve the collaboration among robots allowing for simultaneous inspection and paving way for complex repairs.

As a collective approach, swarm robotics harnesses the power of distributed intelligences. With its synchronized teamwork, it presents a promising trajectory for optimizing equipment maintenance processes through enhanced speed, precision, and resource utilization.

Nano-Robotics for Microscopic Maintenance

Companies like Thermo Fisher Scientific are conducting research to take Nano-Robotics to the next stage. The results will be visible in maintenance, repair, and overhaul operations (MRO). These robots carry our maintenance processes at microscopic levels.

Their minuscule size allows access to confined spaces, ensuring thorough maintenance without disassembly. And so, they will improve the accuracy of detecting and rectifying faults with unparalleled accuracy. By operating at such a microscopic scale, Nano-Robotics will minimize the impact on surrounding components and optimize repair efficiency.

Self-Healing Materials and Robotic Patching

These robotics technologies enable autonomous identification and repair of minor damages. Specifically, self-healing materials possess intrinsic capabilities to mend structural flaws, which enhances durability.

Robotic patching systems, equipped with advanced sensors scan equipment surfaces, pinpointing issues. Then it applies patches precisely. Overall, the amalgamation of these two different robotic concepts offer a paradigm shift in maintenance which will usher in a proactive era for equipment repair and longevity.

Soft Robotics for Delicate Operations

Soft robotics are constructed with adaptable materials. Indeed, it is one of those robotics concepts that is set to revolutionize equipment maintenance. The lightweight and multifunctional nature of these robots, coupled with high elasticity and deformability, enables them to absorb energy and recover swiftly from physical impacts. This unique construction makes them ideal for delicate operations where precision is important.

In industries like aerospace, soft robots can streamline aircraft examinations, reducing maintenance time and enhancing adaptability to unpredictable space conditions. Their potential spans across industries, from electronics to medical applications, since they promise cost-effective and efficient maintenance.

Drone-based Maintenance Inspections

Companies like UPS are using drones to deliver spare parts to ground-based engineering crews at aviation hubs. Electric vertical take-off and landing (eVTOL) drones are already being employed for carrying out inspections and surveys of complex areas such as tanks, vessels, structures, and pipelines remotely. They can capture high-resolution images and videos, allowing for the detection of defects and damage in locations that are difficult for humans to access.

Likewise, drones are demonstrating their prowess in the inspection of confined spaces. This ability of drones to navigate isolated structures and confined spaces is making them particularly suitable for industries such as oil and gas, energy, and construction. So, drones are becoming an epitome of quick and efficient support.

Self-Optimizing Robotic Systems

Self-optimizing robots, rooted in cybernetics, have evolved through key research milestones. Recent applications, such as Pieter Abbeel’s deep reinforcement learning in robot locomotion, are opening new avenues in self-optimization. Such advancements indicate the potential of self-optimizing robots to autonomously enhance performance.

In time to come, these robots will be crucial for framing adaptive maintenance and repair strategies. They will make it easy to operate in dynamic and unpredictable environments, and help maintenance professionals to adjust strategies to changes in weather, equipment configurations, or the surrounding environment. Due to their versatility, they will perform optimally across varied conditions without compromising efficiency or precision.

Biological-Inspired Robotics

Bio-inspired robotics draw inspiration from nature and mimic the adaptability and problem-solving capabilities of living organisms. The development of a multifunctional robotic end-effector will turn out to be a significant leap in enhancing the efficiency of maintenance processes, particularly in hazardous underground mining environments. This has been highlighted in a research that shows how this robotic solution can automatically maintain reconfigurable vibrating screen (RVS) machines, and address the challenges of dangerous conditions.

This approach employs therblig and morphological methodologies to identify maintenance objectives and constraints. The resulting robotic end-effector integrates a worm-gear mechanism with hexagonal-shaped heads and an electric cylinder actuator module, performing functions such as gripping, ungrasping, loosening, bolting, unpinning, and hammering. The future paints a promising picture where bio-inspired robots will be important in industrial upkeep.

Underwater Robotics

Underwater robotics is transforming maintenance practices across industries, particularly in offshore oil and gas operations. These robots, also known as unmanned underwater vehicles (UUVs), are designed to operate underwater without the need for human intervention.

Particularly significant in the offshore oil and gas industry, these robots are expected to be extensively used for repair and maintenance in this sector along with its regular application in drilling. They are also employed for tasks such as underwater surveillance and environmental monitoring. The use of underwater robots in maintenance helps overcome the limitations of traditional methods and human divers, which is important especially from the viewpoint of improving safety.

Transform your Robotic Maintenance with a CMMS

The use of robotic technology is ushering in a new era of efficiency in maintenance, as the future speaks. And as robotics evolves, its application in equipment maintenance, diagnose and repair will undoubtedly redefine industrial efficiency and precision.

Integrating a Computerized Maintenance Management System (CMMS) is crucial in this paradigm shift. A CMMS enables a proactive approach, scheduling and automating routine maintenance tasks for robotic systems. As robotic maintenance are primarily chosen for their ability to work around the clock, it is with a CMMS that you can track if they are maintaining sustainable levels of performance.

Let our experts explain to you how you can get these long-term benefits through a FieldCircle CMMS demo. Connect and know why you must rely on CMMS for adding value with robotics in your maintenance processes.

The post Robotics and Automation in Maintenance and Repair: A look at the Promising Future appeared first on FieldCircle.

However, the ongoing research and efforts towards discovering the applicability of mathematical models for bettering productivity of maintenance processes shed light on the voracious possibilities with AI in predictive maintenance.

So, while PHM helps predict the future health of equipment based on its current condition and historical data Failure Prediction Models help predict when a specific component or system is likely to fail, the theory offers unending possibilities with diverse mechanisms.

Undoubtedly, the transformation brought by AI and Machine learning is revolutionary as its aiding maintenance professionals plan maintenance activities for the best ROI from maintenance operations.

We discuss the length and breadth of opportunities that AI offers through Machine Learning and Deep Learning.

Possibilities with AI and Machine Learning in Maintenance

Artificial Intelligence (AI) and Machine Learning (ML) are continuously evolving, and spearheading predictive analytics towards constant improvement. Steadily most business aspects have been brought under their fold. The reason – the sheer improvement they are bringing. Machine Learning and AI have been shaping predictive maintenance which is the norm of the day. Across categories, we look at some of the mechanisms that offer the potential to reengineer maintenance operations.

Anomaly Detection

• Isolation Forests: Isolation Forests are effective in detecting anomalies or outliers in large datasets. They can be applied to sensor data to identify abnormal equipment behavior, indicating potential issues.
• One-Class SVM (Support Vector Machine): This algorithm is suitable for detecting anomalies in unlabeled data. It can be applied to equipment sensor data to identify deviations from normal operating conditions.
• Autoencoders: Autoencoders, as neural network-based predictive models, can learn efficient representations of normal equipment behavior. During maintenance, they can be employed to reconstruct sensor data and identify instances where the reconstruction error is high, signaling anomalies or unusual patterns that may require attention.
• K-Means Clustering: K-Means Clustering can be used to group similar instances of sensor data. In maintenance, anomalies can be detected by identifying instances that do not fit well into any cluster or belong to small clusters. This can help in identifying unusual patterns that might indicate potential equipment issues.
• DBSCAN (Density-Based Spatial Clustering of Applications with Noise): DBSCAN is effective in identifying dense regions of data. In maintenance, anomalies may be detected by considering data points that do not belong to any cluster or are in low-density regions. This is useful for detecting outliers in sensor data that do not conform to expected patterns.
• Local Outlier Factor (LOF): LOF identifies outliers by comparing the local density of data points with their neighbors. In maintenance, this can be used to identify points with significantly lower density compared to their neighbors, indicating potential anomalies in equipment behavior.
• Mahalanobis Distance: Application in Maintenance: Mahalanobis Distance measures the distance between a point and a distribution, considering the correlation between variables. In maintenance, anomalies can be identified by flagging data points that are significantly distant from the normal distribution, highlighting potential issues in sensor readings.

Condition Monitoring

• Digital Twin Technology: By creating a digital replica of physical assets, maintenance professionals can continuously monitor and simulate the behavior of equipment. Any deviations in the digital twin can indicate potential issues in the real-world equipment.
• Neural Networks for Time Series Analysis: Neural networks, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, are used to analyze time-series data from sensors to predict potential failures or performance degradation in equipment.
• Hidden Markov Models (HMM): HMMs are used for modeling dynamic systems where the underlying state is not directly observable. In condition monitoring, HMMs can represent the hidden states of equipment and transitions between states.
• Kalman Filter: Kalman Filters are recursive mathematical algorithms that estimate the state of a dynamic system over time. They are applied in condition monitoring for real-time tracking and prediction of equipment states based on sensor data.
• Fast Fourier Transform (FFT): FFT is a signal processing algorithm that transforms time-domain data into frequency-domain data. In condition monitoring, FFT is often used to analyze vibration signals and identify frequency components associated with faults.
• Wavelet Transform: Wavelet Transform is a mathematical tool that decomposes a signal into different frequency components. It is useful in condition monitoring for analyzing signals with varying frequencies and identifying transient patterns.

Natural Language Processing (NLP)

• Text Analysis for Maintenance Reports: NLP can be applied to analyze maintenance reports, work orders, and technician notes. Now, we have generative AI to streamline this process, which will help in more easily extracting valuable insights about the history of equipment issues and trends, aiding in preventive maintenance planning.
• Semantic Search for Parts and Procedures: Implementing semantic search using NLP allows maintenance personnel to quickly find relevant information about parts, procedures, and equipment manuals. The technique improves the speed and accuracy of troubleshooting and repairs.
• Knowledge Base Generation: NLP can assist in the automatic generation of a knowledge base by extracting relevant information from maintenance documents, manuals, and reports. It can be used to create a centralized repository of information for technicians and maintenance staff.

Optimization

• Genetic Algorithms: Genetic algorithms can optimize maintenance schedules by considering various factors such as equipment criticality, resource availability, and cost constraints. They can help in finding the most efficient maintenance plan.
• Reinforcement Learning: Reinforcement learning algorithms can be used to optimize equipment maintenance strategies by learning from past actions and feedback. They can adapt maintenance schedules based on evolving conditions and performance data.
• Particle Swarm Optimization (PSO): PSO can be utilized to optimize maintenance tasks by simulating the social behavior of particles in a swarm. It can help in finding optimal solutions for scheduling maintenance activities by considering factors such as equipment reliability, downtime, and resource availability.
• Ant Colony Optimization (ACO): ACO algorithms can optimize maintenance routes and schedules by mimicking the foraging behavior of ants. These machine learning algorithms are particularly useful for tasks such as inspection rounds, where efficient routes need to be planned to inspect equipment or facilities.
• Simulated Annealing: Simulated Annealing is suitable for optimizing maintenance processes that involve finding the global optimum in a complex search space. It can be applied to tasks like optimizing preventive maintenance intervals or resource allocation for activities.
• Tabu Search: Tabu Search is effective for solving combinatorial optimization problems in maintenance. It can be employed in scenarios where certain solutions or paths need to be avoided to prevent inefficiencies or conflicts in maintenance planning.
• Artificial Bee Colony (ABC) Algorithm: The ABC algorithm mimics the foraging behavior of honeybees and can be applied to optimize maintenance tasks. It is particularly useful in scenarios where multiple objectives need to be considered such as reducing maintenance costs, and optimizing resource utilization.
• Ensemble Methods: Ensemble methods like Random Forest or Gradient Boosting can be used in maintenance optimization by combining the predictions of multiple models. They can improve the accuracy of predictions related to equipment failure, helping in better planning and scheduling of activities.
• Fuzzy Logic Systems: Fuzzy Logic can be employed to handle uncertainty and imprecision in maintenance optimization. It is useful in data-driven decision-making processes where variables are not precisely defined, such as determining the optimal time for equipment replacement based on fuzzy criteria.
• Integer Linear Programming (ILP): ILP can be used in maintenance optimization to model and solve scheduling problems with discrete decision variables. It is beneficial for situations where maintenance tasks need to be scheduled subject to various constraints, such as resource limitations and time windows.

Image and Video Analysis

• Computer Vision for Equipment Inspection: Computer vision algorithms can analyze images and videos from cameras or drones to identify defects, wear and tear, or other issues in equipment, which is particularly useful for preventive maintenance.
• YOLO and Faster R-CNN for Asset Localization: Object detection algorithms like YOLO (You Only Look Once) and Faster R-CNN are employed to locate and track specific assets within a facility. Optimizing asset inventory and keeping all assets at their designated locations are some areas where these techniques can be useful.
• U-Net and Mask R-CNN for Condition Assessment:Semantic segmentation techniques are used to segment images into different classes, allowing for a detailed understanding of the condition of different components. These analyses play a crucial role in a facility condition assessment, helping monitor the health of equipment and identify areas that may require maintenance or repair.
• HOG and SIFT for Feature Extraction: Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) are feature extraction algorithms help in identifying distinctive patterns in images, which can be useful for recognizing specific features or components of equipment. These mechanisms will aid in the identification of wear patterns or irregularities.

Implementing for Results

Implementing these algorithms involves several steps, as outlined here:

Data Collection

Collect sensor data measuring parameters like temperature, pressure, and vibration, alongside historical maintenance data. The combination offers crucial time-series insights into equipment condition, aiding in pattern recognition and understanding the relationship between events and failures.

Feature Engineering

Extract relevant features from raw sensor data, emphasizing the health of equipment. Utilize time series analysis techniques such as rolling averages and Fourier transforms to discern patterns and trends vital for predictive maintenance.

Algorithm Selection

Go for regression models like linear and support vector regression for predicting remaining useful life. Embrace survival analysis techniques, along with advanced models like decision trees, random forests, and deep learning algorithms for accurate equipment failure predictions.

Model Training

Train the algorithm through supervised learning on labeled data, incorporating input features and corresponding outcomes like time to failure. In some instances, leverage unsupervised learning for anomaly detection, where the algorithm identifies deviations from normal operations.

Evaluation Metrics

Assess the model using accuracy, precision, recall, and AUROC metrics. These measures gauge how well the model predicts outcomes, its accuracy in positive predictions, and its ability to balance true positive and false positive rates.

Threshold Setting

Determine maintenance thresholds by setting points based on model predictions and acceptable risk levels. A critical step, it ensures timely interventions, aligning with the algorithm’s predictions and the organization’s risk tolerance.

Integration and Deployment

Integrate the predictive maintenance model into existing maintenance systems like CMMS, facilitating seamless coordination. Enable real-time monitoring post-deployment, allowing continuous analysis of incoming data for prompt interventions by maintenance teams.

Continuous Improvement

Establish a feedback loop to continually enhance the model. As more data becomes available, retrain the algorithm to adapt to changing conditions, improving accuracy, and ensuring sustained effectiveness in predicting equipment failures.

Benefits of AI and Machine Learning in Maintenance

Some of the significant benefits that you will experience when diligent implementation of these algorithms and witness a transformation of your maintenance are:

Smart Scheduling

AI-driven predictive maintenance ensures interventions at the optimal time, aligning with equipment conditions to minimize disruptions. Apart from preserving the integrity of assets, it also reduces unnecessary maintenance, resulting in significant cost savings.

Downtime Deterrence

Predictive maintenance schedules activities before failures, ensuring operational continuity and preventing sudden breakdowns. By proactively addressing potential issues, organizations reduce downtime and maintain a seamless production flow.

Resource Harmony

AI optimizes maintenance resources, ensuring efficient deployment of personnel and tools. Additionally, ML predicts spare parts needs, optimizing inventory levels to prevent both excess stock and shortages, contributing to cost-effectiveness.

Prolonged Lifespan

Predictive interventions extend equipment lifespan by addressing issues early, preventing further deterioration. Such proactive care not only enhances reliability but also contributes to preserving the integrity of equipment assets.

Economical Operations

Predictive maintenance diminishes the need for costly emergency repairs, leading to substantial cost savings and budget predictability. By avoiding emergency breakdowns, organizations can allocate maintenance budgets more efficiently and strategically.

Safety First Approach Achieved:

Predictive maintenance identifies safety concerns early, enhancing workplace safety by proactively addressing potential hazards. Early detection and resolution of safety issues also contribute to regulatory compliance and a safer working environment.

Streamlined Efficiency

AI and ML streamline maintenance workflows, enhancing operational efficiency by automating routine tasks and minimizing manual interventions. Productivity improvement in this way results in time and cost efficiencies, allowing organizations to achieve more with existing resources.

Tailored Strategies

ML adapts to specific equipment needs, allowing tailored maintenance strategies that consider the unique characteristics and requirements of each asset. The ability to adapt strategies based on real-time data ensures that maintenance plans remain effective in dynamic operating environments.

Continuous Enhancement

Machine learning models continuously learn from new data, improving accuracy and adapting to changing conditions by incorporating the latest insights. The iterative learning process fosters a culture of continuous improvement, where maintenance processes evolve and become more effective over time.

Conclusion

We’ve illuminated the path towards unprecedented efficiency gains with Artificial intelligence and ML for evolving maintenance needs. Understanding the steps to implement these technologies is pivotal. However, it requires two-fold expertise. First, strong proficiency in AI and ML, and second a robust understanding of industry-specific maintenance processes.

FieldCircle helps you overcome these challenges by offering a computerized maintenance management system (CMMS) that is integrated with advanced AI and ML techniques tailored to solve your maintenance operations. Schedule a demo call to get a better clarity.

The post Transitioning to Smart Maintenance with AI and Machine Learning appeared first on FieldCircle.

Obviously, amidst the dire need to explore every single channel for ROI enhancement, the imperative to cut costs and address skills gaps has been a driving force behind these trends.

The pressure to maintain quality maintenance persists, given that up to 40 percent of abnormal situation losses are caused by human factors such as insufficient knowledge and maintenance mistakes.

With the introduction of smart glasses like Google Glass and Microsoft’s HoloLens, the 2010s witnessed a shift towards enterprise adoption of augmented reality (AR). Some early uses of AR in maintenance can be traced to this period. The developments allowed hands-free AR experiences, particularly beneficial in maintenance scenarios. The release of ARKit and ARCore by Apple and Google in 2015 took AR capabilities to the next level, by making them accessible on mobile devices.

In traditional maintenance approaches there was a room for accommodating inefficiencies, either through run-to-failure strategies or periodic maintenance schedules. However, with the advancement in technology, and especially with AR, stakeholders aren’t willing to sacrifice the quality of maintenance.

Augmented by the Industrial Internet of Things (IIoT), AR provides maintenance workers with powerful insights, enhancing situational awareness and directing them to problem sources swiftly. Beyond this, it carries the potential to transform maintenance schedules into genuinely condition-based programs. The use of connected devices is vital here. They detect early signs of impending issues and optimize work assignments to eliminate unnecessary tasks.

There are many ways augmented reality (AR) is shaping and will shape the maintenance sphere. The current adoptions in the maintenance industry are just planting the seeds, as the transformation AR will bring will be quite significant.

Augmented Reality in Maintenance: A Precursor

The research paper, Augmented Reality in Maintenance—History and Perspectives talks about the AR’s various types. They will guide maintenance professionals to go for area-specific AR implementation.

Marker-based AR: Relies on visual markers like QR codes as reference points for virtual objects. When the system recognizes these markers, virtual content is superimposed on the real view through a camera.

Markerless AR: Doesn’t require specific markers. It employs computer vision techniques like localization or object recognition to detect the user’s environment and place virtual objects in their view.

Location-based AR: Gathers real-time information using GPS or other location systems to overlay AR content based on the user’s geographic position.

Projection-based AR: Applies virtual content to real-world objects via projectors or projector-equipped glasses, eliminating the need for screens.

Recognition-based AR: Recognizes real-world objects or images and overlays relevant information or virtual content onto them.

Outlining-based AR: Uses image recognition to understand the environment, allowing users to create contours or shapes and highlight real-world components with special cameras.

Motion tracking: This type of AR enables systems to follow the user’s movements, adjusting the position and/or orientation of virtual objects for a more interactive and immersive experience.

Simulated Physics and Interactions: Involve virtual objects interacting with the real-world environment or each other, enhancing the realism of AR experiences.

Also read: Total Productive Maintenance: What and Why for SMEs

AR in Maintenance Management: How the adoption will boost the yield

AR has been instrumental in bringing changes in areas that businesses have tapped for its implementation. Prompted by the positive experiences, maintenance professionals are building plans to use augmented reality in maintenance. Let’s understand how the change for betterment has been unfolding.

Predictive Maintenance through AI and AR Integration

Maintenance professionals have already understood the need for transferring from preventive maintenance to predictive maintenance. AR is turning out to be a keystone in this transformation.

By leveraging historical data and real-time sensor information, AR overlays predictions of potential equipment failures onto a technician’s view. Technicians can proactively intervene, reducing downtime and preventing costly breakdowns before they escalate into bigger problems.

For example, a technician wearing AR glasses can see maintenance alerts, generated by AI, highlighting components at risk of failure. In the near years, maintenance teams will rely heavily on AR to empower themselves with such capabilities.

Smart Documentation and Equipment History Visualization

Documentation is central to any maintenance operations. Maintenance teams cannot move even a step forward if they are not thorough with the documentation. Before involving themselves in maintenance tasks they must go through manuals and guides so that they find themselves on the right tracks during the operations.

With AR, rather than relying on static manuals, technicians can access live, interactive documentation linked to specific equipment components. Dynamic, context-aware information overlays offered by AR glasses can display a maintenance history timeline focusing on a particular machine part, detailing past repairs, replacements, and performance metrics.

Technicians have comprehensive, up-to-date information at their fingertips, that will enable them to be quick at initiating important decisions. AR will thus contribute to a more efficient and informed approach to equipment maintenance in the long run.

Immersive Virtual Collaboration for Complex Repairs

Maintenance technicians work with diverse teams, including procurement, finance, etc, based on the nature of the business. At a time when stakeholders are keen on streamlining this process and eliminate all impediments to smooth coordination, AR stands as the promise.

Technicians can use AR devices to share a real-time view of their workspace with remote experts. In cases where the nature of maintenance is intricate and involves multiple challenges, AR can significantly benefit to collaborate.

While guiding the repair process, AR will annotate the technician’s field of view, and even virtually manipulate components. A field technician working on a specialized piece of machinery can receive guidance from an expert located continents away.

Enhancing Asset Lifecycle Management

AR will extend its influence to the entire asset lifecycle, providing a holistic approach to maintenance operations. By integrating with Asset Lifecycle Management (ALM) systems, AR will offer a visual representation of an asset’s journey from procurement to retirement.

So, your maintenance team will be able to use AR to visualize equipment installation dates, track component replacements, and plan for upgrades. Such a comprehensive view will be beneficial for optimal asset utilization. Overall, by adopting AR you can transform maintenance into a strategic asset management function.

AR’s integration into asset lifecycle management provides a visual roadmap of an asset’s journey from installation to retirement. By visualizing past maintenance activities, replacements, and upgrades, technicians will be more accurate in taking appropriate actions. They will perform maintenance, upgrade components, as well as retire assets, optimizing the entire asset lifecycle for maximum efficiency and cost-effectiveness.

Training Simulations with Haptic Feedback

Simulation is a much-used term in the enterprise world. In practice, it’s a coveted exercise as it offers insights into aimed implementation. In maintenance, simulations are key to predictive maintenance. By simulating equipment functioning against various conditions, maintenance teams can define the course of maintenance.

Future AR applications will incorporate haptic feedback technology to enhance training simulations for maintenance personnel. By receiving haptic feedback technicians will develop muscle memory and fine-tune their skills in a risk-free virtual environment. Soon, they will find themselves engaging in realistic, hands-on simulations using AR devices that provide tactile sensations.

Your technicians wearing AR gloves will sense resistance or vibrations mimicking real-world conditions during a simulated maintenance task. As simulations will train them to gauge the impact of vibrations, they will initiate the actions well in time and prevent occurrence of possible issues.

Transforming Inventory Management

Inventory in the context of maintenance has a different connotation – not referring to the inventory of raw materials or processed goods as we commonly imagine for ‘inventory’. Every maintenance process is dependent on implements and parts of equipment that replace the worn-out/malfunctioning parts. These comprise the inventory in maintenance.

Sustaining sufficient levels in these inventories is critical for maintenance operations. In the event of inventory dipping beyond thresholds, maintenance can be adversely affected, coming to a halt for a dumb reason.

AR transforms inventory management by offering instant, on-the-go access to critical information. Technicians can effortlessly view current inventory levels, order status, and part specifications through AR overlays. Detailed insights significantly reduce downtime by ensuring the availability of necessary spare parts when and where they are needed.

Also read: The Impact of Green Maintenance Practices on Business Sustainability

How AR shaped up Maintenance: Using experiences as inspiration

The journey of AR adoption in maintenance is underway, as the stories of some recognized organizations highlight. So, those thinking about the prospects of AR can take inspiration from these stories and craft implementation plans for targeted areas.

ARMAR project expands AR’s horizon in Maintenance

A study by Henderson and Feiner highlighted the ARMAR (Augmented Reality for Maintenance and Repair) project, an AR system for training maintenance interventions. This can be considered as one important early experimentation of AR in maintenance.

It was a system implemented to monitor the user’s head movements, enabling the identification of target components through various augmented reality (AR) output techniques. The aim was to assess the duration spent on each method.

The study was noteworthy for its examination of the Graphical User Interface (GUI) functionality and the performance assessment of gesture recognition. It highlighted some significant benefits of AR such as reduction in head and eye movement, less need of context switching, and optimized repair sequences.

Since then, enterprises across sectors, such as manufacturing, healthcare, and logistics, have adopted AR for maintenance purposes. The use of AR in maintenance has become more widespread as the technology has matured.

Boeing writes a new chapter

Boeing implemented augmented reality technology for aircraft maintenance, particularly with the introduction of its Skylight platform. Skylight is an AR system designed to assist technicians in performing complex tasks by overlaying digital information onto the physical environment. Boeing began testing Skylight in the early 2010s and gradually expanded its use in aircraft maintenance.

The AR program imparted crucial skills in object identification and complex repair processes. Participants engage in activities such as identifying fault sources and receiving step-by-step instructions for repairs, practicing complex repair procedures, and overlaying detailed 3D diagrams of diverse automotive components.

This innovative training approach enables learners to access vital, just-in-time information concerning various automotive objects and procedures. It notably expedited automotive workshop processes, offering a more efficient and effective means of skill acquisition.

BMW stands out in the Automobile sector

World’s leading automobile manufacturer, BMW leverages AR techniques supporting complex maintenance tasks, using markerless CAD-based models and a virtual camera with a 90-degree field of view. The AR system is tested with different hardware setups. It has proven effective in diverse maintenance and repair scenarios, demonstrating its practical utility in the automotive industry.

Bosch sets the course in Industrial Maintenance

Bosch has incorporated augmented reality (AR) technology into its automotive workshops, aiming to enhance the efficiency of service repair tasks. The implementation involves a specialized AR training activity designed for workshop technicians, utilizing tablets and Microsoft HoloLens 2 glasses.

The AR activity offers learners visual guidance through features such as 3D diagrams, on-screen text, and charts, aiding in the identification of objects. Moreover, technicians can access step-by-step instructions tailored to various car models. The HoloLens 2 headsets make it simple for technicians to interact seamlessly with digital objects, further enriching the learning experience.

The AR initiative has paid off by significantly reducing the time required for technicians to complete tasks, concurrently minimizing errors made by technicians by an impressive 15%.

What do studies indicate for future AR adoptions?

Augmented Reality (AR) is proving to revolutionize electrical equipment maintenance. Research conducted by Zhukovskiy and Koteleva reveals that the integration of AR modules significantly reduces maintenance expenses by incorporating scripted tasks, minimizing errors, and improving the overall quality of interventions.

Another investigation utilizes Vuforia, a program for 3D object recognition within an AR system, resulting in cost savings through decreased travel time and heightened technician safety.

Peng et al. in their study on the power sector, used AR to optimize operations and effectively train substation power grid personnel. Enedis, a French electricity company, employs AR applications for low voltage control panels, providing advantages such as reduced intervention time and better operator safety.

How will the application of AR benefit maintenance operations?

Those implementing AR have been witnessing substantial improvements in maintenance. In the long-run, they can find themselves getting benefitted in myriad ways. These include.

Deep Focus on Machine Faults

Augmented reality enhances maintenance by providing insights into machine faults, minimizing downtime and improving efficiency.

Detailed 3D Preview of Machines in Motion

You will have immersive 3D previews for better understanding, training, troubleshooting, and optimizing equipment performance.

Greater Accuracy and Efficiency

AR provides precise instructions, reducing errors and improving accuracy in tasks such as assembly, repairs, and maintenance, thus bringing down the mean time to repair (MTTR)

Transparency in Equipment Audits and Surveys

AR overlays real-time data onto physical equipment, streamlining audits, fostering better decision-making, and ensuring compliance.

Performance Modeling and Simulation of Equipment

You will be equipped with virtual models for in-depth analysis, scenario testing, and optimization of maintenance procedures.

Supporting Equipment Risk Identification and Management

Identifying and managing risks by providing real-time data on equipment health and safety parameters will be possible with perfection.

Hands-Free Tech for Workplace Efficiency

You will have hands-free access to critical information, allowing technicians to focus on tasks at hand and enhance efficiency.

Simplified Entry in Workspaces

AR simplifies tasks by overlaying relevant information directly into technicians’ workspaces, reducing cognitive load.

Data at Your Fingertips for Real-Time Solutions

Process offering real-time access to operational data will undergo further refinement, improving response times and contributing to overall operational efficiency.

Swift Plant Maintenance with On-the-Spot Historical Data

With on-the-spot access to historical maintenance data at their fingertips, maintenance professionals will be able to initiate actions without any delay.

Bridging Gaps Among Technicians

AR will bridge knowledge gaps by providing interactive training materials, on-the-job guidance, and knowledge sharing.

Real-Time Training for Empowering Technician Skill Sets

Real-time maintenance training by overlaying instructions and simulations onto physical equipment to accelerate the learning curve will be a reality.

How can AR-CMMS be a comprehensive solution for your maintenance needs?

Augmented Reality (AR) systems integrated with Computerized Maintenance Management Systems (CMMS) can leap towards high efficiency levels.

Specifically designed for a 3D capability, an AR-CMMS system offers both preventive and predictive measures. Such AR solutions deliver maintenance guidance through text, spoken instructions, and dynamic 3D models or videos. By utilizing a marker to pinpoint the target, the system allows quick responses to unforeseen breakdowns, demonstrating its real-time effectiveness in resolving issues for operators.

Reach out to our experts to know how you can combine the power of AR with a CMMS.

The post Role of Augmented Reality in Maintenance: Gauging the Future Impact appeared first on FieldCircle.

Enterprises are at the forefront of this movement and keen about implementing comprehensive strategies to monitor and reduce their carbon footprint. The correlation between industrial activities, particularly in energy generation and distribution, and carbon emissions is undeniable. The business world has found in maintenance an opportunity to meet their individual as well as global sustainability goals.

The impact of sustainable maintenance extends beyond environmental considerations; it directly influences an enterprise’s bottom line. Effective maintenance strategies can lead to a significant reduction in costs, with up to a 40% decrease in maintenance costs and a 10% reduction in energy consumption.

In this intersection of carbon emissions, preventive maintenance, and corporate commitment to sustainability, it becomes important to gauge the impact of green maintenance. Apart from embracing eco-friendly practices, this maintenance strategy encourages use of improved technology. How does this benefit the business in the long-run? Explore.

What is Green Maintenance?

Green maintenance refers to a set of practices aimed at reducing the environmental impact due to equipment maintenance operations. It aims to replace maintenance with sustainable maintenance, where the longevity of the equipment is improved, and performance enhances as a result. In this endeavor, maintenance professionals implement eco-friendly practices and adopt sustainable strategies throughout the equipment lifecycle. Green maintenance seeks to promote a greener future where a positive impact is made on overall business.

What are the Principles of Green Maintenance?

The notion of green maintenance rests on the following principles which maintenance professionals should also look at as best practices.

• Strive for energy efficiency
• Reduce Waste
• Promote Sustainable materials
• Prioritize renewable energy use
• Reduce carbon footprint
• Focus on life cycle thinking
• Implement green procurement
• Implement green cleaning practices
• Responsibly handle and dispose hazardous materials

How does Green Maintenance make your business sustainable?

Enterprises can make their businesses more sustainable in terms of commitment to reduce their carbon footprint and improve profit margins by adopting green practices which help:

Strike Financial Opportunities

Green maintenance practices offer significant financial advantages to businesses, creating a win-win situation by promoting environmental sustainability and improving cost-effectiveness.

Firstly, by reducing the Total Cost of Ownership (TCO), green maintenance contributes to substantial cost savings for organizations. Eco-friendly maintenance strategies are concentrated around efforts to remove resource consuming processes from operations, and promoting resource-efficient practices where the focus is on using limited resources wisely. Ultimately, these steps lower overall costs.

Investing in energy-efficient equipment is another key step with direct financial benefits. Such equipment is designed to function on optimal energy. Cumulatively, the impact of adoption of such equipment on energy costs is significant. Extending the equipment’s lifespan, these adoptions affect equipment lifecycle management, and defer the need for expensive replacements.

Amidst these promises, access to green financing is an emerging financial opportunity. As businesses adopt eco-friendly maintenance approaches, they become eligible for specialized funding options that support environmentally conscious initiatives.

Make Environmental Impact

Across industries, carbon emission is a key concern. Electricity and heat production contributed around 23% of global GHG emissions in 2022. In 2021, transportation contributed to 16.5% of total GHG emissions in the US.

In each industry, maintenance activities have their share in greenhouse gas (GHG) emissions directly from the trucks, equipment, facilities, and materials used to keep the system in a state of good repair.

There should be a focus towards harnessing energy from sustainable sources like solar or wind power to power machinery. Moreover, adopt energy-efficient maintenance strategies, such as optimizing maintenance schedules and using energy-efficient technologies that minimize energy consumption.

Minimizing energy consumption during upkeep creates a long-term impact. For example, replacing traditional incandescent light bulbs with energy-efficient alternatives like compact fluorescent lights reduces electricity usage significantly. Likewise, using motion sensors in shared spaces not only conserves energy but also enhances safety.

Additionally, the adoption of eco-friendly lubricants, fluids, and cleaning products in maintenance activities contribute to lowering the carbon footprint. These include:

Embracing a circular economy model in maintenance operations promotes the reuse of resources. Instead of discarding used equipment or materials, businesses can explore options for refurbishment, repair, or repurposing. You can go for designing products and systems that can be easily disassembled and components reused.

Optimize Design and Planning

Green maintenance practices focus on designing products and systems with ease of maintenance in mind, reducing the need for frequent repairs and replacements. Companies like Toyota have implemented design principles that emphasize modularity and ease of repair.

Modular robotic arms designed with interchangeable components, allowing quick and targeted replacements during maintenance. Procedural optimization is coupled with environmental benefits – the overall span of equipment extends and targeted replacements reduce energy consumption.

Implementing predictive maintenance using sensors and monitoring systems help detect potential issues before they become major problems. Similarly, implementation of remote monitoring capabilities to diagnose issues without physical access also falls within the scope of the green concept of maintenance.

In sustainable maintenance designing, you have to also establish commonality in components across systems or equipment. For instance, in manufacturing, standardization of production line components make available the common set of elements for streamlined maintenance. While in oil and gas, standardized valve components across multiple extraction sites bring consistency.

Promote Employee Well-being and Engagement

In many industrial settings, maintenance professionals are exposed to hazardous conditions that pose significant health risks. Traditional maintenance often involves the use of toxic substances, increasing the likelihood of respiratory issues and skin problems among workers. However, as we mentioned earlier, adopting green maintenance practices can mitigate these risks by replacing harmful chemicals with environmentally friendly and non-toxic alternatives.

A common hazard in maintenance processes, particularly in manufacturing sectors, is noise pollution. The constant hum and clatter of machinery during maintenance activities can lead to hearing damage and stress among workers. Green maintenance practices emphasize the use of quieter and energy-efficient machinery, coupled with effective noise control measures. By minimizing noise pollution, you can create a more conducive and comfortable work atmosphere.

In fields where workers engage in repetitive tasks, ergonomic strain is a prevalent concern. Use of traditional maintenance approaches can potentially lead to musculoskeletal problems amongst the maintenance staff. If you invest in ergonomic tools and promote proper work techniques, it will reduce the physical strain.

Beyond the direct health benefits, green maintenance practices contribute to improved indoor air quality. The use of low-emission paints and cleaning agents create a healthier indoor environment, preventing respiratory problems and allergies among maintenance professionals.

Build a Sustainable Supply Chain

We have already seen what green maintenance offers, but the impact it makes on the entire supply chain deserves special mention. If we consider the manufacturing industry, maintenance encompasses from maintenance across inbound logistics, plant maintenance, to maintenance in outbound logistics.

While we have discussed promoting use of eco-friendly products in upstream stages such as raw material extraction and processing, the expanse of green maintenance is vast. Like how it can benefit these stages, it can bring improvement in allied processes like procurement.

Green procurement involves the conscientious selection of products and materials that align with environmental and social responsibility. The procurement of eco-friendly maintenance supplies encompasses selecting products that have minimal environmental impact throughout their lifecycle. Also, procurement is a paper-intensive process where you have to create several requests for quotation (RFQs) and requests for proposal (RFPs) on a daily basis. However, using a computer-based process, you can straightaway eliminate the need for processing these requests in paper form and streamline the approval process, thereby massively reducing time for procurement of supplies.

A sustainable procurement practice also extends to the sourcing of supplies from suppliers and manufacturers committed to sustainable practices. You evaluate the sustainability credentials of suppliers, verify if they follow ethical labor practices and use environmentally responsible production methods, before buying their supplies.

Build a compliant ecosystem

Following sustainable maintenance is a primary step to meeting compliances and environmental regulations, may it be any industry. If we talk about the United States (US), maintenance professionals need to consider the following regulatory compliances.

With green practices, you don’t have to set up any special process to align with these practices. Rather you automatically start following these norms. You might bring green maintenance practices, but it is certified only when the concerned authorities stamp their approval for your implementation.

Conclusion

Embracing an eco-friendly approach and implementing sustainable practices in maintenance is a win-win for both the environment and the business. While it improves your ability to carry out maintenance tasks, at the same time, it regulates the impact of maintenance on the environment.

Sustainable maintenance must be looked at from a broader perspective, including activities linked to maintenance and take the adoption process as a long-term assignment. Some results will be quickly visible and some will generate nice outcomes in the long-run. Instead of implementing everything at a time, adopt a practice at a time, observe how it is creating the difference and then move on to implementing another sustainable practice.

At the end of the day, sustainability in maintenance brings tremendous returns. The better you are prepared to implement sustainable maintenance strategies and permeate them in your operations, the better will be outcomes.

The post The Impact of Green Maintenance Practices on Business Sustainability appeared first on FieldCircle.

In the wake of these changes – which nobody knows how fast will translate into unexpected realities – the need to be well-versed across functions and practices has become clearer. The solution to counter this challenge, which is still unfolding, lies in cross-training.

So, to become future-ready, and demonstrate adeptness in managing evolving maintenance operations, cross-training has become a powerful mechanism and invincible tool. Its viability in today’s maintenance management is justified by its efficacy in equipping teams with capabilities to manage complex software interfaces, participate in the application of AI and data analytics, and secure digitally-driven systems from cybersecurity threats. Moreover, it aims to bring out maintenance staff from their traditional isolated role, often looked at as taciturn, and enthuse them to communicate and collaborate across functions.

In a nutshell, cross-training is not just a need but one key practice that is not a choice but an obligation for today’s maintenance functions. We look at the strategies to cross-train your maintenance employees and empower them with diverse skills, and then move to the benefits you will derive as a result.

What is Cross-Training and What is its significance?

Cross-training is the process of training employees to perform tasks and responsibilities outside their primary job roles to build versatility and improve operational flexibility. It equips employees with diverse skills, allowing them to step into various roles when needed ensuring operational continuity during absences or workload spikes. This approach fosters teamwork, enhances employee engagement, and develops a more adaptable workforce. By reducing dependency on specific individuals, it also strengthens organizational resilience.

How to Craft a Cross-Training Program For Maintenance: Strategies to adopt for Cross-Training employees?

Creating a cross-training program for maintenance staff is a strategic way to enhance workforce flexibility, improve efficiency, and ensure smooth operations. By equipping employees with knowledge in multiple areas, such as HVAC, plumbing, electrical systems, and general repair, organizations can minimize downtime and optimize resource utilization. This guide outlines effective strategies to develop a robust cross-training program, from assessing skills to evaluating outcomes.

1. Assess Skills and Roles

To create a successful cross-training program, it’s essential to first assess the current skills and roles within your facility maintenance team. Identify key areas of expertise, such as HVAC, electrical work, plumbing, and general repairs. Evaluate each team member’s strengths and skill gaps to pinpoint where additional training is needed. For example, if several employees excel in HVAC but lack knowledge of electrical systems, prioritize electrical training modules to address this gap. This foundational assessment ensures that your program targets the most critical areas for improvement.

2. Set Clear Objectives

A well-structured program requires clear and measurable objectives. Define what you want to achieve, such as reducing equipment downtime, improving efficiency, or creating more scheduling flexibility. Setting goals like these allows you to measure the program’s success over time. Additionally, outline expected outcomes, such as smoother operations during staff absences or enhanced collaboration among team members. For instance, a cross-trained team can step in during emergencies without disrupting workflow, leading to better resource allocation.

3. Create a Structured Training Plan

Developing a structured training plan is key to ensuring consistent learning outcomes. Divide the program into manageable modules, with each module focusing on a specific skill or area of maintenance. Include hands-on training opportunities, such as role rotations, where employees can gain real-world experience in different tasks. Pair this with a mentorship program by assigning experienced employees to guide less-experienced colleagues. For example, a senior plumber could mentor a technician in advanced repair techniques, fostering both skill development and team collaboration.

4. Utilize Diverse Learning Methods

Incorporate a mix of learning methods to keep training engaging and effective. Interactive workshops can encourage employees to work together and solve practical maintenance problems. Supplement this with online resources, such as video tutorials and manuals, for flexible, self-paced learning. Job shadowing is another effective tool, allowing employees to observe and learn from peers performing specialized tasks. For example, a technician could shadow an HVAC specialist to gain insights into equipment troubleshooting.

5. Monitor Progress and Provide Feedback

Regularly monitoring progress ensures that the training program delivers the desired results. Use tools like a cross-training matrix to track skill acquisition and identify remaining gaps. Assess employees’ progress through practical evaluations, quizzes, or supervisor observations. Provide constructive feedback to reinforce strengths and address areas for improvement. For example, if an employee is struggling with electrical troubleshooting, offer targeted support or additional training resources to help them succeed.

6. Encourage Knowledge Sharing

Fostering a culture of knowledge sharing is critical for sustaining the benefits of cross-training. Encourage peer learning by organizing team meetings where employees can exchange insights and best practices. Create internal documentation, such as step-by-step guides or procedure manuals, to serve as reference material for all team members. For instance, documenting standard HVAC troubleshooting techniques ensures consistent application across the team.

7. Offer Incentives for Participation

Motivate employees to fully engage in the cross-training program by offering meaningful incentives. Recognize their efforts through bonuses, certificates, or public acknowledgment during team meetings. Additionally, highlight how cross-training can contribute to career growth. Employees who acquire new skills are more likely to be considered for promotions or leadership roles, which can inspire them to invest in their own development.

8. Assess the Impact

Finally, measure the program’s overall impact by tracking performance metrics, such as response times to maintenance issues, equipment reliability, and employee satisfaction. Use this data to evaluate the program’s success and identify areas for improvement. For example, if the program has reduced downtime but not improved team collaboration, consider introducing more team-based training exercises. Continuously refining the program ensures it remains aligned with the organization’s evolving needs.

What are the Benefits of Cross-Training for your Maintenance Team?

Establishing harmonized cross-functional collaboration and making maintenance personnel understand the nitty-gritty of the other sides of the business are primary goals of cross-training. This process brings several improvements that reflect across:

Root Cause Analysis Proficiency

Identifying the root causes of issues in maintenance operations is a cross-functional task, where team members from different teams have to arrive and analyze the situation. Cross-training streamlines the process of implementing and employing methods like failure mode and effects analysis (FMEA), to address underlying issues and prevent recurring problems. So, teams can systematically analyze equipment failures and take note of establishing a guideline that won’t lead to the issue in the future.

Predictive Maintenance Implementation

With procedural and technical knowledge, maintenance personnel can apply their skills to implement condition monitoring, IoT sensors, and predictive analytics. Proactive identification of potential failures and the scheduling of maintenance activities before critical issues arise will be two important outcomes of the action.

Integration of Reliability-Centered Maintenance (RCM)

RCM rests on a systematic analysis of critical components and their failure modes, guiding maintenance decisions based on risk and criticality. Cross-trained professionals can effectively implement reliability-centered maintenance methodologies and sustain asset reliability.

Streamlined Procurement

Maintenance professionals by offering accurate insights into equipment specifications and requirements will optimize procurement efforts. They will thus minimize the lead time for procuring the required spare parts and be the key to sustaining optimum levels in the spare part inventory.

Efficient Data Management for Analytics

Technical cross-training enhances data literacy among maintenance staff. This proficiency is crucial for leveraging data analytics in maintenance management. Teams can efficiently collect, analyze, and interpret data to make informed decisions, driving continuous improvement in maintenance strategies and overall operational productivity.

Integrated Health and Safety Protocols

Health and safety don’t form a special function but a responsibility within a function. Technically, maintenance supervisors and managers well-versed and adept in OHS (organizational health and safety) standards mitigate the risk of accidents or incidents, which will have broader implications for the entire organization.

Optimized Energy Consumption

Minimizing energy utilization is one pressing urgency that every team has to address without fail, as enterprises strive to cut down unnecessary energy usage and adopt green measures. Cross-training includes an understanding of energy consumption patterns related to maintenance activities and creating roadways for energy efficiency improvements surrounding cost reductions.

It’s Time you Implement Cross-Training

Implementing cross-training programs for maintenance not only addresses immediate skill gaps but also contributes to the long-term resilience of organizations. It standardizes the maintenance workflow and builds an ideal work environment as bottlenecks associated with isolated teams are eliminated. By cultivating adaptable teams capable of seamlessly navigating industry changes, maintenance practices evolve with the demands of the future.

As organizations strive for success, implementing cross-training becomes a strategic investment in workforce versatility and collaborative problem-solving. We encourage you to consider integrating these strategies within your team, fostering a culture of continuous learning and adaptability. Even adopting tools like maintenance management software is one part of this process. Know how it works by speaking with our experts.

The post Cross-Training Your Maintenance Team: Strategies and Benefits appeared first on FieldCircle.

TPM can empower businesses to uphold their processes through enhanced equipment effectiveness and the integration of digital, data-driven solutions. Since its evolution, TPM has been a lynchpin in bettering maintenance efficiency for those who have adopted and implemented it.

We know how Toyota leveraged TPM to establish a reputation for operational excellence and efficiency. And more recently, giants like Procter & Gamble and Coca-Cola have showcased the value of TPM.

Stories and experiences of success realized through TPM abound today.

Going ahead, we shed light on this transformative maintenance philosophy and how it can generate value for small to medium enterprises (SMEs) across industries.

What is Total Productive Maintenance (TPM)?

Total Productive Maintenance (TPM) is often seen as a concept the seeds of which germinated in the Toyota Production System in Japan in 1970. It is a comprehensive approach to equipment maintenance that aims to maximize the efficiency and effectiveness of production machinery.

TPM goes beyond traditional maintenance practices and suggests that not only maintenance professionals but everyone in the organization must participate in maintenance. Through this “All-inclusive” and “Out-of-the-box” philosophy, it emphasizes and enables proactive and preventive measures to eliminate breakdowns, defects, and accidents.

The key components and principles on which Total Productive Maintenance (TPM) is based are:

Overall Equipment Effectiveness (OEE)

OEE is a key performance indicator in TPM and is used to measure the overall efficiency of a production system. It rests on:

• Availability: The percentage of time that equipment is available for production.
• Performance: The rate at which equipment operates as compared to its designed speed.
• Quality: The percentage of good-quality products produced by the equipment.

– 5S Principles:

• Sort: Organize and eliminate unnecessary items from the workplace.
• Set in order: Arrange necessary items logically and efficiently.
• Shine: Keep the workplace clean and well-maintained.
• Standardize: Establish and maintain standards for workplace organization and cleanliness.
• Sustain: Ensure that the 5S principles are continuously followed and improved.

Kaizen (Continuous Improvement)

TPM promotes a culture of continuous improvement where small incremental changes are made by employees at all levels to enhance efficiency and eliminate waste.

Equipment Life Cycle Management

TPM considers the entire life cycle of equipment, from design and procurement to operation and disposal. It proposes that by addressing maintenance needs at each stage, organizations can improve the overall reliability and efficiency of the equipment.

Visual Management

Visual tools and indicators are used to make information easily accessible to all employees. From displaying equipment status, maintenance schedules, and performance metrics, the visual tools encompass all metrics and information that is important for managing maintenance operations.

Zero Breakdowns, Defects, and Accidents

TPM lays stress on achieving zero breakdowns, defects, and accidents through proactive measures for addressing issues and continuously improving processes.

What are the pillars of Total Productive Maintenance (TPM)?

TPM is built on eight pillars, and each plays an equally vital role in fostering a culture of continuous improvement. We see what these pillars are and how they make TPM a transformative strategy.

Autonomous Maintenance (Jishu Hozen):

Autonomous Maintenance places the onus on operators to actively engage in the care of equipment. By integrating routine maintenance tasks into daily operations, operators become the first line of defense against equipment deterioration.

For maintaining a packaging line in a food processing plant, operators, through autonomous maintenance, regularly inspect machines for wear, clean accumulated debris, and ensure proper lubrication. Apart from preventing sudden breakdowns, it also fosters a sense of ownership among operators, leading to increased attentiveness to equipment conditions.

Planned Maintenance (Keikaku Hozen):

Here operators systematically schedule and execute maintenance activities to prevent unplanned downtime. In a manufacturing facility, as per this principle, you will schedule preventive maintenance regularly to replace worn parts.

For instance, a CNC machine might undergo routine checks for calibration, lubrication, and belt tension. Predictive maintenance, using tools like vibration analysis, allows organizations to anticipate failures and plan interventions proactively. Such scheduled overhauls will ensure that comprehensive maintenance is conducted at planned intervals, and disruptions to production schedules are minimized.

Focused Improvement (Kobetsu Kaizen):

Focused Improvement is the engine driving the continuous enhancement of operational processes. Imagine an automotive assembly line facing recurrent delays at a particular workstation. Through a Kaizen event, a cross-functional team, including operators, engineers, and managers, collaborates to identify root causes.

The use of tools like root cause analysis is central to this principle, which lets stakeholders understand inefficiencies and optimize processes for streamlined workflows. The outcome is an agile and responsive production system that continually evolves to meet or exceed performance expectations.

Early Equipment Management (EEM):

Early Equipment Management emphasizes integrating maintenance considerations during the design and installation of new equipment. So, let’s say, in a scenario where a pharmaceutical company is installing a new blending machine, failure mode and effect analysis (FMEA) is employed to anticipate potential issues.

Now, design for reliability principles will make your equipment not only efficient but also easy to maintain. Installation quality checks further validate that the equipment is installed correctly, which will make your equipment lifecycle reliable.

Quality Maintenance (Hinshitsu Hozen):

It prevents defects and ensures consistent product quality through concepts such as Six Sigma. So, if you are managing a chemical manufacturing plant, you know how critical it is to maintain precise temperature and pressure.

Quality Maintenance involves using statistical process control (SPC) to monitor and control such variables, along with implementing error-proofing mechanisms such as sensors and alarms. Again, this is a proactive approach that will maintain process and product quality, as it will massively reduce the likelihood of defects.

Safety, Health, and Environment (SHE):

Critical to any maintenance operations is the Safety, Health, and Environment (SHE) principle as it calls for a workplace that prioritizes employee well-being and minimizes environmental impact.

In maintenance, rigorous risk assessments are conducted to identify and mitigate potential hazards. As per this principle, comprehensive training programs must be carried out to make employees well-versed in safety protocols and make the supervision staff accountable for adhering to compliances. A robust SHE strategy not only safeguards personnel but also contributes to a positive organizational image.

Education and Training (Kyoiku):

Education and Training form the bedrock of a skilled and adaptable workforce. In an enterprise, operators undergo continuous training on the operation and maintenance of diverse machines.

Here, a skills matrix acts as a ready-to-use tool to identify and address skill gaps, while cross-training ensures that employees can seamlessly transition between different tasks. Through this principle, TPM equips employees with the knowledge and skills needed to navigate evolving technologies and challenges.

Administrative and Office TPM (Shitsuke):

This principle of TPM extends to non-production areas, recognizing the integral role of support functions. It stresses that the standardization of processes in an office setting establishes efficient workflows, akin to the standardized procedures implemented on the shop floor.

Even for administrative functions, visual management tools, such as Kanban boards, provide a transparent view of tasks. Through continuous improvement in administrative processes, the principles of TPM permeate every facet of the organization, which completes your aim to achieve holistic operational excellence.

Also read: Cross-Training Your Maintenance Team: Strategies and Benefits

What transformation does Total Productive Maintenance (TPM) bring?

The effect of TPM can be witnessed across several areas, and there are countless benefits that an organization implementing it will experience. Enlisted below are some ways it will bring improvement in your maintenance operations.

Less Unplanned Maintenance and Equipment Downtime: Through proactive and preventative maintenance, as TPM keeps the equipment in a well-maintained state, it automatically reduces the need for unplanned maintenance and reduces downtime.

Lower Costs: By optimizing equipment availability and performance, TPM can lead to a significant reduction in maintenance and conversion costs over time.

Safer Work Environments: TPM empowers employees to take ownership of equipment, and makes them accountable for adhering to industry-specific regulations, which potentially mitigates the risk of injuries.

Improved Efficiency and Overall Staff Morale: TPM’s focus on continuous improvement and employee empowerment leads to improvement in efficiency and encouragement amongst all employees, who actively participate in maintenance management.

Improvement in Quality and Customer Satisfaction: TPM’s quality maintenance pillar integrates quality improvements in business processes, which can deliver better services to customers, who, as a result, develop a positive impression of the businesses.

Cultural Transformation: TPM brings a cultural shift within the organization, due to its emphasis on continuous improvement, collaboration, and empowerment. As contributing to improvement in processes is a part of every team member’s role, it automatically keeps adding value to processes.

Frequent Continuous Improvement Events: TPM often involves the organization of continuous improvement events, where cross-functional teams come together to address specific challenges or opportunities for improvement. These events, often known as Kaizen events, are focused on achieving tangible results within a short timeframe.

Accurate identification of Root Causes: When equipment issues do occur, TPM emphasizes the importance of conducting a thorough root cause analysis. Stakeholders are committed to the philosophy to investigate underlying factors and implement corrective actions to prevent recurrence.

How can total productive maintenance be used across different industries?

In each industry, maintenance brings its own set of technicalities and challenges. We look at how Total Productive Maintenance (TPM) applies across various industries, against this backdrop:

Manufacturing

• Automotive Manufacturing: TPM focuses on reducing downtime for critical machinery such as robotic arms in assembly lines. Predictive maintenance using sensors and IoT devices can be integrated to anticipate issues and plan maintenance during scheduled downtimes.
• Pharmaceutical Manufacturing: TPM assures the reliability and cleanliness of production equipment in pharmaceutical manufacturing. Clean-in-place (CIP) and sterilize-in-place (SIP) processes need to be well-maintained to meet strict hygiene and quality standards.

Food and Beverage

• Breweries: TPM can be applied to the maintenance of brewing equipment, including fermentation tanks and bottling lines. Preventive maintenance software will avoid contamination and ensure the consistency of the brewing process.
• Dairy Processing: In dairy processing plants, TPM can focus on maintaining pasteurization equipment, and make it operates at optimal temperatures to meet safety regulations.

Energy

• Power Plants: TPM can play a vital role in power generation by optimizing the performance of turbines, generators, and other critical equipment. In nuclear power plants, where safety is paramount, TPM can contribute to ensuring the reliability of safety systems.
• Renewable Energy: In wind or solar farms, TPM can be used to maintain and monitor the performance of turbines or solar panels. Remote monitoring systems can be integrated to collect data for predictive maintenance.

Chemical

• Chemical Processing: In chemical plants, TPM can maintain the integrity of vessels, reactors, and piping systems. Proactive maintenance strategies can prevent leaks or failures that may result in hazardous situations.
• Specialty Chemicals: Industries producing specialty chemicals have unique equipment. TPM can be tailored to address specific challenges in maintaining specialized reactors, distillation columns, and mixing equipment.

Aerospace

• Aircraft Manufacturing: TPM is critical in aerospace for maintaining precision machinery used in the manufacturing of aircraft components. In particular, CNC machines and robotic systems must undergo routine maintenance to ensure accuracy and reliability.
• Airport Operations: TPM can be applied to airport ground equipment, such as baggage handling systems and aircraft de-icing equipment, to minimize delays and ensure efficient operations.

Healthcare Industry:

• Medical Device Manufacturing: TPM is essential in maintaining the reliability and cleanliness of machinery used in the production of medical devices. Strict adherence to regulatory standards requires a robust maintenance program.
• Hospitals: TPM principles can be applied to medical equipment maintenance so that diagnostic and treatment devices are consistently available and meet stringent quality and safety standards.

Conclusion

TPM is not just a maintenance strategy but a transformative philosophy integral to organizational excellence. Its significance extends beyond machinery and applies to every single process within an enterprise.

Aligning TPM with the foundational elements of digitized Standard Operating Procedures (SOPs) and remote expert guidance can usher in a new era for businesses. With this amalgamation, operators can use contextual information, to guide themselves towards achieving the highest efficiency in the process.

Adopting a (computerized maintenance management system) CMMS software can be one great step towards realizing TPM potential. Integrating the tool into your TPM plan will permeate it across all the pillars and help you derive value through TPM implementation.

The post Total Productive Maintenance: What and Why for SMEs appeared first on FieldCircle.

For stalwarts such as Kone and Siemens, implementing automation for reengineering maintenance in their manufacturing processes and production system has been an ongoing journey. But it needn’t be the same for every enterprise, due to myriad challenges that automation brings.

By taking over the tough adoption process, enterprises can bring a massive transformation in their maintenance function. They can optimize the process of maintaining their complex systems and also augment human capabilities.

On one hand, there is an allure of using automation for building a state of the art maintenance framework, due to its potential benefits. On the other hand, maintenance leaders are aware of the fact that they must overcome barriers to secure advantages with automation in the Industry 4.0 environment.

Against this backdrop, we cover the challenges and opportunities that automation offers in maintenance and equip you with the readiness to strategically optimize your maintenance management systems.

Also read: Total Productive Maintenance: What and Why for SMEs

What are the challenges in implementing automation in maintenance?

Automation is poised to change the way maintenance operations are conducted. But to achieve a breakthrough with it, you must overcome hurdles that lay in the path to its successful adoption. So, below we discuss major challenges that maintenance leaders typically encounter when implementing automation.

Building automation competence

The prospect of automation lures most maintenance professionals these days. However, the process requires investing significant resources (time and dollars) and efforts. You must assist your workforce in developing the necessary skills to effectively use and manage automated maintenance systems. You need dedicated support from specialized personnel, including automation engineers, artificial intelligence (AI), and data analysis experts.

From complex software and guidelines to a competent workforce, you need everything that an automation system requires. Apart from financial resources, you must build a culture of continuous learning, possible through regular training programs. All in all, you have to be investment-ready to erect a competent framework for implementing automation for managing your maintenance operations.

Managing complexities

Automation can bring complexity to the maintenance process, especially in two ways. Firstly, in adjusting to equipment variability and secondly, in maintaining reliability.

Adapting to equipment variability demands a flexible automation system capable of adjusting to diverse machinery specifications, configurations, and conditions. You need sophisticated sensor integration and control algorithms to streamline automation across a range of equipment types.

While maintaining reliability, you must address potential system failures, so as to ensure robust fault detection mechanisms and implement redundant systems to minimize downtime.

Balancing the need for adaptability with the imperative of reliability requires meticulous design, continuous monitoring, and timely updates to accommodate evolving maintenance requirements, which becomes a tough process altogether.

Handling Partial Dependence and Partial Independence

Even if automation is implemented for maintenance, it still requires some degree of human intervention. You cannot thus completely transfer the responsibility of managing maintenance tasks on the automation systems, which themselves will work under the supervision of an automation management team. You will have to closely watch if your automation systems are performing their function rightly or not.

Let’s take an example of manufacturing systems; a robotic assembly line may need humans to program specific tasks, adjust machinery, or replace worn-out components. If a robot malfunctions or a sensor requires recalibration, human expertise is crucial to diagnose the issue and implement corrective measures. Likewise, in the medical equipment industry, a fully automated medical imaging device may require manual calibration by skilled professionals for accurate and reliable results.

Building Rapport

The challenge we discussed previously, gives way to a connected challenge – establishing human-machine coordination.

As automated systems become more sophisticated, humans need to collaborate closely with machines for optimal performance. The collaboration spans tasks like programming, diagnostics, and troubleshooting. Establishing a harmonious human-machine rapport involves not only technical proficiency but also a deep understanding of the system’s intricacies. Here, human technicians must navigate complex interfaces, interpret data outputs, and communicate effectively with automated systems.

Moreover, the challenge extends to cultivating trust in automation. Humans must feel confident in the reliability of automated processes, especially when it comes to critical functions, say in precision manufacturing. Now, building such trust creates the need for streamlining communication, training staff, and building strong feedback mechanisms so that the team is fully equipped to make use of automated processes.

Building a Flexible Data-Value Chain

The implementation of automation systems in maintenance heavily relies on a robust data-value chain. It is integral to predictive maintenance that integrates real-time sensor data, historical records, and operational parameters. The challenge lies in orchestrating a seamless flow of data across the entire value chain.

Firstly, disparate data sources, varying formats, and inconsistent quality must be harmonized for accurate predictive models. Next, establishing interoperability among diverse systems is a technical hurdle, which requires standardized protocols. In terms of hardware, you need sensors, IoT devices, and data storage infrastructure, so as to manage large amounts of data.

Last but not least, the data-value chain must be resilient to evolving operational conditions to support automation to adapt to ongoing procedural changes.

Developing a Human Cyber-Physical System (H-CPS)

Automation gives rise to H-CPS, where people, machines, and computer technology all work together. However, managing it is no easy feat. One major issue is the concurrency and synchronization problem, where intricate interactions between humans, physical systems, and cyber technologies can make it tricky to keep everything in synchronization.

The interfaces between human operators and autonomous components present another hurdle. Designing H-CPS requires careful consideration of how humans interact with automated elements and the development of effective control strategies for systems. Similarly, modeling and specifying the requirements for H-CPS can be tough due to the need to incorporate the human factor and the complex interactions between physical and cyber components.

Beefing up Cybersecurity

Securing automated systems with strong cybersecurity mechanisms is an imperative because as you make things smarter, you open the door to more cyber risks. With automation, machines communicate with each other quite frequently, which creates more chances for cyber-attacks.

Without robust cybersecurity measures, there’s a real risk of data breaches, manipulation of automated processes, and disruption of critical functions. It’s akin to fortifying a building – without solid defenses, vulnerabilities increase, potentially undermining the entire automation effort.

Overcoming Resistance

Employees, accustomed to familiar workflows, may express concerns about job security, harbor distrust in automated systems, or exhibit reluctance towards learning new technologies. Addressing these emotional and psychological barriers is essential for successful automation adoption.

Due to these concerns, maintenance leadership can find it tough to shift from established workflows to automated processes. Finally, employee apprehensions cannot disappear in a day, and it takes time for them to ebb away.

Scaling across processes

As operations expand, diverse processes emerge, each with unique demands and intricacies. Implementing automation at scale necessitates addressing varying workflows, data sources, and system interfaces against operational expansion.

In this process, compatibility issues may arise. Further, coordination and synchronization become critical, which creates expectations of careful actions from planners to sustain efficiency.

Also read: How To Develop Effective Maintenance Supervisors For Operational Success

What opportunities does Automation bring in Maintenance?

By building capabilities you can effortlessly overcome the challenges we saw. So, don’t be deterred by them. While there are challenges, there are opportunities, too. So, envision can capitalize on these opportunities and enjoy their benefits.

Smart Condition Monitoring

Automation has been a key lever in enabling enterprises to transition from preventive maintenance software to predictive maintenance systems. Offering remote monitoring capabilities to maintenance teams, across industries, this move can transform the character of condition monitoring across processes such as:

Fault Detection and Diagnostics: Automation enables real-time monitoring of equipment conditions, facilitating early fault detection and diagnostics. By leveraging sensors and data analytics, the system can identify abnormal patterns or deviations from normal operating conditions. As a result, technicians can swiftly respond with corrective actions before issues escalate.

Work Order Generation: Upon detecting potential faults or deviations, an automated system can generate work orders seamlessly. Due to this feature, maintenance teams receive timely and accurate information about the issues that require attention.

Vibration Analysis and Balancing: Vibration patterns can indicate potential imbalances or misalignments in rotating equipment. By integrating automated balancing systems, maintenance managers can address these issues promptly, thereby enhancing the reliability of assets. It will work as a part of a predictive maintenance strategy, where abnormal vibrations can be an early indicator of impending failures.

Thermal Imaging for Electrical Systems: Automation extends to the use of thermal imaging technology for monitoring electrical systems. An automated system can analyze thermal patterns in electrical components to identify potential issues such as overheating.

Battery Health Monitoring: Advanced sensors and automated analytics can continuously assess the condition of batteries in various devices and systems. With these systems, you can anticipate the need for battery replacements or maintenance based on the real-time health status, preventing unexpected downtime and disruptions.

Now, let’s look at some other industry-specific examples that cast light on how automation can be a boon in the monitoring process:

Monitoring and Control of HVAC Systems: Automated systems collect data on temperature, humidity, and air quality and feed this real-time information into a centralized control system. As a primary step that aids predictive maintenance, they help take corrective actions beforehand.

Conveyor Belt Tensioning: Integrated sensors or load cells provide real-time data on tension levels, providing input to automated systems to autonomously adjust the tension of conveyor belts. The predictive analytics algorithms get trained to anticipate tension changes based on historical data.

Corrosion Monitoring in Maritime Structures: Sensors provide real-time data on corrosion levels, which is transmitted to a central monitoring system. The data collection and analysis allow for early detection of corrosion, as algorithms identify trends and patterns that indicate potential corrosion-prone areas.

Advanced Robotics Streamlining Routine Operations

The integration of robotics into various facets of inspection and maintenance streamlines many routine processes such as:

Inspection: In the industrial sector, deploying robots for routine inspections of machinery and equipment reduces the need for human intervention in hazardous environments. Industrial robot maintenance ensures these robotic systems operate efficiently, preventing unexpected failures and optimizing performance. For instance, in the energy sector, robotic systems equipped with advanced imaging technologies can inspect turbine blades for defects and wear, ensuring seamless operations with minimal downtime.

Cleaning Systems: May it be any industry, facilities, often face challenges in maintaining cleanliness which impacts operational efficiency. Automated cleaning systems, driven by robotics, offer a solution. Robots can navigate complex spaces, execute cleaning tasks with precision, improve sanitization, and reduce time for the process. To give an example, robots can navigate solar arrays, remove dust and debris, and improve energy throughput through solar panels.

Pipeline Monitoring and Inspection: Robotic systems designed for pipeline inspection can navigate through intricate networks, detecting leaks, corrosion, or other anomalies.

Alignment in Conveyor Systems: Automated robotics can be employed to monitor and maintain the alignment of conveyor belts. By promptly correcting misalignments, these systems smooth material transport.

Efficient Lubrication and Greasing

Lubrication and Greasing is one elemental process in entire maintenance operations, as no equipment can ever function without lubrication and greasing. This is a periodic activity that can become a headache if technicians do not adhere to schedules, and can lead to upheavals in performance.

Automation brings a paradigm shift as it offers scheduled lubrication and greasing cycles based on real-time usage data, eliminating the inefficiencies associated with manual applications such as over or under-lubrication and greasing. The chances of premature bearing failure are thus remarkably brought down. The condition monitoring sensors provide real-time data on bearing temperature and vibration reinforcing the process of lubrication and greasing application.

Streamlined Calibration

Through automated testing and calibration, the efficiency and speed of these operations are significantly heightened. You achieve consistency in the calibration of measurement instruments, as the risk of errors is eliminated, and results are more reliable.

Automatic calibration and testing bring advantages such as scheduled calibration, remote calibration capabilities, and robust data logging. Scheduled calibration takes away the complexities of adhering to calibration timelines, while remote capabilities prove invaluable for geographically dispersed assets.

Energy Efficiency

By harnessing real-time data, automated systems optimize equipment performance, dynamically adjusting settings for peak energy efficiency. While minimizing operational costs, these systems also extend the lifespan of machinery, as they reduce the overall environmental impact.

The consistency in equipment operations is ascertained through intelligent adjustments based on current conditions. This is also one of the best maintenance practices, which can align with your sustainability and green management goals.

Improvement in Equipment TCO and EBITDA

Total Cost of Ownership (TCO) encapsulates all expenses associated with owning and operating equipment, extending beyond the initial purchase cost. Automation can reduce TCO by introducing efficiency measures in maintenance.

Predictive maintenance helps in the proactive identification of potential issues, facilitating scheduled maintenance and averting costly breakdowns. The maintenance costs are thus eliminated right away. Likewise, efficient energy usage curbs utility costs over the equipment’s lifecycle.

The reduction in TCO through automation translates into tangible benefits in EBITDA. Reduction in downtime, energy efficiency, and optimization of labor utilization frees up capital. Also, due to improvements in asset management, the revenue-generating potential of existing assets is maximized. All these factors potentially contribute to an increase in and positively impact EBITDA.

How a CMMS helps overcome automation challenges and secure opportunities

A computerized maintenance management system (CMMS) can be a one-stop maintenance solution to beating automation adoption woes for transforming maintenance processes and securing opportunities that it offers.

Implementing a CMMS software can thus be the first step in your automation program and offer future directions. By integrating it with other technologies, streamlining big data analytics, and assimilating it into the Industrial Internet of Things (IIoT), a CMMS will enable a smooth transition to fully automated workflows.

Learn from our experts how a CMMS acts as a bridge between human capabilities and intelligent systems. Using FieldCircle’s capabilities, you can harness the transformative potential of automated maintenance systems and capitalize on the opportunities they offer.

The post Maintenance in the Age of Automation: Challenges and Opportunities appeared first on FieldCircle.

So, the approach you choose for managing your maintenance operations will be the cornerstone of your financial success with maintenance.

Now, there are two options: First, you build your own in-house team of capable maintenance experts. Second – it’s simple – you choose to hire and seek the expertise of external maintenance professionals.

When conducting a cost-benefit analysis (CBA) for these options, one thing we must know is that maintenance processes vary across industries, and so do the associated costs. As a result, the intricacies of in-house versus outsourced maintenance require a tailored approach.

Recognizing the diversity of industries and their unique maintenance requirements, we arm you with generic frameworks for cost-benefit analysis for both of these options. Through them, we present a systematic process and empower you to make informed decisions on whether to manage maintenance in-house or opt for outsourcing.

Cost Benefit Analysis and Its Importance in Maintenance

By conducting a meticulous examination of the costs and benefits associated with both in-house and outsourced maintenance, this analysis seeks to equip decision-makers with the insights necessary for informed choices. The primary aim is to offer a clear understanding of the financial implications, operational advantages, and potential drawbacks inherent in each maintenance strategy.

Costs Associated with In-house Maintenance

Establishing and maintaining an in-house maintenance team involves a nuanced breakdown of costs, encompassing both direct and indirect expenditures.

Direct Costs

– Salaries: The most immediate and apparent expense is the salaries of maintenance personnel. This includes wages for technicians, engineers, supervisors, and any additional staff required for specialized tasks.

– Equipment: Acquiring and maintaining the necessary tools and equipment for maintenance tasks constitutes a significant direct cost. This includes the initial investment in machinery, regular maintenance of tools, and potential upgrades or replacements over time.

– Materials and Supplies: In-house maintenance requires a steady supply of materials such as spare parts, lubricants, and consumables. The cost of procuring and managing these materials is a direct expenditure that impacts budget considerations.

– Training: Continuous training is vital for an efficient in-house maintenance team. Costs associated with workshops, certifications, and skill development programs for employees contribute to the overall direct expenses.

Indirect Costs

– Overhead Expenses: These encompass the indirect costs associated with maintaining a physical workspace for the maintenance team, including rent, utilities, and general facility upkeep.

– Administrative Costs: Costs related to management, administration, and supervision of the maintenance team fall under this category. This includes salaries of supervisors, administrative staff, and any additional management-related expenses.

– Downtime Costs: Downtime is one of the important maintenance performance metrics. Unplanned downtime during maintenance activities can result in lost productivity and revenue. While not always easily quantifiable, the financial impact of downtime should be considered as an indirect cost.

– Insurance and Safety Compliance: Costs associated with insurance coverage for the maintenance team and adherence to safety regulations constitute indirect costs. This ensures compliance with industry standards and mitigates potential legal and financial risks.

Intangible Costs

– Management Overhead: Intangible costs associated with the time and effort invested by management in overseeing and coordinating in-house maintenance activities.

– Risk of Errors: Intangible costs related to the potential risk of errors or mistakes during maintenance tasks, impacting overall operational efficiency.

Possible Monetary Benefits of In-house Maintenance

– Reduced Outsourcing Fees: By handling maintenance tasks internally, businesses can optimize maintenance costs by avoiding the fees associated with outsourcing contracts. This approach can lead to significant cost savings over the long term, particularly for routine or predictable maintenance activities.

– Optimized Resource Utilization: In-house maintenance enables businesses to utilize existing resources more efficiently. Skilled staff can be cross-trained for various maintenance tasks, reducing the need for specialized external services.

– Investment in Long-Term Assets: Rather than allocating funds to external service providers, businesses can invest in building an internal team of skilled professionals. This long-term investment can lead to a self-sufficient and capable maintenance department, contributing to the organization’s overall resilience.

– Flexible Scheduling: In-house maintenance allows businesses to have greater control over scheduling and prioritizing tasks. This flexibility can lead to optimized workflow management, minimizing downtime and maximizing operational efficiency.

– Improved Quality Control: Direct oversight of maintenance activities allows businesses to maintain high-quality standards. In-house teams can quickly address issues and ensure that maintenance tasks are performed to the desired specifications, reducing the risk of errors.

Costs Associated with Outsourced Maintenance

Direct Costs

– Service Contract Fees: You will have to pay the fees to the external maintenance service provider as per the terms of the service contract.

– Materials and Parts Costs: These are expenses for materials, spare parts, and consumables used by the external maintenance service provider.

– Specialized Equipment Costs: These costs are related to the use of specialized tools and equipment by the outsourced maintenance team.

– Travel and Accommodation Expenses: You will incur expenses for the travel and accommodation of external maintenance personnel for their planned/unplanned on-site visits.

Indirect Costs

– Administrative Overhead: These costs are associated with the internal administrative efforts in coordinating with the external service provider, managing contracts, and overseeing the relationship.

– Communication Costs: Communication tools, software, and technologies used for collaboration between the business and the outsourced maintenance provider will result in some expenses.

– Downtime Costs: These are the costs caused by potential downtime during maintenance activities, which will impact productivity and potentially lead to revenue losses.

– Quality Control Oversight: Time and resources spent on monitoring and ensuring the quality of work performed by the external maintenance provider will bring some costs.

Intangible Costs

– Loss of Control: These are related to a potential loss of control over the maintenance process, as external providers operate independently.

– Cost caused by Dependency Risks: These are related to the risk of dependency on the external provider, which may pose challenges if the provider experiences issues or delays.

Possible Monetary Benefits of Outsourced Maintenance

– Reduced Labor Costs: External maintenance providers assume the responsibility of staffing and training, reducing the burden of labor costs on businesses. In another way, you don’t have to hire external talent, which leads to significant savings compared to maintaining an in-house team.

– Economies of Scale: Maintenance service providers can achieve economies of scale by serving multiple clients. This allows businesses to benefit from cost efficiencies in materials, equipment, and labor that may not be achievable on a smaller scale.

– Reduced Equipment and Tool Costs: Businesses outsourcing maintenance can avoid the upfront investment and ongoing costs associated with purchasing and maintaining specialized tools and equipment.

– Elimination of Training Costs: External providers are responsible for training their staff. This eliminates the need for businesses to invest in training programs, workshops, and certifications for maintenance personnel.

– Resource Allocation Flexibility: Outsourcing allows businesses to allocate resources more flexibly, focusing on core competencies while relying on external experts for specialized maintenance tasks. This flexibility enhances overall operational efficiency.

– Mitigation of Risk: External maintenance providers often assume certain risks, such as equipment failure or accidents during maintenance tasks. This can mitigate potential legal and financial risks for the business.

– Reduced Administrative Overhead: Outsourced maintenance minimizes administrative tasks related to managing an in-house team, resulting in reduced overhead costs for activities such as recruitment, payroll, and human resources.

Cost-Benefit Analysis for In-House Maintenance Management

We use the cost elements and perceived benefits discussed above with illustrative amounts to demonstrate how you can carry out the CBA for in-house maintenance management. This is a generic framework to which you can add costs and benefits as you may experience in your business.

Net Savings in in-house Maintenance Management

= $630,000 – $570,000

= $60,000

Benefit-Cost Ratio = 1.105

Cost-Benefit Analysis for Outsourced Maintenance Management

Here is an illustrative break-up of various cost elements for the outsourced option along with probable annual investment figures. Based on the nature of your maintenance operations this cost framework may change. It’s difficult to quantify the intangible costs in this case, so we have not included them. However, you must carefully observe each intangible cost element and record the cost, if incurred, on account of it.

Net Savings in Outsourced Maintenance Management

= $400,000– $270,000

= $130,000

Benefit-Cost Ratio = 1.48

What to Keep in Mind When Doing Cost-Benefit Analysis for Your Maintenance Operations?

Irrespective of the option you choose, there are some factors that will influence the result of your CBA. Though not each of them may apply in a given case, you must be careful enough to include or exclude them. These factors are:

– Accuracy: Ensure precise tracking of maintenance costs and benefits to avoid undervaluing or overestimating the overall value of maintenance efforts.

– Volatility of Cash: Acknowledge that maintenance costs can vary, making it important to factor in unexpected repairs or changes when conducting cost-benefit analyses.

– Impact of Income: Consider the financial capacity of the organization when deciding on the scope and type of maintenance activities.

– Intuition: Base maintenance decisions on thorough research rather than intuition, ensuring that data on costs and benefits are well-documented and reliable.

– Subjectivity in Value Assessment: Acknowledge that the value of certain maintenance activities can be subjective and may vary among stakeholders, requiring consideration of diverse perspectives.

– Non-Monetary Benefits: Recognize and account for non-monetary benefits, such as safety improvements or enhanced reputation, in addition to financial considerations.

– Duplication in Measurement: Exercise caution to prevent double-counting of benefits or costs when measuring maintenance projects in multiple ways, ensuring consistency for reliable results.

Drawing the Inference

With the results of CBA for both options at your disposal, you might find that outsourcing your maintenance operations is turning out to be more beneficial. But as we mentioned those were just illustrations to guide you.

Regarding your business context, you must meticulously capture each cost element and the benefits that both options can offer and then arrive at a decision. The ready-to-use frameworks we provide will come in handy in this process, reducing your efforts and time.

The post Comparing Economic Efficiency in Maintenance: Cost Benefit Analysis of In-House vs Outsource appeared first on FieldCircle.