Mitigating Supply Chain Challenges with Predictive Maintenance

Mar 16, 2023
Manufacturing | 7 min READ
This article was originally published in Automation & Digitization - Source link
Manufacturers worldwide are looking for solutions and measures to optimise their operations and improve efficiency. By utilising Predictive Maintenance, manufacturers can lower maintenance expenses and decrease production time.
Jagdeep Chawla
Jagdeep Chawla

Senior Vice President & Global Delivery Head

Manufacturing Vertical


COVID-19 has redefined the way enterprises function. It compelled enterprises to increase digitalisation to handle remote work and adapt to changing demands. The pandemic caused disruptions in logistics and supply chains, leading enterprises to focus on local suppliers and address the cost and demand issues. Solutions were needed to provide control and real-time visibility for better demand planning.
It also emphasised the need for a cross-functional approach to managing supply chains and companies investing in resources to anticipate disruptions and build risk profiles for emergencies.
What is the need of the hour?
To improve their supply chain operations, enterprises must adopt advanced digital technologies such as cognitive planning, AI-driven predictive analytics, and advanced tracking and tracing mechanisms.
These tools will enhance critical supply chain planning capabilities and increase the integrity of secure supply chains. AI-based predictive maintenance is the future of driving consistent results and improving asset performance based on data to avoid potential malfunctions and leave no room for breakdowns. There are three main areas of focus:
To increase asset efficiency
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Despite 85% of manufacturers acknowledging the impact of digital technologies and the opportunity for improvement offered by Industry 4.0, only 15% have specific plans for enhancing asset efficiency. AI-powered anomaly detection solutions are crucial for transportation and manufacturing companies to improve asset efficiency, anticipate equipment failure, and save on operational costs. Regular monitoring helps track operations and diagnose issues. As a result of having real-time insight into the performance of their assets, production efficiency, and logistics processes, global manufacturers have the potential to greatly improve the performance and utilisation of their assets, resulting in an increased return on investment.
Manufacturers always find it hard to find skilled labour and retain it. Besides, certain assembly operations can be dangerous, leading to labour shortages. Automation helps to always achieve efficiency by running simulations on production alternatives, thereby improving production capacity and lowering operational costs. Using IoT devices and smart sensors improves efficiency by reading real-time data on equipment performance, location, and maintenance. This helps manufacturers reach strategic goals and minimise downtime.
Digital Twin
A digital twin in manufacturing is necessary to improve various aspects of the production process, such as optimisation of planning and operations, Predictive Maintenance, quality control, supply chain management, customer experience, and sustainability analysis. It also reduces physical prototyping and testing while increasing operational efficiency and reducing downtime. This results in improved collaboration and decision-making.
Security challenges
The transition towards a technological phase where systems and plant processes are connected can improve the performance of organisations with IoT and interconnected smart systems. But this opens a high risk of sabotage and attack due to digitally connected systems. One such security vulnerability is at the supplier level, which exposes organisations to phishing attacks and the theft of privileged credentials, resulting in a massive data leak.
What could be the ideal situation?
Most enterprises believe digital transformation will boost their competitiveness, while a few believe it may affect their business. Some enterprises argue it could slow down production relocation to low-wage countries, but this ignores the fact that automation often drives the need for local production in new growth markets. The crucial role of innovation in manufacturing competitiveness is to impact both local and global value chains in low and high-cost countries.
IoT sensors connected to machine parts in a smart factory or logistics setting can open up a wide range of possibilities for improving efficiency and optimising operations.
Mitigating Supply Chain Challenges with Predictive Maintenance
Engineers can leverage the power of predictive analytics to construct statistically sound models of equipment longevity based on operational data. This enables them to concentrate on crucial risks that impact operational reliability and availability. By utilising this capability, a maintenance strategy can be devised to enhance efficiency. For example, an analysis may indicate that current maintenance schedules and practises are already optimal or suggest that maintenance should be performed sooner to avoid failures or postponed to reduce unnecessary costs and efforts. Some of the key benefits include:
Real-time monitoring
IoT sensors can provide real-time data on the equipment performance, allowing manufacturers to minimise downtime, quickly identify and address any issues that arise.
Predictive Maintenance
By analysing data collected by IoT sensors, manufacturers can predict when the equipment is likely to fail, allowing them to schedule maintenance and repairs before issues occur.
Optimising production
IoT sensors can provide manufacturers with a wealth of data on their operations, allowing them to optimise production processes and improve efficiency.
Inventory management
IoT sensors can be used to track inventory levels in real time, allowing manufacturers to identify when they need to reorder parts quickly. IoT sensors can also be used to automate specific tasks in the factory, such as reordering parts or scheduling maintenance.
Technology components at your disposal
Supervisory Control and Data Acquisition (SCADA) has been around for almost four decades, but AI and ML have turned things around to a new level. While SCADA was restricted to condition-based monitoring, gaining insight into the future of machinery is now possible with big data.
There are a lot of active data-driven maintenance strategies; the essence lies in recognising the right fit for your business operations.
Let’s look at each of them objectively:
Reactive Maintenance
It served only one purpose, i.e., to trigger a maintenance activity only when the plant registers a breakdown. Reactive maintenance takes less time to implement and is a financially cheaper method. However, it is not a sustainable method in the long run.
Preventive Maintenance
A preventive maintenance schedule helps avoid unwanted breakdowns, reduces cost expenditures and saves time. The technological implementation makes a minor dent in initial expenses due to increased maintenance runs to ensure uptime. It considerably impacts the production cycle and improves machine life and ROI.
Predictive Maintenance
It enables manufacturers to schedule maintenance and repairs before issues occur. It also helps reduce the risk of unexpected downtime and provides detailed analytics. Although the relative investments required to drive it will be higher, the overall costs will be considerably less as failures are caught before impact. Predictive Maintenance aims to prevent failures at all stages, reduce the number of shutdowns, and extend the equipment’s life by stopping the process before failure occurs.
Prescriptive Maintenance
It is the most advanced form of maintenance as it uses advanced analytics and Artificial Intelligence to predict when equipment may fail and recommend the best course of action to take to prevent failure. It can tell precisely what to fix without running onsite test runs and help achieve much-improved uptime and a massive increase in savings.
Addressing challenges
Predictive Maintenance involves analysing usage and environmental data for equipment to identify patterns that correspond to failures. This information is then utilised to build predictive models that assess incoming data, allowing for the prediction of failure probability. The health of equipment is determined by generating scores from this information.
Additionally, the collection of Key Performance Indicators (KPIs) is used for reporting purposes, enabling the identification of assets that deviate from normal behaviour patterns. Rules can be established to generate recommendations when a high probability of failure is detected in a piece of equipment. These recommendations can be integrated into other systems, triggering automatic alerts to relevant personnel.
By implementing a management system, enterprises can better predict when specific assets will need attention and allow for timely action to be taken before major problems arise. Enterprises need to have a strategy in place for implementing a Predictive Asset Management (PAM) system, including timelines, milestones, and Key Performance Indicators (KPIs) to measure the success of the implementation.
Currently, there is an Information Technology with Operational Technology (IT-OT) system gap that needs to be bridged to better integrate data from various interoperable systems. OT has a key role in translating user needs into system requirements and then developing the corresponding systems. This gap can inhibit efficient collaboration between IT and OT professionals, ultimately impacting project timelines, feasibility and quality.
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