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?
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.
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.
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.