Recent changes in global consumption habits and demand, along with growing trends of digitalization and advanced technologies have all led the manufacturing industry to embrace the inevitable disruption of what we call today ‘industry 4.0.’
With customer demand constantly changing, manufacturers had to redesign their workflows in order to support shorter production runs to ultimately increase their brand’s sales and improve customer service and experience. In this increasingly competitive landscape, manufacturers cannot afford being lagged behind with legacy systems and outdated operational workflows. It’s clear that in order to keep their competitive advantage, they need to boost operational management in speed, scale, and simplicity.
As a consequence, traditional workflows that have dominated the manufacturing sector for decades are now giving way to advanced technologies; in fact, without the implementation of automation, IoT, cloud computing, and other innovative technologies, manufacturers risk falling behind their competitors.
Implementing advanced technologies is not enough
However, implementing these innovative technologies across the manufacturing supply chain is not enough; as clearly shown by IDC, only 30% of manufacturers investing in transforming the digital operations of their business will reach their full potential, and the main reason for that is that technologies cannot manage themselves.
The amount of data produced by digitalized processes and connected machinery across the manufacturing supply chain is unprecedented, and in most cases is not run and managed by one platform. According to a Gartner survey, 75% of manufacturers indicate that multiple data-sources constitute as a main hurdle to enterprise-integration, which in turn, is a hurdle to leveraging that data for real business value and operational success.
Turning the problem into the solution
Harnessing the great amount of data gathered by the connected manufacturing supply chain with its intelligent machinery has become one of the greatest challenges for manufacturers, and recent developments in artificial intelligence have proved to be the practical, immediate cure.
Based on machine-learning algorithms, artificial intelligence systems are the only ones able to normalize vast amounts of aggregated data, analyze it automatically, and identify behaviors to detect anomalies and alert in real-time. It is the only viable tool for manufacturers to gain control over their data in order to use it for better decision making.
The benefits of artificial intelligence real-time monitoring in manufacturing are easily visible when considering the risks of not implementing this technology –
Here are some of the highest risks of not using AI in manufacturing:
- Lack of inventory traceability
When there’s no real-time view of current inventory, there’s no real certainty of meeting customer demand, which in turn leads to stockouts or wasteful production surplus.
AI real-time monitoring enables inventory traceability to continuously adapt production capacity to market demand.
- Inefficient processes and errors due to unreliable manual processes
Human monitoring is prone to error by default, which can lead to inconsistent quality assurance, or wasted staff time on manual machinery checks and paper records.
AI leaves maintenance monitoring to automated anomaly detection and real-time alerts, thus enabling staff to focus on higher value tasks.
- Failure to comply with regulations
Growing regulations on product safety or disposal management, though a blessing for consumers, are a heavy burden for manufacturers, especially in highly regulated industries such as the medical sector, the chemical or electronic manufacturing.
AI monitoring in real-time provides visibility into global supply chains to ensure regulatory compliance.
- Longer equipment downtimes
Relying on manual machinery checks can lead to higher equipment failures due to human error and slow response times.
AI-based preventive maintenance not only alerts immediately on technical issues, but also predicts them before they occur and indicates their root cause, thus minimizing downtime, lowering repair and operating costs, and ensuring a safer working environment for the staff.
- Lack of executive visibility into plant-floor processes
When executives are disconnected from the processes on the plant floor, they cannot have real control over performance and problem-solving.
AI-based dashboards and ongoing analysis translate processes from the plant floor to C-level executives, thus tightening the connection between executives and the processes on the ground.
- Inaccurate business intelligence
Using BI solutions to report on past events no longer meets the needs of decision-making executives, and does not leverage the real-time data coming from across the manufacturing supply chain. Though once a valuable reporting tool, BI cannot predict future trends, report on real-time events or automatically identify root causes. It requires expensive staff time and even then produces complex reports that require additional executive time to decipher.
AI makes BI-based management solutions irrelevant, thanks to its ability to show the big picture along with meticulous drill-down, alerting on anomalies in real-time and providing predictive analytics.
Missing out on AI today might cost you your competitive edge tomorrow
Missing out on the opportunity to harness available AI solutions could cost not only in efficiency and expenses, but in the manufacturer’s ability to stay in the game. What started as a machine-learning algorithm now became the greatest competitive advantage in manufacturing, as it harnesses the real power of data, allowing faster response times and driving greater efficiency to maximize high-quality production.
How can AI real-time alerts optimize operational performance to boost production capacity in your factory?