Manufacturing can double down workflows with analytics and AI.
The manufacturing industry currently is in the midst of the industrial revolution, leveraging advanced cutting-edge technologies for smoother operations. With the rise of the fourth industrial revolution, manufacturers are witnessing a significant growth of connected machinery and equipment that generate a voluminous amount of industrial data, which is valuable if processed and interpreted effectively. Unlike generated by various big data sources like social media, industrial data promises much potential in improving the process, machines and envisage future needs of manufacturing.
In order to stand out in today’s highly-competitive business environment, manufacturers must deliver data-driven decisions powered by artificial intelligence. This will enable very lucrative business outcomes for manufacturing companies. Integrating data, analytics and AI together within a business process, firms can even differentiate themselves while minimizing costs and protecting margins.
As AI deployment can cause massive disruption in the manufacturing industry, KPMG found that automation could save companies 75 percent of operating budgets.
Big Data Wave in Manufacturing
Big Data in manufacturing is rapidly taking a stronghold making the industry more complex. And to garner and derive value from it, this large volume of data needs to be processed effectively. Using advanced analytics can assist in decoding complex manufacturing processes, and replaces human decisions with automated algorithms while making production more efficient and faster.
Big Data has positively disrupted the manufacturing industry by creating entirely new business models, services and products. Even it can enable to reuse existing datasets for an unintended purpose. The use of big data to create algorithms could lead a number of applications such as personalized recommendation engines, machine learning for fraud and anomaly detection, and image analysis.
Manufacturers often challenged by predicting product demand, inventory management, efficiency, skilled labor deficiency, and gaining increased ROI. In this way, capitalizing on data analytics can help add a new dimension to these challenges by optimizing production and enhance efficiency. In a predominantly process-driven industry, dig data technologies can also improve product quality and cut manufacturing costs.
Intel, for instance, has developed over a dozen of data-intensive projects that have boosted the company’s operational efficiency and bottom line. The semiconductor manufacturing giant has been unlocking the power of big data for its processor manufacturing for some time now. Using big data and predictive analytics has helped Intel to significantly lessen the number of tests required for quality assurance, enabled the company to save whopping US$3 million in manufacturing costs for a single line of Intel Core processors.
Reaping Benefits of AI
There is no clandestine that AI has the potential to transform and automate every industry, and manufacturing is not an exception. The technology along with the rapid development of robots are already changing all aspects of manufacturing. With the capability of real-time monitoring, AI can encounter production bottlenecks, track scrap rates, and ultimately meet customer delivery times. It is predicted that the global artificial intelligence in the manufacturing market will grow at a CAGR of 55.2 percent from US$513.6 million in 2017 to US$15,273.7 million in 2025.
Applications of AI has also the potential to transform the manufacturing value chain, from optimizing manufacturing supply chains to assisting manufacturers in anticipating market changes. They can formulate predictions of market demands by looking at patterns linking location, socioeconomic and macroeconomic factors, weather patterns, political status, and consumer behavior, among more.