How Data & Analytics is Transforming the Manufacturing Industry

Manufacturing is witnessing a transformation with technology. While the industry had long periods of innovation and technological development throughout history, it has been significantly stagnant for the past few decades. This can be partly attributed to the huge capital expenditure associated with transforming factories and plants. However, digital technology has come to a point where the manufacturing industry can take advantage of it without significant overhauls.

The biggest advantage of digital technology in manufacturing is the fact that it can be applied as a layer over the existing system without disrupting them. Add to it the self-dependent, self-governing and self-improving capabilities of digital and you have the perfect formula for transformation. And the formula points to business intelligence and artificial intelligence as the primary drivers. In a publication by Accenture and Frontier Economics, it was estimated that AI could increase the manufacturing gross value added (GVA) by nearly 45% by 2035. Moreover, the integration of ​data analytics services can provide deeper insights and drive efficiency across manufacturing processes. 

Why AI Analytics is Crucial to Transforming Manufacturing Industry?

The simple answer is, long-term financial benefits of such analytics platforms.. However, this answer packs a variety of factors that will affect the manufacturing industry in the coming future. For a sustainable and thriving future, manufacturers are looking towards disruptive technology that can reimagine (if not revolutionize) their production and related processes. Part of this shift comes from the learnings from the pandemic as well. A survey by the National Association of Manufacturers (USA) reported that 78% of respondents said that the pandemic had a severe impact on their industry.

Manufacturers already have a wide range of challenges that can be dealt with AI analytics efficiently. These challenges range from overall production cycle management to market related needs like demand forecasting. Integrated (and even distributed) AI analytics can significantly increase visibility and decision making capability for manufacturers.

The advantages of AI analytics can be summarized as:

• End-to-end visibility on manufacturing processes and operations.
• Cost efficiency enabled by forecasting, planning, and mitigation in critical areas.
• Resilience against internal and externally dependent disruptions.
• Reduction in dependencies on labor and manual operations, and related errors.
• Increment in production quality and customer fulfillment.

How AI Analytics will be Used in Manufacturing?

The applications of AIOps vary with the integration of technological solutions with data analytics, IoT, imaging technologies, and context-aware computing. Many of these technologies are mature enough to use out-of-the-box while some require further development and/or testing with AI to drive results.

The primary applications of AI analytics and AIOps in general are:

• Predictive maintenence and fault prevention
• Demand forecasting for inventory and production cycle management
• Procurement and logistics planning
• Product development and quality control
• Productivity and event simulations for cost optimization
• Automation and robotics

While many of these operations are conducted either manually or (in the case of robotics) are pre-programmed, AI analytics can further enhance their efficiency and accuracy. The most important aspect of these applications is the low capital cost and high scalability during and after implementation. Only dependency with AI analytics is the availability and generation of consistent data from the dependent systems and/or processes. Furthermore, a data analytics platform enhances the efficiency and scalability of AI analytics in manufacturing. 

A Capgemini study found 51% of European manufacturers are already using AI, followed by Japan with 30% and the U.S. with 28%. Quality assurance takes the major share in their AI applications followed by predictive maintenence and fault prevention. AI-powered technologies are also slated to boost labor productivity by almost 40% (Frontier Economics and Accenture). These numbers are growing year on year as manufacturers continue to adopt AI analytics in their operations and management.

SLK Software is a digital transformation company that has extensive experience in providing AI-enabled digital solutions to manufacturers across the globe. We partner with you in your transformation journey from identifying your problems holistically to providing the best-in-class data analytics solutions that go beyond your expectations.

AI analytics has already found a place in manufacturing. When we consider the scope of the data the manufacturing industry can provide, AIOps can easily shape the next generation of manufacturing. The industry also enjoys advantages that come with its mostly empirical nature. The revolution is already on the rise and there’s a lot to gain for manufacturers from digital technology.