AI in Manufacturing: A Flexible and Intelligent Decision Management Solutions

AI (Artificial Intelligence) is not just a buzz word, it's actually revolutionizing various industries including manufacturing. Some of the organizations have adopted AI in manufacturing in all or some forms. But who still planning to deploy AI has questions like – Will AI help to reduce manufacturing cost plus enhance productivity? How AI will help in quality management? And many other questions.

We tried to get answers to these questions as well as the impact of AI in manufacturing from Vikas Gupta, Managing Director, Wiley India, let's see what he thinks.

Usage and Applications of AI in Manufacturing

There is immense transformation brought in through the implementation of Artificial Intelligence in the manufacturing sector globally. One of the key concerns revolves around the ongoing maintenance of manufacturing equipments and production of line machineries incur huge expenditures due to unplanned downtime.

AI enabled robots can reveal insights on every single stage of the manufacturing procedure and later furnish accumulated data through analytics software resulting in accurate future predictions and behavioural pattern on various circumstances including manufacturing time, quantity and quality.

The application of AI as flexible and intelligent decision management solutions has the potential to transform the manufacturing sector - from engineering, procurement, supply chain management, industrial operations (production and related functions) to marketing, sales and customer services. It is among the main technological building blocks of Industry 4.0 with key applications like –

• Procurement and supplier management

• IoT and Edge computing

• Engineering and Systems Simulation

• Quality, Yield enhancement, Lean manufacturing and Six Sigma

• Workforce safety, resource productivity and efficiency

• Supply Chain and Logistics

• Product R&D abs reduced speed to market

As per a recent article published in Wiley Innovation Black Book on Emerging Technologies 2019 – “Adopting and adapting to these technological changes is no longer an option…it is imperative for manufacturers to respond quickly to changing customer demands and maximise new market opportunities.”

AI to Lower manufacturing cost and Booar Productivity

Minimising cost: The very fact that one can monitor results at each phase of the manufacturing process and enable one to take corrective measures at the initial stage saves a lot of investment that was usually spent on rectifying damaged products or machinery problems.

Besides, AI also ensures predictive maintenance of machines and optimize usage of assets within a specific production unit.

Improving Productivity: AI enables faster and error-free work which is more efficient and productive. Besides intelligent decision making capability paired with comparatively lesser cost of production together better ROI and improve productivity manifold.

Impacts of AI on the manufacturing sector in India

India is expected to become the fifth largest manufacturing country in the world by 2022, aiming to have its manufacturing sector make up to 25% of its GDP by then. According to the survey Data Pulse conducted by Seagate on AI adoption in the Asia Pacific region – titled Maximizing the Potential of Artificial Intelligence, it is evident that about 38% of Indian organizations are already using AI in their supply chain, almost 99% planning to implement the technology across their organization over the next year.

The survey also states that AI will have a growing impact on their organizations in the future, and they must adopt AI to stay relevant.  Its ability to expand network storage capacity quickly and cost-effectively without disrupting service is critical in today’s data-driven world.

Moreover, manufacturers will no longer be just manufacturers – they will take up additional roles of distributors, as retailers will bypass traditional wholesalers and would want to buy directly from the manufacturers.

Advantages of Artificial Intelligence on Manufacturing Industry

Keeping the huge scope in mind, it is time to train our future workforce to deal with these emerging technologies better. Skilled workforce to deal with these newer technological requirements is another challenge that is prohibiting its higher adaptation in the manufacturing domain.

Through the brand umbrella WileyNXT, Wiley is trying to address these skills deficit, prepare the future workforce to work in these emerging technologies efficiently to handle business problems better and contribute towards the development of the sector which will eventually contribute to India’s economic growth.

However, there is continued reluctance among Indian manufacturers to adopt AI-based technologies and lack of awareness among small and medium-sized businesses are some of the factors restraining the growth of AI in the manufacturing segment.

AI impact on the future of manufacturing

Despite AI being there for a few years now, it is only recently that it is becoming an integral part of businesses and is being implemented in different spheres including manufacturing. The next wave will see the emergence of machine intelligence that will leverage unsupervised context-aware learning to self-aware unsupervised learning for applications across automated recommendations, auto correction of operational parameters for maintaining quality consistency and real-time interactions.

AI will become ubiquitous and manufacturers will go through a process of learning on how best to collect, process, analyze and make decisions based on the collective intelligence.

Overall, factors like improved computing power, declining hardware cost, and increasing venture capital investments will together fuel the growth of AI in the manufacturing segment.

AI to empower quality management

Quality is imperative in manufacturing. Quality degradation may occur due to poor packaging of the product, transportation issues, machinery defects, supplier process defects in addition to metallurgical defects. Each of these defects can incur an immense loss in the form of higher customer rejection rate, high cost of production, and eventually loss of revenue for the manufacturing firm. Quality improvement has a bigger role to play in these.

3 Key Differences between AI and Machine Learning

In today’s Industry 4.0 world, increasing reliance on Big Data analytics in predictive manufacturing. An advanced machine learning algorithm can analyse processed data collected from the production system to provide –

a) Early warning for Process Perturbation

b) Predict Product Quality

Using AI, manufacturers can collect data at each phase and act faster on an arising issue – be it raw material quality check, transportation or equipment performance of various machineries used. These eventually help manufacturing firms to reduce the rejection rate of their products while keeping a constant eye on the root causes of each issue surrounding the manufacturing process.