Pricewaterhouse Coopers forecasts that Artificial Intelligence (AI) could contribute up to $15.7 trillion to the global economy by 2030, of which $6.6 trillion is likely to come from increased productivity and $9.1 trillion from consumption-side effects.
In the Manufacturing sector, this increased productivity is expected to be driven by the need for:
- Direct automation
- Predictive maintenance
- Reduced downtime
- 24/7 production
- Improved safety
- Lower operational costs
- Enhanced efficiency and quality control
- Faster decision making
Why is Artificial Intelligence core to the manufacturing industry's future?
Even before the COVID-19 crisis, businesses were adopting (and implementing) AI for manufacturing quality control and to improve operational decision making. The pandemic, however, has worked as the pivot for manufacturers to double down on their use of AI to:
- Make their operations more resilient
- Optimize the allocation, and use, of resources
This increased focus on leveraging AI is a result of it being capable of:
- Completely automating complicated tasks
- Making complex operational decisions on its own
- Preserving, improving and standardizing organizational knowledge
- Reliably delivering predictable and consistent output
- Cost-effectively creating – and maintaining – its own in-house algorithms and intellectual property
- Swiftly adjusting manufacturing strategy and production plans as per need
In a nutshell, AI’s impact on manufacturing is exhibited as enhanced operational adaptability because it outperforms conventional decision making-support tools and technologies. AI-driven high-performance software tools are highly versatile and adaptive to continuously changing equipment and market situations. They are also cost effective because they require minimal maintenance.
Manufacturing businesses that implement AI will benefit from its predictive maintenance abilities across the entire manufacturing value chain – from engineering and testing, to production. Be it enhanced equipment and process efficiency, reduced machine failure costs, or cost-effective scheduled maintenance/ repair of functional equipment before failures occur.
How can manufacturers introduce AI into their operations easily?
The first step for manufacturers who want to introduce AI into their processes is evaluating the nature of their requirement (for the scope of AI-related work within their organizations). To do so, key questions they need to ask themselves include:
- Do I want to automate repetitive tasks?
- What is the level of automation that I need in my business?
- Do I want to completely change the character of work in my factory?
- How much can I spend to introduce AI in my organization?
- Will introducing AI allow me to keep using my existing software?
Depending on the answers to these questions, different companies may opt to introduce different automation levels for different tasks. It is a good idea to adopt a step-by-step approach – start with a ‘human-in-the-loop’ arrangement and gradually move to a completely autonomous process – to drive higher adoption of AI-powered solutions across the organization.
Digitization of operations is a key factor influencing AI adoption by a manufacturing company. The aim? – Building trust in AI-driven data and algorithms over time to gauge which business applications can be supported by data, technology and automation. Studies suggest companies that have digitized core business processes not only lead the AI adoption front but are also the ones that have made the most progress.
Currently, manufacturing businesses have implemented AI in their key functions. However, the need of the hour is to focus on incorporating AI solutions in core manufacturing/ production processes – from engineering to assembly; from product development to quality testing.
For quite some time now, Columbus has been at the forefront of leveraging AI techniques to predict outcomes and trends and prescribe necessary action for manufacturers across industries. We combine our expertise, skill and domain knowledge to uncover new growth potential, increase business efficiency, and provide a competitive edge to manufacturing companies – irrespective of their size. Write to us at email@example.com to learn more.