AI Revolutionising Manufacturing: Fast-Tracking Operations
Jul 05, 2023
As with any technology, no one can deny the power of artificial intelligence (AI). It is everywhere – from our phones to our offices and homes. With the pace at which AI is moving, it has quickly found its way into business functions. Although it is picking up steam, the implications of AI in manufacturing operations are raising concerns—especially about data intensiveness , use cases, and whether the benefits outweigh the associated costs.
Simply put, AI has limitations that will require considerable efforts to overcome. At the same time, it is also a moving target that promises advances to create new opportunities. Before we dive deep into the benefits and the associated costs of AI in manufacturing operations, let us first gain perspective on its use cases.
How is AI playing a pivotal role in building the manufacturing operations of the future
The pace of change is accelerating, here’s how AI is supporting manufacturing operations functions to not just keep up but stay one step ahead.
In situations where visual inspections are required, with a prior trained dataset, AI is effective in eliminating human bias and providing accurate outcomes. A use case example in the automotive industry is accurate visual defect identification on painted surfaces where current levels of subjectivity owing to manual inspection can be easily overcome.
With an ability to track the mean time between failures (MTBF) and detect early warning signals, AI effectively prevents unplanned outage. This is particularly useful in critical equipment where uptime requirements are close to 100%. While there are several such use cases for this, cement companies can leverage this effectively towards timing the shutdown planning of kiln.
With the help of trained data, the computational ability of AI can design solutions quickly and objectively. This aspect of AI can be useful in an engineer to order environment, such as the defense sector, where companies can leverage AI to get the best strength for a protective equipment, for a given cost and weight consideration.
AI, coupled with vast amount of PoS (point of sales) data, and an ability to recognise patterns, can accelerate sales for a variant/depot combination. Recommendations made on supply chain networks and footprints based on emerging trends in consumption can contribute to boosting growth.
Powered by deep learning, AI can optimise transportation index within a plant for mobile material handling equipment and can also suggest layout modifications to reduce transportation index. Leveraging AI in planning milk runs within the plant for movement of the materials according to the production run, along with the optimised transportation index, will boost production.
Through monitoring and analysis of tool usage and tool wear, AI can not only be leveraged to predict the remaining useful life of the tool, but also aid in spares management for tools. One good use case for these is in fabrication shops where the punch and die can be closely monitored through image scanning, and, accordingly, a plan for refurbishment of the tools can be planned well in advance.
Apart from these use cases, AI adoption has a breathtaking range of opportunities in manufacturing operations – from workforce planning, procurement to logistics, store management and so much more. The use cases and applications depend on the type and nature of the industry, maturity of operations, product life cycle and other factors.
AI’s application to business problems extends across nearly every sector of the economy, but when AI is put into action to overcome real-world barriers, its limitations can sometimes discourage leaders to reinvest, which can give others that competitive edge. So how much of AI should be used in business functions? How does one decide when to stop so that the cost does not outweigh the benefits?
What businesses need to do is evaluate AI applications through an impact vs cost matrix and choose areas where the returns on investments are higher.
When it comes to manufacturing operations, AI has been beneficial in the areas of key operational metrics, such as the cost of poor quality (COPQ) reduction, overall equipment effectiveness improvement, order to delivery lead time reduction, among others.
However, organisations need to have some foundational elements in place for successfully leveraging AI, first of which is understanding the concept of “measuring what matters”. Granular data elements, which are generally not collected in day-to-day operations, may need to be collected for effective base lining and, subsequently, understanding reasons behind current baseline performance.
A prerequisite for getting the best outcomes is ensuring the right quality, duration, and source of data, along with investing proactively in data collection and automation of the collection process to witness real-time benefits.
The applications of AI continue to be an evolving area, and manufacturing organisations will require newer skill sets, both in operating as well as managerial positions. It is necessary to have a strong programme management and governance mechanism to ensure progress. Organisations must invest in strong change management initiatives for this change to stick.
The promise of AI is immense, and, like it or not, AI is here to stay. Successful companies will be those who are able to understand the prerequisites well enough and direct their AI efforts to areas where it matters the most. With experts and researchers poised to solve AI’s complex problems, it’s time to understand what the capabilities of AI are, so you can position your organisation to learn, exploit, and unlock new possibilities.
