For the past 30 years, gross domestic product (GDP) across the globe has been shrinking. Where capital investment and increases in labour have been traditional drivers of production, we are now at a crossroad where we are no longer able to sustain the level of investment to drive the levels of growth the world desires. This situation is mirrored in the industrial sector, whereby the average age of America’s factories exceeds 20 years, but for the most part their operations and production levels have not significantly improved since they were introduced.
However, Accenture believes the outlook should not be all doom and gloom, and that investment in AI has the potential to double the annual economic growth rates of major developed economies, such as Germany, and triple Japan’s economic rate by 2035. In the manufacturing industry, Accenture believes the power of AI technologies will increase profitability by 39%, boosting Gross Value Added (GVA) by almost $4 trillion in 2035.
Manufacturing’s high reliance on machinery and its legacy-driven operations environment, whereby teams rely on operator experience to guide their decision-making, makes it a prime candidate to derive high return on investments when the sector invests in AI.
AI can drive productivity and empower workforces to work smarter in three ways:
1. Intelligent automation
2. Workforce and asset enhancement
3. Accelerate innovation
AI and its self-learning capabilities can be especially valuable for industries like automotive manufacturing, which have as many as 300 processes involving human operators and robots working together to create a product for their clients.
Without AI, a human operator will set the different parameters for the process and output based on their experience, but any errors are only found later. The cost of these errors can result in a higher rate of scrap being produced, leading to the manufacturer not fulfilling a customer’s order in time, while also increasing their own production costs.
In an AI-powered environment, the parameters will be controlled by AI; any time the parameters fall outside of the specifications, the AI-powered systems will not just notify but eventually control them before it impacts the quality of the product. This is only possible due to AI-powered analytics systems’ ability to dive deeper into the data that would otherwise take weeks or months to manually analyze the millions of data points that are generated on today’s operations floors. By being able to monitor the quality of production throughout the process, AI-powered environments are producing better quality output, less scrap and their operating costs are reduced. AI brings intelligence into automation – enabling industrial companies to not only be more efficient, but allowing operators the time to derive further value out of their processes and make data-driven strategic decisions.