The Growing Demand for AI-Powered Business Intelligence in Manufacturing

Technology adoption has long been the single greatest driver in making manufacturing faster, cheaper and more efficient. While organizations have routinely continued to double down on investments in next-gen technologies to drive growth and profits, the global pandemic has vastly expedited this trend by highlighting the manufacturing industry’s inherent vulnerabilities when confronted with unexpected disruption to human labor.

AI-powered Business Intelligence allows manufacturers to predict and react to supply chain problems, production challenges and equipment performance before there is an issue.

When having employees on-site — whether at factories or warehouses — suddenly became a safety issue, manufacturers were forced to contend overnight with the reality of managing operations remotely or risk shutting down. After a rocky few months, many have successfully executed their return-to-work strategies, but the road to recovery highlighted the enormous opportunity that data management and advanced analytics tools hold for their business long term.

By siphoning up and analyzing data from their plants, manufacturers immediately have a clearer view of operations and equipment performance, leading to more efficiencies. But the next step is where real opportunity happens: by feeding this data into AI software, their vantage point shifts from reactive to proactive. AI-powered business intelligence means manufacturing leaders can predict and react to supply chain problems, production challenges and equipment performance before there is an issue. In fact, ABI Research expects global manufacturers will spend nearly $20B by 2026 on data management, analytics and other advanced capabilities — up from roughly $5B this year.

The question now isn’t whether manufacturers will continue to double down on automation and business intelligence technologies; it’s how. The investments and approaches companies take today will pave their future success – and smart organizations will be thinking beyond their bottom lines and towards sustainable deployment.

Why Business Intelligence Starts with Understanding Experience

The most successful companies don’t just react to problems as they occur; they try to predict and mitigate potential issues before they ever happen. This knowledge comes from monitoring every interaction people experience with a company in order to spot opportunities for improvement – and answering the “why” behind the “what.”

On the one hand, slow technology adoption can cause manufacturers to fall behind on production efficiencies. On the other hand, if they transition too rapidly, and their workforce isn’t ready, productivity still suffers. As organizations transition to new work processes and technologies, consistent real-time engagement with employees isn’t just a “nice to have” but a necessary prerequisite to future optimization.

Focusing efforts on three key areas can help manufacturers ensure tech rollout readiness and ultimately drive long-term efficiencies, productivity and a culture of innovation.

1. Invest in employee training and consider ideal skill sets. Employees will need new and additional training to elevate them beyond their current roles – but this won’t be a straightforward process. Organizations need to know if their current employee base is ripe for this kind of training (or if they’ll need to look elsewhere) and if the skills they deem critical are having the expected impact. Of the 4.6 million manufacturing jobs that are anticipated to become available in the next decade, 2.4 million are expected to go unfilled due to the skills gap, according to Deloitte and The Manufacturing Institute. With the ripe talent pool presented by the COVID economy, now is a great time for manufacturing leaders to consider what skills their organizations will benefit from and explore potential new hires.
2. Implement experience checkpoints. Employee retraining and tech adoption must move along at an identical pace. Traditional operational metrics will indicate if there are issues with a new deployment, but only experience checks can reveal what those issues are.
3. Identify the tipping point where humans and automation dovetail. An organization must be confident in its workforce’s ability to fulfill new roles before they transition to deeper automation. It’s not a matter of digital capital replacing human capital, but that automation will make peoples’ work and contributions more meaningful.

A company’s efficiency and foresight will ultimately determine its long-term success, but there is mounting evidence, spurred by the pandemic, that how a company treats its employees is having a strong impact on consumer decision making. Time will tell, but the companies who weigh their technology investment alongside their employees’ successes will be the intelligence enterprises who thrive in the future.

(Source: devprojournal.com)

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