AI presents opportunities for cost optimization in manufacturing

Management


Emerging technologies — such as artificial intelligence (AI) and especially machine learning — allow organizations to streamline their operations and better realize growth opportunities. Importantly, they can also prevent costly defects and avoid operational inefficiencies. While COVID-19 sped up the pace of adoption for many industries, including industrial manufacturing, manufacturing companies have historically embraced new ways of working.

Manufacturers were early endorsers of Kaizen, Six Sigma, and Lean, known business improvement models with direct impacts to the continuous improvement methodology critical to manufacturing processes. And now, AI is being embraced for its ability to make supply chains more flexible — mostly to evaluate vulnerabilities identified during the COVID-19 pandemic among their suppliers and in the supply chain itself — reduce costs, and fully leverage human talent and intelligence.

According to a new KPMG report, Thriving in an AI World, 93% of industrial manufacturing respondents indicated they have moderate or fully functional AI, primarily machine learning technologies, implemented into their processes. In particular, AI capabilities are being readily adopted in three areas: defects monitoring, predictive and preventive maintenance, and forecasting accuracy.

Manufacturers may wish to examine these areas carefully for opportunities to use machine learning to optimize costs and perhaps drive growth.

Defects monitoring

Since much of the costs of sales for many manufacturers is in material, the ability to identify defects and to correct them on the spot can be the difference between growing margins and maintaining operations … and shutting down. Minimizing scrap and rework helps manufacturing companies minimize costs and increase margins.

We have seen robotic sensors located above or within the production line to identify defects in the manufacturing process in real time rather than waiting until production is complete. This significantly reduced scrap and downtime in the production line.

Leveraging the analytical skills inherent to the role, CPAs are well positioned to monitor the results of any defects and look for patterns or trends that may help to identify the root cause.

Predictive and preventive maintenance

AI, especially machine learning, is also being used to more frequently predict and prevent costly maintenance issues. First, the technology is helping maximize efficiency by identifying areas where the useful life of machinery could be diminished, using data ingested as part of the analytics process. Second, machine learning is providing early identification of maintenance needs before a plant requires a full shutdown.

In addition to KPMG’s report, which found that 41% of respondents within industrial manufacturing have already started implementing AI technologies for maintenance operations, a recent IBM survey of executives from companies including commercial vehicle manufacturers and ancillary manufacturers reported that about one-third believe that preventive vehicle maintenance and predictive equipment maintenance will be most important to their company’s success over the next 10 years.

Accounting and financial leaders within the organization should prioritize learning what areas of AI their company is currently implementing and what the road map is for the coming years. Many companies are already planning to implement more machine learning in their production process over the next few years, so those who proactively engage in those efforts will be well positioned to bring strategic insight to the decision-making table.

Forecasting accuracy

More recently, manufacturers have begun using AI to analyze historical data and market trends to do more predictive modeling and increase the accuracy of their forecasts. This can help them improve margins, maximize the top line, and/or better predict what will be needed in their supply chain.

Accounting teams within the organization should understand current reporting systems for forecasts to ensure that they are up to speed on the types of data readily available and what tools could be available to them, based on the existing enterprise resource planning (ERP) or enterprise performance management (EPM) system.

Engaging with industry organizations and professional services firms on share forums and knowledge sharing can help executives within the organization get educated and up to speed on where AI technologies are headed and the possibilities to improve operational efficiencies.

With COVID-19, there were significant delays with shipping and obtaining the parts that U.S.-based manufacturers needed from China and other suppliers. Also, with the impacts from the Ever Given’s weeklong blockage of the Suez Canal in March still being assessed, the global manufacturing sector has even more impetus to accelerate capabilities that will allow companies to model scenarios that just a year ago might not have even been conceivable. And, while predicting what could be needed in their supply chain is only a single use case, three of the top four threats to manufacturers’ growth over the next three years are supply chain related.

With better forecasting accuracy, manufacturing companies are also able to be more precise with what they need and reduce storage costs. Additionally, they don’t see the lag time that can increase costs due to expedited shipping or penalties from customers.

The increased use of AI and data and analytics to predict future demand for a manufacturer’s products or the costs to obtain and manufacture goods could affect accounting estimates, such as excess and obsolete inventory reserves or asset impairment recoverability assessments. A company’s internal control environment, including risk assessment, may need to evolve as these technologies affect the underlying accounting processes and estimates.

Looking ahead

As digital transformation continues apace, the prevalence of AI will drive further opportunities for organizations across the industrial manufacturing landscape to assess cost structures — including the cost of labor. As a labor and people-intensive industry, the skill sets required by the industrial manufacturing workforce will continue to evolve. And people with the desire to learn data, analytics, and problem-solving will become increasingly valuable across the industry.

Anne Zavarella, CPA, is a partner and National Audit Industry Leader for Industrial Manufacturing for KPMG LLP in Columbus, Ohio. To comment on this article or to suggest an idea for another article, contact Ken Tysiac, the JofA’s editorial director, at Kenneth.Tysiac@aicpa-cima.com.

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