AI and Predictive Analytics: Manufacturing’s Opportunity For Navigating COVID-19


As COVID-19 tightens its grip on daily life around the globe, multiple industries have scrambled—and in many cases, struggled—to respond. In particular, the manufacturing sector has been hit hard as mounting pressure to respond quickly to fluctuations in demand increases. While many businesses continue to search for solutions, the pandemic has highlighted the vital role of advanced data analytics in manufacturing. This umbrella term refers to a host of tools that can help manufacturers make sense of the large volumes of data they collect from daily operations. However, the real strategic advantage lies in artificial intelligence (AI) and predictive analytics solutions. This powerful subdivision of advanced data analytics is an important competitive factor for navigating the current crisis into the post-pandemic era.

The Role of Predictive Analytics in Navigating the COVID-19 Era

Whether you’re dealing with the challenges presented by a pandemic, an economic crisis, or both, predictive analytics give manufacturers the power to expect the unexpected. This is critical in an industry where constant shifts in consumer behavior are part of the norm, especially in the consumer-packaged-goods (CPG) sector. With business leaders needing to act quickly, predictive analytics solutions allow CPG manufacturers to analyze vast quantities of data, extract meaningful insights, and drive action.

Manufacturing workers

How Do Predictive Analytics Solutions Work?

In short, predictive analytics can take both past and current data and offer predictions of what could happen in the future. The software aggregates and analyzes historical data provided by sensors that are embedded in assets throughout the facility. Statistical modeling then gives particular variables in a data set a certain weight or score, which is then used to calculate the probability of a certain event occurring in the future. However, many predictive analytics solutions today rely on old-school models—such as linear regression—to make predictions, and are overly simplistic and subject to overfitting, among other issues. This is where artificial intelligence (AI) and machine learning come into play.

While predictive analytics solutions can and have run without AI and machine learning, these technologies are best for making sense of large, complex data sets. Combining AI and predictive analytics will enable CPG manufacturers to make faster, more informed decisions and effectively optimize operations. How? Machine learning is inherently designed to help AI-based predictive models to continuously adapt and learn over time. Even better, AI and machine learning alleviate many of the difficulties associated with predictive analytics, such as addressing speed and scale and alleviating the cost and time of model upkeep. 

Applying AI and Predictive Analytics: COVID-19 and Beyond

Now that you understand the basics of how predictive analytics works, let’s discuss how you can apply predictive analytics solutions to help your CPG manufacturing company navigate the current pandemic and beyond.

advanced data analytics

Minimizing Downtime and Operating Costs

Preventing asset failures and unplanned downtime is critical for the CPG industry to thrive. Unexpected incidents can grind operations to a halt for extended periods of time, which is unacceptable when consumer and business demands constantly evolve — especially during a pandemic that introduces additional demand volatility. Unfortunately, most of the CPG manufacturing industry still relies on preventive maintenance to avert failures. Not only does preventive maintenance waste valuable production and personnel time, but it also fails to catch unexpected or unusual asset failures. AI and predictive analytics enables operators to better understand the health of their assets.

Predictive analytics solutions, like SparkCognition’s SparkPredict® predictive analytics product, use artificial intelligence and machine learning to generate meaningful insights from the data collected from sensors embedded in critical assets. As machine learning algorithms ingest historical data from sensors throughout the facility’s operations, the data can be used to build a model of what normal operations look like. The normal behavior model can then analyze the data in real time and flag any values that deviate from the established norm. This enables operators and subject matter experts to pinpoint exactly when and how an asset failure will take place, allowing them plenty of time (often days of advance warning) to conduct maintenance. 

Beyond CPG manufacturing, multiple industries have leveraged AI and predictive analytics to avert costly failures. One hydropower utility was able to use this technology to predict and prevent a failure that would have cost an estimated $1.5 million. The CPG manufacturing sector can and should be enjoying these same benefits, without wasting unnecessary resources, time, and manpower on scheduled maintenance or having to scramble to recover from unexpected failures.

Reducing Waste and Maintaining Quality 

In our last blog, we talked at length about how AI and predictive analytics can reduce waste to achieve operational sustainability. During the COVID-19 crisis, manufacturers have ramped up production and often work around the clock to quickly produce in-demand goods in large quantities. However, with rising pressure from consumers and facilities strapped for resources, this increases the likelihood of quality degradation and can have devastating consequences. Therefore, it’s important that CPG manufacturers maintain high-quality standards during production and all the way through the supply chain process before their products arrive at consumers’ homes.

For example, SparkCognition worked with a Fortune 50 beverage producer that was challenged by operational inefficiencies at their manufacturing plants. The company leveraged SparkCognition’s SparkPredict product to identify process anomalies that caused inefficient water usage during both production and non-production time. AI and predictive analytics  enabled them to maintain quality control by reducing machine downtime and ensuring production only used the necessary amount of water to produce their goods. Whether you’re manufacturing life-saving medical equipment, household cleaning items, or beverages, CPG manufacturers must conserve their resources to maintain standards and achieve increased production KPIs.

Demand Forecasting, Planning, and Supply Chain Resiliency

Predictive analytics is all about predicting future trends and occurrences such as new demand for particular products like hand sanitizer, toilet paper, or food during the pandemic. With unprecedented demand surges and shifts, forecasting models couldn’t adapt fast enough. This led to countless shelves in grocery stores and beyond to be completely empty. That means suppliers, in tandem with CPG manufacturers, need to be able to battle out-of-stocks so families are prepared to combat COVID-19 and any other crisis. Rather than being a company that waits a few months before reacting, AI and predictive analytics can help you make sure the right amount of products are in stock.

While demand is never linear, predictive analytics solutions can help CPG manufacturers and supply chain professionals analyze past and present trends, including economic forecasts, to predict demand for certain products. In addition, predictive analytics can determine optimal inventory levels to satisfy demand while also reducing stock. 

A Look Into the Post-Pandemic World 

This entire article is based on the premise that AI and predictive analytics give CPG manufacturers the power to be more proactive rather than reactive. Many companies—and industries as a whole—continue to grapple with a lot of unknowns, and the constant influx of new information will make it difficult for them to keep adapting. Their experience with the pandemic should serve an important opportunity to recognize the value of technologies like AI and advanced data analytics, and that the need and application of these technologies is broader than the challenges presented by COVID-19. By transforming data into future insights, CPG manufacturing can keep up with demand no matter what the “new normal” brings — that includes any future health, economic, or weather crisis. This is a call to action for CPG manufacturing as consumers value reliability, quality, and safety in the face of disruption.

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