For food manufacturing companies, Artificial Intelligence (AI), Asset Performance Management (APM) and Industrial Internet of Things (IIoT) have become vital ingredients for business success. Together, these technologies can have a significant impact on operational processes at these businesses, ultimately enabling them to deliver higher quality products to customers.
Companies can use AI algorithms to analyze the data they are gathering from a variety of sources — including sensors, supervisory control and data acquisition (SCADA) systems, meters, event logs and others — in industrial settings, such as food and beverage processing plants.
The algorithms examine asset behavior, identify patterns and predict future operating conditions based on the data they’re receiving. With AI and IIoT platforms in place, decision makers at industrial plants can track the impact of asset performance on revenue metrics automatically and continuously. That way they can quickly make adjustments as needed to keep processes going smoothly.
Among the potential benefits of improved asset performance are cost savings, reduced energy consumption, extended life of assets and better overall efficiency within industrial processes.
The Power Of Data
Food manufacturers have more data at their disposal than ever, and IIoT will deliver exponentially more information to these companies. Up to now, the Internet of Things (IoT) has mainly focused on consumer applications, such as creating smart connected homes, appliances and devices.
IIoT is quickly becoming a focal point for companies in manufacturing markets, however. As consulting and IT services firm Accenture has noted, the IIoT market can potentially add $14.2 trillion to the world economy by 2030.
IIoT links computers, databases, analytics platforms and other IT systems with data gathered by sensors, making operational processes much more efficient. It’s helping to rejuvenate industries that need digital transformation to improve processes that have been conducted in much the same way for years.
The volume of data that will be generated through IIoT technologies is immense, and it will continue to grow as companies deploy more sensors and other devices to track more assets.
As the volume of data increases, APM will become more important for food processes, and food manufacturing professionals will be able to leverage this information and the insights gleaned from AI to lower their operational costs and energy consumption. In many cases, they might even be able to leverage data they already have at their industrial sites.
The emergence of APM as a powerful framework for enhancing processes could not come at a better time. Many executives at industrial companies, such as food processors, are under pressure to increase efficiency and reduce resource consumption. There is huge potential for AI and IIoT to help companies address the challenges they face, keep costs down and increase profit margins.
APM centered on AI can address the inherent weaknesses of the legacy systems many food processing companies have in place in industrial or production settings.
A good example of the value of these new technologies comes from a company in the beverage market, which runs bottling plants to produce bottled water. A single process analyst at the company's headquarters is responsible for maintaining assets for up to 12 plants. In each plant, there are multiple types of software systems that house the data, within complex and aging on-premise environments.
As a result of this arrangement, industrial plant operators make decisions based on years of on-the-job experience and ponderous tools to conduct asset performance management. With the right technology in place, however, process analysts can extract data from their own systems, determine if the data can solve their particular problems, then apply AI to analyze and provide a greater level of data intelligence.
The end result is analysts at the water bottling company can better manage and track the revenue-impact of assets, including the system that produces the water.
Companies such as this one will be able to more effectively track and deliver on their key performance indicators (KPIs) and goals. Leveraging the large volumes of readily available historical and real-time data in plant systems, APM can uncover blind spots previously unknown to analysts because they have not been able to link all the data together. Now, they can create a bridge between the data and the business metrics, which is critical for success.
AI tools allow analysts to make more informed decisions, deliver higher throughput efficiency, and increase revenue retention.
Finally, deploying AI-based APM solutions enables food processing companies to be more proactive. Rather than using unreliable tests and static rules, decision makers at these companies can use data-driven approaches.
The data that’s being generated at food processing plants has enormous value, and AI tools can transform this data into valuable information on which to base decisions. It helps predict trends and measure KPIs, which links back to business metrics that companies can use to boost efficiencies and improve the bottom line.
Prateek Joshi is CEO of Plutoshift