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Why Food Manufacturers are Turning to Industrial AI

logoDue to stringent health and hygiene regulations, the food and beverage sector has often been slow to adopt new disruptive technologies, such as industry 4.0, for fear that they may affect compliance.

So, why is an increasing number of food manufacturers turning to Industrial Artificial Intelligence (AI) technology?The application of Industrial AI — most commonly through Machine Learning — offers capabilities that now have proven business benefits too powerful to be ignored.

These include software solutions for:

  • Predictive Maintenance – Reduces time-to-repair and cost-to-repair through accurate issue categorization and predictive alerts.
  • Digital Twin – Visualizes assets and manufacturing processes with performance data for remote monitoring, increased throughput, and higher quality.
  • Automated Root Cause Analysis – Utilizes Machine Learning and anomaly detection to identify factors that cause defects or quality deviations.
  • Condition Monitoring – Real-time monitoring of equipment health to maximize overall equipment effectiveness (OEE), reduce maintenance costs, and cut downtime.

Predictive maintenance using the Seebo platform. Data is continually captured from sensors on assets and analyzed in real time.

Predictive maintenance using the Seebo platform. Data is continually captured from sensors on assets and analyzed in real time
Predictive maintenance using the Seebo platform. Data is continually captured from sensors on assets and analyzed in real time

Leading food manufacturers understand that Industrial AI technology should be embraced to stay ahead in a competitive market.

Another compelling factor is that smart factory technology has become cheaper, with the costs of hardware and software tools — sensors, edge devices, cloud services and analytics — decreasing significantly in recent years.

The Top Challenges of Food Manufacturing Today

Demand for Quality

Quality is as important as ever. Product recalls are a huge financial setback and can damage a brand’s reliability in the market. Meeting regulations demands consistently high-quality output and customers have come to expect nothing less than world-class products and excellent service.

Containing Costs

Keeping down the cost of production is always a priority. In the food and beverage sector, compliance with safety regulations demands tight monitoring and control of the manufacturing lines’ equipment, recipes, and processes. Other unavoidable and significant costs include pest control, microbial testing, hygiene consultation, and other services.


Food manufacturing incorporates extremely elaborate processes. Liquefaction, emulsification, baking, pasteurization, packaging — these processes require the use of complex machinery, making efficient maintenance a real challenge.

Read More: Setting the Stage Now for Artificial Intelligence Success in 2019

What Industrial AI Brings to the Table

The heart of AI’s gift to manufacturing lies within the power of Predictive Analytics — the ability to predict machinery failures and quality issues.

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This is revolutionary: Instead of being reactive in nature, maintenance, resource management, and quality efforts become predictive, offering a host of advantages.

AI for Quality

AI algorithms identify correlations between parameters in the production process in order to be able to predict situations that will likely lead to deviations in quality.

For example, root causes for quality failures — abnormal machine behavior; deviations from recipe; changes in raw material suppliers — can be discovered automatically. This reduces the time it takes to resolve critical quality issues and minimizes the likelihood of them recurring.

AI for Maintenance

AI techniques such as machine learning and artificial neural networks power predictive maintenance, offering a number of benefits that positively affect the bottom line.

  • Reduced maintenance time – Proactive repairs cut down maintenance time by 20% to 50% and reduce overall maintenance costs by up to 10%.
  • Increased efficiency – Insights are based on the analysis of data collected from historians and captured live from the factory floor, improving OEE (Overall Equipment Effectiveness).

AI for Inventory & Supply Chain

The predictive capabilities of AI mean that the inventory of spare parts and raw materials can be kept minimal.

Supply chain management is also simplified through prediction — customer demand is recorded and analyzed forming the basis of predictions regarding the necessary output. This prevents a surplus of product and unnecessary stress on production systems.

Read More: Why Manufacturers Are Turning to Seebo for Process-Based AI

Automated Root Cause Analysis

Accurate Root Cause Analysis (RCA) is imperative to continual optimization. Utilizing Machine Learning algorithms, automated RCA can identify correlations that may not be intuitive to human rationale. This puts a stop to recurring problems by uprooting them at their source, and not just dealing with surface symptoms.

What’s more, automated RCA can uncover the root cause of malfunctions and quality issues before they disrupt production. In a recent case study, a food manufacturer returned to expected production capacity after only 6 hours of investigation using automated RCA. The problem was solved using a team half the size of traditional RCA methods, and solving the problem led to an increase in production of 4.7%.

AI in Food Manufacturing Isn’t “Set and Forget”

AI is already proving to be a game changer in the food manufacturing industry. As our understanding of how to apply the technology grows, so do the rewards, but deploying AI so that it has a positive ROI requires planning and knowledge.

Companies without in-house expertise or the required human resources will do well to partner with industry experts. This will ensure a positive ROI on their AI and Industrial IoT efforts for lasting impact.

Read More: New and Exciting Ways Brands Are Using AI on Social Media

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