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5 Important Reasons Why the F&B Industry Requires Big Data Analytics

The Food and Beverage Industry is vast, diverse, and constantly expanding. There are multiple challenges associated with running a business in any realm of this industry. For instance, in food and beverage processing, ensuring that the taste, flavor, and quality have to be consistent as per the brand standards and underlying consumer expectations. With F&B Data Analytics, monitoring the process becomes a lot easier. Manual checks are bound to falter at some point in time or other. But AI-run sensors and algorithms ensure that the consistency is always maintained, batch after batch. Even from the point of view of F&B Service and Marketing Campaigns, big data plays a crucial role. Here are five critical reasons why the F&B industry requires big data analytics:-

Preventing Deviations in Batch-wise Food/Beverage Processing

Be it to bake batches of cookies with uniformity or to ferment vats of Ale batch-wise, with F&B data analytics process deviation is immediately detected and alerted. A prediction engine is devised with all the variables involved in the process. All the process parameters are monitored in real-time by AI-run sensors. Then, the process trajectory is mapped by the prediction engine to forecast deviations well in advance. It gives the operators ample time to make batch-wise adjustments accordingly.

Significantly Reduces Food Wastage

Degrading shelf-life and resultant wastage have always been a problem for the food and beverage industry. With advanced data analytics, one can achieve accurate shelf-life prediction and take advanced measures to counter wastage. Whether it is raw materials in transit or processed food cans in warehouses, cutting-edge stability testing algorithms are being used by F&B manufacturers to ensure timely utilization to avoid wastage.

Maintaining Consistent Quality Control in Processed Food and Beverages

As per the latest food and beverage industry trends, consumers today are more brand-conscious and aware of labels more than ever before. So for F&B manufacturers today, there is absolutely no scope for any deviation of taste, flavor, aroma, color, shape, size, and so on. From infra-red spectroscopy to AI-run regression models, all the possible variants like raw material quality, processing temperature, etc., are taken into account for exercising thorough quality control. This data-driven process is much more holistic and fool-proof than manual inspections.

Ensuring Authentic Food Labels

The modern-day consumer is highly discerning about supply chain transparency and labels like non-GMO, organic, gluten-free, sugar-free, vegan, etc. Any kind of label fraud, procedural errors, or quality control issues can land you in legal trouble. Data-driven fingerprinting of food and beverage samples is a sure-shot way of ensuring authentic food labels. Statistical control and real-time monitoring also ensure supply chain transparency for F&B brands today.

Tailoring the Best F&B Marketing Campaigns

Not just at the manufacturing, transport, or storage phases, data analytics are also instrumental in crafting the right marketing messages for food and beverage brands. A vast repository of consumer data is available with all major F&B corporations today. Yet, only a handful of them knows how to use it to their advantage. A professional data analytics team can tap into your databases for valuable and actionable insights. This will help create an enticing marketing campaign to grab the attention.

Conclusion

Since the very outset, businesses have been grappling with unpredictable food and beverage industry trends, escalating hygiene compliances, and logistical difficulties. Furthermore, the precision-based control required in food processing has been incredibly challenging for F&B manufacturers. With the advent of advanced data analytics, most of these obstacles have been efficiently resolved. In the foreseeable future, F&B experts predict this industry being transformed to be entirely data-driven across all sectors and businesses alike.

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