Predictive Analysis improving the Food Industry

The food industry is being transformed by data science and analytics. Companies in the food sector face their own barriers with constant development of consumer purchase behavior. The increasing appetite of customers pushes businesses to become ever more consumer-focused and produce new products, strategize customer involvement and enhance their marketing strategies. This can be done by adopting unique blend such as data analytics and machine learning in order to provide valuable insights into the data acquired by these companies.

Companies can connect knowledge and insight in data science. AI technology with a machine learning framework will allow sales staff to identify their market more effectively, focus resources and spot lost opportunities with reliable sales predictions. (‘Predictive Sales analytics for Food and Beverage Industry’, no date)

What would data science and analysis do?

• Predict shelf-life how(‘How data science and analytics changes the food industry’, 2019)

The shelf life of food is changing or expiring over time. Data scientists can predict the shelf life of the manufacturing process through applying machine learning and analytics to provide insight for precautionary measures to reduce the waste produced and to save time or money.

• Sentimental Analysis

Neuro-linguistic programming helps companies to examine the behaviors and trends that disclose the most popular foods and beverages of the season through social networking sites. This helps companies, restaurants and other organizations’ to learn and respond to the most common recipes. This experience will help companies adapt to customer demand further efficiently.

• Better accountability in the supply chain (Garver, 2018)

Supply chain management is a top priority for all food businesses, due to the recent rules on food safety as well as the rising need for transparency. Data science and analytics help create accountability within supply chains, so their consumers can be more honest. Transparency also helps solve problems and improve supply and logistics efficiency.

• Improved safety care

The data science and analytics enable food health and cross-contamination organizations to be protected. In combination with satellite and remote sensing techniques, geographical data enable data analysts to find changes. This knowledge combined with data on temperature, soil and urban proximity will determine which part of the farm will be pathogens contaminated and respond prior to contamination with the goods.

Conclusion

Data analysis has given various sectors a successful growth, like healthy foods and beverages, as the sector would face other problems. Given the projected increase of the world population, climate change and land desertification, several challenges for the sector to overcome.

Bibiliography

Garver, K. (2018) 6 Examples of Artificial Intelligence in the Food Industry, Food Industry Executive. Available at: https://foodindustryexecutive.com/2018/04/6-examples-of-artificial-intelligence-in-the-food-industry/ (Accessed: 30 May 2020).

‘How data science and analytics changes the food industry’ (2019) Selerity, 30 July. Available at: https://seleritysas.com/blog/2019/07/30/how-data-science-and-analytics-changes-the-food-industry/ (Accessed: 30 May 2020).

‘Predictive Sales analytics for Food and Beverage Industry’ (no date) softwebsolutions. Available at: https://www.softwebsolutions.com/sales-forecasting-in-food-beverage-industry.html (Accessed: 30 May 2020).