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How Data Science Is Reshaping the Food Industry

How Data Science Is Reshaping the Food Industry

The food industry is vast, encompassing businesses producing, processing, preserving, packaging, and transporting food products. Sales of spreads, convenience food, meat, pet food, vegetables, fruits, baby food, snacks, bread, cereals, and sauces all contribute to the total value of the food industry. The foodservice industry includes cafeterias and restaurants.

Like all industries, the food industry’s capitalized on data science to improve operations and increase profits. Businesses use potent computers to pool and process data. Let’s look at how data analytics works and how data science impacts businesses in the food industry.

How does data analytics work?

Data science refers to using algorithms and scientific processes to evaluate various types of data, such as unstructured data. Using multiple methods to evaluate data enables data scientists and data analysts to generate helpful information about sales trends, operating costs, and future demand.

Companies using data science and big data analytics to generate helpful information employ data mining software to harvest data. They may physically transfer or copy data and combine data from various sources. They may also opt to use data virtualization to access data from multiple sources without moving or copying the data.

Predictive analytics software searches for patterns in the data sets. Identifying trends enables data science software to anticipate future demand for various goods and services. For example, while historical data may indicate rising demand for meat products over the last decade. It could identify that the growth rate’s slowing, enabling food industry companies to anticipate that demand levels will stabilize.

Data experts use data visualization software to produce reports. Businesses may distribute reports to executives, boards, or shareholders and support operational recommendations.

How is data science impacting the food industry?

All businesses within the food industry can use data to increase efficiency. For example, food production and distribution companies benefit from having logisticians evaluate various shipping routes to determine how to reduce shipping times and costs. Data analytics can also evaluate population trends, enabling logisticians to anticipate increased or decreased demand levels in specific regions. They can use that information to build supply routes to areas with higher demand.

Food production companies can use data science to determine which products to discontinue. Identifying reduced demand from data trends enables businesses to determine when producing various goods will not be profitable. Food producers can use that data to develop a business strategy, determine how to reassign staff members, and implement a marketing strategy to reduce criticism from consumers.

Analyzed data can also identify ingredients causing health issues. Food production companies may invest in research and development to identify alternate ingredients and modify recipes to produce healthier products. The food industry can also identify consumption trends and identify the most popular flavors, enabling them to determine how to use popular flavors to boost sales of existing products. For example, a company producing turnovers may determine that blueberry filling is more popular than apple filling. Consequently, they may introduce blueberry turnovers to their products. Companies already producing blueberry turnovers may increase production, reduce the production of apple turnovers, and invest in a blueberry turnover marketing campaign.

Restaurant supply companies can use data analysis to anticipate product demand. For example, the location may influence the type of restaurant equipment Wenatchee Valley food industry businesses to need. Wenatchee Valley contains millions of acres of grapes used to produce wine. Consequently, local wine producers and wine tasting businesses may fuel demand for refrigerators to store ingredients and preserve wine after it’s opened. Analytics could demonstrate local restaurants are more likely to serve wine, increasing their need for bottle coolers. They may also note higher demand for pizza prep tables, prompting them to increase their stock of those types of prep tables.

Food industry businesses use data scientists to modify shipping routes, identify discontinued products, determine how to allocate research and development funds, and identify in-demand food prep equipment. Data analysis can also influence marketing campaigns.