Several trends in the food and beverage industry are leading to challenges for manufacturers that can be best addressed with data analytics. With growing digitalization, more companies have access to the kinds of data that can transform their processes to meet the latest consumer demands as well as to shorten time to market, reduce costs, and shrink health and safety risks.
Data analytics can support food and beverage manufacturers in adjusting production processes to accommodate the latest food trends such as vegan, dairy-free, gluten-free and other products with ingredient substitutions.
By moving toward a data-driven approach, manufacturers can more easily adjust their processes to accommodate the latest trends in the food and beverage industry. This includes current trends such as:
- Organic and eco-friendly products
- Healthier alternatives and substitutions
- Plant-based foods
- Protein drinks
- Functional foods
- Food fraud prevention
“The food industry is about 10 years behind other industries in digitalization and using data-driven information to make decisions and create processes.”
Data from IDG Communications shows that of 89% organizations across all sectors have adopted or have plans to adopt a “digital-first” business strategy. And Data Analytics tops the list of technologies already implemented in other industries, at 58%.
How much more successful would your company be if using a data-driven approach rather than “winging it”?
Addressing key challenges with data
In the food and beverage industry, some key challenges that can be addressed with data analytics include:
- Fast time-to-market demands for new products
- Being able to get production processes under control
- Optimizing new processes with respect to yield, sustainability, quality
- Testing and developing new production methods, like fermentation
- Assessing and mitigating safety and contamination risks
A look at the key trends in the food industry
Some food trends come and go, but several are continuing to shape the food and beverage industry. Here’s a look at six we see continuing:
· Organic and ecofriendly products
The demand for organic and ecofriendly products continues to rise around the world. This leads both to a demand for organic alternatives to traditional foods, as well as to expectations about how products are produced, packaged and shipped. Reducing the carbon footprint of a production process as well as its packaging can become important.
Multivariate data analytics (MVDA) can help optimize a production process to make it more efficient: using less energy, less water or steam, and generating less waste. In addition, it allows analysis of the overall carbon footprint of a process. MVDA can also help verify or determine local sources for products by analyzing region specific trace elements (such as for Cava) or spectra data for chemical or pesticide residues.
Read more about counterfeit modeling and "fingerprinting” Cava samples.
• Alternatives and ingredient substitutions
The rise in alternative ingredients is fueled both by health and dietary needs, as well as by a demand for novelty or sustainable food choices. Consumers increasingly want foods free from artificial dyes, sweeteners, fillers, GMOs, sugar, gluten, and high-fat ingredients, leading many food manufacturers to reformulate their tried and true recipes. But only 5-10% of new product development succeeds.
Maintaining signature flavor profiles and textures can be a challenge while also keeping costs in check. New formulations are needed in order to remove artificial ingredients or make substitutions, which can require different types of processing, equipment or regulation compliance. Add to that the fluctuating price of ingredients, and making this type of switch can become complicated and costly.
Multivariate data analytics can help manufactures find the right combinations of product substitutions and processes that will meet a variety of requirements including taste, shelf-life, manufacturing steps and other considerations such as costs. MVDA lets you factor in responses from sensory panels combined with consumer taste tests along with other elements, such as cost and processes (such as optimizing for less environmental impact or use of resources).
Design of Experiments (DOE) can be used to create better recipes, reducing the cost and improving success of formulation testing.
Real-time process monitoring using data analytics can help ensure that a process stays on track and allow you to make early adjustments to correct any deviations that would affect the final product taste or quality.
Read an example: optimizing ingredient selection for chocolate.
• Plant-based foods
Plant-based proteins are growing in popularity in grocery stores and restaurants as consumers view these products as healthy and environmentally friendly. New versions of these faux meats come very close to looking and tasting like real meat.
Plant-based options are also infiltrating other foods, such as pizza crust made with cauliflower, pasta made from chickpeas and butternut squash, and bread fortified with beets and carrots. The trend is buffeted both by a general movement toward healthier eating as well as popular diets such as Keto (low carb, higher protein).
As with ingredient substitutions, data analytics supports manufacturers moving toward plant-based foods in making formulations better. The goal is to give products taste and texture similar to the meat-based products they are replacing. Data analytics helps find the right combination of factors to develop a process that can be replicated consistently.
Only about 5-10% of new product development in the food industry succeeds.
· Protein drinks
In addition to the trend toward plant-based proteins, there is a general trend toward high-protein content in foods and beverages. A key player in this industry is the protein drink. You might think of protein drinks as primarily meant for body-builders, but today’s trend goes beyond that to meet general health and lifestyle needs.
Creating a shelf-stable product that looks good and tastes great is a challenge for many beverage brand owners, which is why they often turn to specialized blends of different types of proteins like plant and whey to create their finished product. Pea protein and rice protein are becoming more popular within the nutrition industry as well.
Creating a cost effective process and formulation can be aided by using data analytics, including MVDA and DOE.
· Functional foods
Functional ingredients in food and beverages are dominating the industry. Today’s nutrition is about meeting individualized, or personal needs. What is healthy for one person is not necessarily healthy for all. Products such as gluten-free, nut-free, egg-free, low-carb, sugar-free, keto-friendly and vegan are increasingly in demand by consumers.
In addition, there is a trend toward meeting several of these needs all in one product.
Catering for multiple dietary requirements as well as offering a specific functional element to a product is a way that food and beverage manufacturers are trying to stand out.
Recently, we’ve seen growth in probiotics, collagen, protein and plant-based food and beverages, as well. In the future we may see more custom nutrients through fermentation.
Some of the issues facing manufacturers include restrictions such as food safety requirements from HACCP and others. By incorporating data analytics into the production process manufacturers can help satisfy various safety concerns and ensure more consistent manufacturing processes.
• Reducing food fraud
The fraudulent and intentional substitution, dilution or addition to a raw material or food product, or misrepresentation of the material or product for financial gain (by increasing its apparent value or reducing its cost of production) or to cause harm to others (by malicious contamination) is known as food fraud. It has been increasingly in the news for more than a decade, for being a threat to consumer health, endangering industry reputation and trust, and causing losses in the order of USD 35 billion in 2018 (in lost profits, legal cost, brand value).
Data analytics can help mitigate food fraud by providing methods to screen and test for authenticity of ingredients (such as actually organic, locally sourced, minimally processed, etc.
Read more about detecting and preventing food fraud with data analytics.
Similarly, food safety can be supported with data analytics. For example, consider the example of yogurt production. Data analytics can provide a complete picture of the process from the time the milk arrives at the facility through the various manufacturing steps (including monitoring risks such as pH of milk) to assessing factors during delivery (such as temperature, exposure to air, etc.) that could increase risk.
Data analytics can help determine which factors will increase risk and how to adjust processes accordingly, test for contamination, and know when a product must be discarded.
Data analytics is also an essential part of back casting: Reviewing your process after something goes wrong or to assess areas for improvement. It can help answer questions such as: What could have been done differently? How could contamination be prevented or the process improved?
Using data analytics to address food trends
Data analytics can support product development, improve production processes and help improve food testing. For food and beverage manufacturers, using data analytics tools such as Design of Experiments (DOE) can mean a faster time to market and increased market success with product formulas based on statistical comparisons.
Process monitoring and control using multivariate data analysis (MVDA) can result in reduced down time, and optimization of processes leading to better use of ingredients or raw materials and lower energy consumption, as well as less waste generation. MVDA can also be used to help inclassifying and discriminating products in food authenticity testing toprevent food fraud.
Want to know more?
Watch this webinar about how data analytics supports the latest trends in food and beverage industry manufacturing.