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Innovation on the Menu: How Tyson Foods Engineering is Revolutionizing Food Tech

Published Sep 12, 2023

Updated Aug 22, 2024

3 min read

In this podcast episode, Tracy Lee, the host of the Engineering Leadership series, interviews Danyel Bischof-Forsyth, the Chief Technology Officer (CTO) of Tyson Foods. The episode provides insights into Danyel’s journey to becoming the CTO of Tyson Foods and discusses her role in leading technology initiatives within the company.

Danyel introduces herself as the CTO of Tyson Foods and shares her background, which includes being a computer science graduate with an MBA. She started her career in technology 33 years ago and previously held the position of Business Unit CIO for Hallmark Cards.

The conversation then moves to Danyel’s decision to move to Tyson Foods. She explains that the opportunity to lead technology transformation in a company that was rapidly growing and investing in digital technology intrigued her. She highlights her willingness to take on new challenges, learn about different industries, and work with emerging technologies as key factors in her career choice.

Danyel also discusses the exciting technologies Tyson Foods has been implementing. These technologies include computer vision, IoT sensors, artificial intelligence, and predictive analytics. She explains how these technologies are used to monitor and optimize various aspects of Tyson Foods' operations, such as production lines, employee safety, and product quality.

Tracy and Danyel speak about the decision-making process for technology initiatives at Tyson Foods. She describes a structured approach that involves a technology committee comprising C-suite members and their delegates who prioritize technology projects based on their value to the organization. The committee uses a scoring process to make these decisions.

The episode also covers Tyson Foods' three-year technology roadmap, which aligns with the company's business goals. Danyel explains that her team works closely with business units to integrate technology into their strategic plans. They prioritize projects based on their potential impact and align them with the roadmap.

Tracy asks about Tyson Foods' approach to innovation. Danyel reveals that the company encourages innovation through an initiative that involves generating ideas, investigating them, piloting select ideas, and scaling the most promising ones. She emphasizes the importance of fostering a culture of innovation.

The conversation shifts to Danyel’s involvement in women in technology programs. She shares that she has initiated and led women in tech programs at both Hallmark Cards and Tyson Foods. These programs aim to connect, grow, and inspire women in the technology field. They offer mentorship, training, and networking opportunities.

Tracy and Danyel discuss the significance of having a clear mission and goals for women in tech programs, as well as the importance of retaining women in technology roles.

In summary, this podcast episode explores Danyel’s journey to becoming CTO, the technology initiatives at Tyson Foods, the decision-making process for technology projects, technology roadmaps, innovation strategies, and women in technology programs. It provides valuable insights into the role of a CTO in a leading company and the evolving landscape of technology in the food industry.

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

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Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. 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Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

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Innovation on the Menu: How Tyson Foods Engineering is Revolutionizing Food Tech

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