Developing new crops

Case study: Developing a new crop

We helped a Global Fortune 500 company develop a new crop 60% faster and achieve tens of millions of savings

Using predictive analytics within the crop development process shortened time to market significantly, and helped to deliver savings in R&D spending, together with a huge first-mover advantage.

The challenge

Developing principally new crops is a very long undertaking. It takes at least 20 years of monitoring yields, pest tolerance and resistance before anything sellable can be obtained.

A Global Fortune 500 company with a gigantic R&D is on a mission to deliver best food products and they want to do this quickly. Our clients’ team had the ambitious goal to push a new crop to the market in just 15 years (instead of 20). In the first three years they spent several million dollars to collect piles of experimental data and were preparing a 12-year action plan for the new experiments. With 400 knobs to turn for the experimental design (if you decided to only change one variable at a time) - a LOT of experiments needed to be done to get a representative collection of data.

The solution

A bright project leader decided to use predictive models on how children crops would behave based on the properties of their parents and environmental conditions they grow in, instead of actually planting them, waiting for the year to pass, harvesting and calculating the results. This is where DataStories came in.

We helped the client with building models and discovering which properties of parent crops impact the Key Performance Indicators - like yield and resistance. This allowed the team to focus on 10 features instead of 400 during only several weeks. They could also run “in-silico” experiments on a computer by varying only the features that matter. Ten knobs to turn instead of 400 created the necessary focus for the team. The ability to instantaneously predict the likely outcome of the experiments allowed to save massive amounts of time and resources.

The impact we created

Quickly discovering what worked and what didn’t work, and using predictive models to guide future experiments the team was able to come up with the final product in just six years instead of the planned 15. The company managed to reduce time to market by 60% in a course of just two and a half years. This equated not only to tens of millions of savings in R&D expenses, but more importantly – it gave the company 9 years of competitive advantage of being the first on the market.

60% shorter time to market

Tens of millions costs saved

Hundreds of millions ROI gained

...all because they knew what to do with their data!

If knowing what to do with your data is something you are struggling with...

Be aware that the solution to your problem exists. Introducing advanced analytics during the data collection process always results in massive ROI improvements and a shorter time to market for new products.

This is not difficult. To reap the benefits of data-driven product design go through this simple check list.

Check list

1. Think about your data collection process.

Are you extracting all the benefits of all that effort?

2. Think about what you truly need to know to optimize your product.

Make a list of your KPIs. Are you collecting data for each of them?

3. Assuming you would know what truly drives your KPIs, think about how it would impact your personal and business goals.

Write this down.

4. Is your biggest challenge not knowing what the true KPI drivers are?

If yes, consider design of experiments which would allow you to capture informative data for your entire search space. In this way you can maximize the knowledge of your problem with a smaller amount of experiments.

5. Is your biggest challenge not knowing how exactly the KPI drivers impact the KPIs?

If yes - check non-linear predictive analytics and ensemble-based modeling, which would allow you to build more robust predictions to guide future experiments.

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Our core expertise is in business-driven applications of predictive analytics and data science to solve complex business challenges which directly impact the bottom line.