Our client, an agricultural market leader, relies heavily on a pest that is being impacted by climate change. They needed to understand how the evolving environment will affect the bug over time and the impact on demand for their products.
- Understanding potential negative cost impact to the business
- Strategic decision-making to account for adjustments in market conditions
- Gaining foresight on when and why dynamics will change
- Assessing direct and indirect implications from environmental externalities
Cost of not finding a solution
The client risks losing significant market share by not adjusting their supply chain, product availability, marketing, and sales to the new and changing demand cycle.
We developed a predictive model that incorporated environmental factors and future climate scenarios to forecast the emergence patterns of the key target pest. Using Machine Learning Models, we analyzed pest emergence cycles and peaks, identifying shifts in patterns and linking these shifts to new demand patterns for the client's products.
Our insights were instrumental in helping the client save millions in supply chain-related costs, provide products to end-users when needed most, and achieve inventory management efficiency. This led to customer retention and product life cycle extension.