Mitigate risks in supply chain for manufacturer of agricultural equipment


 US-based multi-business firm that is a global leader in design, manufacture, and distribution of a wide range of agricultural equipment


The client wanted to mitigate internal risks and vulnerable links in the supply chain. The client wanted to focus on top suppliers of agricultural sub-appliances. The client faced challenges in automating the data capture process and minimizing manual efforts.


SG Analytics data analytics team created a model for risk estimation associated with each component delivery based on the need to maintain the flow of supply chain and to avoid surplus/shortage:

  • The SG Analytics team gathered data for various components of a given product type to be used as a demand value and in risk prediction in the supply chain
    The SG Analytics team developed the demand value of  these components and the risk associated with their delivery
    The team used attributes such as country, logistical, performance, supplier, and supply quantity as predictors
    SG Analytics monitored the estimated value error continuously and improvised models for better results
    The team used techniques such as artificial neural network, linear regression, and support vector machine to estimate demand value, and predict risk probability as well as risk severity (High, Medium, Low)


Reduced risk of component shortage/ surplus by accurately forecasting demand values.
Enabled 25% cost savings from supply chain process optimization.
Ensured cross-utility and adaptability of the model for other inventory types across various products.
Calculated vendor ratings to gauge performance.