Embracing AI & ML, Kargo Introduces Demand Planning for Enhanced Logistics and Sustainable Supply Chain in Indonesia
If you know how many shipments will happen by next week or month, what will you do today? Understanding what the future looks like helps both Shippers and transporters in Indonesia to navigate high demand volatility, reduce waste and increase profit margins. Predictability is key in the supply chain.
Traditionally, manufacturers and logistics companies in Indonesia have relied on passive prediction methods, such as historical sales data, to establish predictability. However, this approach must consider various uncertainty factors, such as economic conditions and customer churn. Furthermore, relying on spreadsheets for predictions limits the ability to model what-if scenarios and manage uncertainty and probabilities. It often leads to manual efforts and human errors through frequent email exchanges.
What is the solution to this problem? There is a massive movement in the global economy, where businesses are starting to use machine learning for demand forecasting. The graph below will help us understand the differences.
Does this solution even exist in Indonesia today? Yodi Aditya, CTO of Kargo Tech, proudly answers this by introducing Kargo Demand Planning — enabling next-gen shipment prediction leveraging Machine Learning (ML) that is trained over multiple years of enterprise shipment data, both internal and external data, and focuses on the Indonesia market trends over multiple industries.
By embracing Kargo Demand Planning, businesses can reap numerous benefits:
- Shippers can get a better sense of when and where demand is likely to be high, which can help them plan their shipments more efficiently.
- Carriers can get a better sense of where demand is likely to be high, which can help them plan their routes more efficiently.
- Both shippers and carriers can benefit from Kargo’s demand prediction by avoiding overbooking and underbooking, which can save them money.
Kargo Demand Planning encompasses many features, including seasonality and trend analysis, transporter segmentation, fleet profiling, route analysis, fulfillment rate patterns, cohort analysis, and a budget estimation based on shipment probabilities across multiple transporters and routes. These features are already utilized internally by the Kargo team to build a resilient supply chain.
As of today, the Kargo Data Scientist team has trained the Kargo Demand Planning machine learning model using over 10 million rows of data across 26 different metrics. Remarkably, our model can generate accurate predictions with as little as 7 days of shipment data.
To leaders and visionaries, are you ready to build a sustainable supply chain for better logistics? Let us help you build a resilient and efficient logistics network. Please follow this link https://kargo.tech/dp/ to receive contact from our team.
