Demand Planning For Global Supply Chain

Demand Planning For Global Supply Chain
About the Company

One of the world’s biggest container shipping companies handles ocean and inland freight transportation and port operations. It has subsidiaries and offices across 130 countries and around 83,000 employees worldwide in 2020. The pandemic has had a long-standing effect on Shipping carriers companies as the consumer product industry experienced a surge in the search and demand for products. Hence, the shipping industry saw a boost in profitability in revenue in recent times. As for the financial year 2021, the shipping company’s revenue has been about ~ 61.8Bn USD.

Challenges

A retail giant suffers from revenue loss and over time losses customers to competitors. Since their inventory is filled with out-of-stock products. As a distribution partner, our client realized there could be a supply chain mismanagement. Either the production is skewed or the goods distribution needs a revamp.

The client relied on Factspan’s team to come up with a demand planning solution to anticipate customer demand for products as efficiently as possible while avoiding any disruptions along the supply chain. Moreover, they were unable to plan the customer demand accurately. As they did not have visibility on the historical usage at the lowest possible origin and destination.

They aim to get planning that should tell ideal inventory levels, shipping detail, production schedule, and purchase schedule needed to meet demand.

Solutions

Factspan reimagined a pre-planning tool that enables the client team to choose the ports in the trade route. In an actual scenario, a retail giant (client’s customers) wants to ship all of its FFE across various ports in the world, and our tool helps in recommending available ports. The tool uses historical demand data and seasonal data to forecast demand in future periods across various customers, products, and destinations.

The importance of recommending shipping ports helped the client discover which port could use more vessels. This way the retail giants were able to utilize all the available vessels without overburdening one port with all their goods.

The team designed the above flow of trade routes from east to west. The trade route comprised various ports. In an actual scenario, there were a total of 300 ports. The system helped the client team to separate the ports into two different routes based on unique origin destinations. Further, the groups were divided into unique corridors. In other words, one of the corridors will cover all the ports from Singapore to Visakhapatnam.

Tools Used: Python – Data Modelling, Azure Databricks

In our pre-planning tool, there are two panels: Monthly FFE and Corridor trade routes. In the section of monthly FFE, the client teams set a target FFE. It shows the retail giant is looking to ship 420 FFE for the month of June. With the help of historical data, the system is able to distribute the target FFE among different corridors. In real-time, the historical data suggested that in the month of June corridors 2  and 3 have a greater demand. Hence, the client was able to save time and costs by optimizing their shipping vessel operations.

Results:
  1. Demand planning enabled the client to predict customer demand in accordance with customer needs.
  2. Enhanced user experience for customers with the pre-planning tool.
  3. Forecasting the FFE at the corridor level helped the business gain 60% more accurate planning data points than the previous model.
  4. By using a new digital + AI-assisted workflow, the business was able to plan better, reducing the planning process from 3-4 months to 1 month.

Major credits to the Factspan team for their valuable contribution Vikas Chavan and Nikitha Shatdarsanam

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