Case Study: Optimising Inventory and Distribution Strategy
Case Study: Optimising Inventory and Distribution Strategy
Client
An EU logistics and supply chain management company.
Problem
The client aimed to optimise its inventory distribution strategy to reduce costs while ensuring timely deliveries. Despite having demand forecasting in place, the client’s system could not react to real-time demand fluctuations.
Achieving this objective required effective management of complex logistics and strategic stock balancing across multiple warehouses. Some of the warehouses were cold, and part of the stock had shelf life restrictions that had to be taken into account to minimise spoilage.
The first hurdle was that vast amounts of data needed to be piped to a central silo and develop an efficient model capable of addressing the dynamic nature of the supply chain variables. Additionally, the data was dispersed across multiple sources and software systems.
Because the problem involved fast moving goods with limited shelf lives, the client’s data team believed that using nonlinear mathematics to describe the demand forecast would be much more precise, but the resulting optimisation problem seemed impossible to solve.
How We Helped
We constructed a data pipeline to aggregate information from various sources into a centralised database, enabling real-time data integration into the mathematical models. This allowed us to develop a comprehensive optimisation model that incorporated constraints and parameters such as shipping costs, warehouse capacities and types, nonlinear demand forecasts, and service levels. Our solution employed advanced optimisation algorithms and real-time adjustments to continuously refine the distribution strategy based on current data.
Testing revealed that using nonlinear mathematics to describe the demand forecast was indeed much more precise, and as the client expected, unsolvable within the necessary timeframe. We trained Octeract Neural, our AI algorithmic generation framework, on the client’s data and within 3 weeks the system discovered a custom algorithm that could solve that specific problem very well. This custom solution was then tested and deployed on the client’s backend infrastructure.
Impact
The solution significantly improved supply chain efficiency, reduced shipping costs, and optimised warehouse utilisation. The system became capable of reacting to real-time demand fluctuations, allowing dynamic inventory adjustments that ensured timely deliveries, enhanced customer satisfaction, and reduced spoilage. This led to substantial cost savings and improved operational performance. A side benefit was that the ability to leverage nonlinear demand forecasting enabled better predictions over the long term.