Development of a Data-Driven Dynamic Soybean Procurement Strategy for ABT-CPD
Problem Overview
Soybean is the primary raw material for ABT-CPD, and its market price is highly variable. Soybean is available through both domestic and international markets, with significant variations in price, seasonality, availability and procurement economics. Historical domestic and international soybean price data, along with ABT-CPD’s procurement data, provide an opportunity to develop a scientific and data-driven procurement strategy. The challenge is to develop a decision-support system that can analyze historical and current market trends and recommend when to buy, how much to buy, and whether domestic or international sourcing is economically preferable, while considering inventory, lead time, landed cost and business requirements. The objective is to reduce procurement cost and price risk while ensuring uninterrupted availability of quality soybean.
Industrial Context & Background
AI & Machine Learning / Data Analytics & Business Intelligence / Supply Chain & Logistics / Agriculture & Agri-Tech
Technical Requirements
1) Historical domestic and international soybean price data. 2) Historical ABT-CPD procurement data, including quantity, price and date. 3) Relevant parameters such as inventory level, consumption rate, lead time and procurement constraints. 4) The model should accommodate changing market conditions and allow manual updating of market inputs. 5) The solution should provide understandable recommendations rather than only statistical predictions.
Suggested Technology Stack: Python / R / SQL / Power BI or equivalent BI tools / Machine Learning / Time-Series Forecasting / Predictive Analytics / Optimization Algorithms / Web or Dashboard-based Decision Support System
Expected Solution & Prototype
A prototype Soybean Procurement Decision Support System that: 1) analyses historical domestic and international soybean price trends; 2) identifies seasonal and cyclical price patterns; 3) forecasts short-term/medium-term price trends; 4) compares domestic procurement with international sourcing based on total landed cost; 5) recommends optimum procurement timing and quantity; 6) provides scenario analysis for different market conditions; and 7) generates a simple procurement recommendation/dashboard for management decision-making.
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