Should Be Cost

The “Should-Be Cost” model combines demand forecasting and machine learning-driven cost prediction to help companies procure raw materials at optimal prices. By integrating internal historical data with external market signals—such as commodity price movements, macroeconomic trends, and supplier performance metrics—this model helps businesses determine the “fair” or “should-be” price for key raw materials and goods. By analyzing market data, historical trends, and macroeconomic indicators, it determines a fair baseline or “should-be” cost, guiding procurement teams to negotiate more effectively and avoid overpaying. Companies using this model typically achieve at least 10% reduction in procurement costs, as it continuously refines pricing strategies, improves supplier relationships, and provides real-time market insights for smarter, data-driven decisions.

Key Features:

Demand Forecasting:
Utilizing state-of-the-art time series analysis and predictive models, the framework accurately forecasts future demand for various raw materials. This enables companies to plan procurement activities proactively, adjusting order quantities and timings to capitalize on favorable market conditions.

Machine Learning-Based Cost Prediction:
Advanced ML algorithms ingest a wealth of market data—commodity indices, supplier quotes, historical transaction records, and economic indicators—to predict the expected cost of goods. By understanding likely price fluctuations well in advance, procurement teams can negotiate more effectively and avoid overpaying.

Data-Driven Negotiation and Supplier Management:
Armed with a clear understanding of what the cost “should be,” companies gain stronger leverage in supplier negotiations. This knowledge fosters more transparent discussions, improves supplier relationships, and ensures that contractual terms reflect true market value rather than reactive pricing.

Real-Time Market Intelligence:
The model continuously updates its predictions as new data becomes available, providing ongoing intelligence. This keeps procurement teams informed about shifting cost dynamics, potential supply disruptions, and emerging trends—all of which can influence when and how much to buy.

Actionable Recommendations and Alerts:
The framework can issue automated alerts when actual supplier quotes deviate from the predicted should-be cost by a significant margin. This proactive approach ensures that procurement teams can quickly adjust tactics, renegotiate terms, or source alternative suppliers to achieve cost-efficiency