Key Points
Amid an onslaught of inflationary pressures, logistical issues and energy price rises, the sheer volatility in component costs has made it difficult for retail and consumer packaged goods companies to predict cost impact at the finished goods level. This, in turn, has made it difficult to forecast company profitability. Predictive analytics is thus a critical requirement to understand how each component in the Bill of Materials (BOM) is affected by market changes.
The journey to BOM optimization begins with structuring data, which lies at the root of sensitive forecasting algorithms. Advanced analytics platforms are then employed to ingest the datasets, run artificial intelligence and machine learning algorithms and forecast the cost impact of external drivers on every BOM component. Finally, leaders need to access the right data at the right time for enhanced decision-making and efficient management of supply chains and distribution.