In the wake of the pandemic, companies are in an exceptionally fast-moving business climate. Customer behaviors and expectations are evolving quickly and as more and more businesses adopt optimized supply chain practices and cloud-connected business networks, competition is getting fierce.
Demand forecasting is important to the supply chain because it helps to inform core operational processes such as demand-driven material resource planning (DDMRP), inbound logistics, manufacturing, financial planning, and risk assessment.
At its best, demand forecasting combines both qualitative and quantitative forecasting, both of which rely upon the ability to gather insights from different data sources along the supply chain.
Qualitative data can be curated from external sources such as news reports, cultural and social media trends, and competitor and market research. Internally-sourced data – such as customer feedback and preferences – also contributes greatly to an accurate forecasting picture.
Quantitative data is typically mostly internal and can be gathered from sales numbers, peak shopping periods, and Web and search analytics.
Modern technologies employ advanced analytics, powerful databases, and use artificial intelligence (AI) and machine learning to analyze and process deep and complex data sets. When modern technology is applied to qualitative and quantitative forecasting and predictive analytics, supply chain managers can provide ever-increasing levels of accuracy and resilience.