AI Systems Rely on Demand Intelligence to Enhance Forecasts

Artificial intelligence (AI) is defined as the simulation of human intelligence and decision making in machines. In turbulent business environments that many organizations are currently in, AI systems can be leveraged to understand business patterns and use this to supply information used for strategic decisions. Specifically, emerging AI systems are being used by data teams to improve forecasting accuracy, and businesses that are adopting these approaches are seeing results. But in order to build effective AI systems and ML models to perfect your time series forecasting methods, it's essential to invest in the right data.

Data challenges when adopting AI systems 

To effectively train artificial intelligence systems for demand modeling, teams must think about the data they’re using.  While that sounds obvious to forecasting experts and data gurus, a McKinsey report found that only 33% of organizations are effectively using internal and external data to take advantage of AI capabilities. 

It’s easy for businesses to leverage historical sales data and seasonality trends. But there are a vast amount of external demand causal factors that cause unexplained demand anomalies that significantly impact the bottom line. 

Demand intelligence is forecast-grade data that provides insight into those external demand anomalies. You have the ability to train AI systems to learn the patterns of demand causal factors causing business disruptions and pivot strategies around inventory management, product pricing, workforce optimization, and more. 

There are various challenges data teams face when using external data sources and training AI systems, but two notable issues are data diversity and data quality. Demand intelligence mitigates these issues so your team can focus on fine tuning AI systems and ML methods to improve demand prediction. 

Data diversity 

Artificial intelligence systems use algorithms to assess datasets, identify demand patterns and use propensity models or forecast aggregators to start making predictions. To prevent data bias and forecasting errors, it’s essential to provide systems with a breadth of data. PredictHQ uses hundreds of external data sources and proprietary data to ensure models have the depth and diversity needed to identify demand patterns. We cover 18+ event categories that represent many demand causal factors, allowing AI systems to learn from a variety of data. 

Data quality 

The quality of data plays a huge role when training AI systems. If your systems are ingesting non-standardized, duplicative, spam data, artificial intelligence systems won’t be able to accurately identify patterns. PredictHQ’s data processing system aggregates, standardizes, dedupes, and filters millions of raw data points into a single format. Our team uses The Quality Standards for Processing Demand Causal Factors (QSPD) and has created more than a thousand machine learning models to identify incorrect details and validate data by using our knowledge graph and entity system. This ensures that you’re training your AI systems with high quality data that’s accurate. 

AI systems used in forecasting 

When demand intelligence is paired with internal data sources, teams can build effective AI systems that can be used to improve forecasting accuracy. Popular AI systems used in forecasting are neural networks, expert systems, and belief networks.

• Neural Networks enable artificial intelligence machines to recognize and learn patterns in supply and demand. 

• Expert Systems replace any judgement by creating ‘knowledge’ stores in if-then rules.

• Belief Network based systems are structured in a tree format. The nodes represent variables and the branches the conditional dependencies between variables.