AI-driven Operations Forecasting in Data-light Environments

 Integrating accurate forecasting is essential.

AI-driven operations forecasting in data-light environments

For better forecasting in operations management, AI is proving essential. And limited data is no longer the barrier it once was.

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AI-driven operations forecasting in data-light environments    February 15, 2022  

By Jorge Amar, Sohrab Rahimi, Zachary Surak, and Nicolai von Bismarck

Too many companies still rely on manual forecasting because they think AI requires better-quality data than they have available. Nowadays, that’s a costly mistake.

What do internal functions as diverse as risk assessment, capital-expenditure planning, and workforce planning have in common? Each is fundamentally about understanding demand—making demand forecasting an essential analytical process. Amid rising pressure to increase forecasting accuracy, more companies have come to rely on AI algorithms, which have become increasingly sophisticated in learning from historical patterns.

AI models have clear advantages over traditional spreadsheet-based analytic methods. Applying AI-driven forecasting to supply chain management, for example, can reduce errors by between 20 and 50 percent—and translate into a reduction in lost sales and product unavailability of up to 65 percent. Continuing the virtuous circle, warehousing costs can fall by 5 to 10 percent, and administration costs by 25 to 40 percent. Companies in the telecommunications, electric power, natural gas, and healthcare industries have found that AI forecasting engines can automate up to 50 percent of workforce-management tasks, leading to cost reductions of 10 to 15 percent while gradually improving hiring decisions—and operational resilience  (Exhibit 1).  .... 

Enabling AI-driven forecast models

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Automated AI-driven forecasting promotes these benefits by consuming real-time data and continuously identifying new patterns. This capacity enables fast, agile actions because the model anticipates demand changes rather than just responding to them. In contrast, traditional approaches to demand forecasting require constant manual updating of data and adjustments to forecast outputs. These interventions are typically time-consuming and do not allow for agile responses to immediate changes in demand patterns.  ....'