Decades ago, in the 1970s and 80s, businesses were bewildered by the complexities that demand forecasting presented. With the onset of enterprise computing at the time, there wasn’t enough customer data available to draw insights on demand. This resulted in massive losses when projected demand didn’t meet actual demand. Between 1980-81, a staggering $500 billion was invested by the petroleum industry in infrastructure and services, as they were anticipating a higher demand for oil. However, when the demand didn’t materialize, massive losses were suffered triggering an industry-wide price collapse.
The current scenario
Today, every business has vast amounts of data at their disposal – something that was unimaginable in the 1980s. You would expect forecasting demand to be easier theoretically, but businesses have still suffered. While we have improved from before, the age-old, simplistic business rules and reliance on outdated historical data works against us. Our current problem is dealing with the huge inflow of big data to create real-time interventions that are effective and profitable.
Forecasting demand with precision
With behavioral analytics, artificial intelligence and machine learning, businesses are poised to revolutionize demand forecasting:
- Behavioral analytics focuses more on future-looking customer data rather than historical data, to derive insights into choice patterns and buying decisions. By focusing on the “how” and “why” of customers’ actions, you can get a more accurate reading on expected buying behavior. Combining behavioral analytics with business intelligence can help you take seemingly unrelated data to extrapolate, predict and decipher future trends in customer choice patterns.
- Artificial intelligence (AI) helps businesses come to strategic interventions with little to no human input. When it comes to forecasting demand, AI enables financial and demand planners to extract knowledge from huge datasets assembled from various external and internal sources. By testing and refining advanced models, AI can go far beyond traditional demand forecasting to predict demand accurately. AI also adapts to changes in supply chain, competition, new products and channels, all in real-time. Machine learning, an application within AI, helps to draw insights and trends that generally go undetected, unlike human-created forecast algorithms. Machine learning allows systems to automatically learn against some measure of truth and improve without being explicitly programmed. By running through vast quantities of data, self-improving algorithms can be created that accurately forecast demand.
Future of demand forecasting
Through a combination of behavioral analysis, AI and machine learning, “intelligent automation” can be applied to enhance existing demand forecasting models. Using applications that leverage AI and machine learning, algorithms can self-learn by examining outputs against a measure of truth to constantly update themselves and evolve. It’s no wonder that 55% of organizations are looking to make major investments in AI over the next two years when it comes to supply chain management, according to a leading industry report. By leveraging future-looking data to run these algorithms, organizations can aim to predict demand more accurately than ever. By using transformative technology, you too can go beyond traditional forecasting!
How OSG can help forecast demand
OSG’s bespoke behavioral and cognitive analytics platform Illuminate, can help you predict demand accurately. Using a combination of historical data and future-looking metrics, Illuminate offers one of the most powerful forecasting methodologies. With Illuminate’s behavioral analytics, historical, transactional and sales data can be modeled with forward leaning behavioral measures. By combining our powerful AI and machine learning algorithms, we can help your business arrive at accurate, real-time forecasts in localized geographies of your choice.
OSG used Illuminate’s forecasting capabilities to help an American seasonal products company optimize their product shelf stocking with one of their channel partners at a store level. By analyzing data from over 4500+ channel partner stores across 1600+ SKUs, OSG was able to forecast that an additional $10 million could be earned in revenues by increasing product availability across channel partner stores.
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