The current state of the supply chain
Supply chains today are being disrupted like never before. Consumers are demanding the best product selection at the best prices. They also want rapid delivery and customizability options while the world faces uncertainties brought about by climate change, trade tensions, resource scarcity, and, most recently, the outbreak of COVID-19.
Forecasting is a critical activity that helps companies predict future demand, mitigate potential risks, and capitalize on emerging opportunities. However, due to the increasingly volatile environment, businesses are forced to depart from traditional forecasting methods, siloed processes, and legacy technologies. Instead, they are focusing on digitally evolving their forecasting capabilities and operations, lest they risk continued value leakage throughout the company.
Next-generation technologies such as artificial intelligence (AI), specifically machine learning (ML), can significantly increase a business’s forecast accuracy, help it navigate a volatile demand landscape, and ensure its continued growth.
The limitations of traditional forecasting
Traditional forecasting methods are limited as they mainly use historical data to predict future demand. While this approach may have worked in the past when demand was relatively stable, rapidly fluctuating demand means that companies need to shift toward utilizing the wealth of real-time, external data about the market to create more accurate forecasts. However, most companies are not leveraging these external drivers of demand because they are still using the traditional approach based on historical data.
The emergence of leading indicators of demand
Leading indicators of demand—data that has predictive value for a forecast—are increasingly being leveraged to increase forecast accuracy. Broadly spread over two categories, market knowledge such as consumer demographics, Gross Domestic Product (GDP), and interest rates, and external data such from the Internet of Things (IoT), social media, review sites, and the news, all help create significantly more comprehensive forecasts. Depending on the industry and business, real-time external data such as local weather, events around the stores, road conditions and traffic, and search trends on the Internet can add even more dimensions to a forecast.
But to make meaningful inferences from these demand drivers, this data must be converted into actionable knowledge to improve forecasting accuracy and, ultimately, decision-making. It must also be done promptly, allowing a business to capitalize on these insights quickly. Dispensing with time-series forecasting and embracing intelligent forecasting through AI techniques such as ML forecasting provides a decisive advantage to companies.
The future of forecasting with machine learning (ML)
Furthermore, these next-generation technologies can take leading indicator data and create a forecast free of human bias. They can constantly learn which leading indicator data best predicts demand for more accurate forecasts down to the location, item, and time level.
A deeper understanding of how these drivers influence demand combined with the learning abilities of AI and ML forecasting enables a higher degree of automation of demand forecasting. This frees up planners to address exceptions and more complex cases. For the chemical industry, the ability to link macroeconomic data at the sector and country level allows for the accurate identification of different demand streams. It creates a composite forecast across horizons regarding contracting and tenders.
For the food industry, connecting leading indicators of demand such as weather forecasts and satellite images and utilizing machine learning algorithms to recognize patterns, outliers better, and seasonality to predict optimal harvest time creates yield and supply chain efficiencies. As a result, planner productivity gains of up to 60% have been reported.
In the end, automated intelligent ML forecasts do not just increase productivity; they produce plans optimized to the degree that neither manual nor traditional solutions can deliver.
If you are interested in learning more, read our white paper, which discusses how AI techniques like ML forecasting can significantly improve forecasting accuracy, optimize how you plan for demand, and evolve your company’s DNA from traditional to digital.