Artificial Intelligence has been widely praised for its ability to transform the supply chain for the better, from more accurate demand forecasting to real-time, automated inventory management. Zeroing in on that critical component of demand planning, an accurate forecast, AI-powered forecasts outperform traditional models, in some cases driving double-digit percentage gains in accuracy.

Sounds like a silver bullet, right? And it is. So why are many businesses still operating with poor, outdated forecasting processes? In IBF’s June 2018 survey, 70% of respondents said AI will be the future of demand planning in 2025. Forecasting practitioners are well-aware of the benefits, but the pitfalls of AI forecasting projects are preventing widespread adoption. Without coding or data science skills, it can be difficult for planners, analysts, and others tasked with improving forecasting processes to implement the most advanced AI-powered forecasting solutions.

Do Demand Planners Need Data Scientists To Exploit The Power OF AI?

When a demand planner has a centralized data science organization to collaborate with they have an opportunity to access the skills to exploit AI but typically, development cycles are long, costly and complicated. Many projects fail completely, often with a hefty price tag. At a leading Fortune 500 company I work with, the planners on the forecasting team are currently debating whether to rely on the company’s centralized data science team or hire their own data scientists. The strategic discussions have focused on these two options. Now, with new software platforms that automate the process of building AI models, they have a third option of incorporating advanced AI—Automated Machine Learning (AutoML) and Automated Deep Learning (AutoDL)—into their workflow.

These allow a demand planning function to achieve the same objective that they would get in options one and two, but faster and with less cost and risk associated with the project. The forecasting practitioners and demand planners don’t necessarily have a data science background but that’s not a limitation as these platforms empower them to take advantage of the latest in AutoML/DL to create process improvements and drive forecast accuracy—not in 2025, but now.

How Deep Learning Solves Common Forecasting Challenges

Tools powered by AutoML and AutoDL not only solve common, complex demand forecasting challenges with ease, but do so in a fraction of the time. Manufacturers of fresh, perishable goods create their own algorithms that precisely forecast daily sales for each SKU in each store, reducing wasted inventory and creating efficiencies in transportation and logistics. Apparel brand manufacturers are utilizing these platforms to improve forecast accuracy for new product introductions that have little historical data, mitigating risk in a way not possible with traditional forecasting methods. Fast food chains are predicting sales of individual menu items down to half-hour increments with greater accuracy than ever before, in turn allowing for better staffing and inventory management.

The biggest factor impacting accuracy is the data, and unlike traditional methods, Machine Learning and Deep Learning-powered methods allow forecasters to incorporate different types of data, structured or unstructured, and combine those data sets into the forecasting model. This holistic approach incorporates all data that could impact future sales, leading to major gains in accuracy.

Automation Reduces Tedious Manual Work

Automation is a critical component with any Machine Learning or Deep Learning software as without it, development processes are often still lengthy and failure rates high. AutoML and AutoDL forecasting platforms can automatically factor in seasonality, holiday and trend effects removing the need for tedious manual work for planners.

AI Helps Forecast New Products

While Machine Learning has provided many improvements over legacy demand forecasting methods, Deep Learning is uniquely well-suited for forecasting. Deep Learning acts like a long-term memory function and is better at learning patterns over time, and when historical data is incomplete or non-existent, like forecasting new products, AutoDL excels by quickly iterating to fill in these gaps.

AI Does Something Statistical Models Can’t Do – Identify Non-Linear Relationships

Deep learning solutions have greater capabilities to handle unstructured data and learn the correlations by understanding complex non-linear relationships. Sophisticated models are better suited for this type of task, however these advanced Deep Learning and AutoDL models require significant processing power. Largely made possible by GPU (Graphics Processing Unit) computing, these advanced forecasting algorithms can discover much finer patterns in the data than traditional methods.

AutoML and AutoDL solutions put the power of these unique capabilities into the hands of non-technical users while also solving pain points for data science personnel, like automating feature engineering, and model design, training and deployment.

When considering how to best incorporate the latest AI technology into forecasting, practitioners are no longer restricted to either partnering with an internal data science team, hiring their own, or simply relying on traditional methods. AI platforms that leverage AutoML and AutoDL can be valuable not only for data scientists but also for those business users who are fluent in data but perhaps not coding. This new breed of software is putting the power of Machine Learning and Deep Learning directly into the hands of planners and analysts, so they can build better forecasts today without the overhead, creating more accuracy and better decision-making throughout the supply chain.

IBF’s 2018 research report entitled The Impact Of People and Process On Forecast Error revealed that forecasting error of cause-and-effect models (AI) is more than four percentage points lower than Time Series models (20.79% vs. 24.86%). Click the link to download.