Want to achieve ghost-accurate demand forecasts for your customers?
Spreadsheet-based, old-school demand forecasting is out. Noisy data, volatile markets and rapidly changing consumer demands have taken over.
Here’s the lowdown…
Legacy forecasting methods are simply not up to the task. Manual models assume the future resembles the past. They struggle to account for complex data sources and over-rely on human intuition. When unforecastable events occur, accuracy plummets.
Meet machine learning, the future of demand forecasting.
Machine learning can process massive datasets, detect hidden patterns and quickly adapt to dynamic changes in real-time. Companies that use ML for forecasting see the results.
Table of Contents
What you’ll learn:
- Reasons Traditional Forecasting Falls Short
- How Machine Learning Changes The Game
- Key ML Techniques For Demand Prediction
- Tangible Business Benefits You Can Expect
- How To Get Started With ML Forecasting
Reasons Traditional Forecasting Falls Short
Let’s call a (data) spade a spade…
Manual, spreadsheet-based and old statistical forecasting approaches are still what McKinsey research shows make up a whopping 73% of all demand forecasting tools in use at enterprise level.
That’s a problem for demand forecasting.
Legacy approaches rely on traditional statistical models that have served for decades. These tried-and-tested methods include moving averages, linear regression and exponential smoothing.
But they have glaring limitations that lead to inaccuracy:
- Historical data is the sole basis for predictions
- Models can’t simultaneously account for multiple variables
- Adaptation to market changes is slo
- Complex interactions between variables get missed
No business that’s serious about gaining or maintaining a competitive edge can afford to ignore the need for advanced demand forecasting tools.
The digital age has brought exponential increases in data, real-time data feeds, e-commerce, global supply chains and data complexity that legacy models are ill-equipped to handle.
Global supply chains and real-time data have become the norm, rendering linear models obsolete.
How Machine Learning Changes The Game
ML is the complete opposite of traditional models.
Whereas static spreadsheet-based models have limited predictive accuracy, machine learning systems evolve and improve as they are exposed to more data. ML is perfectly suited to today’s fast-paced and unpredictable business environment.
The best part is…
ML-based demand forecasting models can yield forecast errors that McKinsey Digital notes are between 20% and 50% lower in supply chain network systems. Improved demand forecasting accuracy reduces lost sales due to out-of-stock items by 65%.
That’s right.
ML algorithms can simultaneously ingest and process data from thousands of variables. This includes sales history, market trends, weather, social sentiment and economic indicators. The key advantages of ML-based demand forecasting include:
- The ability to simultaneously consider thousands of variables
- Detecting patterns that humans wouldn’t recognise
- Automated adaptation to rapid market shifts
- Continually learning and improving over time
Legacy methods don’t handle non-linear, complex interactions well. ML algorithms such as Random Forest, XGBoost and LSTM neural networks understand and model these connections between demand drivers.
Key ML Techniques For Demand Prediction
But it’s not a one-size-fits-all scenario…
Different machine learning methods and algorithms each have their own strengths and applications. There’s no one-size-fits-all solution for every demand forecasting problem.
Time Series Models
Time series models such as ARIMA have been around for a long time. ML-enhanced versions handle non-linear patterns and longer-term prediction horizons.
Neural Networks
Deep learning models are particularly adept at uncovering demand patterns that more simplistic approaches miss entirely. LSTM networks are a type of neural network with powerful time-series capabilities.
Ensemble Methods
Algorithms like XGBoost and Random Forest build and combine predictions from multiple individual models to boost overall accuracy. These approaches shine at taking numerous factors into account.
Clustering Techniques
Machine learning can help with predicting demand for new products without prior sales data. Algorithms will identify clusters of existing products with similar characteristics and use them to model new product forecasts.
ML model parameters can be updated and retrained as needed, allowing companies to quickly adapt to disruptive events.
Tangible Business Benefits You Can Expect
The business benefits of ML-based demand forecasting extend far beyond superior forecasts themselves…
Better forecasting accuracy has a cascade effect. Procurement, production, distribution and logistics all become more optimised. Efficiency increases. Waste gets cut. Cash flow becomes tighter.
Here are the numbers that are really changing lives:
- Warehousing costs dropping by 5% to 10%
- Administration costs getting slashed by 25% to 40%
- Inventory levels able to be cut by 20% to 50%
- Service levels going up by 65%
Most importantly for most businesses…
Accurate forecasting allows teams to focus on higher-level strategic problems rather than constantly reacting to unexpected demand changes. The business stops wasting precious time and energy firefighting staff and rescheduling production runs or manual order adjustments.
ML provides a safety net to catch and learn from demand fluctuations so they don’t wreak havoc. Enhanced customer service and availability also win more sales opportunities than in the past.
How To Get Started With ML Forecasting
Ready to take the leap into the world of ML-based forecasting?
Jumping straight in is tempting but there are some key first steps that companies need to take. Too many organisations will claim that they’re “pretty good” on their data readiness, but that’s simply not the case. Longstanding business process shortcuts and workarounds will be there, waiting to be automated and rubber stamped by technology.
Begin with these steps:
- Cleanse and consolidate your historical data
- Identify the variables that are most likely to influence demand
- Define clear KPIs that accurately measure results
- Start with a narrow product category
Don’t try to boil the ocean at the outset. Begin with a smaller set of items and focus. It’s not necessary to implement ML-based forecasting for your entire portfolio from the start.
Bringing It All Together
Machine learning has rewritten the demand forecasting rulebook. ML algorithms crunch data at scale, understand non-linear connections between variables and just keep on getting more accurate over time.
The benefits of machine learning are measurable, direct and tangible. Spreadsheet forecasting is a relic of the past, and companies still using it are leaving money on the table.
Upsides include lower error rates (by up to 50%), stockouts (down 65%) and inventories (down 50%), while customer service levels go up (by 65%). The proof is in the pudding.
The challenge for demand forecasting decision-makers is getting data-ready, taking an incremental approach and scaling up ML tools across a broader portfolio.
Act now while your competitors are asleep at the switch.
