Supply chain forecasting is the process of predicting the demand, supply, or price of inventory for a given period.
When you plan a trip, you probably check your weather app to know what kind of clothes to pack—otherwise you risk shivering (or sweating) all week. The same principle applies to supply chain planning.
The better insights you have into your supply chain, the more resilient your business will be.
Supply chains are complex systems that are influenced by all sorts of variables, trends, and unforeseen surprises. It would be great if business owners had a crystal ball to know exactly how much inventory to order at any given time. But the next best option is supply chain forecasting.
Having a system to make evidence-based decisions about operations is essential to meet customer demand and get an edge over your competitors. This article breaks down the benefits of supply chain forecasting and explores nine forecasting methods your business can use to meet customer demand and improve cash flow.
What is supply chain forecasting?
Supply chain forecasting refers to predicting the future demand for products and the necessary materials and resources to fulfill that demand. This process involves analyzing historical data, customer behavior, market trends, or a combination of factors to make accurate predictions.
Companies use these forecasts to plan their production schedules, pricing, shipping, transportation, storage, and other logistical processes to maximize profits.
Forecasting is a crucial part of supply chain strategy, as it helps organizations streamline their operations, cut unnecessary costs, and reduce manufacturing waste.
Why is supply chain forecasting important?
Supply chain forecasting helps businesses make evidence-based decisions about their business logistics. Without accurate predictions on demand, pricing trends, and customer behavior, it’s difficult to steer your organization in the right direction.
1. Make smarter business decisions
Supply chain forecasting yields insights that can inform your production schedules, procurement strategy, inventory management, pricing, and labor needs. This becomes especially important leading up to new product releases or busy times like holidays. Without forecasting, you leave critical matters up to guesswork and clunky spreadsheets.
2. Customer satisfaction
If you can’t accurately predict the amount of product you need (and when you need it), you can negatively impact customer experiences. Shopify reports that 67% of US consumers expect same-day, next-day, or two-day delivery. These rising expectations mean companies need their forecasts to be spot-on, otherwise you could be dealing with shortages, stockouts, and backorders.
3. Improve inventory management
Buying excess inventory leads to bloated warehousing costs, not to mention the cost of spoilage if your products have a short shelf life. Supply chain forecasting gives you an accurate picture of how much inventory you need for a given period, so you carry only what you need.
9 supply chain forecasting methods
There are two main categories of supply chain forecasting models: quantitative and qualitative. Let’s explore each type.
Quantitative supply chain forecasting methods
Quantitative forecasting uses historical data and statistical models to project future trends.
- Moving average forecasting: Examines data points by creating an average series of subsets from complete data. This average is used to make predictions for an upcoming time period.
- Exponential smoothing: Uses weighted averages of historical data, but gives more weight to recent observations. This method is ideal for short-term forecasting.
- Adaptive smoothing: Uses multiple variables to provide deeper insights into how trends change from period to period. Adaptive smoothing requires machine learning tools that collect and aggregate data in real-time.
- Auto-regressive integrated moving average (ARIMA): A highly-accurate forecasting method best suited for time frames of 18 months or less. This method tends to be costly and time-consuming.
- Multiple Aggregation Prediction Algorithm (MAPA): This is a relatively new forecasting technique that works well to predict seasonal trends. It smooths out trends to help prevent under- or over-estimating demand.
Qualitative supply chain forecasting methods
Qualitative forecasting relies on firsthand insights and experience from industry experts to make predictions. This type of forecasting can be used when historical data is hard to find, or if you’re launching a brand new product.
- Market research: This involves using customer surveys, polls, or focus groups to gain insights into your target demographic.
- Historical analogies: Analogy forecasting predicts future sales by assuming a new product will yield similar trends to a current product.
- Delphi method: This method gathers insights from a group of experts or advisors, which are then analyzed by a third-party. After review, the information is summarized, emphasizing trends and patterns.
- Sales force composition: Also called “collective opinion,” this method gathers personal insights from the sales team, managers, and other staff to establish a forecast. This method isn’t the most reliable but can be used when other methods aren’t available.
Supply chain forecasting challenges
Supply chain forecasting is far from an exact science. Here are some challenges to be aware of.
Global events
Unexpected events can disrupt even the most detailed forecasts. The most obvious example of this was the COVID-19 pandemic, where businesses of all shapes and sizes struggled with supply chain shortages, changing regulations, staffing issues, and increased shipping fulfillment costs.
Seasonality
Demand forecasting gets more complicated when you factor seasonal changes into your projections. High-demand periods require longer lead times that should be factored into your forecasts.
Changing consumer trends
Historical data is helpful, but consumer spending habits can fluctuate depending on the economy, world events, and other factors outside your control.
Best practices for supply chain forecasting
Here are five tips to improve your supply chain forecast accuracy:
- Use hard data when possible—qualitative forecasting can be biased.
- Take advantage of inventory management software to get real-time data and minimize errors.
- Avoid changing SKU IDs for identical products.
- Take inventory stock levels into account during demand forecasting.
- Analyze each sales channel separately (for example, online vs. brick-and-mortar).
How QuickBooks Enterprise supports supply chain forecasting
Just like weather forecasts require sophisticated technology, supply chain forecasts require software that offers a clear line of sight across all operations.
From real-time inventory visibility to more than 200 built-in reports, QuickBooks Enterprise is an integrated solution designed to improve decision-making within your supply chain.
