Supply chain forecasting is the process of predicting future customer demand based on historical data. This helps ecommerce and retail brands make more informed decisions about budgeting and inventory, ensuring they stock the right products during peak periods.
IHL Group research puts the global cost of inventory distortion (the cost of both out-of-stocks and overstocks) at $1.73 trillion a year. It names supply chain disruption as the single largest contributor at $301 billion. Poor supply chain forecasting can leave businesses with costly shortages and excess inventory.
This guide covers nine supply chain forecasting methods, how to choose among them, the challenges that reduce forecast accuracy, and the trends shaping supply chain planning in 2026.
What is supply chain forecasting?
Supply chain forecasting analyzes historical product demand to inform decisions about planning, budgeting, and inventory. Seasonal changes, supply chain trends, economic conditions, and global events can all lead to spikes and slow periods that affect inventory control.
Accurate forecasting helps a retail business keep enough product on hand to meet customer demand without running out of stock or carrying unnecessary excess inventory.
Supply chain forecasting vs. demand forecasting
These two terms are often used interchangeably, but they’re not the exact same thing.
Demand forecasting looks at historical sales data and market trends to predict what customers will buy and in what quantities.
Supply chain forecasting incorporates that data and determines how to meet the demand. This includes deciding how much to produce, when to reorder, how much to hold at each location, and what it costs to move inventory around.
A demand forecast tells a retail business that they’re likely to sell 10,000 units next quarter. A supply chain forecast takes that number and determines how much inventory the business needs and where to hold it.
It answers questions like how many units the business needs to order from their supplier, how to divide them among warehouses, and how much safety stock to carry in case demand spikes.
How supply chain forecasting works
Forecasting in supply chain management follows a repeatable cycle. Here’s a common five-step sequence a retailer might follow::
- Gather historical data. Pull sales history, inventory levels, lead times, and returns from every channel into one place. Make sure the data is accurate before moving on to the next step, or the final numbers could be off.
- Add external inputs. Consider factors like seasonality, planned promotions, market trends, and known supply constraints, which can affect the amount of supply your business needs.
- Choose a forecasting method. Match the method to the data and the time frame. A quantitative model fits a product with years of sales history, while a qualitative approach fits a new product without any historical data.
- Generate the forecast. Run the numbers to predict demand, then translate that projection into supply decisions like order quantities, reorder points, safety stock, and distribution across locations.
- Track accuracy and adjust. Compare your forecast against actual sales, measure how far off it was, and use that data to improve the next forecast.
The “Three V's” framework: Volume, variety, and volatility
Three factors shape how hard a product is to forecast, and which method works best. Considering each one before choosing your forecasting method can help you avoid a simple model when you need a more complex one, or vice versa.
- Volume: How much a product sells. Products that have steady sales generate enough data for quantitative methods to work well. Lower-volume products produce less data, and a single large order can sway the forecast.
- Variety: How many distinct products, variants, and locations a forecast has to cover. A single product type sold in one store is straightforward, but most retail brands don’t work like that. An accurate forecast has to account for hundreds of variants across multiple channels and warehouses.
- Volatility: How much demand swings and how predictably. A product with stable year-round sales has low volatility and is easier to forecast. A trend-driven or seasonal product has high volatility, so demand can jump or drop in ways that make it harder to predict.
These three dimensions play a major role in how precise any forecast can be.
A high-volume, low-variety, low-volatility product is easier to forecast with high accuracy. A low-volume, high-variety, high-volatility product is much more difficult to predict.
No prediction will ever be 100% accurate, but the goal is to consider these factors and how much they can affect forecast accuracy. Low-volume, high-variety, and high-volatility products may need a much larger margin of error than those on the opposite side of the spectrum.
Why is supply chain forecasting important?
Supply chain forecasting affects a retail brand’s ability to meet customer demand, maintain profitability, and keep inventory at the right level. Accurate predictions help retailers avoid costly mistakes like stockouts and excess inventory.
Build supply chain resilience
Forecasting helps businesses anticipate disruptions so they can build more resilient supply chains. For example, your team may have dealt with a supplier switching to a longer lead time, a seasonal port slowdown, or a regional shortage.
Building those risks into your forecast helps ensure you have stock on hand when a global event leads to shipping delays.
For example, 2025 tariff activity disrupted global trade. In McKinsey's "Supply Chain Risk Pulse 2025", 82% of companies said new tariffs affected their supply chains, with 20% to 40% of supply chain activity impacted. Every company that identified a tariff impact had already prepared or implemented countermeasures like increasing inventory (45%), dual sourcing (39%), and nearshoring suppliers (33%).
Better meet customer demand
Effective supply chain forecasting helps keep products available when customers want them.
“To deliver orders fast and inexpensively, you need to have inventory in stock,” says Kristina Lopienski, director of content marketing at ShipBob. “Tracking inventory velocity over time involves monitoring best-sellers and staying ahead of production, even as demand changes.”
By accurately predicting demand, you can:
- Maintain appropriate stock levels
- Reduce the risk of stockouts
- Improve customer satisfaction and loyalty
Optimize inventory levels
Forecasting also helps retailers maintain the right balance in inventory management. It can help them:
- Avoid understocking: Prevent lost sales and damaged customer relationships.
- Prevent overstocking: Reduce warehouse costs and avoid tying up capital in excess inventory.
- Manage product lifecycles: Account for items with short shelf lives.
ShipHero cofounder Nicholas Daniel-Richards says, “Stale inventory sits in a warehouse gathering dust and accumulating fees. The only way to salvage such situations is by selling at cost or at steep discounts, or selling in bulk to clearance houses.”
Maintain profitability
Accurate forecasting has a direct impact on a company's bottom line.
“If supply chain forecasting isn't accurate down to a couple of weeks, it can cause costly ripple effects that will zap the profitability of an entire quarter or half-year,” says Leandrew Robinson, general manager of mesh logistics at Auctane.
Accurate forecasting helps maintain profitability by:
- Reducing storage costs for excess inventory
- Minimizing lost sales due to stockouts
- Optimizing production and logistics costs
Preserve brand reputation
Supply chain forecasting can help protect a positive brand image. Brands that can't fulfill demand risk losing both immediate sales and long-term customer relationships.
When forecasting is inaccurate:
- Late deliveries can damage brand reputation..
- Stockouts during peak sales periods can frustrate customers and drive them to competitors.
- Customer acquisition costs (CAC) can increase when brands are unable to fulfill demand.
Adii Pienaar, founder of Cogsy, says that stockouts can also impact your ability to convert demand.
“Many brands go out of stock during their biggest sales of the year, so they're spending money on ads to create demand to then find themselves unable to convert that demand,” says Adii. “This drives CAC way up and negatively affects brand affinity.”
Five quantitative forecasting methods
Quantitative forecasting uses historical data to estimate future sales. These supply chain forecasting methods largely assume that the future will mirror the past. They use complex mathematical formulas typically performed by computer software.
Here’s a quick comparison of how they work and when to use them:
| Method | How it works | Accuracy | Best for |
|---|---|---|---|
| Moving average | Averages subsets of historical data, weighting all points equally | Low; ignores seasonality and trends | Low-volume items with steady sales |
| Exponential smoothing | Weights recent data more heavily; variants add trend and seasonality | Moderate; prone to lag | Short-term forecasts or nonseasonal items |
| Auto-regressive integrated moving average (ARIMA) | Analyzes time-series data to model patterns and project trends | Very high | Time frames of less than 18 months |
| Multiple aggregation prediction algorithm (MAPA) | Smooths trends across multiple aggregation levels to handle seasonality | High for seasonal cases; less proven | Seasonal items |
| Bottom-up | Builds from detailed inputs (supplier schedules, marketing plans) up to revenue | High; small errors can amplify | Scaling brands |
Here’s a closer look at each quantitative forecasting method.
1. Moving average forecasting
One of the simplest forecasting methods, moving average forecasting examines data points by creating a series of averages based on subsets of historical data.
Because it’s based on historical averages, moving average forecasting doesn’t account for whether recent data may be a better indicator of the future and deserve more weight. It also doesn’t allow for seasonality or trends. As a result, this method is best for forecasting low-volume items with steady sales.
A bookstore might use a three-month moving average to predict demand for a steady-selling book, basing each month’s forecast on sales from the previous three months. This approach wouldn’t work for seasonal items like calendars, which sell out at certain times of year.
- Pros: Easy
- Cons: Doesn’t allow for seasonality or trends
- Best for: Low-volume items
2. Exponential smoothing
Exponential smoothing analyzes historical data and gives more weight to recent observations. It’s similar to adaptive forecasting, which also accounts for seasonality.
Variations of exponential smoothing include Holt’s forecasting model (sometimes called trend-adjusted exponential smoothing or double exponential smoothing) and the Holt-Winters method (also known as triple exponential smoothing), which factor in both trends and seasonality.
For instance, a fast-fashion retailer might use exponential smoothing to forecast clothing sales because it lets the business focus on the latest trends and adjust to changing consumer preferences.
- Pros: Easy; takes historical and recent data into account
- Cons: Can be prone to lag, causing forecasts to fall behind actual demand
- Best for: Short-term forecasts or nonseasonal items
3. Auto-regressive integrated moving average (ARIMA)
Auto-regressive integrated moving average (ARIMA) analyzes time-series data based on past performance to identify patterns and predict future trends. This time-series forecasting method is one of the most accurate, although it’s best suited for time frames of 18 months or less. It can also be costly and time-consuming to use.
An ecommerce brand could use ARIMA to forecast sales based on data from the 18 months leading up to a major product launch. This could help the brand allocate marketing spend and prepare the supply chain.
- Pros: Very accurate
- Cons: Costly and time-consuming
- Best for: Time frames of less than 18 months
4. Multiple aggregation prediction algorithm (MAPA)
MAPA is designed to account for seasonality. It smooths out trends to help prevent over- or underestimating demand. Although less popular than Holt’s or Holt-Winters, research has shown MAPA performs better.
MAPA is useful for forecasting fashion sales, which may be influenced by multiple seasonal patterns like spring and summer collections, autumn collections, and cyclical trends.
- Pros: Prevents over- and underestimating demand
- Cons: Still relatively new and not as proven
- Best for: Seasonal items
5. Bottom-up forecasting
This method estimates a company’s future performance by starting with detailed data and building toward revenue projections. It considers data such as suppliers’ production schedules, key growth assumptions, and marketing plans to create a more accurate forecast than a top-down approach.
This approach can help a brand operate more strategically, for example, by ordering only stock it expects to sell and avoiding unnecessary capital tied up in excess inventory.
“Brands can then bring this forecast to their suppliers to negotiate a discounted unit price or better ongoing terms,” says Adii. “Any predictability brands can offer manufacturers becomes leverage in the conversation. This way, brands lower their cost of goods sold and spend less to make each dollar of revenue. As a result, they become more profitable without raising prices.”
- Pros: More accurate forecast than traditional top-down approach (which fails to optimize for unit economics)
- Cons: Errors at the micro level may amplify at the macro level
- Best for: Scaling brands
Four qualitative forecasting methods
Qualitative supply chain forecasting predicts future supply chain trends and demand based on expert judgment. It's an approach that uses non-numerical techniques to anticipate future supply chain needs and challenges.
Qualitative methods are also useful when there’s little to no historical data to draw from. This makes them useful for new businesses or recently launched products.
Here’s a quick-reference comparison of these four methods:
| Method | How it works | Accuracy | Best for |
|---|---|---|---|
| Historical analogy | Assumes a new product will track the sales history of a similar existing product | Poor short-term; better over the medium to long term | Similar items |
| Sales force composite | Gathers the collective judgment of experienced managers and staff | Poor to fair; carries team bias | Cases where quantitative methods aren't feasible |
| Market research | Draws on surveys, polls, and focus groups from the target demographic | Varies; strong on customer intent but time- and cost-intensive | New product launches |
| Delphi method | Aggregates anonymous expert questionnaires across rounds until consensus | High for the qualitative group; reduces bias | Long-term supply chain planning |
Here’s a closer look at each method.
1. Historical analogy forecasting
Historical analogy forecasting predicts future sales by assuming a new product will have a sales history similar to that of an existing product already sold by the business or a similar competitor. This comparative method tends to have poor accuracy in the short term, although it may be more accurate in the medium and long term.
For example, when launching a new video game, a publisher may compare it to a previous title with similar themes that was released under comparable market conditions to predict sales. This comparison may provide a good baseline estimate, but it does not account for changes in market dynamics or consumer tastes.
- Pros: May be more accurate in the medium to long term
- Cons: Poor accuracy in the short term
- Best for: Similar items
2. Sales force composite forecasting
Sometimes called “collective opinion,” this method relies on the insights and opinions of experienced managers and staff, gathered as a team exercise. Panels of this nature tend to produce forecasts with poor to fair accuracy.
When introducing a new product line, the sales team can draw on direct customer interactions to provide insights that may not appear in quantitative data alone. However, this method is subject to bias and can vary based on the sales team’s point of view.
- Pros: Easy to collect
- Cons: Poor to fair accuracy
- Best for: Cases where quantitative methods aren’t feasible
3. Market research
To gauge the potential success of an upcoming product or feature, an ecommerce business can conduct online surveys or analyze past customer feedback. Direct input from the target market can help the business tailor product offerings to better meet customer needs.
This research may include surveys, polls, or focus groups with your target demographic.
- Pros: Provides insights into your target demographic
- Cons: Can be time- and cost-intensive
- Best for: New product launches
4. The Delphi method
In this technique, individual questionnaires are sent to a panel of experts, with responses aggregated and shared with the group after each round until the panel reaches a consensus. Since the panel doesn’t collaborate, the method reduces group pressure and some forms of bias.
This is considered one of the most effective and dependable qualitative methods for long-term forecasting.
- Pros: Reduces group bias
- Cons: A lengthy process that can lead to experts dropping out
- Best for: Long-term supply chain planning
What is the best method of supply chain forecasting?
If you’re relying on Excel spreadsheets, Adii says a moving average that focuses on recent sales velocity is your best bet. But if you’re using programmatic software, time-series methodologies are the most relevant, with the most popular including ARIMA, CNN-QR, Deep-AR, and Prophet.
“Their forecasting accuracy depends on the type of retail data they’re working with,” he says. “The best option here is to compare statistical significance and confidence levels of all those algorithms and pick the strongest for your data.”
Regardless of which method of supply chain forecasting you use, there will be inherent errors because of assumptions, so it’s impossible to achieve 100% accuracy. However, short-term forecasts are often more reliable than long-term forecasts.
Many businesses use hybrid approaches that combine quantitative and qualitative models. This is particularly useful when forecasting new products, market changes, or situations where historical data is limited. Pull from the sales data you have, while also accounting for qualitative methods like team input and market research.
One limitation of qualitative methods is that they rely almost entirely on the opinions of consumers and market or industry experts, which are subjective and harder to validate than numerical data.
“The strongest method of supply chain forecasting is quantitative and trend forecasting based on hard data and analysis,” says Nicholas.
Forecast accuracy expectations and measurement
No forecast can be 100% accurate, so it’s important to set realistic expectations from the start. Forecasts for high-volume, low-volatility products can be more precise than forecasts for other products.
Every forecast will contain some error, but measuring it can help improve future predictions.
Use these three metrics:
- Forecast error: The difference between predicted and actual sales. Mean absolute percentage error (MAPE) is a common measure, expressing the average miss as a percentage so it’s comparable across products.
- Forecast bias: Whether a forecast runs consistently high or low over time. A forecast can be close on average but still run slightly low, which can drain safety stock, or slightly high, which can create excess inventory.
- Forecast accuracy: The flip side of error, often expressed as 100% minus MAPE, which shows teams how close the forecast is to actual results and gives them a measure to track over time.
Tracking bias is also important. Because no forecast will be completely accurate, businesses often carry some safety stock. But if a forecast consistently runs low, that buffer can eventually run out. Adjusting the forecast based on consistent bias helps protect against that risk.
One practical way to account for uncertainty is to forecast in ranges rather than single numbers. For example, an estimate of 10,000 units feels precise. But using a range of 8,000 to 12,000 accounts for uncertainty. The wider the range around the midpoint, the more room a business has to plan for variation.
Supply chain forecasting challenges
Even a sound method can run into obstacles. These are the challenges that most often reduce forecast accuracy.
Changing regulations and geopolitical disruptions
Events of the past few years have made it clear what supply chain experts have long known: The global logistics network is incredibly vulnerable to political instability, natural disasters, and regulatory changes, all of which are happening with increasing frequency and severity.
Adii says this has caused brands to start diversifying their supply chains by working both on- and offshore. As already mentioned with the 2025 tariffs, these disruptions need to be built into your supply chain forecasting process so you can account for the unexpected.
“Building a supply chain to meet decentralized demand will be key to growth,” he says, noting that many brands don’t sell only on Shopify. They may also sell products on marketplaces such as Amazon and Etsy, and natively on social media platforms.
“There will be a shift from ‘supply chain management’ to ‘demand chain management,’” Adii says, adding that Cogsy is currently building a tool to give manufacturers more visibility and predictability in how a brand generates demand and sales.
Product returns and reverse logistics
Free returns are now considered a cost of doing business, and they’ve also changed how customers shop. It’s not unusual for online shoppers to order multiple sizes, colors, or products, find the right fit, and then return the rest.
NRF’s "2025 Retail Returns Landscape" found that total returns were expected to hit nearly $850 billion in value in 2025 alone. Making returns easy is good customer service, but it can complicate supply forecasting. The percentage of products returned can vary widely based on the products a business sells and their seasonality.
Track your brand’s typical return rate so you can incorporate it into your supply chain forecasting. When products are returned in resellable condition, businesses need less new inventory on hand.
Trends and changing demand patterns
Trends and fads come and go, and without sufficient stock, a business may miss out on a surge in demand.
For ecommerce merchants with brick-and-mortar locations, managing these shifts can be even more complex, as customers can suddenly change the channels they shop on, making it difficult to predict where to stock inventory. Economic fluctuations and seasonal trends must also be taken into account when forecasting to avoid misalignment between supply and demand.
Matt Warren, CEO of Veeqo, a company that helps ecommerce merchants manage omnichannel inventory management, says this is why retailers are increasingly turning to a hybrid online/offline approach. He cites the case of one of Veeqo’s clients, a large US fashion retailer with a big physical retail footprint.
“They used Veeqo to turn each of their stores into a mini fulfillment location, allowing them to optimize delivery times for online customers,” Matt says. “They can also seamlessly marry stock level data with all their online/offline sales data, which enables a more sophisticated demand forecast. It’s the kind of innovative, hybrid online/offline approach to commerce that the industry has been talking about for a while.”
Seasonality and event-driven demand
“Not factoring in seasonality and current events is one of the biggest mistakes I see ecommerce merchants making when it comes to supply chain forecasting,” says Leandrew from Acutane. “It’s hard to react to a booming holiday sales period a few weeks before.”
Looking at past seasonality can help predict the future, but there are predictable periods of higher demand. The end-of-year holiday season , for example, has made up nearly 20% of all annual retail revenue since 2019.
Holiday shopping revenue is only growing, with the NRF’s 2025 holiday projection exceeding over $1 trillion for the first time ever.
Supplier and manufacturer lead-time variability
Prior to founding Veeqo, Matt ran an online luxury watch retailer. His experience taught him that predicting demand was only ever half the battle.
“Each supplier, and sometimes each individual SKU, needs a different lead time,” he says.
It’s important to take into account warehouse and shipping lead times, which may be affected by overseas holidays.
For example, Chinese New Year may slow fulfillment from China, while other holiday peaks may cause delays or congestion at ports, slowing deliveries. This is where building strong relationships with your suppliers can be key.
Siloed data across systems and channels
Matt also says that siloed data can hurt the accuracy of supply chain forecasting.
“Too many merchants use different software for different parts of their business. Add in working across multiple websites, marketplaces, and fulfillment locations and you can see where the headache comes from,” he says. “It’s worth either investing in all-in-one software to unify your sales and inventory data, or putting the hard yards in to pull it all together via spreadsheets.”
Skewed data and data quality issues
“Brands can’t create accurate forecasts with skewed data,” says Adii. “Merchants can infuse real-time data into their forecasting process to have a better idea of where they stand and where they can expect to be in the future. With better data in hand, they can chart a path that ensures they get there."
Adii says that to improve data accuracy, merchants need to avoid common inventory forecasting mistakes by:
- Shortening the time it takes to update data in their systems
- Avoiding changing products’ SKU IDs
- Taking inventory stock levels into account when performing demand forecasting
- Identifying limited edition products to interpret their data accordingly
- Linking demand for all versions of the same product
- Analyzing each channel separately
Historical data limitations in high-growth environments
“Quantitative methods that rely on historical data only are not reliable in fast and hyper-growth environments where most of our ecommerce customers are operating,” says Kristjan Vilosius, CEO and cofounder of Katana, a provider of supply management software for makers and manufacturers. He says that people are better at making sense of events after they’ve already happened.
“Investing in tracking and early warning systems and finding ways to make the supply chain management leaner and less dependent on stock levels is often a better investment, rather than trying to find the best forecasting methods,” he says.
Adii agrees, saying that many brands report struggling with the time it takes to create these processes. This can cause delays in taking action, hindering a brand’s ability to capitalize on opportunities and mitigate risks.
“The challenge with using time-series forecasting methodologies is that historical data often lags, especially in high-growth environments,” he says. “At Cogsy, we believe in additional future plans, such as marketing events, and assumptions or growth modeling, on top of a baseline forecast that was created by analyzing historical data. This creates the most holistic perspective on future demand.”
Modern forecasting technologies
Modern technology can help make forecasting more accurate. These modern supply chain planning and forecasting tools help retail brands make better decisions based on their data.
AI and machine learning for demand prediction
Traditional statistical methods like moving averages and ARIMA rely on historical sales. Machine learning models bring in more factors, learning demand patterns and finding relationships across additional variables like price, weather, promotions, web traffic, competitor activity, and macroeconomic indicators.
Some applications of AI and machine learning include:
- Dynamic demand sensing: Adjusts near-term forecasts as real-time activity (like current sales, web traffic, and cart activity) comes in, rather than waiting for the next planning cycle
- Promotional impact prediction: Estimates how much a specific discount or campaign will lift demand, so stock is ready for the spike
- New product forecasting: Predicts demand for items with no sales history by learning from the launch curves of comparable products
- Anomaly detection: Flags sudden, unexplained changes in demand, whether a spike or a drop, early enough for a business to react before they affect the forecast
Real-time visibility and control tower operations
A supply chain control tower is a central dashboard that pulls data from across the supply chain, including suppliers, carriers, warehouses, and stores, into one live view. Instead of checking separate systems to track orders and inventory, a business can see the whole network in one place.
That visibility supports supply chain planning and forecasting in a few ways:
- Real-time shipment monitoring: Tracks orders in transit so a business knows where inventory is
- In-transit inventory visibility: Counts stock already on the way as part of available supply, improving reorder timing
- Multi-location inventory coordination: Shows stock levels across every warehouse and store at once so inventory can be moved where it’s needed
- Earlier disruption detection: Flags supplier delay or logistics bottleneck while there's still time to reroute or reorder
Advanced analytics and predictive modeling
Modern supply chains generate more data than a team can analyze manually, and advanced analytics helps turn that volume into forecasts. Predictive models draw on market trends, economic indicators, and historical sales to project demand, so a business can adjust production schedules, inventory levels, and shipping logistics to match.
Next steps to take with supply chain forecasting
When it comes to determining the best forecasting method to use, you’ll need to consider a number of factors:
- What is the lifespan of the products? Are they perishable or can they remain on shelves in a warehouse indefinitely?
- How often are the products sold?
- How are sales affected by seasons, months, and special sales events?
- What are the warehouse fees associated with a particular item?
- By what date do you need to reorder inventory for each product?
- What are your standard reorder points?
- Do you require safety stock?
“Supply chain forecasting shouldn’t be guesswork, but that’s the reality for many ecommerce merchants today. Online merchants need to understand the difference that real-time data and app integrations could make on their inventory replenishment capabilities,” says Nicholas.
“It’s the difference between being in-stock or out-of-stock, it’s the difference between having stale inventory or not, and it’s the difference between running a successful supply chain or not,” he adds.
Start with a simple approach and iterate from there. The more forecasting you do, the easier it becomes to decide which method works best for your business, and the more accurate your predictions can become.
Working with supply chain, inventory, shipping, and fulfillment experts can help your business adapt to disruptions and simplify this process.
A full logistics service provider, the Shopify Fulfillment Network can help you build a resilient supply chain, with a vast network of strategically located fulfillment centers nationwide.
Veeqo, Katana, ShipHero, ShipBob, and ShipStation are just some of Shopify's management and shipping partners who can help.
Supply chain forecasting FAQ
Why is forecasting important in supply chains?
Forecasting allows ecommerce merchants to ensure they have the right amount of product in stock, prevent back orders and dead stock, and improve customer service. Done properly, merchants can fill orders on time, avoid unnecessary expenses and tied-up capital, keep customers happy, and be prepared for potential disruptions in the supply chain.
What is the best method of forecasting in supply chains?
Quantitative supply chain forecasting methods tend to be more accurate than qualitative methods, which are subjective and based on the opinions of consumers and market or industry experts.
How accurate should supply chain forecasts be?
No business will achieve a 100% accurate supply chain forecast. The closer a forecast comes, the better. A good supply chain forecast should achieve 75%–85% accuracy.
What tools help with supply chain forecasting?
Tools like Veeqo, Katana, ShipHero, ShipBob, and ShipStation help with supply chain forecasting. A good fulfillment center can support forecast accuracy, and the Shopify Fulfillment Network is a great place to start.
How does AI improve supply chain forecasting?
AI helps retailers analyze historical data to generate predictive insights that improve forecasting.
