Supply chain forecasting and weather forecasts have more than one thing in common.
Both make predictions based on past and present information. Both use hard data, and sometimes intuition, to varying degrees of accuracy. And in both cases, something that didn’t appear on the radar can leave you feeling caught out and unprepared—whether that means without an umbrella in the rain or without the inventory needed to fill an order.
Understanding how to properly forecast your supply chain needs is critical to your ecommerce store’s success. Getting demand planning right can lead to better supplier relationships, increased customer satisfaction, and more capital to grow and scale your business.
Learn from supply chain management, fulfillment, and shipping experts to find out how supply chain forecasting can make or break your store’s next quarter—and what you can do to get and stay ahead.
What is supply chain forecasting?
Supply chain forecasting consists of looking at past data about product demand to inform business decisions around planning, budgeting, and stock inventory. It can help a business prevent loss, especially during the holidays.
As its name implies, supply chain forecasting is based largely on analyzing supply. But customer demand also plays into it. Factors such as seasons, supply chain trends, the economy, and global events can all lead to spikes or sluggish sales, which can affect inventory control.
Why is supply chain forecasting important?
Supply chain forecasting directly impacts your ability to meet customer demand, maintain profitability, and optimize inventory levels. Accurate predictions help you avoid costly mistakes such as stockouts and overstock situations.
Other benefits of supply chain forecasting include:
Better meet customer demand
Effective supply chain forecasting ensures that products are available when customers want them. As Kristina Lopienski, director of content marketing at ShipBob, explains, "To deliver orders fast and inexpensively, you need to have inventory in stock. 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 strike the right balance in your inventory management:
- Avoid understocking: Prevents lost sales and damaged customer relationships
- Prevent overstocking: Reduces warehouse costs and avoids tying up capital in excess inventory
- Manage product lifecycles: Particularly important for items with short shelf lives
Nicholas Daniel-Richards, cofounder of ShipHero, warns about the consequences of poor forecasting: "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. Leandrew Robinson, general manager of mesh logistics at Auctane, emphasizes this point: "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."
Proper 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 plays a big role in maintaining a positive brand image. 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 due to inability to fulfill demand.
Adii Pienaar, founder of Cogsy, notes: "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. This drives CAC way up and negatively affects brand affinity."
Five quantitative forecasting methods
Quantitative forecasting uses historical data to estimate future sales. These methods work largely on the assumption that the future will mirror the past, and involve complex mathematical formulas, which are typically performed by computer software.
1. Moving average forecasting
One of the simplest methods for forecasting, this method examines data points by creating a series of averages based on subsets of historical data.
As it’s based on historical averages, moving average forecasting doesn’t take into account that recent data may be a better indicator of the future and should be given more weight. It also doesn’t allow for seasonality or trends. As a result, this method is best for inventory control for low-volume items.
A bookstore might use a three-month moving average for predicting demand for a steady-selling book, basing each month’s forecast on sales from the previous three months. This wouldn’t work for seasonal items like calendars, which sell out at certain times.
- Pros: Easy
- Cons: Doesn’t allow for seasonality or trends
- Best for: Low-volume items
2. Exponential smoothing
Picking up where average forecasting leaves off, this method analyzes historical data, but gives more weight to recent observations. It’s similar to adaptive forecasting, which takes seasonality into account.
Variations on 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 them focus on the latest trends, adapting quickly to consumer preferences.
- Pros: Easy; takes historical and recent data into account
- Cons: Can be prone to lag, causing forecasts to be behind
- 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 better understand the data set, or to predict future trends. Costly and time-consuming, this time-series forecasting method is also one of the most accurate, although it’s best suited for forecasting within time frames of 18 months or less.
An ecommerce brand could use ARIMA to forecast sales using data from the 18 months leading up to a major product launch. This could allow the brand to allocate marketing spend and prepare the supply chain.
- Pros: Very accurate
- Cons: Costly, time-consuming
- Best for: Time frames of less than 18 months
4. Multiple aggregation prediction algorithm (MAPA)
A relatively new method that’s specifically designed for seasonality, MAPA smooths out trends to help prevent over- or underestimating demand. Although not nearly as popular as Holt’s or Holt-Winters, research has shown MAPA performs better.
With its ability to handle seasonality, 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
- Cons: Still relatively new, not as proven
- Best for: Seasonal items
5. Bottom-up forecasting
This method estimates a company’s future performance by starting with detailed operational 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 compared to a top-down approach.
This approach can help a brand operate more strategically, such as by ordering only stock that will actually sell, thereby preventing the unnecessary tying up of capital.
“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 compared to traditional top-down approach (which fails to optimize for unit economics)
- Cons: Errors at the micro level may become amplified as they approach the macro level
- Best for: Scaling merchants
Four qualitative forecasting methods
Qualitative supply chain forecasting refers to predicting future supply chain trends and demands that rely on expert judgment. It's an approach to forecasting that uses non-numerical techniques to anticipate future supply chain needs and challenges.
1. Historical analogy forecasting
Historical analogy forecasting predicts future sales by assuming a new product will have a sales history parallel to an existing product already sold by you or a similar competitor. A comparative analysis, it has 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 and released under comparable market conditions to predict sales. It may provide a good baseline estimate, but it doesn’t take into account 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 personal insights and opinions of experienced managers and staff, gathered as a team exercise. Panels of this nature typically have a poor to fair accuracy.
When introducing a new product line, he sales team can draw upon direct customer interaction to provide insights that are not obvious from quantitative data alone. However, this method is subject to biases and can vary significantly based on the sales team’s point of view.
- Pros: Fairly easy to collect
- Cons: Poor to fair accuracy
- Best for: When 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 previous customer feedback. The target market’s direct input can help you tailor product offerings to more effectively meet their needs.
This research may include surveying, polling, or conducting focus groups for your target demographic.
- Pros: Provides insights into your target demographic
- Cons: Can be time- and/or money- 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 they reach a consensus. Since the panel doesn’t collaborate, bias is eliminated from the process.
This is considered one of the most effective and dependable qualitative methods for long-term forecasting.
- Pros: Unbiased
- Cons: Slow, long process, leading to the risk of 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 using a moving average focusing 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 ones being 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 what method of supply chain forecasting you use, there will be inherent errors due to assumptions, so it’s impossible to achieve 100% accuracy—although you’ll generally find that much like with the weather, short-term forecasts are more accurate than long-term forecasts.
Our experts agreed on one thing, though: Qualitative methods rely on the opinions of consumers and market or industry experts, which are ultimately subjective and less accurate.
“The strongest method of supply chain forecasting is quantitative and trend forecasting based on hard data and analysis,” says Nicholas.
Supply chain forecasting challenges
Changing regulations
Events of the past few years have made common knowledge 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 now happening with increasing frequency and severity.
Adii says this has caused brands to start diversifying their supply chains by working both on- and offshore.
“Building a supply chain to meet decentralized demand will be key to growth,” he says, noting that many merchants 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 predicts, adding that Cogsy is currently building a tool to give manufacturers more visibility and predictability in how the brand generates demand and sales.
Product returns
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.
Between Thanksgiving and January alone, millions of returns are made every year, amounting to over $171 billion in goods. Making returns easy is good customer service, but it can complicate supply forecasting. The percentage of your products being returned can vary widely based on the products you sell and their seasonality.
Trends and changing demand patterns
Trends and fads come and go—and without sufficient stock, you can miss out on a surge in demand altogether.
For ecommerce merchants with brick-and-mortar locations, managing these demands 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—which helps support ecommerce merchants in their 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 of products
“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. “It’s hard to react to a booming holiday sales period a few weeks before.”
Supplier or manufacturer lead time
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.
In addition, 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 fulfillments from China, while other holiday peaks may cause delays or congestion at ports, slowing deliveries. This is where building strong relationships and communication with your suppliers becomes vital.
Siloed data
Matt also cautions that siloed data can hurt the accuracy of a supply chain forecasting method.
“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
“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 in order for data to be accurate, 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 completing 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 isn’t enough
“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, which offers supply management software for makers and manufacturers. He makes the point that we’re 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, noting that many brands report struggling with the time it takes to create operational plans—which 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.”
Supply chain forecasting trends
AI-assisted management
This trend involves the use of AI to actively assist in decision-making, not just by analyzing large datasets but also by learning from past decisions to improve future outcomes.
Gartner refers to this as “Actionable AI,” defined as the use of AI to assist with decision-making based on analysis of past problems and solutions combined with conditions in real time. In this way, AI can support supply chain operations in a more nuanced, context-aware way, as a co-pilot in decision-making instead of just a data-analysis tool.
Having “control tower” visibility
AI is being used to improve visibility across the supply chain, acting as a “control tower” for operations, as described by KPMG Global.
Real-time visibility means being able to see beyond your own company and into your broader network of suppliers, partners, and logistics. Through predictive foresight and agility, AI-driven visibility tools can help you anticipate problems, respond to changes quickly, and collaborate better.
With these tools, you could track shipments, predict potential delays, and provide alternative logistics solutions proactively—plus facilitate collaboration with suppliers by sharing demand forecasts and inventory levels, enhancing overall efficiency.
Advanced analytics
IDC’s supply chain survey highlighted advanced analytics and AI as the most important technologies for supply chains over the next three years. Modern supply chains are complex and generate a lot of data. AI can process, analyze, and extract meaningful insights from that data.
Artificial intelligence-driven demand forecasting tools could be used to predict future sales patterns based on market trends, economic indicators, and historical sales data, so you can adjust production schedules, inventory levels, and shipping logistics accordingly.
Next steps to take with supply chain forecasting
When it comes to determining the best forecasting methods 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 different 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.
Working with supply chain, inventory, shipping, and fulfillment experts can help keep you safe in stormy weather 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 backorders and dead stock in warehouses, and improve customer service. Done properly, merchants can fill orders on time, avoid unnecessary expenses or tied-up capital, keep customers happy, and be prepared for potential clogs in the supply chain.
How do you forecast supply and demand?
Supply and demand can be forecast using qualitative or quantitative methods, the latter of which are tied to historical data. With both, it’s impossible to achieve 100% accuracy, but quantitative methods tend to be more accurate.
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.
