Supply Chain Forecasting Methods: Preventing Storms and Predicting Trends


Illustration by Diego Blanco

Supply chain forecasting and weather forecasts have more than one thing in common.

 Both make predictions based on past and present information, and both use hard data, and, at times, 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’s without an umbrella or without the inventory needed to fill an order.

Understanding how to properly forecast your supply chain needs is critical to ensuring your ecommerce store’s success. Getting it right can lead to better supplier relationships, increased customer satisfaction, and more capital to grow and scale your business.

We spoke with supply chain management, fulfillment, and shipping experts to find out how supply chain forecasting can make or break your store’s next quarter—and the best methods for doing it.

What is supply chain forecasting? 

Supply chain forecasting is looking at past data about product demand to help make business decisions around planning, budgeting, and stock inventory. It can help a business from experiencing a loss, especially during the holidays.

“To deliver orders fast and inexpensively, you need to have inventory in stock,” says Kristina Lopienski, director of content marketing at ShipBob, a global logistics platform that fulfills ecommerce orders for direct-to-consumer brands.

Tracking inventory velocity over time involves being able to monitor bestsellers and stay ahead of production—even as demand changes.” 

Key factors may include:

  • Turnover rate of products
  • Lead times needed for each supplier or product
  • Freight transit times
  • Warehouse receiving times
  • The cost of storage 

As its name implies, supply chain forecasting is based largely on analyzing supply. But 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.  

Table of Contents:

  1. Why is supply chain forecasting important?
  2. 5 forecasting methods used in supply chains 
  3. Qualitative supply chain forecasting methods
  4. What is the best method of supply chain forecasting?
  5. What makes supply chain forecasting difficult?
  6. Next steps to take with supply chain forecasting

Why is supply chain forecasting important?

You don’t have to be a regular reader of the Journal of Supply Chain Management to know that timing means everything.

“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 with shipping and software experts Auctane (which includes ShipStation, ShippingEasy, ShipWorks, and ShipEngine).

Products arriving late to your warehouse or shipping center won’t make it to customers in time in an era when 67% of US consumers expect same-, next-, or two-day delivery. This doesn’t just damage your brand’s reputation, it leads to a loss in sales and digs into customer acquisition costs (CAC). If you don’t have it in stock or it’s on backorder, your customers will go elsewhere.

“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,” says Adii Pienaar, founder ofCogsy, a forecasting operations platform for DTC companies. “Fast-growing brands tend to duct tape their operational issues as they arise, but patching up problems won’t scale.”

On the flip side, inventory arriving before you need it can lead to increased warehouse costs, or losses if products have a short shelf life. It also ties up capital, which could otherwise be used to scale or improve different aspects of your business.

And if you order the wrong amount or the wrong products? You may be left with deadstock.

Stale inventory sits in a warehouse gathering dust and accumulating fees,” says Nicholas Daniel-Richards, co-founder of ShipHero, which offers warehouse management software and shipping solutions. “The only way to salvage such situations is by selling at-cost or at steep discounts or selling in bulk to clearance houses.”

5 forecasting methods used in supply chains 

Quantitative projective forecasting methods use historical data to estimate future sales.

Working largely on the assumption that the future will mirror the past, these involve complex mathematical formulas, are typically performed by computer software, and may include (but are not limited to):

  1. Moving average forecasting
  2. Exponential smoothing
  3. Auto-regressive integrated moving average
  4. Multiple aggregation prediction algorithm
  5. Bottom-up forecasting

1. Moving average forecasting

  • Pros: Easy
  • Cons: Doesn't allow for seasonality or trends
  • Best for: Low-volume items

One of the simplest methods for forecasting, this method examines data points by creating an average series of subsets from complete 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.

2. Exponential smoothing

  • 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 non-seasonal items 

Picking up where average forecasting leaves off, this method takes into account historical data, but gives more weight to recent observations. It’s similar to adaptive forecasting, which takes into account seasonality.

Variations on exponential smoothing including Holt’s Forecasting Model (sometimes called trend-adjusted exponential smoothing or double exponential smoothing) and Holt-Winters Method (also known as triple exponential smoothing), which factors in both trends and seasonality.

3. Auto-regressive integrated moving average (ARIMA)

  • Pros: Very accurate
  • Cons: Costly; time-consuming
  • Best for: Time frames of less than 18 months

One method that fits within the ARIMA category is Box-Jenkins. Costly and time-consuming, this time series forecasting method is also one of the most accurate, although it’s best suited for forecasting within timeframes of 18 months or less.

4. Multiple aggregation prediction algorithm (MAPA)

  • Pros: Prevents over and under estimating
  • Cons: Still relatively new; not as proven
  • Best for: Seasonal items

 A relatively new method that’s specifically designed for seasonality, MAPA smooths out trends to help prevent over or under estimating demand. Although not nearly as popular as Holt’s or Holt-Winters, research has shown it performs better.

5. Bottom-up forecasting

  • 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

This method estimates a company’s future performance and how it will work “up” to revenue. It considers a brand’s suppliers’ production schedules, then layers key growth assumptions and scheduled marketing events onto this solid foundation. This method results in a more accurate forecast compared to a top-down approach, with brands only ordering stock that will actually sell, in turn 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 Pienaar.

“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.”

Qualitative supply chain forecasting methods

In the case of new product or business launches when data is nonexistent or hard to come by, it can be difficult to make supply chain forecasts. There’s also the case of historical data becoming irrelevant or less accurate, such as when a global pandemic has skewed historical data. That’s where qualitative forecasting comes in. 

Methods include:

  1. Historical analogies
  2. Sales force composition
  3. Market research
  4. The Delphi method

1. Historical analogies

  • Pros: May be more accurate in the mid- to long term
  • Cons: Poor accuracy in the short term
  • Best for: Similar items

Historical analogy forecasting predicts future sales by assuming a new product will have a sales history parallel to a present product (either one sold by you, or a product sold by a similar competitor). A comparative analysis, it has poor accuracy in the short term, although may be more accurate in the medium and long term.

2. Sales force composition

  • Pros: Fairly easy to collect
  • Cons: Poor to fair accuracy
  • Best for: When quantitative methods aren’t feasible

Sometimes called “collective opinion,” this method relies on the personal insights and opinions of experienced managers and staff, gathered as a team exercise. According to Harvard Business Review, panels of this nature typically have a poor to fair accuracy.

3. Market research

  • Pros: Provides insights into your target demographic
  • Cons: Can be time and/or money intensive

 This research may include surveying, polling, or using focus groups of your target demographic.  

4. The Delphi method

  • Pros: Unbiased
  • Cons: Reliability is uncertain

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.

What is the best method of supply chain forecasting?

If you’re relying on spreadsheets, Pienaar says that using 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 ones being ARIMA, CNN-QR, Deep-AR, and Prophet.

“Their accuracy depends on the type of retail data they’re forecasting,” he says. “The best option here is to compare statistical significance and confidence levels of all those algorithms and pick the one that’s strongest for your data.”

But 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 the weather, short-term forecasts are more accurate than long-term forecasts.

 There is one thing our experts agreed on, 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 Daniel-Richards. 

What makes supply chain forecasting difficult? 

Supply chain shortages and changing regulations

 COVID-19 wreaked havoc on supply chain forecasting systems, in more ways than one. This probably isn’t news, but just in case you’ve been off the grid for the past two years and have only just emerged from the woods (lucky you), we’ll get you up to speed.

 At the same time that online shopping became everyone’s favorite lockdown activity (by May 2020, online orders had nearly doubled what they were the previous year), supply chains were crippled.

Ecommerce merchants sourcing products or supplies from China saw lead times increase from mere days to entire months. Bottlenecks at borders, ports, and airports were created by staffing issues and new health regulations, alongside soaring shipping costs, which continued well into 2021 and 2022.

 Much less sudden was the long-coming Brexit, which impacted cross-border sales in the EU and UK, as suppliers changed their models to comply with new regulations. Then, more recently, sanctions stemming from the Russian invasion of Ukraine have resulted in regional manufacturers being unable to source raw materials that typically come from Russia, Belarus, and Ukraine.

 Events of the past few years have made common knowledge what supply chain experts have long known: the global logistic network is incredibly vulnerable to political instability, natural disasters, and regulatory changes, all of which are now happening with increasing frequency and severity. For example, significant disruptions to manufacturing production occur every 3.7 years on average, according to research by McKinsey.

 Pienaar says this has caused brands to start diversifying their supply chains by working both on- and off-shore. “Building a supply chain to meet decentralized demand will be key to growth,” he says, noting that many merchants don’t just sell directly on Shopify—they also sell products on marketplaces such as Amazon and Etsy, natively on platforms such Facebook, and will soon be selling in the metaverse.

 “There will be a shift from ‘supply chain management’ to ‘demand chain management,’” he predicts, adding that Cogsyis currently building a tool to give manufacturers more visibility and predictability in how the brand generates demand and sells.

“Brands will have to forge better relationships with their suppliers to ensure manufacturing availability and favorable economic units.”

Product returns

 Free returns are now considered a cost of doing business, but 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 $100 billion in goods. It’s good customer service, but it can complicate supply forecasting.

 “The percentage of products being returned and the reasons those returns happen can vary widely based on the product category you sell and seasonality,” says Karen Fitzgerald, senior marketing manager at Returnly, a provider of digital return experiences for direct-to-consumer brands. 

 According to Alex McEachern, marketing manager at Loop Returns, an app that allows Shopify brands to automate the returns process, the highest months for returns are December and January, while the lowest month is February. 

“Many brands forget to include returns when forecasting inventory,” he says. “It’s important to have an idea of what percentage of returns are able to be restocked and resold. 

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 bricks-and-mortar locations, managing these demands can be even more complex, as customers will change channels where they shop, making it difficult to predict where to stock inventory.  

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-fulfilment location, allowing them to optimize delivery times for online customers. 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,” he says.

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 Robinson. “It’s hard to react to a booming holiday sales period a few weeks before.”

Supplier or manufacturer lead time

Prior to founding Veeqo, Warren 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 to recognizing that different products require different lead times, it’s important to take into account warehouse and shipping lead times, which may be affected by overseas holidays.

Chinese New Year may slow fulfillments from China, while holiday peaks may cause peak delays or congestion at ports, slowing deliveries. This is where building strong relationships and communications with your suppliers becomes vital. 

Siloed data

Warren also cautions that siloed data can affect 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 fulfilment 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 Pienaar. “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." 

Pienaar says that in order for data to be accurate, merchants need to avoid common inventory forecasting mistakes by:

  1. Shortening the time it takes to update data in their systems
  2. Avoiding changing SKU IDs for the same product
  3. Taking inventory stock levels into account when completing demand forecasting
  4. Identifying limited edition products to interpret their data accordingly
  5. Linking demand for all versions of the same product
  6. 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 co-founder of Katana, which offers supply management software for makers and manufacturers. He points out that we’re better at making sense of events after they’ve 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.

Pienaar 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.”

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 Daniel-Richards.

“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.”

“Supply chain forecasting shouldn’t be guesswork.”

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.

Cogsy, Veeqo, Katana, ShipHero, ShipBob and ShipStation are just some of Shopify Plus’ management and shipping partners who can help.

Where to learn more:

Supply Chain FAQ

Why is forecasting important in supply chains?

Forecasting allows ecommerce merchants to ensure they have the right amount of product in stock, to prevent backorders and dead stock in warehouses, and to improve customer service. Done properly, merchants will be able to 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 performed 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 chain? 

Quantitative supply chain forecasting methods tend to be more accurate than qualitative methods, which are subjective based on the opinions of consumers and market or industry experts.

About the author

Jessica Wynne Lockhart

Jess is an award-winning Canadian freelance journalist and editor currently based in Australia. Her writing has appeared in ChatelaineenRouteThe Globe & Mail, and The Toronto Star, amongst others. Learn more about her work at