Shopify

Supply Chain Forecasting Methods: Preventing Storms And Predicting Trends

supply-chain-forecasting-methods:-preventing-storms-and-predicting-trends

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

The process of making predictions based on past and present information, they 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.

Table of Contents:

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

What is forecasting in supply chain management? 

Regardless of whether you’re ordering whole products or raw goods that need to be assembled, forecasting in supply chain management is the act of determining when suppliers will have a product ready and at what point it should be ordered.  

“To deliver orders fast and inexpensively, you need to have inventory in stock. Tracking inventory velocity over time involves being able to monitor best-sellers and stay ahead of production—even as demand changes,” says Kristina Lopienski, director of content marketing at ShipBob, a global logistics platform that fulfills ecommerce orders for direct-to-consumer brands.

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, 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?

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).

“If supply chain forecasting isn’t accurate down to a couple of weeks, it can cause costly ripple effects”

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

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

Quantitative 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

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.

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.

Auto-regressive integrated moving average (ARIMA)

  • Pros: Very accurate
  • Cons: Costly; time-consuming
  • Best for: Timeframes of <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.

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 or Holt-Winters, research has shown it performs better.

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

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.

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.

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.  

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?

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. He adds, joking: “The weakest is qualitative and intuitive forecasting based on Mercury being in retrograde.”

What makes supply chain forecasting difficult? 

Changing regulations

COVID-19 has 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 last 12 months and have only just emerged from the woods (lucky you), we’ll catch 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.

But that’s just one example of how a sudden change in legislation can affect supply chains. Much less sudden was the long-coming Brexit. It’s predicted that Brexit will significantly impact cross-border sales in the EU and UK, as suppliers change their models to comply with new regulations.

“Analysts expect that due to Brexit changes in 2021, cross-border ecommerce in the UK will decline year-over-year. This is why it is important to adopt a UK-specific ecommerce fulfillment strategy,” says ShipBob’s Lopienski.

“It’s important to adopt a UK-specific ecommerce fulfillment strategy”

For now, ShipBob reports that the four leading carriers in the United States have achieved their pre-COVID average delivery times, despite the persistent challenges. But even with vaccines rolling out, future pandemics are likely. Even more likely are changes to the supply chain caused by political or economic instability, and natural disasters.

That’s why ShipHero has hopes for President Joe Biden’s Buy American campaign.

“We anticipate it will bring business to US-based manufacturers, and in doing so will allow supply chains to focus on eco-friendly transportation options like ground and rail freight,” says Daniel-Richards. Using domestic markets may have higher upfront cost but it helps to reduce risks in the supply chain, which can have dividends in the long-term.

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, colours 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 is 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.”

“Many brands forget to include returns when forecasting inventory”

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 omni-channel 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 American 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.”

“Not factoring in seasonality and current events is one of the biggest mistakes I see… [in] supply chain forecasting”

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.” 

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 e-commerce 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 suggests.

“Investing in ways to make the supply chain management leaner is often a better investment than trying to find the best forecasting methods”

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. 

Veeqo, Katana, ShipHero, ShipBob and ShipStation are just some of Shopify Plus’ Management and Shipping partners who can help.

Where to learn more

Special thanks to our friends at Shopify Plus for their insights on this topic.
I'm also on