Demand planning can sometimes feel like taking a shot in the dark. Maybe you feel like you’re relying on guesswork to guide your plans and simply hoping for the best when making your inventory forecasts. After all, you’re literally trying to predict the future.
But when your demand planning misses the mark, it’s detrimental to your direct-to-consumer (DTC) brand. Order too little inventory, and you’re susceptible to stockouts. Order too much stock, and you could be overstocked with SKUs that don’t sell. In either scenario, your profits suffer.
Consider this: On average, global ecommerce sales bring in about $4.2 trillion per year, but stockouts cost retailers an astounding $1.1 trillion per year. That’s a whopping 25% of global revenue lost annually due to out-of-stock products.
Fortunately, demand planning accuracy can save your brand from being part of this statistic.
Demand planning forecast accuracy means correctly predicting how many products customers will want over a specific period. And creating accurate demand planning forecasts relies on sales forecasting, inventory management, and supply chain management to generate precise predictions.
On that note, demand planning forecasts have to be both accurate and precise:
*Actual demand represents what customers actually ordered (without any adjustment for product availability).
If your forecasts are accurate but not precise—or precise but not accurate—you risk stockouts and overstocks galore (both of which are terrible for your bottom line).
On the other hand, accurate and precise forecasts ensure you have enough inventory available. That way, you can always meet customer demand.
We know that crunching numbers for forecasts can get boring (we’re stifling a yawn ourselves). But demand planning accuracy is crucial to carrying optimal inventory levels at all your locations. It also helps you prepare for demand fluctuations and reduces your total operational costs, among many other benefits.
Let’s face it: Stockouts are a pain for retailers and consumers. Not only do they frustrate your customers, but they also cost your brand a lot of money in lost sales.
The good news is that accurate demand planning helps you maintain order accuracy by predicting how much inventory you need on hand to keep up with future sales. That way, you order the right kinds (and quantities) of stock at precisely the right time, so you never have to turn anyone away.
On the flip side, accurate demand planning ensures you don’t overstock either—which can be just as costly as under-stocking. After all, excess inventory racks up tons of unnecessary carrying costs the longer those products go unsold.
The goal is to order exactly what you need. No more, no less. As daunting as that might sound, demand planning can make it happen.
Accurate demand planning aligns all your sales and forecast data, informing your inventory replenishment needs. In turn, this streamlines your restocking process – all while lowering your risk of overstock and dead stock.
Fluctuations in demand come with the territory when you’re a DTC retailer. You’ll likely encounter demand spikes and surges in sales, particularly around Black Friday-Cyber Monday and the weeks leading up to Christmas.
For instance, a lot of retailers stocked out during the 2020 holiday season due to supply chain issues and increased demand for consumer goods.
So, some brands got more aggressive with their holiday forecasts to avoid stocking out again during the 2021 holiday season. That way, they could (hopefully) have enough stock to meet rising demand. But their approach backfired as a return to normal shopping habits (coupled with inflation) caused a big drop in demand.
The result? Brands like Target were left overstocked, needed to offer deep discounts, and struggled to maintain their profit margins.
Our point is that you could end up in a similar situation when you’re unprepared for demand fluctuations and constantly reacting to the demand chain.
How do you avoid this kind of predicament? Simple—you anticipate changes in demand rather than reacting once they’ve already happened (like trying to offload tons of extra stock).
Accurate demand planning can predict both short-term trends and long-term stock needs. Moreover, accurate plans protect your bottom line from seasonal stockouts and keep your cash flow steady during the year’s busiest, most profitable times.
That’s because demand planning accounts for seasonality, market changes, DTC trends, and historical data so you can better understand where things are headed.
It’s never a bad time to reduce your operational costs, right? Well, accurate demand planning can help you cut down on overhead expenses in a big way.
You already know that demand planning helps you hit optimal inventory levels. But let’s connect the dots even further: Optimal inventory translates to less safety stock, higher inventory turnover, and decreased holding costs.
Think about it: When you always have the products your customers want in stock, you’re not losing out on sales. Meaning, your merchandise moves off your shelves faster, and you pay less in holding costs since stock continually moves through your warehouse.
Simply put, accurate demand planning ensures you don’t carry inventory longer than necessary. So, the risk of ending up with dead stock is slim to none—which is great news since dead stock really drags down your profits.
In fact, Target had to offload so much unexpected excess stock that their operating profits dipped 87% in 2022 as they tried to reset their entire inventory infrastructure.
Forecasting demand for new products is tricky since you don’t have any sales history to lean on. But when you don’t accurately plan for new product production, it can lead to stockouts, overstocks, and significant profit losses as a result.
More specifically, planning errors can create an under or oversupply of new product inventory.
As you know, an undersupply puts you on the fast track to a stockout, which can chip away at customers’ confidence in your brand (and maybe even send them to a competitor).
Sure, 69% of customers will choose a substitute item when they encounter their first stockout. But after 3 stockouts, 70% of shoppers will change brands altogether.
On the flip side, an oversupply can hike up your carrying costs since you must keep paying for those new products to collect dust on your warehouse shelves.
But your brand can successfully launch new products with some proactive forecasting. Accurate demand planning tells you how much new product you’ll need and when—so you can budget accordingly (and figure out when orders need to go into production).
And when you make accurate production plans in advance, you can ensure suppliers have everything ready. The best part? Advanced communication reduces potential supply chain mishaps like delays, disruptions, and longer lead times.
As much as 50% of a company’s value depends on supplier relationships, so it’s no shock that prioritizing these relationships will really pay off for your brand.
But how do you improve these relationships? You focus on accuracy and transparency. For instance, you might share your 12-month demand plan with your suppliers. This, in turn, leads to more accurate demand and production planning.
That’s because when your demand planning is full of errors, it causes a ripple effect throughout the supply chain—like if you’re constantly canceling purchase orders. Suppliers who can’t trust you to deliver accurate plans might start relying on their own predictions.
Worse yet, the supplier’s predictions could end up hurting your brand. When suppliers lean on their own forecasts, they’ll likely be conservative. So, if demand spikes beyond your supplier’s projections, it will be tough to catch up on fulfillment.
Fortunately, accurate demand planning can improve your supplier relationships almost immediately. And leaning on tried-and-true demand planning software like Cogsy can help.
Cogsy’s planning feature maps out accurate growth plans and inventory needs for the next year (without ever using a pesky spreadsheet). You can share this 12-month plan with your suppliers to negotiate better terms and reduce your total lead times.
In other words, Cogsy helps you clearly communicate what you need and when to your vendors. And this level of clarity and communication can do wonders to improve your supplier and fulfillment partner relationships.
If customers aren’t happy with your products or their availability, they might take their business elsewhere—like to a competitor with in-stock products ready to ship. That’s why it’s so critical to always have the right products in stock.
Accurately forecasting demand is one way you can make this happen. With these forecasts, you ensure you have the products customers want available, regardless of seasonality or supply chain disruptions.
This also means faster fulfillment and delivery times (because you’re not waiting for shipments to fulfill orders), which translates to happier customers. In fact, around 73% of shoppers say shipping is key to a positive shopping experience. So, there’s no doubt faster fulfillment will put satisfied smiles on your customers’ faces.
To that end, accurate demand planning supports greater customer retention. After all, happy customers are loyal ones who will come back for more. An impressive 73% of consumers say a good experience greatly impacts brand loyalty—so why not provide the most exceptional experience you can?
All the advantages of accurate demand planning (optimized inventory, reduced costs, maximized customer satisfaction) boil down to the same thing: More sales and revenue for your DTC brand.
When you always have what customers need (when they need it), conversion rates will naturally increase. Why? Because customers can count on your brand to have the products they’re looking for, which makes them more likely to spend their money with you.
Along with that, inventory optimization (via accurate demand planning) lowers your operational costs. As noted earlier, having the right inventory in stock speeds up inventory turnover, cutting down on your warehousing and holding costs.
So, what does all this mean for your brand? Higher conversion and lower costs (AKA, improved profit margins.
Putting together an accurate demand plan is no easy task. If it were, every retailer would get it right every time. So, you’ll need to consider a handful of factors when creating your forecasts to be as accurate as possible.
Ecommerce sales boomed during the pandemic as people stuck indoors turned to online shopping. In 2021, ecommerce sales hit about $5.2 trillion worldwide as a result. This figure is expected to balloon to a whopping $8.1 trillion by 2026.
Despite these massive sales numbers, pandemic-era growth wasn’t all smooth sailing. Case in point: 2020’s fast-shifting (and sometimes turbulent) customer demand. Retailers saw a 15-30% increase in customers who made online purchases across almost every category, from home furnishings to fitness to footwear.
While this increase should theoretically mean more revenue, it actually caused stockouts for many retailers. In the fitness category, kettlebells were sold out everywhere. And in footwear, Birdies ran out of all their slippers and seasonal items by November 2020 (leaving them unable to meet year-end demand).
Social media drove even more stockouts—as seen with Aerie’s Crossover Leggings. The same month Birdies stocked out their slippers, a TikTok clip featuring Aerie’s leggings went (what we can only describe as) mega-viral.
As a result, these pants sold out more than 6 times and racked up a waitlist of over 156,000 people. This level of demand was totally unexpected. And Aerie had nowhere near enough stock to keep up.
The point is what’s popular influences demand—but popularity isn’t always predictable. Needless to say, these dramatic, unexpected market shifts can make demand planning accuracy a challenge.
There’s a good chance 2022 will be remembered as the year of inflation. Consumer prices were up 9.1% from June 2021 to June 2022, the largest price increase in the last 40 years.
Among other consequences, this spike in prices has curbed consumer spending. With less disposable income, customers started prowling for cheaper alternatives to their everyday essentials (like gas and groceries) while abandoning non-essential purchases (like jewelry and home decor).
🧠 Keep in mind: While decreasing discretionary funds means consumers are more frugal, the opposite is also true. When the economy is doing really well, households have more money to spend on big-ticket purchases and luxury goods.
Of course, inflation impacts retail from the other direction, too. Brands must be prepared for higher raw materials prices, leading to more expensive finished goods. More often than not, these inflated costs lead to smaller margins and lower revenue for your brand.
All in all, macroeconomic factors have a big influence on demand planning accuracy—since demand tends to rise and fall according to how much customers can spend.
If your products have high and low demand periods due to seasonality, keep in mind that these trends can affect your demand planning.
While many ecommerce sellers see a peak from October to December, others might see a spike in sales during the summer months instead. Sunscreen brands are a prime example of this kind of summertime surge.
As exciting as increased sales can be, they also put your brand at a higher risk of stockouts. For that reason, the most effective demand planning considers each product’s unique lifecycle (including seasonal fluctuations in demand).
Accurate demand planning ensures you’re prepared for an influx in demand—but it also accounts for longer lead times for holiday inventory. This might mean placing orders with your supplier further in advance or ordering extra safety stock to protect against potential delays.
📝 Note: When suppliers are flooded with tons of orders all at once (like during the holidays), it takes longer for them to get these orders out the door. It’s best to get your purchase orders in early (to stay ahead of your competitors) and to keep plenty of safety stock on hand (to combat delivery delays). Fortunately, accurate demand planning helps you do both.
Whether you’re estimating demand for a new product or a best-selling SKU, you’ll need to consider the effects of your advertising and promotions.
This means anticipating any lift in sales from your promotions and discounts and adjusting your plans accordingly to meet that extra demand.
Generally speaking, the lower the price, the higher the demand for that product. With that in mind, you can use discounts to control consumer demand. Offer a discount, and demand will increase. Sell at full price, and demand will likely slow.
You can create a similar effect with product bundles or setting unlockable minimum order quantities (MOQs).
For example, bundles are a great way to get rid of SKUs that aren’t selling well. You simply package them with complementary items from the same product line. By bundling products together, you increase the perceived value (which can increase demand for those goods).
Unlockable MOQs, on the other hand, encourage customers to buy more to reach a volume discount. For example, you might set an MOQ where customers get free shipping on orders of $75 or more.
The whole idea behind bundles and MOQs is getting customers to buy more in a single purchase, increasing your average order value. But here’s the catch: You’ll need more inventory in stock to satisfy these larger orders.
One of the lingering issues from the hyper-growth during the pandemic is the shortage of available warehouse space. While nearly 400 million square feet of new warehouse space are in the pipeline, all this square footage is being leased rapidly. Some estimates even suggest retailers need another 400+ million square feet by 2025 to meet this demand.
Because of all the competition around warehouse space, there are limits to what your brand can sell and how many orders you can fulfill. That’s because it doesn’t matter if your brand can sell 1 million units if you only have the warehouse space to hold 500,000.
You can apply the same logic to your production capacity. After all, you can only increase production if there’s somewhere to manufacture your products. That’s why keeping a few suppliers on the back burner might be worth it—just in case you need to switch to one with greater warehousing or fulfillment capacity.
A product like Flexe can help brands get matched with unused warehouse space so they can scale their warehouse availability up and down. Plus, retailers don’t wind up paying for storage space they don’t need.
Demand planners can use a few formulas to measure forecasting accuracy and calculate error rates. Among the most common (and most reliable) formulas are bias forecasting, MAD forecasting, and MAPE forecasting.
Bias forecasting tells you the difference between your forecasts and your sales (that is, the difference between what you planned to sell and what you actually sold).
The formula for bias forecasting is:
forecast bias = (forecasted demand – sales)
If the bias is a positive number, your forecast overestimated sales. If the bias is negative, your forecast underestimated sales. If the result is 0, no bias is present, which means your forecast was just right.
To look at bias as a percentage of sales, divide your forecasted demand by your total sales and multiply by 100.
More simply, the bias forecasting percentage looks like this:
forecast bias percentage = [(forecasted demand ÷ sales) x 100]
Any results over 100% mean you’re over-forecasting, and those below 100% suggest you’re under-forecasting.
Let’s say your company sells T-shirts, and you predicted you’d sell 15 shirts in a single week. In reality, you sold 30 shirts within that time frame. In this example, your bias equals -15 (15 – 30). Meaning, you underestimated sales by 15 T-shirts or by half (50% [(15 ÷ 30) x 100]).
Mean Absolute Deviation (MAD) is a popular way to detect forecasting errors. MAD calculations rely on the absolute value of forecast errors (basically, the difference between sales and projected demand) and averages them over the forecasted time period.
📝 Note: When using absolute value, if the difference between sales and forecasted demand is a negative number, that number becomes positive. So, if you get an answer of -2, the absolute value of deviation is 2. (The same is true for MAPE errors down below.)
The MAD calculations formula looks like this:
MAD error rate = (forecasted demand – sales)
Let’s go back to the T-shirt example above. In this scenario, your MAD value would be 15. That’s because the difference between your forecasted demand (15) and your sales (30) equaled -15. Expressed as a positive number, you get an answer of 15.
You now know there were 15 errors within your weeklong forecast.
Another way to calculate forecast error is by using Mean Absolute Percentage Error (MAPE)—statistically defined as the average percentage of errors.
The MAPE formula is more involved than your MAD calculations. It looks like this:
MAPE error rate = [(forecasted demand – sales) ÷ sales] x 100
Using this formula, we find that the MAPE percentage for your T-shirt business equals 50%. That is, [(15 – 30) ÷ 30] x 100 = -50%. Expressed as a positive, you get an answer of 50%.
Be mindful that since MAPE measures errors, a high percentage is bad, and a low percentage is good. This 50% calculation lands smack-dab in the middle (but it definitely leaves room for improvement).
Some of the best ways to improve demand planning accuracy include obtaining accurate data, testing different forecasting methods, and making short-term forecasts.
One of the bestbest practices for demand planning is ensuring all your inventory data is accurate and up-to-date. After all, you can’t achieve successful demand planning without having up-to-date data to work from.
That’s what makes it so important to gather your data from a reliable source, like demand planning software.
You can trust the software to deliver precise calculations and the most current inventory counts—which are exactly the insights you need to make better demand predictions.
Another way to pull in accurate data is by using the right integrations. Integrations combine all your favorite apps and tools to empower your demand planning and help you make smarter, more profitable actions.
The bottom line here is that bad data leads to bad decision-making. And ultimately, those decisions harm your demand planning accuracy.
🔥Tip: Cogsy integrates with the tools your brand already relies on (including Shopify, Amazon, and ShipBob ), so you centralize your operational data and make smarter, faster decisions.
There’s no one-size-fits-all approach to forecasting methods. One method might be a better fit for your business than another. To determine which forecasting strategy makes the most sense for your DTC brand, you can try a few different approaches and compare the results.
The most popular demand forecasting techniques include:
By testing different methods, you might find that top-down inventory planning gives you a good ballpark for budgeting, whereas a bottom-up approach is more accurate. Each method will account for different factors, but it’s up to you to determine which improves your forecasting accuracy the most.
That said, you can also combine methods by looking at your answers and determining:
📝 Note: Cogsy forecasts use a layered approach just like this to increase planning accuracy. Your forecast will then update automatically whenever new data is introduced, preserving its accuracy over time.
Earlier, we mentioned how Cogsy helps you create accurate inventory plans for the next 12 months. While those long-term forecasts are great for big-picture planning and budget setting, the further out the projection goes, the less accurate it becomes.
For instance, a 3-day weather forecast will always be more accurate than a 10-day forecast. Demand planning works the same way. If you act on long-term forecasts and something changes, your brand could be in trouble.
So, what can you do? Create short-term forecasts and continuously update your long-term forecasts as you get new info (which will improve accuracy over time). (Luckily, Cogsy does this too.)
Short-term forecasts are a great alternative because they’re more actionable and tell you everything you need for the next 90 days. By not looking as far into the future, you can better ensure your forecasting and planning accuracy (and have a stronger chance of avoiding stockout slash overstock events).
Cogsy’s demand planning software is second-to-none, with a suite of top-notch ops tools that do all the heavy lifting of demand planning on your behalf.
For example, Cogsy’s actionable dashboard leverages real-time sales data and historical insights to inform each of your demand forecasts.
By tracking all this data around the clock, Cogsy provides the most accurate, up-to-date information to guide your planning process. This way, your team can eliminate errors, ignite your growth, and maximize your working capital.
Cogsy is also tech-stack agnostic, which is a fancy way of saying it integrates with the other ops tools you’re already using—or it’ll build the integration for you if it doesn’t already exist.
And if your brand struggles to keep products in stock? In that case, Cogsy can help you stay ahead of supply chain disruptions so you always maintain optimal stock levels. (More on how in a minute.)
Here’s the thing: Forecasts will never be 100% accurate. Even the best predictions can under or overestimate demand to some degree. However, the more accurate and precise you can get your forecasts, the more efficient your operations (and the better your bottom line looks).
One of the best ways to improve your planning accuracy is by running a few less likely—but still possible—outcomes you might need to prepare for.
For instance, inside Cogsy’s growth planning feature, you can run “what-if” scenarios to identify your best-case, worst-case, and most probable inventory strategies. That way, you avoid expensive mistakes (like stockouts and excess inventory) that prevent you from reaching your revenue goals.
These scenarios are typically based on visible risks and external factors that can impact future demand plans. For example, you can run a test to see what might happen if your supplier needs to catch up or your stock runs out a month before replenishment.
From there, you can build contingency plans based on these scenarios. That way, if demand shifts, your brand can move with it. There’s no need to reconvene and replan retroactively, which wastes your team’s time and money.
For instance, offering backorders is a solid contingency plan when your demand planning underestimates actual customer demand. By shifting to a backorder model, your brand can prevent customers from spending money with competitors.
How so? Well, selling on backorder leads to only a small drop in conversions compared to selling that SKU in stock (compared to seeing a 0% conversion rate when you mark products as “out of stock”).
Cogsy provides ecommerce brands with accurate forecasts by combining a variety of demand forecasting methods as well as leaning on machine learning.
Cogsy also automatically updates your demand forecasts as new info becomes available. And since your predictions are based on the most up-to-date data, your forecasts continue to improve.
But Cogsy doesn’t just transform how you forecast. It also helps you:
Brands who partner with Cogsy can even factor upcoming marketing events, new product launches, and subscription orders into their forecasts, increasing accuracy as much as possible. Few tools on the market have these functionalities. And none do it as well as Cogsy.
In fact, Shopify merchants that use Cogsy generate 40% more revenue and save 20 hours a week on average.
Ready to generate more accurate forecasts and get closer to planning perfection? Try Cogsy free for 14 days!
The most reliable methods for calculating a retail brand’s demand forecasting accuracy (including their rate of error) are bias forecasting, MAD forecasting, and MAPE forecasting (see our sections above for more details on these formulas!).
Forecasting accuracy depends on how much a forecast matches actual demand. The more accurate your projections, the better because accurate forecasts mean you can fully meet demand without accumulating excess stock or stocking out.
To create an accurate forecast, the data the prediction is based on (like historical sales data, changes in seasonal demand, etc.) needs to be up-to-date and error-free.
The ideal percentage for demand forecasting accuracy is 100%, meaning the forecast is perfectly aligned with actual demand. But 100% accuracy is nearly impossible for retailers to achieve. That said, brands can increase their forecasting accuracy by leveraging up-to-date data, testing different forecasting methods, and using comprehensive planning software.