With demand planning, retailers can prevent costly stockouts and overstock. All while also optimizing their resources to ensure customer demand is always met.
And with little room for error, here’s how to get it right.
Demand planning is the process of forecasting and estimating future customer demand for a product or service. It involves analyzing historical data, market trends, customer behavior, and other relevant factors to predict the number of goods or services that will be required in the future. Effective demand planning plays a crucial role in supply chain management and helps businesses optimize their inventory levels, production schedules, procurement, and overall operational planning. It enables retailers to predict future demand for their products, then implement inventory strategies to meet that demand.
In most cases, demand planning combines sales forecasting, inventory management, and supply chain management to help brands generate the most accurate predictions possible.
Demand planning analyzes real-time and historical sales data (plus consumer trends) to ensure reliability when generating demand estimates. You can then use these estimates to procure what you need to fulfill customer demand.
In retail, demand planning and forecasting are more like siblings than identical twins. Meaning, they’re closely related but not exactly the same.
Demand forecasting anticipates consumer demand by looking at historical data and customer buying behaviors to predict future demand. Your forecasts will also consider how seasonality and supply chain disruptions might influence these demand projections.
Meanwhile, demand planning takes demand forecasting a step further. Rather than stopping with a projection, demand planning ensures you have enough resources (time, materials, manpower) to meet this demand in full.
That said, many retailers will use the phrases “demand forecasting” and “demand planning” interchangeably when discussing demand management.
|🤿 Dive deeper: A comprehensive guide to demand forecasting.
Demand planning lays the foundation for a well-run supply chain. Not only does this process optimize your resource management, but it also helps prevent stockouts and reduce backorders simultaneously.
Demand planning helps DTC sellers align their inventory levels with all kinds of peaks and valleys in demand.
In other words, demand planning makes it a lot easier to adapt to fluctuations in demand — due to seasonality and unprecedented shifts in DTC trends. That way, you can keep your stock levels right where you want them.
This adaptability boosts resource management since you won’t be spending money on excess inventory that doesn’t leave your warehouse (more on this in a minute).
Plus, on top of saving you a pretty penny, demand planning also saves you considerable time.
All the hours you devoted to managing unsold inventory? Those can now be spent growing your brand, developing new products, and creating exceptional customer experiences.
It’s safe to say nobody enjoys a stockout situation. Stockouts frustrate your customers (because they can’t complete their purchases). But they also cost you a frustrating amount of money (since selling out means leaving money on the table).
The good news is demand planning helps you identify how much inventory you need to meet future demand. All without creating a surplus of products sitting at your warehouse.
With demand planning, you can order the right amount of inventory at exactly the right time. This way, your stock levels never drop too low, and you don’t have to worry about causing a stockout.
Demand planning also creates a continuous flow of inventory that allows you to fulfill orders accurately and on time, which will likely positively impact customer satisfaction (and retention).
This might be an unpopular opinion, but there are times when selling on backorder is a saving grace. Period.
This is especially true if you run out of inventory during a new product launch or experience unprecedented demand leading up to the holidays.
Moreover, when brands go viral, and their products start flying off the shelves, backorders are the perfect way to pivot without losing momentum.
That’s because backordering lets customers place orders even when inventory isn’t in stock. So your cash flow keeps, well, flowing, and customers get the satisfaction of making a purchase.
Still, most brands prefer to keep products in stock at all times than rely on backorders (we don’t blame you). That’s where demand planning comes in.
Thanks to demand planning, DTC brands can better manage their stock levels and replenishment cycles to prevent stockouts and backorders from occurring.
Too much inventory is just as bad as not having enough (sometimes, it’s even worse). That’s because the longer that excess inventory goes unsold, the more carrying costs you’ve got to pay.
Even worse, if your products are perishable or time-sensitive (like holiday decorations), they could expire before they’re sold. Needless to say, this dead stock means lost revenue and a lot of waste.
Of course, you would ideally order just enough inventory at just the right time. Nothing more, nothing less. No earlier, no later.
Demand planning encourages this by aligning your sales and forecast data. That way, you can make smarter inventory replenishment decisions that lower your risk of overstocking.
|🤿 Dive deeper: How to increase demand to offload excess inventory.
Customers expect a lot from their favorite brands. For instance, they want your website to be intuitive, so they can easily place orders. And they expect products to be available and ready to ship whenever they want them.
It’s a bit of a tall order. But one you can easily fulfill with demand planning.
When demand planning is done right, you’ll always have the products your customers are shopping for in stock. Meaning, not only can you meet customer demand, but you can also guarantee faster delivery.
A Future of Commerce report found that 60% of consumers said that fast shipping (same-day or next-day delivery) had a “significant influence” on their decision to purchase and their satisfaction level.
So, always having the products your customers want available means you not only meet customer demand but you’re also well-positioned to increase your retention levels.
Understanding why demand planning is important provides some great context — but it doesn’t tell you anything about how to carry out the demand planning process. So, here are the 5 key steps of executing demand planning on your own:
Demand planning kicks off with in-depth data collection from internal and external sources. Essentially, you want to collect all the relevant data that will predict or influence future demand for your products.
Typically, internal data includes things like your order history and product performance. If you’re using an ERP system (enterprise resource planning) or operations software, this information will be stored in one place where you can access it at any time.
You might also want to discuss things with your sales or marketing teams. These folks have insights that add color to your inventory metrics, like turnover and conversion rate.
For example, your sales team has a pulse on how stockout rates impact turnover — while your marketing crew knows how different promotional campaigns might impact demand. All of this information can help inform how you eventually purchase inventory.
After you’ve checked in with these departments, you can compare their qualitative remarks alongside the quantitative information from your software.
But don’t forget about external data like market trends and patterns in consumer behavior. For instance, you can gather consumer behavior from industry briefings and current market conditions from recent news articles.
Lastly, record any anomalies — like supply chain disruptions or global crises — that could affect your demand planning down the line. It’s always better to find yourself with too much data than not having enough.
What’s the use in collecting data if you don’t take the time to analyze it, hmm?
The next phase of the demand planning process is statistical analysis, where you examine all the sales numbers and inventory data you rounded up in the previous step.
There are a few metrics you’ll want to pay special attention to during your analysis:
These 3 metrics are great indicators of product movement and will be the biggest help in predicting consumer demand.
In addition, if your brand has periods of high and low demand throughout the year, be sure to analyze product performance for peak and off-peak months.
Digging into internal and external data is the only way to gain critical insights that inform your inventory planning and purchasing decisions.
|🔥 Tip: The best demand planning software (like Cogsy) will complete this analysis for you with little to no effort required from your brand. It’ll even spot trends that take human analysts hours to identify in seconds.
Next comes demand modeling, which uses historical sales data to predict future customer demand. In short, an accurate model gives way to more accurate demand forecasts since demand modeling is like a blueprint for demand forecasting.
With that said, there’s no one-size-fits-all approach to forecasting models. In fact, forecasting is often broken down into 2 different categories: qualitative and quantitative.
Qualitative forecasting is used when brands don’t have access to historical data because they’re just starting out or they’ve never kept track of their inventory records.
Without any data to reference, qualitative forecasting seeks the opinions of customers, salespeople, and industry experts to predict future sales. Examples of qualitative methods include customer surveys and sales team surveys (among others).
Quantitative forecasting, on the other hand, looks at all your past data to predict future sales.
Most quantitative forecasting uses statistical models. Meaning, it relies on mathematical data to estimate demand and inform supply chain planning. Examples of quantitative methods are trend projection, seasonal index, and moving average.
The right forecasting technique for your brand is whichever one meets your current needs and can scale with you as you continue to grow.
For instance, if your brand doesn’t have any wholesale business, it likely doesn’t have a sales team. So, you wouldn’t use sales team surveys to guide your forecasting. Instead, you’d rely more on quantitative data like your sales history.
Once you’ve put your forecasting method into motion, you can move forward with plan creation. At this point, demand planners decide what (and how much) inventory is needed to meet demand in the coming months.
That said, even after you’ve created a plan for your product lines, you’ll still want to compare your forecasting roadmap to your current inventory levels.
Meaning, you’ll want to check the inventory needs you forecasted against what you already have in stock.
You might find you’re forecasting too much replenishment for a low-selling SKU already inching toward overstock territory. Or that if anything doesn’t go as planned, you’re at high risk of stocking out. These sorts of demand planning mistakes typically have a compounding effect over time.
Say your forecasting mistakes continue to go unnoticed. Then, your brand might go from slightly overordering every PO to being wildly overstocked on that SKU (since you’re stocking up much faster than you’re selling through).
But say a SKU is instead always at risk of stocking out. Then, you might want to loop some safety stock into your plans. This stock offers a buffer if customers order more inventory than anticipated or if you run into unexpected delays along the supply chain. That way, you don’t feel the need to overorder your next purchase order to compensate for lost sales.
(Big-name brands like Nike, Target, and Allbirds are all currently scrambling to offload excess inventory and missing Wall Street projections after overcompensating for their 2020 stockouts.)
The last piece of the demand planning puzzle is monitoring and modification.
More often than not, it’s in your brand’s best interest to review your forecasting plans with key stakeholders or department leads. Depending on their insights, you can adjust or adapt your plans to improve forecast accuracy.
After your forecasts are finalized (and replenishment is received), you can start monitoring how well you did.
Specifically, you can measure the results of your demand planning by using inventory management KPIs.
These metrics gauge how your forecasted sales compare to the actual sales you brought in. And they can be used to assess the effectiveness of your planning so you can make changes moving forward.
Looking for ways to improve demand planning? Ones that’ll also maximize efficiency and boost revenue? Check out these tried and true best practices:
There’s no doubt that data is the driving force behind accurate demand planning. And that’s exactly why it’s so important to use internal and external data during the planning process.
Gathering information from different sources and perspectives paints a clear picture of what your inventory is doing in the past, present, and future.
When your historical data is used in conjunction with current buying trends, you can recognize areas of improvement and identify where you can push into replenishment a little further.
|🔥 Tip: Cogsy’s software stores real-time and historical data to create a single source of truth for Shopify merchants and Amazon sellers. That way, you can always access the most up-to-date and reliable inventory information. Try for free.
We’ve mentioned KPIs a few times now, yet it’s difficult to overstate their importance.
Key performance indicators are the most reliable way to know whether you’re on the right track with demand planning or missed the mark completely.
And as far as that goes, how can you possibly streamline your operations (or bring in more money) if you don’t know where you’re thriving and dropping the ball?
Metrics like turnover ratio and order lead time expose a lot about how your products sell, and they’re a great reference for demand decision-making.
|🤿 Dive deeper: The 15 KPIs every DTC brand needs to track.
There are many ways your brand can forecast demand depending on your unique needs, capabilities, and resources. Common forecasting methods include moving average, linear regression, and trend projections.
But you can increase the effectiveness of your plan by combining several of these demand planning methodologies.
The moving average method analyzes a range of data points by creating a series of averages from the full data set.
Simply put, this strategy finds the average from a select set of time-stamped data and then applies that average to help forecast demand for your products.
It’s called a “moving” average because you can move the range of data to suit your specific needs. This “motion” makes the moving average method more flexible than a static spreadsheet model.
Because moving average is a statistical forecasting method, it’s best used for predicting short-term trends and long-term demand.
|🤿 Dive deeper: Why demand planning with Excel doesn’t work.
Linear regression examines the relationship between a dependent variable and 1 (or more) independent variables in your retail operations. That is to say, this technique examines the relationship between 2 continuous, numerical variables.
With the linear regression model, your independent variables are actually used to predict the value of your dependent variable.
Independent variables include customer demographics and economic factors, whereas dependent variables might be your production planning or product price.
The purpose of linear regression is to estimate the impact of each variable and determine whether their relationship will have significance to your forecasting predictions.
Trend projection is the most straightforward approach to the future of demand planning. This method uses your past sales data to predict future demand. It’s really as simple as that.
The idea behind trend projection is that the factors responsible for past trends (and spikes in demand) will continue at the same rate. More simply, it’s assumed that demand and demand forecasting will follow the same pattern as in the past.
Still, you will need to adjust your calculations for seasonality or other anomalies in demand.
It’s important to note these unusual factors in your historical data, so they don’t influence your forecasts for new products or product categories.
Selling on backorder is perhaps the best contingency plan for times when your demand planning doesn’t align with the actual demand you experienced.
By shifting to a backorder model, your brand can convert demand into revenue as you wait for more stock to arrive. This means customers can buy your out-of-stock products rather than spending their money with a competitor.
The benefit of selling on backorder is that it sets clear expectations with your customer base.
They still get the satisfaction of completing their purchase. Still, they’ll also appreciate the honest communication about wait times and delivery windows.
Not only that, but often, backorders generate a sense of urgency and excitement around buying a product. Meaning, that the item has increased value in the customer’s eyes.
But perhaps the greatest benefit of backordering is that it converts customers at nearly the same rate as selling products in stock. So, you know you’re not leaving revenue on the table if (scratch that – when) a stockout occurs.
|🔥 Tip: Cogsy has a dedicated backordering feature that makes pivoting to selling on backorder easier than ever. Try for free.
Last but certainly not least, implementing a demand planning software can do wonders for your operational efficiency. That’s because the leading software solutions (like Cogsy) will handle the heavy lifting that comes with demand planning on your behalf.
Demand planning software automates a slew of tasks, from inventory tracking to calculating optimal stock levels to inventory replenishment. All that’s on your plate is a few quick clicks to lead this system in the right direction.
By automating your demand planning process, your brand saves time. Plus, it generates more revenue when you’re not stuck dealing with stockouts and overstock. For instance, Cogsy saves retail brands 20+ hours a week and helps them generate 40% more revenue on average.
Cogsy is the demand planning tool that supercharges how you meet customer demand. With it, brands can place orders faster, reduce the frequency of stockouts, and minimize the amount of excess stock hanging out at their warehouse.
To do all this, the Cogsy dashboard monitors your stock levels around the clock. That way, you always know how much inventory you have available (what’s just sitting there and when you need to replenish).
Cogsy then uses that data to build 12-month demand forecasts with pinpoint accuracy. You can even run “what-if” scenarios within the tool to find your best-case, worst-case, and most profitable inventory strategies.
Best part? As new information becomes available, your demand forecasts update automatically, improving your forecast accuracy.
But don’t take our word for it – try Cogsy free for 14 days.
Intelligent demand planning uses advanced algorithms and machine learning to forecast more accurately. This artificial intelligence can generate more precise demand plans and forecasting estimates than static spreadsheets (or other manual methods).
Collaborative demand planning uses a combination of forecasting algorithms to predict future demand for your products. This approach ensures market forecasts are handed down directly from the supply chain and provides meaningful information-sharing among all key stakeholders.
Demand planning is a process for predicting consumer demand to inform and guide your company’s supply chain operations. By contrast, supply planning focuses on managing your inventory supply to ensure it meets the targets in your forecasts.
Supply chain forecasting faces several challenges, including demand volatility caused by changing customer preferences and market trends, limited or incomplete historical data, data quality and accuracy issues, the complexity of modern supply chain management processes, variability in lead times, limited visibility and information sharing, forecast bias and human error, and external disruptions. These challenges can impact the accuracy of forecasts of supply and demand and lead to inventory shortages or excesses, inefficient operations, and decreased customer satisfaction.