A beginner’s guide to demand planning in the retail industry

What is demand planning in retail?

Demand planning helps retailers forecast future customer demand for products by analyzing historical data, buying behavior and market trends.

According to Gartner, demand planning is the most widely used machine learning (ML) application in supply chain planning — and it’s easy to see why. When demand is accurately predicted, retailers can optimize inventory levels and improve supply chain efficiency to boost customer satisfaction and maximize cash flow.

With the right tools and strategy, demand planning can centralize your data and give you all the information you need to make better product decisions and optimize every aspect of your supply chain.

Why is demand planning important?

Better cash flow. More in tune with what customers want. Less wasted cash on bloated inventory, unneeded safety stock and warehouse costs. 

Demand planning in retail has its perks. If you can predict future sales trends and make accurate decisions about how much inventory to stock, it’ll keep costs down across the board. 

Let’s start with how important it is to have accurate forecasts. The more accurate your data, the more inline projections about future revenue will be. This is important as retail revenue revolves heavily around customer demand for products and fluctuations like the economy, seasonal conditions and market trends. 

Accurate demand planning can also help with:

Stock shortages or overstocking

Retailers can accurately forecast customer demand and maintain optimized inventory levels. This helps to minimize overstocking, set optimal safety stock levels and maximize cash flow.

Meet customer demand

When a customer orders a product, successful demand planning ensures you actually have it in stock.

Supply chain efficiency

Demand planning streamlines operations and resources to optimize the supply chain and improve production processes.

Perhaps the most crucial reason for demand planning in retail is how it can help businesses survive unexpected turbulence.

When Deloitte looked at how companies handled supply chain disruption caused by the COVID-19 pandemic, it found those with strong supplier relationships and insight into their broader supply networks were best equipped. In other words, retailers that could quickly calculate how seasonal conditions and suppliers would be affected could pivot quickly, while those that lacked supply chain visibility and accurate projections struggled to adjust to the volatility. 

Even if you now think demand planning sounds like a no brainer, there are still some challenges and consequences that you need to think about…

What are the challenges in demand planning?

Without accurate data, the right resources and smooth internal communication across departments, a demand planning strategy can easily derail. 

But even though 80% to 90% of all planning tasks can be automated, some businesses still use manual processes for forecasting and inventory management to run their stores. 

This is risky. 

One wrong forecast, spreadsheet formula failure or data input error can throw off a demand plan and lead to overstocking or product shortages due to inaccurate calculations. When this happens, businesses must pay more to store bloated inventory, tying up cash unnecessary in stock that often sits gathering dust in the warehouse. This is also a problem if retailers are hit with unforeseen market disruptions or supply challenges, as a lack of storage space will make it harder to adjust stock and meet customer needs. 

Poor demand planning can also lead to product shortages. Customers may find the product they want is out of stock (maybe for a long time) which can lead to a poor buying experience and may even see them turn their attention towards one of your competitors. And of course, they can’t buy your product — which means your bottom line will take a hit. 

It’s also crucial to remember demand planning is not the same as demand forecasting. Let’s take a quick look at the key differences 👇

Demand planning vs. demand forecasting: what’s the difference?

Demand planning and forecasting are both powerful techniques used to predict customer demand, but they have different scope and purpose.

Let’s start with demand forecasting. At its core, this is basically an estimation of how much product you should expect to sell over a specific timeframe, and is calculated using historical data, market trends and other relevant factors.

Traditional demand forecasting used internal data to make these predictions, but thanks to the emergence of artificial intelligence (AI), it’s becoming increasingly accurate. In fact, using AI can reduce mistakes by 30% to 50% in supply chain networks and reduce lost sales due to inventory out-of-stock issues by up to 65%.

Demand planning uses these demand forecasts to plan and manage inventory levels. Demand planning can help create production schedules and control other aspects of the supply chain.

The most effective demand planning methods for retail

Before a company can implement effective demand planning, it’s important to choose the method that is best for the business and its ways of working. Some retail stores will benefit from forecasts that lean heavily on market research and expert inputs, while others can feel the impact from just monitoring seasonal trends. 

Here are four different methods and approaches that you can take for your company’s retail demand planning 👇

1. Time series forecasts

If you are just getting started with demand planning, time series forecasting is the simplest method on our list to take historical sales data and predict future sales. 

This method assumes that past sales patterns and levels will continue in the future, and it also takes into account peaks and troughs like seasonality. It then uses this information to forecast demand for a specific SKU or category of products.

2. Linear regression

Linear regression uses sales data to find relationships between variables that impact product demand. 

It starts by looking at activities like seasonal fluctuations, pricing and promotional activities. These variables are then weighed against each other to see if they affect product demand and inventory to predict future demand for each item. For example, if you analyze historical sales data of an item you sold at Christmas time and find customers are still willing to buy it at an increased price, you could study this pattern to predict future demand after a price increase.

3. Qualitative forecasts

We put this method of demand planning in the ‘old school’ basket. 

It’s a mix of market research, surveys and expert predictions (like consultants and consumer groups) to form a qualitative forecast. Although these sources are trusted, this method of demand planning relies more on intuitions than hard data, so be aware of any human bias creeping into a forecast. 

But qualitative forecasts are great for unpredictable demand patterns or for new products with no historical data.

4. Rolling mean

Rolling mean analyzes historical data to calculate a rolling average of demand over a certain period of time. 

This demand planning method will look at data over a week, month, quarter or year and spot sales fluctuations and spikes. For example, retailers that sell seasonal clothing (think snowboarding gloves in winter) may calculate the rolling average of demand for each product line over the past three winter seasons and use this data to predict demand for the upcoming season. 

Once you pick a demand planning method that fits your retail model, it’s time to put it into action…

How to put demand planning into place in four simple steps

The best way to build an accurate demand planning strategy is to create a process that responds to market trends and inventory changes in real-time. 

Accurate demand planning requires more than just picking a method. You must invest in people, processes and technology for it to pay off. Here are the four essential steps to put demand planning into place to ensure it’s a success👇

1. Invest in a team who know what they’re doing

Build a foundation so the people in charge of demand planning are set up for success. 

A demand planning team not only needs to understand the nuances of forecasting and inventory management, but they should also have some serious knowledge around your purchasing and supply chains and customer needs. 

Demand planning can also be enhanced by data science teams who can use their skills to analyze large datasets and spot seasonal trends in sales reports. Using technology like AI, you can collect data and analyze every aspect of an inventory in real time. For more on that, take a look at Peak’s Dynamic Inventory application to see what’s possible.

2. Collect and organize accurate data

Sales and inventory data is the most important element of any demand planning strategy. 

Aim to collect and organize data from any source impacting inventory, sales or revenue. Think point-of-sale (POS) systems, inventory management systems, warehouse logistics and even customer relationship management (CRM) systems. All of this data is like gold — build it up and input it into a centralized system to get a better view of every moving part in your supply chain. 

Once the data is in one place, delete any errors or duplicates as it will make inventory levels and product sales analysis more accurate.

3. Focus on optimizing inventory

Next, optimize your inventory by getting into the nitty gritty of every single SKU in your product portfolio.

We recommend you try to find a balance between holding enough stock to meet customer demand without carrying too much stock as it leads to waste (and ties up your cash flow). To optimize inventory, retailers use demand forecasts to manage stock levels and streamline logistics to ensure you always have products available that customers order. 

The best way to do this is to use SKU-level demand forecasts to accurately predict required stock levels. It’s pretty much impossible for humans to track entire stock levels accurately, so a demand forecasting tool is a game-changer here to suggest optimal reorder quantities and stock numbers. For example, an AI platform like Peak can:

• Collect transaction, product, pricing and warehouse data to create models and accurate demand forecasts
• Suggest minimum order quantities based on forecasting methods and seasonal changes to optimize warehouse capacity
• Track stock levels in real-time so each time a customer purchases a product, its SKU is updated and you are alerted if stock is running low

Instead of wasting time with your head stuck in a spreadsheet, you can use these optimized SKU-level forecasts to create concrete plans for stock orders and supply chain co-ordination to boost sales.

4. Pick the right metrics and KPIs to keep demand planning on track

Last but not least, keep demand planning on track by measuring the right metrics. 

Choose metrics and key performance indicators (KPIs) to track inventory levels and sales to see if demand plans are in line with forecasts and predictions. Some KPIs are more important to track than others, so we recommend keeping an eye on: 

Inventory turnover rate (cost of goods sold ÷ average inventory)

This is the number of times you sell and replace stock in a specific timeframe, usually a quarter or annually. Use this metric to track if you are holding too much inventory versus actual sales, and then compare it to your demand forecasts to determine how accurate they were.

Sell-through rate ((# units sold ÷ units received) x 100)

This metric takes a granular look at your warehouse and compares inventory sold to the number of products ordered from your manufacturer. It’s perfect for looking at supply chain efficiency and determining stock reorder levels.

Gross margin return on investment (gross margin ÷ average inventory cost)

This metric (known as GMROI) tracks how much revenue you make off of each product sale once manufacturing and supply chain costs are accounted for. It’s a good surface level indicator of supply chain efficiency and helps calculate gross returns on specific SKUs so you can make  more effective decisions around inventory. 

Tracking these metrics also helps you spot areas that need improvement, so make sure you review them regularly and make any changes to your demand plan if necessary.

Wrapping up

Successful demand planning in retail takes work. It takes investing in the right processes, and it requires hiring the right people. 

A lot of the success around demand planning will depend on building the right foundation. Creating reliable demand forecasts and keeping accurate datasets are part of this, but investing in demand planning tools to do a lot of the dirty work is the real secret to success, increased efficiency and greater productivity for your teams.  

If the foundation is built early on, the reward is a demand plan that can help you optimize inventory, improve sales and boost your bottom line 💰