Constantly missing the mark on the right inventory levels? Learn how to accurately predict customers’ demand so you can make better inventory stocking decisions.
Every successful business relies on accurate demand forecasting. This information determines the restocking rate and volumes, directly impacting inventory management and customer satisfaction.
Check out Ware2Go’s latest eBook to discover how accurate forecasting and effective fulfillment strategies enhance customer satisfaction and grow your business.
Without precise forecasts, businesses risk excess inventory leading to obsolescence or stockouts resulting in lost sales.
This article discusses demand forecasting, its methods, techniques, factors influencing customer demand, and key considerations for accurate forecasting.
What Is Demand Forecasting?
Demand forecasting is the process of estimating the level of consumer demand for a product over a specific period of time. It involves analyzing historical data, understanding customer behavior, and using statistical models and machine learning techniques to predict future sales.
Merchants decide how much inventory to buy based on demand forecasting models, and strategic businesses even determine the location of their inventory distribution based on geographic demand.
Accurate demand forecasting is important for avoiding stockouts (which result in missed sales opportunities) or slow inventory turns (which lead to inventory obsolescence and high inventory carry costs).
5 Steps to Simplify Your Demand Forecasting Skills
Proper demand forecasting requires a commitment to data integrity and a structured approach to modeling. It can be difficult to allocate time and resources to developing a robust demand forecasting solution, but doing so will enable your business to be resilient and adapt to an increasingly dynamic marketplace. Ultimately, doing this will optimize operating costs and improve customer experience.
Here’s a breakdown of these steps and tips for each to help you implement a solid demand forecasting strategy.
1. Select a Forecasting Method & Technique
Carefully consider the nuances of your business and industry, and choose a method (more on demand forecasting methods later) that optimizes resource allocation. Some important factors to consider are the amount of historical sales data you have available, access to general industry data, and the skill set of your team.
2. Identify Factors Influencing Demand
Understand factors that influence demand, such as your consumer preferences and competition. It’s important to think on both a micro-scale (your marketing budget and seasonal sales patterns, for example) and a macro scale (like the general economic climate and state of the supply chain).
3. Collect & Analyze Data
Any demand forecast is only as accurate as the data it’s based on. The larger data set you have, the more accurate your forecast will be, and a healthy mix of first-party and third-party data adds diversity to your data set.
4. Implement Forecasting Results
Use your forecasting insights to make practical changes in how you manage your inventory and resources. By aligning your business operations with expected demand, you can ensure you have the right resources available at the right time, improving efficiency and meeting customer needs effectively. It’s important to share your demand forecast with all of your partners, including fulfillment and marketing partners, to set them up for success.
5. Continually Optimize
Assess the effectiveness of your forecasting efforts by comparing your forecast to actual sales. Refine your forecasting strategies by using feedback from your suppliers, distributors, and other stakeholders. Explore customer surveys, regular market research, and input from your sales team to stay agile in dynamic market environments.
Methods of Demand Forecasting
There are several methodologies for forecasting demand and planning. The method your business employs will depend on the sales history available, the length of your sales cycles, and your business goals. Below are a few common demand forecasting methods:
- Passive Demand Forecasting is best for very small or hyper-local businesses. It’s based on limited historical data and makes broad assumptions. This method isn’t likely to support aggressive growth goals.
- Active Demand Forecasting considers historical sales data and external market forces and trends. It’s best to employ active demand forecasting before major changes to marketing tactics or expanding into a new market.
- Short-Term Projections are only meant to forecast demand for the next business quarter to business year. Businesses use this method primarily to manage just-in-time supply chains. It’s best suited for businesses with a higher risk tolerance.
- Long-Term Projections are best for high-growth businesses with aggressive growth goals. They take into account increased market share over time and determine capital expenditures and long-term marketing plans.
Demand Forecasting Techniques
For each of the demand forecasting methods above, there are many techniques to accompany them. Choosing the right technique can make all the difference in how effective your demand forecasting method is.
Here are some commonly used demand forecasting techniques.
1. Time Series Analysis
Time series analysis looks at past data, like previous sales, to spot patterns that repeat over time. Understanding these patterns helps businesses predict busy times and plan better, making sure they have enough products and setting prices wisely to meet customer needs and boost profits.
2. Moving Averages
Moving Averages smooth out the ups and downs in data, helping you focus on the main trends. Here’s how you do it:
- Pick a time frame, like each week or month
- Calculate the average for each time frame
- Draw these averages on a graph
This method helps clear up the clutter in the data, making the important trends easier to see.
3. Exponential Smoothing
Exponential smoothing is similar to Moving Averages but gives more importance to recent data. This method adjusts more quickly to recent changes, helping businesses stay responsive to current trends and demand shifts.
4. Regression Analysis
Regression analysis helps forecast future demand by examining how different factors relate to each other. For example, it can show how changes in marketing spend influence sales. This analysis helps businesses understand and predict how changes in one area can affect outcomes in another.
5. Delphi Method
The Delphi method involves gathering insights from experts through structured discussions. This technique pools diverse perspectives to challenge existing assumptions and develop more accurate forecasts by collectively addressing uncertainties and blind spots.
6. Simulation Models
By exploring various ‘what if’ situations and analyzing their potential impacts, companies can better anticipate risks and opportunities, leading to more informed decision-making under uncertainty.
These forecasting techniques allow you to foresee market changes, manage your resources effectively, and keep your business competitive, ensuring you’re always one step ahead.
Factors That Influence Customers’ Demand
Accurate demand forecasting is essential for effective business planning, allowing companies to anticipate customer needs, optimize inventory, and reduce waste. A thorough understanding of the factors influencing demand helps refine forecasts and align them with real-world conditions, making supply chains more resilient and responsive.
Seasonality
Seasonal demand patterns fall at different times of year across industry and vary in nature:
Natural seasons: Changes in the weather, like an increase in demand for winter coats during colder months
Cultural seasons: Such as holiday shopping spikes or back to school shopping
Business season: Such as increased demand for tax services during April in the US
Type of Goods
The nature of a product strongly impacts demand trends. Fast-moving consumer goods, like groceries, tend to have consistent demand, while durable goods, like appliances, follow different purchasing cycles. Luxury items and products seen as discretionary are more sensitive to economic shifts, while necessity items maintain stable demand regardless of external factors. For instance, a mattress company might see steady demand throughout the year, with repurchases averaging every 7-10 years.
Marketing and Promotions
Marketing campaigns and promotional discounts can create fluctuations in demand. Advertising, sales promotions, and viral social media can drive temporary surges, often boosting short-term sales. It’s very important to align marketing strategies with inventory availability to avoid stockouts.
Accessibility
Product availability and shopping convenience play significant roles in demand. Physical store locations, robust e-commerce platforms, and convenient shipping options can all increase demand. For example, 69% of consumers are more likely to click on an ad that mentions fast shipping.
Economic Factors
Broader economic conditions such as GDP growth, inflation, unemployment, and consumer confidence influence whether consumers are likely to spend on essentials or luxury items. For example, during the 2008 financial crisis, global demand for luxury goods dropped by 9%, while necessities stayed relatively stable.
Customer Experience
A positive brand experience is crucial for driving long-term demand. Product quality, reliable service, and brand reputation build customer loyalty, with 73% of consumers citing customer experience as a major influence on their purchasing decisions, according to PwC.
7 Factors to Consider in Demand Forecasting
Understanding the factors influencing demand forecasting can help you succeed with your chosen forecasting method and techniques. Here are some factors to consider for sustained growth.
1. Economic Conditions
Keeping a finger on the pulse of economic indicators is critical to anticipating shifts in consumer spending patterns. These economic indicators include unemployment rates, Gross Domestic Product (GDP) growth, inflation rate, interest rates, and income levels. With these indicators, you can gain insights into consumer sentiment. Their perception of the economy and their financial outlook helps you gauge their ability and willingness to buy your products.
2. Competition
Understanding competitive dynamics, such as your competitors’ pricing strategy, product assortment, market share, and marketing strategy also helps you assess the level of demand to expect for your products.
3. New Product Introductions
Demand forecasting for a new product launch is a tricky process. With no historical sales data to measure against, you must have a deep understanding of your current customer base and the competitive market. You should also decide which outcome is more favorable to your business long-term: under-ordering and selling out or over-ordering and being left with a product to further market or liquidate.
4. Consumer Trends
Tracking consumer trends, such as on-demand shopping, customization needs, fast shipping, and eco-conscious shopping, provides businesses with insights into evolving preferences and behaviors.
5. Price
Fluctuations in pricing directly impact consumer purchasing behavior and demand levels. Considering your pricing strategies when forecasting demand equips you to optimize your revenue and profitability.
6. Availability
Ensuring product availability through effective supply chain management is crucial for meeting customer demand, maximizing sales, and providing customer satisfaction and loyalty. However, it’s important to balance the risk of stocking out against the risk of carrying too much inventory.
7. Geography
Geographic factors, such as location, climate, and demographics, play a pivotal role in demand forecasting. Businesses must tailor their forecasting strategies to specific regions or markets to optimize sales and customer satisfaction.
The Benefits of Accurate Demand Forecasting
Accurate demand forecasting can benefit businesses in several ways, including:
Control Shipping Costs with a Distributed Fulfillment Network
Understanding where your customers are located and accurately forecasting demand enables you to stock the right SKUs in the right quantity in the most optimal location in anticipation of purchases. This level of forecasting helps you meet your customers’ demands for 1- to 2-day shipping without depleting margins with costly solutions like next-day air.
Alignment Between Marketing and SKU-Readiness
Poor demand forecasting leads to poor supply chain performance. A marketing campaign should geo-target audiences based on available inventory at the SKU level. Otherwise, customers will encounter stockouts and shipping delays, ultimately leading to lost revenue.
Sales aren’t the only measure of success for a marketing campaign. Emphasizing sales over customer experience or SKU-level profitability might lead to back orders piling up while a replenishment order is still coming from the supplier. This results in a frustrating experience for customers, who may not return for a repeat purchase.
Instead, marrying your demand forecasting with your marketing efforts will enable you to squeeze the most ROI out of your marketing dollars without sacrificing customer experience. By closely monitoring SKU-level performance, you’ll know which campaigns to turn off and which ones to ramp up.
Lower Inventory Carry Costs
Inventory turns really shouldn’t be longer than 1 month. Otherwise, your inventory carry costs will drag down your profitability, eventually leading to inventory obsolescence.
Some merchants focus only on supplier discounts to create margins. They order more inventory than they can sell in a quarter to get the steepest volume discount. Then, they let inventory sit on the shelves of a costly storage facility for 6–12 months. By the time the inventory sells, the storage costs have exceeded the amount saved through the supplier discount.
An outsourced fulfillment partner with a technology-first approach to logistics can provide an accurate demand forecast. Using custom forecasting models, you can strike the perfect balance between getting the best supplier discount possible without over-indexing storage costs.
Profitable Seasonality
Some seasonal demand spikes are virtually industry-agnostic. Most merchants expect seasonal spikes around the holidays and Amazon Prime Day. Sometimes, stocking for these seasonal demands can feel like an educated guess. Merchants may reach the end of a holiday season feeling confident about sales velocity only to find that their SKU-level profitability was down.
There is also an element of risk that you’ll have to determine for your business. Businesses with a higher tolerance for the risk of running out of inventory can fine-tune their inventory levels for the least amount of capital expenditure. Taking the high-risk approach yields the highest profits and frees up capital to invest in other areas of your business.
If your business has a lower risk tolerance, you can optimize inventory levels to insure against stockouts. This low-risk approach requires greater capital expenditures but ensures a seamless customer experience. Once you determine your risk appetite, you can choose the inventory level that will yield either the most profitable or the most satisfying season for your customers.
Other businesses, however, may be surprised by seasonal patterns in their business. Whether due to weather, sporting events, or back-to-school, your customers may find more need for your product at different times of the year.
Analyzing your sales cycles will help you identify seasonal trends, stock up accordingly, and align your marketing strategy with seasonal demand to double down on profitability.
Demand Forecasting Examples
To accurately forecast demand, businesses with highly seasonal demands like winter weather gear will want to look at historical sales data, such as exactly when demand began to spike in years past. They may also look at the average temperatures and weather patterns during demand spikes and compare those to long-term forecasts for the coming year.
Long-term forecasts clearly work on many assumptions and don’t guarantee accuracy, but they can help make general predictions. The forecast should also consider regional weather patterns to have inventory ready at the right time and place to best service their end customers.
A business that’s less seasonal, like supplements and nutraceuticals, will want to forecast demand based on historical sales data and marketing strategy and growth plans. Past marketing campaigns can determine how much sales will grow with incremental marketing spend.
Common Challenges With Demand Forecasting
Demand forecasting is crucial for efficient operations, but brands often face several challenges that hinder accurate predictions. Here are five key issues that complicate the process:
Data Collection and Analysis Issues
High-quality data is the backbone of accurate demand forecasting, and inaccuracies can severely impact forecasts. Mistakes in data entry introduce errors that skew analysis, while incomplete data sets can hide trends and produce unreliable results. Outdated information fails to reflect current market conditions, leading to irrelevant predictions, and a lack of standardization—such as inconsistent formats or measurement units—creates confusion during analysis, making forecasts less dependable.
External Factors
There will always be uncontrollable external events, like the COVID-19 pandemic or recent technology outages that can instantly disrupt demand patterns, rendering even the most meticulously prepared forecasts obsolete.
Rapidly Changing Consumer Behavior
In volatile markets or with products that have intermittent demand, predicting customer behavior is a significant challenge. Intermittent demand patterns make it hard to spot reliable trends, while volatile markets with frequent changes add further unpredictability. Demand is also shaped by a variety of factors—such as seasonality, economic shifts, and regulatory changes—making accurate forecasting highly complex.
Balancing Data and Intuition
While historical data is valuable, relying solely on past trends may lead to inaccurate predictions, especially in fast-changing markets. As markets evolve, new factors emerge that aren’t reflected in historical data, while innovation or disruptive business models can significantly alter demand. Additionally, shifts in consumer behavior—driven by cultural or societal changes—can quickly render previous demand patterns outdated.
Demand Forecasting Best Practices
Demand forecasting is both an art and a science, requiring businesses to strike a balance between data-driven analysis and strategic planning. Below, we’ll outline five best practices to improve forecast accuracy and align supply chain operations with customer needs.
Use a Demand Forecasting Software
Demand forecasting software streamlines data analysis and improves forecast accuracy. You can use it to analyze historical sales data, identify trends, and automate repetitive tasks. Advanced analytics and visualization features can help you gain deeper insights into demand patterns.
Prioritize Accurate Data Collection
The foundation of effective demand forecasting is high-quality data. Avoiding biases in data collection and maintaining consistent, accurate records ensures your forecasts are built on reliable information. Eliminating errors and inconsistencies improves prediction accuracy and supports better business planning.
Break Down Data Silos
To gain a holistic view of customer behavior, sales data must be shared across the entire supply chain. By integrating data from different departments, businesses can align operations and improve demand forecasts, ensuring every team has the insights needed to make informed decisions.
Track Forecast Results
Consistently tracking forecast performance helps identify gaps and optimize future predictions. Analyzing what worked and what didn’t, enables businesses to refine their forecasting methods and make smarter inventory allocation and procurement decisions over time.
Diversify Your Supply Chain
Over-reliance on a single supplier can limit your ability to respond effectively to forecasted demand. Building a diversified supply chain reduces risk and ensures the flexibility needed to execute your demand-driven strategies, even in the face of disruptions.
Future Demand Forecasting Trends
The field of demand forecasting is rapidly evolving, with new technologies and strategies shaping how businesses predict and respond to customer needs. Here are three key trends that are defining the future of demand forecasting:
Greater Automation
Advanced technologies like artificial intelligence (AI), machine learning (ML), self-serve applications, and cloud-based software are transforming demand forecasting. These tools analyze vast amounts of data to identify patterns and provide more accurate predictions. Brands can use these solutions to improve responsiveness, optimize inventory, and deliver better customer experiences. These tools have also made advanced business functions more accessible to growing businesses.
More Robust Integrations
Demand forecasting software is increasingly designed to integrate seamlessly with other supply chain tools, such as warehouse management systems (WMS), inventory management platforms, and ERP software. This integrated approach enables greater visibility and connectivity to align demand forecasting with inventory planning and vendor relationships.
Demand Sensing
Demand sensing is a cutting-edge trend that leverages real-time data from points-of-purchase, social media, and other dynamic sources to predict shifts in demand more quickly. By reacting to immediate market signals, businesses can adjust their forecasts and operations in real-time to stay ahead of changes in customer behavior.
FAQs Demand Forecasting
For more clarity and insights into the demand forecasting process, here are some questions that commonly surround demand forecasting:
How do I choose the right demand forecasting technique for my business?
To determine the right forecasting technique, consider factors such as the nature of your products or services, the availability and quality of historical data, and the accuracy and complexity levels required. Also, assess the technique’s scalability and adaptability to accommodate future business needs.
What data do I need for demand forecasting?
You’ll need historical sales data, market trends, customer demographics, promotional activities, economic indicators, and any other relevant factors that may impact demand.
How often should I update my demand forecasts?
The frequency of updates depends on factors like market volatility, seasonality, and the speed of demand changes.
Update forecasts regularly to ensure accuracy and relevance. Fast-moving goods might demand weekly or even daily forecasts. For industries with fairly stable demand patterns, you can opt for monthly or quarterly updates.
What are the limitations of demand forecasting?
Demand forecasting is subject to uncertainties such as unexpected market shifts, changes in consumer behavior, and inaccurate data inputs.
It’s essential to acknowledge and mitigate these limitations through robust forecasting methodologies.
Ware2Go: The Ultimate Demand Forecasting Partner
As a fast-growing business owner, it’s common to feel confused about demand forecasting or to assume that your business lacks the resources to predict customer demand accurately.
However, this couldn’t be further from the truth. Small businesses have an advantage that makes demand forecasting more effective—agility. With fewer moving parts and stakeholders, small businesses can act on demand forecast predictions quickly and efficiently.
By pairing this agility with a tech-focused 3PL or on-demand warehousing partner like Ware2Go, you can gain a distinct edge to grow your market share and compete in today’s rapidly changing marketplace.
Looking for robust demand forecasting capabilities for your business? Reach out to our in-house experts.
