5 Demand Planning and Forecasting Methods that Accurately Predict Demand
Keeping up with demand and market trends challenges most supply chain operators. Accurately forecasting sales with fluctuating demand cycles is difficult to achieve, and yes, there’s a direct impact: the less accurate the sales forecast, the less precise the stocking levels. The lack of forecasting accuracy can easily spill hardship into other departments.
Ultimately, overproducing and underproducing lead to the same results – lost sales and unhappy customers.
Supply chain operators need to take a step back and closely examine their demand forecasting process to optimize supply chain operations, reduce costs, and improve customer satisfaction.
Since demand forecasting is more than just looking at historical data and making educated guesses, we’ve compiled our know-how in an “all-you-need-to-know” blog post to shed some light on this topic and provide actionable advice.
We’ll explore the art and science of demand forecasting, including proven methods and techniques that can help you create more accurate forecasts and gain a competitive advantage in today’s complex global economy.
Let’s start with the definition.
What Is Demand Forecasting?
Demand forecasting is the process of predicting future demand for a product or service, which helps businesses plan and allocate resources effectively. It involves analyzing historical sales data, understanding market trends, and considering various factors influencing demand, such as economic conditions, consumer behavior, and competitor activities.
By accurately forecasting demand, businesses can optimize their production, inventory management, and pricing strategies to maximize profitability and customer satisfaction.
What are the Benefits of Accurate Demand Forecasting?
When demand is accurately forecasted, companies can optimize their inventory levels, reducing carrying costs and minimizing the risk of stockouts. Accurate forecasts also enable businesses to plan their production more effectively, ensuring they can meet customer demand while reducing waste and inefficiencies.
Improved supply chain resilience is another massive benefit of accurate demand forecasting. By accurately predicting future demand, businesses can better prepare for potential disruptions, such as supply chain disruptions, natural disasters, or changes in consumer behavior.
There’s also the benefit of identifying new market opportunities and developing new products and services that meet customer needs, leading to increased revenue and profitability.
As you can see, there are plenty of benefits to having an accurate demand forecasting system in place. Other than the apparent benefits mentioned earlier, we’ve pinned down the four primary reasons why your supply chain needs precise demand forecasting:
Improved Decision-making:
Reliable forecasts enable businesses to make data-driven decisions about production, inventory management, and pricing strategies, leading to better overall performance.
Reduced Costs:
When demand is correctly and accurately anticipated, businesses can optimize their supply chain operations accordingly, reducing overproduction, inventory management, and logistics costs.
Enhanced Customer Satisfaction:
Having accurate forecasts allows businesses to effectively meet customer demand, leading to higher customer satisfaction and loyalty.
Increased Profitability:
Better decisions reduce costs, improve the supply chain’s bottom line, and increase profitability.
Now that we’ve defined demand forecasting and described how accurate demand forecasts benefit your supply chains, let’s talk about the 5 techniques that will bring your demand forecasting to the next level.
5 Demand Planning and Forecasting Methods That Make a Difference
Even though supply chain operators and manufacturers rely on numerous methods spanning from traditional historical data analysis to leveraging advanced machine learning software, there are five tested and proven methods you should implement in your demand forecasting process.
Historical Data Method
Start forecasting demand by analyzing past sales data. The historical data method helps you get a rough estimate of demand for your products or services by monitoring past high and low periods of demand. It enables you to get a baseline prediction.
Bonus tip – leverage an AI-powered tool to help you get this baseline in minutes. The solutions powered by emerging technologies like AI or ML take into account your past sales data to make future predictions, so you don’t have to sit and sift through vast chunks of data.
Market Research and Delphi Method
What better way to understand consumer demand than talking to and collecting their data? Even though this direct market research approach involves considerable efforts to send out surveys to consumers and collect and analyze their feedback, it also gives you valuable first-hand insights into the minds of consumers.
Similarly, the Delphi method involves talking to market experts to get their opinion on market demand. Both methods involve human interaction and can help you draw on the knowledge of people with different areas of expertise.
The success of these methods depends mainly on your available resources and time. Collecting, collating, and analyzing data collected through these methods is time-consuming and will require expertise to leverage the information in predicting market demand.
Again, make it easier by deploying a data-driven tool that collects data from the end consumer and includes local market knowledge. Combining market reports with expert knowledge gathered from various sources to provide forecast accuracy makes the task more seamless and faster, making the relevant data available for quick decision-making.
Demand Sensing Method
One of the most efficient demand planning methods is demand sensing, which uses machine learning to capture real-time variations in purchase behavior. AI and ML software help you build a data-driven supply chain.
How is that helpful? Demand sensing helps you leverage real-time demand signals so that your supply chain develops the capability to respond quickly to unplanned demand changes.
A powerful, AI-enabled demand sensing tool can help you get real-time visibility into short-term demand, improving service levels and forecast accuracy.
Predictive Sales Analytics Method
Predictive supply chain analytics helps you estimate demand and understand what factors drive sales and how consumers will behave under certain conditions. The visibility provided by the combination of ML algorithms and advanced IoT highlights every step of the supply chain to build a demand forecast.
With a proper ML-powered tool, you can aggregate historical and new data from market surveys, social media, customer feedback, ERP, CRM, and others. You can develop predictive models to spot likely consequences and determine relationships between various factors.
External Macro Forecasting Method
Analyzing trends in the broader economy and determining how they will affect your business goals helps you understand the larger external market forces. You also get to monitor the availability of raw materials and other factors that can directly or indirectly affect your manufacturing and the overall supply chain.
Like previous methods, AI software helps you gather and analyze the correct information and present it as a proper report.
Accurate demand forecasting can benefit from these techniques, but to achieve ultimate success in forecasting demand, it’s vital to address obstacles and consider the significant variables influencing the predictions.
What are the Challenges of Demand Forecasting?
Demand forecasting is a complex process that has its own set of challenges. The success of demand forecasting largely depends on the industry in which the business operates.
One of the most significant challenges of demand forecasting is the accuracy of the data used in the process. Companies must rely on historical sales data, market trends, and consumer behavior to predict future demand. However, this data is often incomplete or unreliable, making it challenging to make accurate forecasts.
Another challenge of demand forecasting is the ever-changing market conditions. Economic factors, such as inflation and recession, can significantly impact consumer behavior and demand for products. Changes in technology and competition can quickly disrupt the market, making it challenging to predict future demand accurately.
Demand forecasting isn’t a one-time process. It requires continuous monitoring and adjustment to ensure that the forecasts remain accurate. While there are challenges, companies must understand them, make data-driven decisions, and ultimately, be willing to adapt their strategies and forecasts as market conditions change.
What Factors Impact Demand Forecasting?
Factors like economic changes, such as recessions or booms, can significantly impact consumer behavior and demand for products or services. Most of the factors fell into one of four major categories:
Seasonality:
Many products or services have seasonal demand patterns, such as winter clothing or holiday gifts, which can take time to forecast accurately.
Competitor Activities:
Competitor pricing, promotions, and product launches can impact a business’s product or service demand.
Consumer Behavior:
Changes in consumer preferences, tastes, and buying habits can impact demand for products or services.
External Events:
Natural disasters, pandemics, and other external events can disrupt supply chains and impact demand for products or services.
Understanding and incorporating these factors into the demand forecasting process helps businesses create more accurate forecasts and better manage their supply chains.
Demand Forecasting Real-life Examples
Demand forecasting has numerous real-life applications in modern supply chain companies.
So far, we’ve covered the basics of demand forecasting, now let’s learn from the pros.
Here are three examples of accurate demand forecasting implemented in modern supply chains:
Amazon:
Amazon uses demand forecasting to manage inventory levels and ensure timely customer delivery. Amazon can accurately predict product demand and optimize its supply chain operations by analyzing historical sales data, market trends, and other factors.
Procter & Gamble:
Procter & Gamble uses demand forecasting to manage its production and inventory levels for its various consumer goods brands. By accurately forecasting Demand, Procter & Gamble can optimize its supply chain operations and reduce overproduction and inventory management costs.
Walmart:
Walmart uses demand forecasting to manage its inventory levels and ensure that its stores are stocked with the right products at the right time. By analyzing sales data, market trends, and other factors, Walmart can accurately predict product demand and optimize its supply chain operations to meet customer demand.
As you can see, all major supply chain operators rely heavily on AI-based software to better manage their demand forecasting. This brings us to the final piece of the forecasting puzzle – artificial intelligence in demand forecasting.
What’s the Role of AI Software in Demand Forecasting?
AI-based software is increasingly being used for demand forecasting in supply chains. Since the software analyzes vast amounts of data and identifies patterns and trends that traditional statistical methods may miss, it can make more accurate demand forecasts to optimize supply chain operations.
Some benefits of using AI software for demand forecasting include:
Improved Accuracy:
AI software can analyze large amounts of data and identify patterns and trends that traditional statistical methods may miss, leading to more accurate demand forecasts.
Faster Processing:
AI software can process data much faster than humans, enabling businesses to create demand forecasts more quickly and efficiently.
Better Decision-making:
By providing more accurate and timely demand forecasts, AI software can help businesses make better decisions about production, inventory management, and pricing strategies.
Reduced Costs:
AI software optimizes supply chain operations and reduces overproduction and inventory management costs, resulting in reduced costs and increased profitability.
Throughput’s Demand Forecasting Solution
With the supply chain uncertainties on the rise, and demand volatility creating crisis testing the resilience of every business, those adopting data-driven demand forecasting solutions have a better chance of adapting to the change and responding to the changes in demand.
For this, you’ll require an all-powerful tool that can not only capture demand signals and fluctuations in real-time but also adjust the existing predictions to accommodate the changes.
ThroughPut’s AI-powered demand planning and forecasting tool, can help you accurately analyze and correlate demand insights. It uses historical data, analyzes short-term and external market trends, and identifies and predicts future demand patterns so you can get a 360-degree view of market demand for your products and services and adapt your demand plans to the changes in patterns.
Explore how ThroughPut can make a difference to your demand planning efforts. Talk to the experts at ThroughPut and book your demo today!
