Using AI & Machine Learning for Demand Forecasting

Introduction

Demand forecasting is the process of developing the best possible understanding and estimations about future customer demand. Forecasting demand for products and services is a tricky business. A new product may be all the rage one day, only to be replaced by something new the next. And this isn’t just an issue with demand trends or fads either – markets are always changing. The last thing any company wants is to produce too much inventory, which ends up costing them more money than they’re bringing in through sales, but at the same time they also don’t want to invest too little into production, risking running out of stock when customers come calling.

The old strategy was guessing what might happen in the future based on past performance – but now thanks to artificial intelligence (AI) coupled with data sets that can predict outcomes, businesses have a whole new way of gauging demand forecast, and it’s changing the game.

“Using AI for demand forecasting really has two parts to it,” explains Matt Straz , CEO of KeepTruckin . “The first is being able to take historic data – past events, past trends, past behaviors – and be able to predict what may happen in the future.”

What Is AI And How Does It Work?

What is AI?

There’s no one-size-fits-all answer to this question, as AI can be used in a variety of ways, but at its most basic level, AI is a process of programming a computer to make decisions on its own. This can be done by teaching the computer how to learn through data sets, or by giving it rules to follow.

How does AI work?

Again, this depends on the specific application, but in general, AI works by using algorithms to analyze data. This data can come from a variety of sources, including social media, financial reports, or climate data. Once the computer has analyzed the data, it can use it to make predictions about the future.

How Is AI & Machine Learning Being Used For Demand Forecasting?

There are a number of ways that businesses are using AI to improve demand forecasting. Some companies are using it to analyze historical sales data in order to make predictions about future demand. Others are using machine learning to study consumer behavior in order to anticipate changes in buying patterns. And still others are incorporating human judgment into their forecasting process in order to help forecast the unexpected.

“It’s not just about utilizing historical data,” says Straz, “it’s being able to look at the future-state landscape of your business and change it so you can actually see what it looks like.”

Why Use AI For Demand Forecasting?

There are a number of reasons to use AI for demand forecasting. The first is that, while you can take predictions from the past and compare them to what happened in the future, it’s difficult to be accurate with these predictions. Plus, there are always unknown factors that come into play when forecasting future trends. With AI, these unknown factors can be analyzed thanks to machine learning.

The second reason is that integrating both humans and machines will help make better predictions about the future. Rather than one or the other, humans and computers work together to make a more accurate prediction. “The combination of human and machine is really where the power comes in,” says Straz.

How Businesses Are Using AI To Improve Demand Forecasting

There are a number of different ways that businesses are using demand forecasting software, and each business has its own specific needs that AI can help meet. Here are a few examples:

  • Maruti Suzuki is using artificial intelligence to predict future demand for what parts will be needed in their cars
  • Tesla has begun using AI to predict the most efficient route possible for each car on the road
  • Disney World uses AI with sensors in order to track wait times throughout the park, which helps them plan better for future crowds
  • Amazon is using AI forecasts to predict what product demand will be like for different areas of their warehouses. This way they can make more efficient use of inventory space and employees’ time
  • Google uses AI to anticipate how many cars might need to drive through an intersection at a specific time, making it easier for them to plan their delivery routes

The Benefits Of Using AI To Forecast Demand

There are a number of benefits to using AI for business demand forecasting. Some of the most important ones are:

  • Accuracy: With the help of AI, businesses can make more accurate predictions about future demand. This is due to the machine’s ability to analyze data in a way that humans can’t.
  • Speed: AI can process data much faster than a human can, so businesses can get predictions more quickly.
  • Efficiency: With the help of AI, businesses can become more efficient in their forecasting process. This means they can make better use of their time and resources.
  • Flexibility: AI allows businesses to be flexible in their forecasting process. They can change their predictions based on new data that comes in, and they can experiment to determine the best way for their forecasting process to work.

Demand Forecasting Types

Passive Demand Forecasting

Passive demand forecasting is a technique used to predict future sales. This method relies on historical sales data to identify patterns and trends. Once these patterns have been identified, the forecast can be made by extrapolating from the past data. Passive demand forecasting is most effective for products that have a predictable sales cycle.

Active Demand Forecasting

Active demand forecasting is a technique used to predict future sales. This method relies on current and projected market conditions to identify patterns and trends. Once these patterns have been identified, the forecast can be made by extrapolating from the past data. Active demand forecasting is most effective for products that have a volatile or unpredictable sales cycle.

Short-term Demand Forecasting

Short-term demand forecasting is a technique used to predict future sales in the near future. This method relies on current market conditions to identify patterns and trends. Once these patterns have been identified, the forecast can be made by extrapolating from the past data. Short-term demand forecasting is most effective for products that have a volatile or unpredictable sales cycle.

Long-term Demand Forecasting

Long-term demand forecasting is a technique used to predict future sales in the long term. This method relies on current and projected market conditions to identify patterns and trends. Once these patterns have been identified, the forecast can be made by extrapolating from the past data. Long-term demand forecasting is most effective for products that have a predictable sales cycle.

External Macro Forecasting

External macro forecasting is a technique used to predict future sales by looking at the overall market conditions. This method relies on current and projected market conditions to identify patterns and trends. Once these patterns have been identified, the forecast can be made by extrapolating from the past data. External macro forecasting is most effective for products that have a volatile or unpredictable sales cycle.

Internal Business Forecasting

Internal business forecasting is a technique used to predict future sales by looking at the company’s own market conditions. This method relies on current and projected market conditions to identify patterns and trends. Once these patterns have been identified, the forecast can be made by extrapolating from the past data. Internal business forecasting is most effective for products that have a predictable sales cycle.

Demand Forecasting Methods

Methods of demand forecasting are important for businesses to predict future sales and plan their production. The most popular methods are trend analysis, correlation, and regression analysis. Each method has its pros and cons which need to be considered while forming the forecasting model.

Trend Analysis

Trend analysis is a simple and effective way of projecting future sales. It tracks past trends and projects them into the future using suitable statistical methods such as moving averages, exponential smoothing, etc. This approach works well when there is no seasonality or cyclical nature to the data. If there is seasonality in sales, it can be corrected by considering seasonal factors like monthly temperature or humidity for clothing for example.

Correlation Analysis

Correlation analysis has similar advantages to trend analysis but it also takes into account correlation between different variables. For instance, if the number of widgets sold last year is positively correlated with price (i.e. higher the price, higher the sales), then this information can be used to develop a better forecast model. Correlation analysis is best suited when there are several variables that have an impact on future sales.

Regression Analysis

Regression analysis measures the relationship between two or more variables and quantifies it in the form of a curve. Unlike correlation analysis, regression analysis takes into account the strength of the relationship between variables. It provides a forecast model which is more reliable than that using either trend or correlation analysis alone. In addition to providing a forecasting model, regression analysis also identifies influential factors that may have an impact on future sales.

Numerous Regression models can be used for market research and demand forecasting. The most popular among them are weighted-moving average model, double exponential smoothing method, and Holt’s Linear Regression.

Weighted Moving Average

Weighted moving average takes into account the time lag between two consecutive observations when creating a forecasting model. The longer the time lag in a data set, the more significant that observation will be considered for forecasting future values. For instance, if the demand for a product in January is 100 units and in February it is 80 units, then this data will be more significant than that of June (where demand may be only 50 units). The higher weights are assigned to recent observations which results in making a better forecast.

Double Exponential Smoothing

Double exponential smoothing technique gives exponentially decreasing weightage to past data. The exponentially decreasing factor is used to measure the rate of decay in forecasts based on historic demand patterns. For example, if there are 20 months of historical data available for estimating future demand, then last 6 months’ data will be given more weightage than first 14 months’ observations.

Holt’s Linear Regression

Holt’s Linear Regression approach uses an exponential curve to map the relationship between demand and other factors influencing it. The exponential curve is derived from historical data itself. In this approach, a trend equation is created based on regression analysis which helps in establishing a close approximation of the future sales value. Holt’s model works well when there are several influencing factors for sales which have different impacts on sales during different seasons. In other words, Holt’s method is capable of identifying the primary drivers for sales as well as their relative contribution to future sales value.

Artificial Intelligence’s Future Impact On Businesses And Society As A Whole

It’s clear that businesses are beginning to realize the benefits of using AI for demand forecasting. As this trend continues, we can expect to see even more businesses using AI to help them plan for the future. Additionally, we can expect to see more collaboration between humans and machines, as this is where the real power lies for accurate forecasting. This type of collaboration between humans and machines (AI) makes it easier for businesses to make accurate predictions about future demand. “It’s not about replacing people, it’s about using the best work of both to make better decisions” says Straz.

In the future, we can expect to see a lot more businesses using AI in their demand forecasting processes. This is one way that AI will help businesses become more flexible when it comes to planning for the future. Additionally, this will allow businesses to make more efficient use of their time and resources. Business leaders are learning from their data to improve in areas such as sales, inventory management, retail planning, supply chain processes and more. AI’s ability to analyze data quickly will also help speed up the demand forecasting process.

Conclusion

It’s clear that businesses are beginning to realize the benefits of using AI for demand forecasting. As this trend continues, we can expect to see even more businesses using AI to help them plan for the future. In addition, we can expect to see more collaboration between humans and machines (AI), as this is where the real power lies for accurate forecasting.

To learn more about using AI for Demand Forecasting at your company, visit our website. Our DATA BOSSES are happy to help you add demand forecasting backed by AI to your business.



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