How Machine Learning Can Improve Demand Forecasting

How Machine Learning Can Improve Demand Forecasting

Machine learning has seen one of the most rapid growths, its market value increasing from $1.03 billion in 2016 to $15.44 billion in 2021. This innovation has also outperformed expert predictions and gained a strong foothold in the commercial industry. The reason is that businesses have benefited from its applications, succeeding at enhancing demand forecasting with machine learning.

Of course, demand forecasting is one of many aspects that have improved due to this technology. However, this aspect significantly impacts a company’s operations, costs, revenue, and business strategy. Therefore, it is one of the most critical parts of the productivity and growth equation.

This blog post will focus on machine learning, examine its impact on demand forecasting, and explain the benefits. It will also provide tips on optimally leveraging the technology for this purpose.

What Is Machine Learning?

Machine learning is a branch of artificial intelligence that focuses on using historical and real-time data to learn in a human-like manner. The goal is to leverage existing and incoming information to make accurate predictions or models, adapting to changes continuously.

ai vs machine learning vs deep learningSource: Microsoft Documentation

The learning mechanism allows this technology to go beyond its core programming, adjusting the algorithm to reflect knowledge from new information. 

How Does Machine Learning Affect Demand Forecasting?

Demand forecasting is an application of data analytics that uses comprehensive information to provide data-centric predictions for future demand. Hence, there is a similarity in how demand forecasting and machine learning operate, making the latter a complementary innovation.

As mentioned above, machine learning can leverage data to ‘learn’ and make more accurate predictions. Hence, experts can optimize demand forecasting with machine learning by incorporating the learning to make relevant adjustments to forecasts.

This difference is critical because demand forecasting cannot factor in new variables and adjust their inter-relationships without human intervention. In contrast, machine learning is self-sufficient and can perform this role.

Enhancing Demand Forecasting With Machine Learning

The definitions and impact make it evident that pairing demand forecasting with machine learning is the ideal solution for improvement. This integration carries significant benefits for forecasting and growth strategy, the most vital of which are mentioned below:

1. Higher Analytical Capacity

Machine learning technology was made with the expectation that it would conduct advanced calculations and manage extensive data simultaneously. Hence, it can work with several variables without letting the wide range of calculations affect its efficiency and effectiveness.

This trait makes it perfect for demand forecasting in the modern world, where trends change consistently, often increasing the number of factors in a predictive model. Machine learning effectively manages the additional data and details, allowing companies to get better results.

2. Demand Sensing

Demand sensing is detecting short-term trends and adjusting your planning to incorporate them. Machine learning enables businesses to pick up on these trends and adjust their orders accordingly in time to benefit from the opportunity.

You need ML for demand sensing because it will automatically detect the change. A typical demand forecast will require a person to adjust settings to incorporate the added factors, while machine learning will alert the analyst. 

Hence, this intelligent innovation makes your operations more adaptive and fluid.

3. Incorporates Unknown Factors 

Markets and consumer behavior change with time, and trends often emerge, leading to discrepancies in buying patterns. Sometimes, the trends are so subtle that analysts find it difficult to pinpoint the core reasons for the difference in sales or marketing data.

Machine learning helps fill this gap with its ability to account for unknown factors affecting demand forecasts. The algorithm will adjust to include the new variable in computation, creating a new equation. This process mostly happens automatically since the machine is capable of learning and adjusting on its own.

4. More Accurate Predictions

Machine learning’s prediction capabilities are far superior to any previous technology. The algorithm is structured to continue extracting available data and incorporating it to improve prediction accuracy. Hence, demand forecasting with machine learning provides an updated, relevant, and optimal forecast for the defined objective.

However, the accuracy is heavily contingent on the availability of quality data. Hence, integrating the system with relevant data-sourcing technological solutions is the best solution. 

5. Quicker Processing Times

The best part about machine learning is that it retains efficiency despite the complexity of calculations. The program accommodates large amounts of data and processes it more quickly than various analytics software.

However, the efficiency is relative to other solutions doing the same task. The processing time does slow down slightly the more information-intensive the projects get. But, you can rest assured that you’ll get the best times using machine learning.

6. Accommodates Internal and External Factors

One limitation of regular demand forecasting is that analysts often have to choose between internal and external factors when computing predictions. Even collaborative solutions can only incorporate a limited number of both to prevent the system from collapsing due to the extent of data.

Machine learning does not have such restrictions, so incorporating it would allow you to add all relevant internal and external factors to the consideration.

7. Optimal Utilization of Historical and Current Data

Lastly, machine learning relies on historical data AND current data. It can combine both and, with suitable data supply, will keep adjusting the forecasts based on the latest developments. Additionally, it will also provide short-term and long-term forecasts so that both ends will be sorted.

Tips for Using Machine Learning for Demand Forecasting

Enhancing demand forecasting with machine learning can improve the results significantly. However, a few tips can help you maximize the value you get from combining the two.

1. Supply Maximum Relevant Data to the Prediction Model

Firstly, the quality of data you add to the machine learning technology dictates how accurate its results are. Incorporate historical data, integrate relevant tools, like social listening, SEO keywords, etc., and provide appropriate location or market-specific information.

The more detailed and relevant your data, the better the results.

2. Be Specific about the Problem

Machine learning is incredible, but it isn’t magical. You need to identify what you want to know. For example, asking, ‘What will the demand for my product X look like in the next five years?’ is a specific question that machine learning can approach.

Putting something vague or open-ended will complicate the computation because it will require several times more variables than the system can handle. Hence, the best solution is to be specific about what information you’re looking for.

3. Set Data Quality Parameters

Lastly, set data quality parameters. You will feed the initial data to the system and can control the quality; however, that is only the beginning. The system will also gather more real-time data and needs to know which information to keep based on its authenticity, relevance, reliability, etc.

Final Thoughts

In short, mixing demand forecasting with machine learning will enhance the overall accuracy of your results. Your forecasts will be more relevant and updated to incorporate the latest trends in the industry. 

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