Will Machine Learning Revolutionize Demand Planning?
AI and machine learning are becoming increasingly sought-after in various fields, including robotics, advanced driver assistance systems (ADAS), computer vision, speech recognition, and supply chain and logistics.
Every industry is keen to integrate deep learning into at least one of its processes and automate its systems. By utilizing AI and ML technologies, supply chain operations are quickly evolving and heading toward effective digitalization.
Supply chain elements, including demand forecasting, logistics & transportation, inventory management, production planning, and procurement, can all benefit from the use of AI and ML ideas.
Today, we are going to have a closer look at one of the supply chain sectors that machine learning can help with. It entails planning and forecasting demand. The supply chain cannot function without it, and this necessitates the need to make the process more efficient. Several industries, however, still need help with it or are only employing crude techniques.
Many of the problems that planners run into later on can be successfully resolved if the process is carried out correctly. This new-found value can be utilized for better sourcing, prudent inventory management, solid production planning, and timely logistics and transportation.
What is Machine Learning?
Artificial intelligence has a subfield called machine learning that focuses on leveraging past and present data to make machines learn in a human-like way. Using both already-collected data and newly-arrived information is the aim in order to continuously adapt to changes and produce precise forecasts or models.
Why is Demand Planning Important?
Demand forecasting is the technique of estimating and predicting future consumer demand for a good or service by applying predictive analysis of previous data. Simple Moving Average, Holt’s Winter Method, Croston Method, and Syntetos-Boylan Approximation are some of the most popular statistical forecasting techniques opted for by businesses.
Time Series Analysis, Regression Modeling, and Deep Learning Modeling are the most highlighted machine learning methods of forecasting adopted by brands to improve their decision-making.
How To Apply Machine Learning for Demand Planning?
Machine learning may dramatically enhance demand forecasting, resulting in better predictions. Nevertheless, if you follow a few pointers, you may get the most out of fusing the two. Let’s take a look at them!
- Supply Maximum Relevant Data to the Prediction Model
First off, the accuracy of the expert systems’ technology’s results depends on the quality of the data you provide. This includes historical data, applicable technologies (such as social listening, SEO keywords, etc.), and relevant location- or market-specific data. Your data will yield greater outcomes if it is more comprehensive and pertinent.
- Be Specific About the Problem
Although machine learning is fantastic, it is not magical. Thus, you must determine the information you need to know. Machine learning can specifically address questions like “What will the market for my product X look like in the next five years?” as an example. The computation will be more difficult to do if you enter something ambiguous or open-ended because it will demand more variables than the machine can manage by a significant margin. Being clear about the data you are looking for is, therefore, the best course of action.
3. Set Data Quality Parameters
The process will begin with you providing the system with the initial data, over which you have quality control. When more real-time data is gathered, the system will need to decide which data to maintain based on factors like reliability, relevance, and authenticity.
In other words, combining demand forecasting and machine learning will increase the general accuracy of your findings. The most recent industry trends will be incorporated into your forecasts, making them more current and accurate.