Utilizing human experts on demand forecasting does no harm. However, if there is enough data to train a model, machine learning is the best alternative you may have.
Having an accurate demand forecast depends on multiple parameters, some of which are hard to collect and clean. However, Latest improvements in AI/ML technologies proved that neural networks formed by those technologies are far more complex and beneficial than the organic neural networks. That is, thanks to improvements in the AI/ML area, said technologies are now working far more efficient than their human counterparts. Being efficient may seem like a dim concept here. Not when there are 4 years of sales data for every day, and you are trying to construct a model for forecasting later years.
Why to forecast demand?
If you have come this far in the endless sea of internet, this is a dull question to ask. Why to forecast demand? To make more profits and be ready for the upcoming uncertainties. This is easy as it sounds. Nevertheless, you’d be surprised if you knew the number of enterprises lacking the proper tools and methods for forecasting demand. There are a ton of companies that are only keeping loose records of sales, supply, and logistics. Mostly relying on a universal prediction model, they are making decisions almost solely backed by their gut. Problem is that there is no such thing as a universal prediction model. A prediction such as every summertime car sales must increase is totally biased towards a hidden pattern invisible to human eye, but obvious to machine learning algorithm. So, algorithm shows more accurate models on why, how, and how much.
How Machine Learning Changes the Game?
Machine Learning almost completely changes the structure of strategic planning. Prediction models fed by accurate/enough data, can be interpreted by various methods such as visualization and moving averages. Important thing here is that the stages of machine learning can be differentiated according to the needs and capital in hand. Therefore, you don’t have only single solution to your forecasting schema. That way, ML not only speeds things up but also it suggests way more alternatives than manual demand forecasting. That is, you can spot the seasonality effects without needing an ML algorithm; however, seasonality effects are the easiest one. When it comes to spot the other anomalies in data such as outliers, hidden patterns, cointegrations, substitutions, and non-financial movements you have no luck with manual work; thus, ML is the only way to go. That changes the game as accuracy of the prediction models is outstandingly increased.
How Machine Learning Works?
This question all by itself is another writing’s topic. However, taking demand forecasting into account, we will answer how machine learning forecasts demand? There are two types of techniques for Machine Learning: supervised and unsupervised learning. Former trains a model as an expert provides inputs and outputs, later works on input data to spot hidden patterns. Both techniques are used to construct prediction models as they have no superiority over each other. However, supervised learning is being used a little more compared to unsupervised learning in demand forecasting. Linear regression, decision trees, and neural networks can be utilized for demand forecasting to train a model to make a prediction.
What are its Benefits?
Benefits of machine learning or benefits of demand forecasting? Actually, both! Benefits of demand forecasting with machine learning is what we are looking. The most basic ones are speed of application and accuracy. If you have enough data to train a prediction model and enough capital to utilize machine learning, you can easily spot seasonality effects, hidden patterns, substitution effects and much more via machine learning algorithms. That way you can visualize the demand movements for your product, demand trends in the market for your product and drifts of consumer demand. That valuable information would help you to act without being too late.
What Does Enhencer Offer?
You’ve seen the machine learning and its benefits. Now, think that you don’t need to know programming, and this process is automated. Utilizing machine learning without knowing programming is almost impossible if you would like to do it by yourself. A proper knowledge on MySQL, R and/or Python etc. is enough to start a simple model. However, a machine learning model is complex even its simplest form. Thus, when things go a little complex you will need to have an expert. However, with Enhencer, you don’t need to know programming. Feeding it with data and choosing the prediction method is enough to utilize machine learning!