One of the most important parts of any organization that relies on products is demand forecasting. Precise projections result in increased productivity, improved inventory control, and avoided cost overruns. AI-based models have played a significant role in increasing this function’s transparency and accuracy. Two categories of models exist.
Black box models, in which the model’s explanation and forecasting are separate. An explanation of the model is crucial for comprehension as it enhances transparency. Here, the researchers create unique algorithms to comprehend the model.
Glass Box Models: These are models in which an AI-based algorithm can justify its forecast.
In order to better comprehend the prediction’s soundness and gain more control, forecasting planners need an explanation of the variables and data that were used in each forecast. A model’s explanation ought to provide logical justification and relevant data (Pedreschi et al., 2018). In addition, actionable information and maybe counterfactuals should be included in the model. The following characteristics could be present in the technology and algorithm used.
Give information about the dataset’s characteristics.
The features of the ranges and the dataset.
Give the user explanations in the appropriate language and context.
Now let’s examine why models based on AI and machine learning are recommended.
Forecasting techniques based on machine learning versus tradition.
The accuracy of traditional statistical forecasting models, which rely on past time series data, is reliant on stable market circumstances. Nevertheless, conventional approaches neglect to include shifts in customer behavior and market disruptions brought about by innovation and technology advancements.
More timely and relevant forecasts can be produced using machine learning algorithms by utilizing real-time data from both internal and external sources. The Institute of Business Forecasting and Planning lists a few datasets that can be used with contemporary machine learning methods. Sales data, website analytics, clickstream data, geolocation, social media, POS data, macroeconomic indicators, and third-party syndicated data are a few examples of what they entail.
Large datasets with intricate linkages between different datasets are used by machine learning algorithms to identify trends and gauge demand. The intelligent models adapt to changing market conditions by continuously retraining themselves as new data becomes available. The forecasts that result are far more precise and trustworthy.
But as we covered previously in the chapter, it is essential that a human comprehends the model’s characteristics, how each dataset affects the result, and the reasoning behind the forecast.
Analytics for Predictive
The most popular use of machine learning algorithms in conjunction with statistical methods is predictive sales analytics. In specific situations, it aids the business in understanding customer purchasing behavior and demand.
To forecast demand, the predictive models incorporate external macroeconomic data, several external variables, and internal firm data. It enables a business to make far more educated decisions on product releases, event planning, and consumer purchasing.
One of the main disadvantages is that because to the complexity of the datasets involved, medium-term forecasting is the best it can look at. Techniques such as Demand Sensing are employed to have a closer understanding of demand.
Sensing Demand
Real-time sales data is integrated into demand sensing algorithms, which generate short-term projections as variations in purchasing patterns emerge. It gathers daily information on sales, warehouses, and other sources to show changes in the medium-range projections. In addition, the models provide extremely brief time forecasts for daily operations and explain the reasons behind each variable as well as the importance of each component.
Widespread Use of Machine Learning for Demand Prediction
It is crucial to comprehend how an organization can get the most out of machine learning as these algorithms are expensive to implement and need massive amounts of data and processing power. Here are a few of the best application cases.
Introduction of New Product: The absence of historical sales data makes it challenging to estimate demand for new products. Expert opinions based on experience have traditionally been a popular method for making such forecasts. On the other hand, machine learning algorithms employ product life cycle curves and historical sales data of related products to do more precise and fact-based forecasting. This influences the decisions made by an organization’s supply chain, marketing, and manufacturing departments.
Products with Short Life Cycles: Fashion items usually have a few-month life cycle and are seasonal. Demand Sensing techniques are quite helpful in this situation since they provide a sense of control and predictability throughout the whole process.
Seasonal Products: Items such as winter coats, umbrellas, and summertime refreshments are weather-dependent and necessitate extremely short-term forecasts. Businesses can create different scenarios and see how each affects demand with the use of machine learning.
Useful Examples
The largest eyeglasses company in the world, Luxottica, launches about 2000 new models annually. The company’s estimates have improved by 10% as a result of using clever forecasting procedures, which has led to significant savings and control.
To avoid overstocking and stockouts, the UK National Health System utilizes machine learning (ML) based algorithms to determine the amount of blood needed in hospitals. Blood is a vital life-saving substance, so it’s crucial to have precise and trustworthy forecasts because stockouts can have disastrous consequences and overstocking causes waste. This technique has led to a 30% decrease in hospital overstocking, which has decreased waste in certain hospitals and stockouts in others.
Artificial Intelligence in Predicting Demand
Editors Corner
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