The supply chains of the Canadian forest products industry are largely dependent on accurate demand forecasts. The USA is the major export market for the Canadian forest products industry, although some Canadian provinces are also exporting forest products to other global markets. However, it is very difficult for each province to develop accurate demand forecasts, given the number of factors determining the demand of the forest products in the global markets. We develop multi-layer feed-forward artificial neural network (ANN) models for demand forecasting of the Canadian forest products industry. We find that the ANN models have lower prediction errors and higher threshold statistics as compared to that of the traditional models for predicting the demand of the Canadian forest products. Accurate future demand forecasts will not only help in improving the short-term profitability of the Canadian forest products industry, but also their long-term competitiveness in the global markets.

AI developers DataRobot and Palantir Technologies Inc have entered into a new partnership designed to create unique, agile and real-time solutions to help solve the most pressing demand forecasting problems.

PredictHQ has announced its Demand Impact Pattern for severe weather events, with data sets to help businesses prepare for major weather events and mitigate overall impact by integrating into machine learning models for demand forecasting.

By Rudrendu Kumar Paul and Bidyut Sarkar

Embracing the Future of Supply Chain Management: An Exploration of AI's Transformative Impact on Demand Forecasting and its Potential to Navigate Global Uncertainties.

I've never been much of a fan of forecasting approaches to intermittent demand. In situations like intermittent demand (or other areas where we have little hope of reasonably accurate forecasts), my thinking is "why bother?" If we can't expect to solve the problem with forecasting, we need a different approach. [...]

The post Intermittent Demand Forecasting (new book by Boylan and Syntetos) appeared first on The Business Forecasting Deal.

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.

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