Demand management solutions have improved on many fronts. One area where some solutions have made particular progress is in the area of providing highly flexible solutions that can evolve as the company's business changes. Demand management solutions need to be flexible! Over time, companies often go on a journey where their forecast accuracy is improved by using more and more data and by forecasting at a more granular levels (For more on this topic, see this article which describes stages in demand forecasting maturity). To support this journey demand management solutions need to be flexible.
This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.
We present a scalable and robust Bayesian method for demand forecasting in the context of a large e-commerce platform, paying special attention to intermittent and bursty target statistics. Inference is approximated by the Newton-Raphson algorithm, reduced to linear-time Kalman smoothing, which allows us to operate on several orders of magnitude larger problems than previous related work. In a study on large real-world sales datasets, our method outperforms competing approaches on fast and medium moving items.
Maybe you are doing your demand forecasting completely wrong. To be more precise, there are two equally important outputs of demand forecasting and you may be focusing nearly all your energy on only one, and maybe even the wrong one. And the impact is that you may not be getting the forecast accuracy you want. Or even more important, that you may not be getting the service levels and inventory efficiencies that you need. And if that's true, you are not alone.
The key to success for any CPG company is its ability to predict demand and agility in response to changes in consumer demand. In a situation where a CPG company is left with no choice but to find options to please its customers to keep the business running, technology and data can be a savior. With technology expansion, the CPG industry stands a big chance at success. CPG companies have many digital technologies and predictive analytics tools at their disposal. The digitization of the entire manufacturing and supply value chain is gradually becoming a reality.