Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. (Wikipedia)
Verdis is a supply chain data analytics software that uses Opportunity Intelligence, a proprietary technology model, that identifies opportunities for maximizing performance in the supply chain. Opportunity intelligence utilizes Artificial Intelligence and Machine Learning to analyze data, identify patterns, build causal relationships and then using augmented analytics to push insights to the decision makers. These insights are powerful action drivers that help the SCM function to optimize the performance between the responsiveness and efficiency of the operations at all levels and across different stages. Therefore whether it is identifying the areas for optimizing inventory levels or SCM costs, Verdis is able to provide predictions and give recommendations that help to elevate the performance delivery of the function. As it is driven by powerful learning algorithms, Verdis is constantly learning the individual business context of your organization which enables it to deliver your context-specific insights.
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. Papers published at the Neural Information Processing Systems Conference.
Intermittent demand, where demand occurrences appear sporadically in time, is a common and challenging problem in forecasting. In this paper, we first make the connections between renewal processes, and a collection of current models used for intermittent demand forecasting. We then develop a set of models that benefit from recurrent neural networks to parameterize conditional interdemand time and size distributions, building on the latest paradigm in "deep" temporal point processes. We present favorable empirical findings on discrete and continuous time intermittent demand data, validating the practical value of our approach.
Ride-sourcing services are becoming an increasingly popular transportation mode in cities all over the world. With real-time information from both drivers and passengers, the ride-sourcing platform can reduce matching frictions and improve efficiencies by surge pricing, optimal vehicle-trip assignment, and proactive ridesplitting strategies. An important foundation of these strategies is the short-term supply-demand forecasting. In this paper, we tackle the problem of predicting the short-term supply-demand gap of ride-sourcing services. In contrast to the previous studies that partitioned a city area into numerous square lattices, we partition the city area into various regular hexagon lattices, which is motivated by the fact that hexagonal segmentation has an unambiguous neighborhood definition, smaller edge-to-area ratio, and isotropy.
Machine learning has become a vital component to get solutions in everyday life. It is adding intelligence in every product we are using today. Marketing software and demand forecasting are using ML to a great extent. In the latest generation, the data is available in bulk, but we need more tools to handle this data. Machine learning is the only solution to so this task as it allows the computer to learn from data for improved analysis.
Businesses in every industry are facing increasing demand volatility. Additionally, with rapidly evolving market conditions, it has become vital for businesses to stay prepared and anticipate the future. To cater to these fast-changing market dynamics, the practice of demand forecasting began. Today, several businesses, especially those belonging to the FMCG sector, have sophisticated demand forecasting models in place, which help them stay ahead of the market. An area of predictive analytics, demand forecasting takes into account the historical data of a business and uses that to harnesses the demand for their goods and services.
Many companies are still relying on complex demand forecasting processes that are generated from the bottom, up. Often, this is a byproduct of assumptions made by one's sales force, marketing team, manufacturing team, product management team, and other internal stakeholders. For mid to large companies, the simple act of gathering this information and turning it into an actionable plan is arduous and time-consuming. By the time the forecast is complete, the information is already out of date.
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.
Well, I work in this area now, and since this is upvoted a bit I'll give my thoughts. And I'll assume you're constraining the term "demand forecasting" to how its often used in business contexts....as well as your your recent posts on issues getting RNN/LSTM to work your time-series data. IMO the best tool for most product/service demand prediction tasks is domain knowledge for good feature engineering and for getting your data to be more stationary. Why? Product/service demand forecasting problems often start with only few explanatory variables as well as those variables not explaining the variance well (more precisely, low mutual information) relative to the number of actual factors going into the demand. Contrast this with areas getting more media such as deep reinforcement learning, where states and actions are fully representable/observed (e.g., AlphaGo).
Another problem is that the more granular the forecast – SKU at store level by week, for example – the higher the forecast error tends to be. "For sure, the greater degree of error in the store-level forecast, the greater the impact on the lost sale calculation," Fenwick said. "However, even if we hit a 70% accuracy measure, we're still capturing 70% of the potential lost demand in the store due to stock outs. Which, from a forecasting perspective, is a lot better than capturing zero lost demand. As the saying goes, 'if you only forecast to sales, you'll only ever stock to … what you sold.'"