Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. (Wikipedia)
LLamasoft published the results of a global retail supply chain study, which revealed that 73% of retailers believe artificial intelligence (AI) and machine learning can add significant value to their demand forecasting processes. Meanwhile, over half say it will improve 8 other critical supply chain capabilities. The research also found that while 56% of overperforming retailers, also known as'retail winners', use technology to model contingency plans for severe supply chain interruptions, a mere 31% of retailers who are not overperforming do the same. Overall, 56% of retailers surveyed are struggling with the ability to respond to rapid shifts, and the lack of flexibility has cost them during the disruptions such as COVID-19, with many seeing a huge drop in revenue as a result. In addition, 73% of'retail winners' have the foresight and ability to monitor capacity, which allows them to prepare for sudden shifts in demand and supply, compared to 35% of'other' or'under-performing' retailers.
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.
What is the top pain point for business executives? Gartner, the world's largest IT research firm, gives a clear answer: demand volatility. Too many factors from weather fluctuations to posts by social media influencers -- impact buyers, causing them to frequently change their minds. Worse still, things reshaping customer intentions happen quite unexpectedly. Think, for instance, of the teenage climate activist Greta Thunberg.
Forecasting of Energy Supply performs a vital role in the electric industry, as it gives the basis for giving decisions in power system planning and operation. A numerous variety of techniques for predicting power demand are being used by electrical firms, which are appropriate to short-term, medium-term or long-term forecasting. In such a changing environment common forecasting techniques are not sufficient, and more advanced methods are required. The aim is to completely untangle all the circumstances that lead to demand change and to discover the underlying problems. But analyzing many personal and social factors is difficult.
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.
Build-to-order (BTO) supply chains have become common-place in industries such as electronics, automotive and fashion. They enable building products based on individual requirements with a short lead time and minimum inventory and production costs. Due to their nature, they differ significantly from traditional supply chains. However, there have not been studies dedicated to demand forecasting methods for this type of setting. This work makes two contributions. First, it presents a new and unique data set from a manufacturer in the BTO sector. Second, it proposes a novel data transformation technique for demand forecasting of BTO products. Results from thirteen forecasting methods show that the approach compares well to the state-of-the-art while being easy to implement and to explain to decision-makers.