Powering Retail with AI: Transforming raw numbers into strategic insights using Timeseries Forecasting

Retail and e-commerce companies using AI elevate decision-making with intelligent forecasting, gain a competitive edge and increase customer satisfaction. Decision Intelligence is the here to unlock efficiencies.

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September 29, 2023
minutes read
Data Science

Facing the forecasting challenge

Navigating the turbulent waters of demand forecasting, a vital part of supply chain management, can often be likened to finding your path around a maze blindfolded. You sense there's a tangible method to the madness but can't quite pin it down. The standard guesswork and intuition can seem enticing, but these measures rarely cut it in the long run. It's crucial to base predictions on accurate data analysis - taking into consideration vast amounts of relevant data including historical patterns, market trends, seasonal shifts and sales records. When done right, it gives your retail business improved control over the supply chain and a competitive edge.

Elevating decision-making with intelligent forecasting

Intelligent demand forecasting can help your retail business grow in unprecedented ways. It serves as the backbone for efficient budget planning, equips you to optimize inventory levels, speeds up delivery times, and boost resource management efforts. Think of it as an X-ray machine, helping you decide which product needs to be in what quantity, where and when. Such precision promotes cost-saving, improves product and service quality while varying according to the forecasting method employed.

Incorporating Machine Learning for Retail Timeseries Forecasting

Having narrated the relevance of accurate forecasting, let's delve into how we make it a reality. At paretos, we use a unique algorithm that blends Machine Learning with timeseries forecasts. The core principle it relies on is finding a correlation between an input and an output based on historical observations. It ventures to predict future data points even for inputs that haven't been tested before.

Gaussian Process Regression: Our trusted tool

For instance, consider a retail scenario where 'x' is the price of a product and 'y' is the sales it generates. Using an array of mathematical tools, such as the Gaussian Process Regression, we take all possible functions into consideration and assign a probability to each. We then align with the more likely functions, using a density function similar to normal distribution, while adding the convenience of pulling from a vector of numbers. As additional information is fed into the system, the algorithm updates the probability distribution, increasing the overall accuracy of the forecast.

Harnessing the power of adaptability and uncertainty

One of the advantages of using Gaussian Process Regression is its inherent adaptability. As more data points are observed, it recalculates and aligns the distribution accordingly. Additionally, its probability-based nature allows it to quantify uncertainty. Thus, it doesn't just make the 'best guess,' but provides a range within which most functions are expected to fall. This is particularly useful in retail, where market dynamics shift rapidly, and preparedness for multiple possibilities can be a lifesaver.

Stepping into the future of retail with us

Sophisticated yet grounded in simplicity, the Gaussian Process Regressor serves as a potent tool in the retail industry's arsenal. With paretos, let your business embark on a journey from raw data to strategic decision-making, leveraging the power of AI-based timeseries forecasting. Stay ahead in the highly competitive retail world and empower your supply chain management with unparalleled precision and prowess.

We are the leading AI-based decision intelligence platform for effective, data-driven decision-making processes in companies. No more bad decisions!
Fabian Rang
CTO & Co-Founder
My passion lies in mathematical methods for processing complex data structures and models. Traded my engineering job for a PhD in Systems Engineering and Mathematics and spent several years researching efficient optimization modeling.

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