With the recent global and regional socio-economic disruptions caused by the pandemic, industries such as retail, consumer products, manufacturing, pharmaceutical, and life sciences all struggle to align production and stocking with rapidly shifting purchasing demands. At the same time, some channels have surged ahead: online retailers, delivery services, and pharmacies are thriving. In this latest Data Science Central podcast, we discuss how injecting AI into existing business intelligence solutions can greatly enhance the ability of organizations to predict future demand for goods, even in uncertain and dynamic times.

As we inch closer to Black Friday and the start of the holiday buying extravaganza, retailers are putting the final touches on the demand forecasts they're using to predict the mix of goods they'll carry this winter. There are lot of variables to juggle, including COVID, the economy, and the weather. It seems like a perfect use case for the increasingly sophisticated machine learning models that are in vogue in the industry. But can they trust their predictions? Over the past decade, retailers and other companies in the consumer goods supply chain have started upgrading their demand forecasting systems in hopes of gaining ground in this super competitive industry. Forward-looking retailers, in particular, are replacing the largely deterministic approaches that were favored in the past–which used simple linear regression models based on historical data with relatively static assumptions about the state of the world–with probabilistic approaches that bring more data into the equation and rely on more sophisticated machine learning algorithms, like neural nets and XGBoost, to generate more detailed forecast ranges.

"Make everything as simple as possible, but not simpler." This quote often attributed to Albert Einstein surmises a conflict that business leaders are facing with the adoption of artificial intelligence (AI) and machine learning (ML). When it comes to automated forecasting and decision-making, many are now embracing the idea of these advanced predictive solutions--but only until they begin to delve beyond the norms and already-familiar data. In this respect, just like the scientific community, business leaders crave "simplicity." However, they often have different definitions of the word, focusing more on how data can affirm their own gut feelings, rather than how deeper insight and newly proved theories can override that personal experience.

I was a welder/metal worker for nearly 40 years, with only dreams of writing, having abandoned college literature and science classes for the trade to ward off starvation and frostbite.

Using artificial intelligence to generate accurate product recommendations is one of the most beneficial applications of artificial intelligence in eCommerce. An AI system can analyse data from previous purchases, page visits, wishlists, clicks, searches, and other parameters to recommend the right products at the right time. These insights assist online retailers in custom product recommendations and provide a uniform user experience across all devices and channels. Once confined to the realm of scientific research, artificial intelligence is now rapidly infiltrating the eCommerce industry as an indispensable tool for many businesses. When it comes to the eCommerce industry, artificial intelligence revolves around the concept of algorithms and learning technologies, which serve as the foundation for automation and so much more. AI enables today's online retailers to provide an exceptional customer and user experience in eCommerce while also making intelligent business decisions based on customer data.

The Predictive Retailer is a retail company that utilizes the latest technological developments to connect with its customers to deliver an exceptional personalized experience to each and every one of them. Today, technology such as AI, Machine Learning, Augmented Reality, IoT, Real-time stream processing, social media, and wearables are altering the Customer Experience (CX) landscape and retailers need to jump aboard this fast moving technology or run the risk of being left out in the cold. The Predictive Retailer reveals how these and other technologies can help shape the customer journey. The book details how the five types of analytics--descriptive, diagnostic, predictive, prescriptive, and edge analytics--affect not only the customer journey, but also just about every operating function of the retailer. An IoT connected retailer can make its operations smart.

Today, Amazon SageMaker Canvas introduces the ability to use the Quick build feature with time series forecasting use cases. This allows you to train models and generate the associated explainability scores in under 20 minutes, at which point you can generate predictions on new, unseen data. Quick build training enables faster experimentation to understand how well the model fits to the data and what columns are driving the prediction, and allows business analysts to run experiments with varied datasets so they can select the best-performing model. Canvas expands access to machine learning (ML) by providing business analysts with a visual point-and-click interface that allows you to generate accurate ML predictions on your own--without requiring any ML experience or having to write a single line of code. In this post, we showcase how to to train a time series forecasting model faster with quick build training in Canvas.

Model deployment in machine learning (ML) is becoming increasingly complex. You want to deploy not just one ML model but large groups of ML models represented as ensemble workflows. These workflows are comprised of multiple ML models. Productionizing these ML models is challenging because you need to adhere to various performance and latency requirements. Amazon SageMaker supports single-instance ensembles with Triton Inference Server.

Predicting the behaviour of shoppers provides valuable information for retailers, such as the expected spend of a shopper or the total turnover of a supermarket. The ability to make predictions on an individual level is useful, as it allows supermarkets to accurately perform targeted marketing. However, given the expected number of shoppers and their diverse behaviours, making accurate predictions on an individual level is difficult. This problem does not only arise in shopper behaviour, but also in various business processes, such as predicting when an invoice will be paid. In this paper we present CAPiES, a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of entities at a time, we improve the predictive accuracy but at the potential cost of usefulness since we can say less about the individual entities. CAPiES is developed in an online setting, where we continuously update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation in two real-world scenarios: a supermarket with over 160 000 shoppers and a paint factory with over 171 000 invoices.

A fundamental challenge of recommendation systems (RS) is understanding the causal dynamics underlying users' decision making. Most existing literature addresses this problem by using causal structures inferred from domain knowledge. However, there are numerous phenomenons where domain knowledge is insufficient, and the causal mechanisms must be learnt from the feedback data. Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact. Also for this reason, most existing solutions become inappropriate since they require data collected free from any RS. In this paper, we first formulate the underlying causal mechanism as a causal structural model and describe a general causal structure learning framework grounded in the real-world working mechanism of RS. The essence of our approach is to acknowledge the unknown nature of RS intervention. We then derive the learning objective from our framework and propose an augmented Lagrangian solver for efficient optimization. We conduct both simulation and real-world experiments to demonstrate how our approach compares favorably to existing solutions, together with the empirical analysis from sensitivity and ablation studies.