We consider a multi-retailer supply chain where each retailer can dynamically choose when to share information (e.g., local inventory levels or demand observations) with other retailers, incurring a communication cost for each sharing event. This flexible information exchange mechanism contrasts with fixed protocols such as always sharing or never sharing. We formulate a joint optimization of inventory control and communication strategies, aiming to balance the trade-off between communication overhead and operational performance (service levels, holding, and stockout costs). We adopt a common information framework and derive a centralized Partially Observable Markov Decision Process (POMDP) model for a supply chain coordinator. Solving this coordinator's POMDP via dynamic programming characterizes the structure of optimal policies, determining when retailers should communicate and how they should adjust orders based on available information. We show that, in this setting, retailers can often act optimally by sharing only limited summaries of their private data, reducing communication frequency without compromising performance. We also incorporate practical constraints on communication frequency and propose an approximate point-based POMDP solution method (PBVI/SARSOP) to address computational complexity. Numerical experiments on multi-retailer inventory scenarios demonstrate that our approach significantly improves the cost-service trade-off compared to static information sharing policies, effectively optimizing the schedule of information exchange for cooperative inventory control.

The last few years have witnessed rapid growth in the on-demand delivery market, with many start-ups entering the field. However, not all of these start-ups have succeeded due to various reasons, among others, not being able to establish a large enough customer base. In this paper, we address this problem that many on-demand transportation start-ups face: how to establish themselves in a new market. When starting, such companies often have limited fleet resources to serve demand across a city. Depending on the use of the fleet, varying service quality is observed in different areas of the city, and in turn, the service quality impacts the respective growth of demand in each area. Thus, operational fulfillment decisions drive the longer-term demand development. To integrate strategic demand development into real-time fulfillment operations, we propose a two-step approach. First, we derive analytical insights into optimal allocation decisions for a stylized problem. Second, we use these insights to shape the training data of a reinforcement learning strategy for operational real-time fulfillment. Our experiments demonstrate that combining operational efficiency with long-term strategic planning is highly advantageous. Further, we show that the careful shaping of training data is essential for the successful development of demand.

Today's global supply chains face growing challenges due to rapidly changing market conditions, increased network complexity and inter-dependency, and dynamic uncertainties in supply, demand, and other factors. To combat these challenges, organizations employ Material Requirements Planning (MRP) software solutions to set inventory stock buffers - for raw materials, work-in-process goods, and finished products - to help them meet customer service levels. However, holding excess inventory further complicates operations and can lock up millions of dollars of capital that could be otherwise deployed. Furthermore, most commercially available MRP solutions fall short in considering uncertainties and do not result in optimal solutions for modern enterprises. At C3 AI, we fundamentally reformulate the inventory management problem as a constrained stochastic optimization. We then propose a simulation-optimization framework that minimizes inventory and related costs while maintaining desired service levels. The framework's goal is to find the optimal reorder parameters that minimize costs subject to a pre-defined service-level constraint and all other real-world operational constraints. These optimal reorder parameters can be fed back into an MRP system to drive optimal order placement, or used to place optimal orders directly. This approach has proven successful in reducing inventory levels by 10-35 percent, resulting in hundreds of millions of dollars of economic benefit for major enterprises at a global scale.

Serverless query processing has become increasingly popular due to its advantages, including automated hardware and software management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the cost of owning a data analytic system. However, it is still a significant challenge for non-expert users to transform their complex and evolving data analytic needs into proper SQL queries and select a serverless query engine that delivers satisfactory performance and price for each type of query. This paper presents PixelsDB, an open-source data analytic system that allows users who lack system or SQL expertise to explore data efficiently. It allows users to generate and debug SQL queries using a natural language interface powered by fine-tuned language models. The queries are then executed by a serverless query engine that offers varying prices for different service levels on query urgency. The service levels are natively supported by dedicated architecture design and heterogeneous resource scheduling that can apply cost-efficient resources to process non-urgent queries. We envision that the combination of a serverless paradigm, a natural-language-aided interface, and flexible service levels and prices will substantially improve the user experience in data analysis.

Inventory control using discrete-time models is a wellstudied problem, where orders of items to hold in stock must anticipate future demand [1, 2]. By defining the costs of insufficient stocks, it is possible to find cost-minimizing policies using dynamic programming [3, 4, 5]. In practice, however, maintaining a certain service level of an inventory control system is a greater priority than cost minimization [6, 7]. Under certain restrictive assumptions on the demand process - such as memoryless and identically distributed demand - there are explicit formulations of the duality between service levels and costs [8].

Cloud service owners need to continuously monitor their services to ensure high availability and reliability. Gaps in monitoring can lead to delay in incident detection and significant negative customer impact. Current process of monitor creation is ad-hoc and reactive in nature. Developers create monitors using their tribal knowledge and, primarily, a trial and error based process. As a result, monitors often have incomplete coverage which leads to production issues, or, redundancy which results in noise and wasted effort. In this work, we address this issue by proposing an intelligent monitoring framework that recommends monitors for cloud services based on their service properties. We start by mining the attributes of 30,000+ monitors from 791 production services at Microsoft and derive a structured ontology for monitors. We focus on two crucial dimensions: what to monitor (resources) and which metrics to monitor. We conduct an extensive empirical study and derive key insights on the major classes of monitors employed by cloud services at Microsoft, their associated dimensions, and the interrelationship between service properties and this ontology. Using these insights, we propose a deep learning based framework that recommends monitors based on the service properties. Finally, we conduct a user study with engineers from Microsoft which demonstrates the usefulness of the proposed framework. The proposed framework along with the ontology driven projections, succeeded in creating production quality recommendations for majority of resource classes. This was also validated by the users from the study who rated the framework's usefulness as 4.27 out of 5.

Predicting future resource demand in Cloud Computing is essential for optimizing the trade-off between serving customers' requests efficiently and minimizing the provisioning cost. Modelling prediction uncertainty is also desirable to better inform the resource decision-making process, but research in this field is under-investigated. In this paper, we propose univariate and bivariate Bayesian deep learning models that provide predictions of future workload demand and its uncertainty. We run extensive experiments on Google and Alibaba clusters, where we first train our models with datasets from different cloud providers and compare them with LSTM-based baselines. Results show that modelling the uncertainty of predictions has a positive impact on performance, especially on service level metrics, because uncertainty quantification can be tailored to desired target service levels that are critical in cloud applications. Moreover, we investigate whether our models benefit transfer learning capabilities across different domains, i.e. dataset distributions. Experiments on the same workload datasets reveal that acceptable transfer learning performance can be achieved within the same provider (because distributions are more similar). Also, domain knowledge does not transfer when the source and target domains are very different (e.g. from different providers), but this performance degradation can be mitigated by increasing the training set size of the source domain.

As a firm and individually, we have been actively studying the use of artificial intelligence (AI) in wealth management--and more specifically how investment advice generated through AI is sought out and applied--for the past three years in its many different applications across the industry. In our view, the wealth management industry is built and will continue to evolve around human relationships, clients' personal values and their most meaningful choices. The question is how advancements in AI could help the wealth management industry even more. I believe that AI can improve the advisor-client relationship significantly through the combination of richer information, meaningful service, and superior efficiency. Earlier this year, financial advisors (FAs) shared with us their beliefs around how to effectively use AI for their day-to-day business.

Roadrunner, transportation's greatest comeback story, implemented its updated proprietary Load Plan 2.0, further speeding up its network across 130 major lanes, representing its fourth round of transit time improvements over the past 18 months. Leveraging the industry's most advanced Machine-Learning ('ML') algorithm to optimize its less-than-truckload (LTL) network operations, Roadrunner now offers more direct long-haul metro-to-metro shipping than any other LTL carrier. This major network enhancement is enabled by Roadrunner's proprietary ML algorithm and the new Load Plan 2.0 that leverage Driver Partner Teams, multiple daily departures, and customized dock automation and reflects Roadrunner's Smart Technology focus on becoming the best LTL carrier in the industry. Recognized by Newsweek as one of America's most trustworthy companies and awarded Most Improved LTL Carrier by Mastio, Roadrunner continues to win service quality awards from multiple shippers. "Roadrunner's Weekend Plus advantage gives shippers access to its network on weekends. Our Chicago-to-SoCal and SoCal-to-Chicago lanes represent the fastest transit times in the industry, offering expedited service at LTL rates," said Phillip Thalheim, Director of Network Analytics for Roadrunner.

Many large enterprises use one form or another of a supply chain application to help manage their supply chains. Supply chain vendors have been touting their investments in artificial intelligence (AI) for the last several years. Alex Pradhan, Product Strategy Leader John Galt Solutions, told me that "all planning vendors have bold marketing around AI." But the trick is to find suppliers with "field-proven AI/ML algorithms" that "have been delivered at scale." Further, while artificial intelligence helps solve certain types of problems, Jay Muelhoefer - the chief marketing officer at Kinaxis pointed out - optimization and heuristics work better for other types of planning problems. This article, which is focused on the different types of artificial intelligence used and the types of problems they are solving, is aimed at helping practitioners cut through the hype.