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.

Pricing strategy is a cornerstone for businesses across various sectors, profoundly influencing their success and market position. This strategy intricately balances multiple factors, including supply and demand dynamics, competitor pricing, brand positioning, perceived value, and overarching business strategies. Despite its critical importance, many companies still rely on traditional, manual approaches to pricing. These methods often depend on the intuition and experience of domain experts, supplemented to some extent by data-driven insights. However, a paradigm shift is emerging in this domain, led by more innovative companies. For instance, companies like Lyft have revolutionized their approach to pricing. By leveraging advanced reinforcement learning techniques, they have managed to automate their pricing policies effectively (Qin et al., 2022).

The European Green Deal aims to achieve climate neutrality by 2050, which demands improved emissions efficiency from the transportation industry. This study uses an agent-based simulation to analyze the sustainability impacts of shared autonomous shuttles. We forecast travel demands for 2050 and simulate regulatory interventions in the form of replacing private cars with a fleet of shared autonomous shuttles in specific areas. We derive driving-related emissions, energy consumption, and non-driving-related emissions to calculate life-cycle emissions. We observe reduced life-cycle emissions from 0.4% to 9.6% and reduced energy consumption from 1.5% to 12.2%.

In a sea of vendors who promise to optimize e-commerce and boost sales, wading through the advertisements to find the right match for the right process can be incredibly difficult. German-based international clothing retailer Orsay recently set out on a journey toward inventory optimization through machine learning tools. They chose to automate their timing and pricing of markdowns in order to maximize profit based on factors specific to consumers and their country. In this Q&A, Katrin Starke, head of business development at Orsay, details the company's journey to using inventory optimization machine learning tools from project conception, decision-making, to implementation of a tool called Luminate Clearance Price (LCP), sold by Arizona-based technology vendor JDA Software. Orsay has been running the software for nearly a year.

The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.

Recurrent neural networks (RNNs) with rich feature vectors of past values can provide accurate point forecasts for series that exhibit complex serial dependence. We propose two approaches to constructing deep time series probabilistic models based on a variant of RNN called an echo state network (ESN). The first is where the output layer of the ESN has stochastic disturbances and a shrinkage prior for additional regularization. The second approach employs the implicit copula of an ESN with Gaussian disturbances, which is a deep copula process on the feature space. Combining this copula with a non-parametrically estimated marginal distribution produces a deep distributional time series model. The resulting probabilistic forecasts are deep functions of the feature vector and also marginally calibrated. In both approaches, Bayesian Markov chain Monte Carlo methods are used to estimate the models and compute forecasts. The proposed deep time series models are suitable for the complex task of forecasting intraday electricity prices. Using data from the Australian National Electricity Market, we show that our models provide accurate probabilistic price forecasts. Moreover, the models provide a flexible framework for incorporating probabilistic forecasts of electricity demand as additional features. We demonstrate that doing so in the deep distributional time series model in particular, increases price forecast accuracy substantially.

The global artificial intelligence (AI) in transportation market is projected to reach $3.5 billion by 2023. Due to the rising concerns for vehicle and driver safety, increasing focus on lowering the transportation costs, and advancements in autonomous vehicles, the artificial intelligence (AI) in transportation market is witnessing significant growth. In 2017, the market valued at $1.4 billion, and it is predicted to generate a revenue of $3.5 billion by 2023, exhibiting a CAGR of 16.5% during the forecast period (2018–2023). AI in transportation involves the use of computer vision, deep learning, and natural language processing technologies. Video camera, radio detection and ranging (RADAR) sensors, and light detection and ranging (LiDAR) equipment are some of the AI-based hardware installed in fully autonomous vehicles which are under trial.

For decades retail companies have been exploiting analytics within the different segments of their businesses, including marketing and operations. Such analytics are dusty, however, and have now come to an end. Traditional analytical methods are outdated; they require a lot of manual steps and the insights extracted cannot be easily generalized. Using analytics ultimately provides a low return if you include the amount of manpower needed allocating to run them. Machine learning (ML) can be viewed as an extension of analytics.

Safety stock is among the most critical elements in the pharmaceutical supply chain. Yet safety stock has also proven very difficult to manage and optimize, even as it locks down working capital and drives up inventory costs. Pharmaceutical companies typically maintain high levels of safety stock to achieve better service levels that maximize revenue of high-margin products and drive customer satisfaction. Also called buffer stock, it provides a safety net against variability such as unanticipated delays in raw materials or transportation, or unusually high demand. Stockouts that result from inadequate safety stock could be highly damaging to the business, with millions in lost revenue and potential brand damage if vital medicines are unavailable.

Service supply chain management is to prepare spare parts for failed products under warranty. Their goal is to reach agreed service level at the minimum cost. We convert this business problem into a preference based multi-objective optimization problem, where two quality criteria must be simultaneously optimized. One criterion is accuracy of demand forecast and the other is service level. Here we propose a general framework supporting solving preference-based multi-objective optimization problems (MOPs) by multi-gradient descent algorithm (MGDA), which is well suited for training deep neural network. The proposed framework treats agreed service level as a constrained criterion that must be met and generate a Pareto-optimal solution with highest forecasting accuracy. The neural networks used here are two Encoder-Decoder LSTM modes: one is used for pre-training phase to learn distributed representation of former generations' service parts consumption data, and the other is used for supervised learning phase to generate forecast quantities of current generations' service parts. Evaluated under the service parts consumption data in Lenovo Group Ltd, the proposed method clearly outperform baseline methods.