We describe a novel decision-making problem developed in response to the demands of retail electronic commerce (e-commerce). While working with logistics and retail industry business collaborators, we found that the cost of delivery of products from the most opportune node in the supply chain (a quantity called the cost-to-serve or CTS) is a key challenge. The large scale, high stochasticity, and large geographical spread of e-commerce supply chains make this setting ideal for a carefully designed data-driven decision-making algorithm. In this preliminary work, we focus on the specific subproblem of delivering multiple products in arbitrary quantities from any warehouse to multiple customers in each time period. We compare the relative performance and computational efficiency of several baselines, including heuristics and mixed-integer linear programming. We show that a reinforcement learning based algorithm is competitive with these policies, with the potential of efficient scale-up in the real world.

The use of artificial intelligence (AI) is becoming more common in many branches of industry and online retailing. Traditional lines of work, such as transport logistics and driving, are developing in a similar direction although mainly out of public view. Scientists at the University of Göttingen have now investigated how efficient the use of AI can be in the commercial management of trucks. Their answer: the best option is an intelligent combination of human decision-making and AI applications. The study was published in the International Journal of Logistics Management.

Machine learning may sound like a complicated term but it is basically a branch under artificial intelligence. Machine learning is very important today because it is being used in so many software, bots, and apps. it simply makes your programmed software more intelligent.In the logistics industry, every step from carrier selection to quality control processes can be improved through the smart algorithms of machine learning. According to Allied Market Research, There is a big scope of logistics management as the logistics industry is estimated to reach USD 15.5 trillion by 2023. Artificial Intelligent features help in the accessibility of information, while also monitoring inventory and load capacity so trucks don't mistake during the delivery. The technology can also secure and manage the suppliers; inventory in the warehouse and the number of trucks that are available for delivery.

The shockingly fast sell-out of the 2018 Neural Information Processing Systems (NIPS) conference put industry on notice: artificial intelligence is poised to break out of the hype cycle and become an important element in strategic business planning. A lot has been written about AI over the last year, but as the many papers and presentations from NIPS showed, much of what has been offered for public consumption has been superficial and, in some cases, misleading. AI is neither a simple panacea poised to solve all our business problems, nor is it a looming evil ready to usher in a dystopian future. It is a powerful tool that can aid decision making in a fast-moving, digital business climate--and there's ample evidence that it's already happening. Among the lucky few who were able to secure credentials to the conference, and as machine learning practitioners who have spent the better part of their careers involved with AI and machine learning, we came away with a look into the future of AI.

Artificial intelligence is poised to break out of the hype cycle and become an important element in strategic business planning. A lot has been written about AI over the last year, but much of what has been offered for public consumption has been superficial and, in some cases, misleading. AI is neither a simple panacea poised to solve all our business problems, nor is it a looming evil ready to usher in a dystopian future. It is a powerful tool that can aid decision making in a fast-moving, digital business climate--and there's ample evidence that it's already happening. As machine learning practitioners who have spent the better part of their careers involved with AI and machine learning, here is a look into the future of AI.