AI Articles and Analysis about Logistics

Nike was founded as Blue Ribbon Sports in 1964 and originally operated as a distributor for athletic shoes made by a Japanese firm. By 1971, Nike sold its own line of shoes and began using the company’s iconic Swoosh design. By the time Nike went public in 1980, the company had achieved a 50% market share in its domestic athletic shoe market.   

Supply chains contain every material, component, product and packaging for the objects that together compose the world we live in. However, there is an often invisible ingredient to successful supply chains: data. 

Logistics in the military encompasses more functions than most people realize. In modern warfare, that means large quantities of data to sift through in order to make decisions regarding supply, transport, communications, and so on. Using artificial intelligence (AI) and machine learning (ML) in one or more areas in logistics could help speed up that process and make it more agile.

The oil and gas industry is usually divided into three major operational sectors: upstream, midstream, and downstream. Upstream involves the exploration and production of oil and natural gas. Midstream usually refers to transportation and storage stages. Downstream encompasses the various processes involved in refining and selling oil.

McKinsey reported that most oil and gas operators have not maximized the production potential of their assets. A typical offshore platform, according to the 2017 report, runs at about 77% of its maximum production potential. Industry-wide, the shortfall comes to about 10 million barrels per day, or $200 billion in annual revenue.

In our previous report, we covered the current use cases for AI in construction and building. As of now, numerous companies claim to assist building maintenance managers in aspects of their roles from optimizing energy usage in building to improving the comfort of building occupants.

One highly sought after engineering role at major tech companies today is the natural language processing, or NLP, engineer.

The healthcare domain seems ripe for disruption by way of artificial intelligence in the form of predictive analytics. Accenture estimates the AI in healthcare market will reach $6.6 billion by 2021.