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Logistics of the Future – Dark, Cold, and 100% AI-driven

Whether managing complex warehouse processes or calculating the most cost-effective routes for vehicle fleets, process optimization is the name of the game in logistics. Leveraging the latest advances in artificial intelligence (AI) and deep learning, plus the vastly increased power of quantum computing, players in the sector can now drive substantial improvements throughout their supply chains – boosting efficiency and making major savings that they can pass on to customers or reuse in R&D. More importantly, human beings look likely to disappear completely from some traditional logistics roles as the technologies rapidly gain ground.

Faster, More Efficient, More Flexible

In today’s logistics space, speed, and utilization are essential. Ideally, providers want to make decisions in real time and ensure their supply chain is as fast, efficient, and agile as possible. And rapid, reliable analysis of relevant data is key to mastering changing situations and making the right choices.

These days, there’s certainly no shortage of information – ranging from the locations of products in warehouses to vehicle routes and delivery schedules, right through to publicly available resources, such as traffic bulletins and weather reports.

Until recently, however, harnessing the value of this data for business decision-making was far from easy – not least because of the time and effort required to sift through the mountains of information. (https://www.supplychaindive.com/news/artificial-intelligence-disrupt-logistics/521655/)But this is now changing with the advent of solutions combining AI, deep learning, and today’s considerably increased computing power to automatically draw valuable conclusions from big data in real time.

From Big Data to Smart Data

In the five or so years since the term “big data” emerged, data volumes have grown exponentially. But the information overload that once posed major challenges for businesses and their analytics solutions is now opening up attractive opportunities.

By combining cutting-edge AI, deep learning, and quantum computing to analyze the ever-rising tide of facts and figures, logistics companies can transform big data into smart data – driving better decision-making and delivering significant business benefits.

The methods applied are nothing new and have underpinned analyses for some time. What has changed is that, through AI and deep learning, analytics solutions are now capable of enhancing their analytics skills themselves. (https://www.rtinsights.com/how-ai-makes-real-time-analytics-more-real/)

Add to this the advent of quantum computing, giving businesses access to 1000 times more computational power than was previously available, and you have the foundation for automatically running ad hoc analyses against formidable data volumes in real time.

You also have the technological basis for replacing human workers and even decision-makers across large areas of logistics operations – from the warehouse floor right through to supply chain planning and design.

No End of Potential Use Cases

The insights delivered by these technologies can be applied in a variety of logistics use cases. In route optimization(a core task traditionally handled by humans), AI-driven solutions can rapidly analyze huge quantities of constantly changing information, allowing logistics companies to determine the most favorable routes for their vehicles on the fly. This could enable trucks to be redirected to avoid congestion. It could also help avoid making costly empty trips, by directing drivers to pick up goods on their return journey.

In warehousing, AI can be deployed with robotics and IoT solutions to automate and streamline time-consuming manual work and move goods via the fastest routes – lightening the load on employees and freeing them up for value-creating tasks. In fact, some experts believe that automation of this kind could radically reduce the number of humans working in factories – and, by implication, in warehouse facilities – turning them into cold, dark places. (https://www.mckinsey.com/business-functions/operations/our-insights/five-fifty-lights-out?cid=fivefifty-soc-lkn-mip-mck-oth-1806&kui=Ch_SJtNjWSpSB9f0hOs5Zw)

In supplier selection, the new technologies could help logistics providers rapidly identify potential new partners and see how they shape up to the competition – enabling companies to deliver products and services of the same (or higher) standard at a lower cost.

One particularly appealing use case is predictive analysis. This involves AI scouring data sources such as flight schedules to determine expected delays well in advance and propose countermeasures, for example. Here, too, some observers see the tech as poised to take over from humans and are even heralding the death of traditional supply chain management. (https://hbr.org/2018/06/the-death-of-supply-chain-management?utm_source=twitter&utm_campaign=hbr&utm_medium=social)

The Future is Now

This sounds both appealing and perhaps more than a little daunting. But how far advanced is this technology today? And, more importantly, is it ripe for deployment in the real-world scenarios outlined above?

The fact of the matter is that a number of logistics players are already successfully deploying the new tech in a wide range of applications. Solutions currently in place for the use cases presented above include:

“Cainiao Future Park” Smart Warehouse Management System

(http://www.alizila.com/cainiao-logistics-future-park-china/)

Combining Internet of Things (IoT) applications, big data, edge computing, and artificial intelligence (AI), this solution from Cainiao Network significantly reduces manual tasks throughout the warehouse, replacing humans with robots for some time-consuming tasks.

Benefits

  • 30% increase in personnel efficiency

Locus B2B Enterprise Platform

(https://yourstory.com/2018/06/f-word-logistics-platform-locus-raises-4-m-pre-series-b-funding/)

Developed in India, Locus is a logistics decision-making engine that leverages deep learning and proprietary algorithms to reduce human decision-making in package transportation. It is deployed in areas including the following:

  • Intelligent route optimization

  • Real-time tracking

  • Insights and analytics

  • Vehicle allocation and utilization

Benefits

  • Greater efficiency

  • Higher profitability

  • 10 to 15 percent cost savings in real-world scenarios

Intel’s Supplier Identification and Selection Solution

(https://www.logisticsmgmt.com/article/nextgen_supply_chain_drop_ins_at_mits_crossroads_2018)

The leading chip manufacturer uses AI to identify potential suppliers and rate them against current suppliers. This solution draws on sources including social media to pinpoint appealing candidates.

Benefits:

  • Ability to handle supply chain tasks that would be impossible using manual approaches or legacy technology

  • Greater transparency and seamlessness in the supply chain

DHL’s Predictive Analysis Tool

(https://www.supplychaindive.com/news/artificial-intelligence-disrupt-logistics/521655/)

DHL and IBM recently identified AI-generated predictive analysis as the most revolutionary development for the logistics sector. The machine learning-based tool developed by DHL can:

  • Analyze multiple parameter from internal data

  • Predict air freight transit time delays

Benefits:

  • Enables DHL to proactively determine the effects of predicted transit times and take appropriate action

And that’s not all: In an Indian pilot, DHL’s SmarTrucking solution slashed transit times by 50%. (http://railandroad.logistics-business-review.com/news/dhl-launches-innovative-road-transportation-across-india-250518-6165768). And UPS’s ORION route optimization solution is already saving the company some 100 million miles a year – equivalent to 10 million gallons of fuel. (https://www.pressroom.ups.com/pressroom/ContentDetailsViewer.page?ConceptType=Factsheets&id=1426321616277-282).

Mission Critical: Getting the Technical Architecture Right

The value potential of the new tech is clear. But to tap into it logistics players must understand the importance of the underlying enterprise architecture and data strategy. They need to consider this carefully and strike the right balance between the level of integration and complexity. After all, keeping complexity manageable is key to ensuring the constant agility required to respond to customer’s ever-changing expectations.

What’s more, they have to consider the implications of highly automated processes for their staffing. While AI and deep learning may do away with some traditional human roles entirely, it will also call for new specialists with expertise in logistics and technology to design the tech-driven supply chains of tomorrow.

Potentially Rich Rewards for First Movers

While there may be some skepticism regarding the impact of AI (reference to https://www.supplychaindive.com/news/artificial-intelligence-disrupt-logistics/521655/), the successes to date strongly suggest that now is the time for logistics companies to examine just how solutions of this kind could benefit their business and set them apart from the competition. Because one thing is certain: The competition will not want to pass up the potentially massive savings on offer. And that means logistics companies must act now if they are to avoid being saddled with time-consuming manual processes and the prohibitively high operating costs these entail.

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