The world finds itself in the grip of an unprecedented pandemic. In many countries, the manufacturing of most commodities, apart from essential goods has come to a complete halt. Shipping and logistics operations too have been greatly affected, which is why industries world over are placing more emphasis on Artificial Intelligence (AI) and automation. In a nutshell, the idea of an autonomous supply chain that offers unprecedented information security finds immediate appeal.
But for those who are not tech savvy, a quick look at the basics first: The simplest way to define AI is to have a software mimic human behaviour that includes ability to learn, think and decide. If you’d extend that simplest of definitions to logistics, there are several opportunities that spring up to enable self learning, correction and decision making in the logistics industry. Logistics in general has at least two distinct resources - the freight/cargo and the firms that move that freight. There is reassuring evidence that both firms and freight are increasingly becoming smart at their atomic levels. This accelerated adoption of technology is due to the inherent desire of knowing the status of freight - where is it, how is it and when will it arrive?
For a supply chain network that covers large geography, AI can provide the much needed granular visibility into where and how your cargo is, learn to recommend corrective actions such as route optimisation, intervention to save the condition of cargo and predict accurately the flow of goods through touch points. “Artificial intelligence is the ability of machines to make smart decisions with the available datasets. By implementing AI in logistics and supply chain, companies can save up to $2 trillion every year,” reveals Purnendu Shekhar, CEO, Cogoport.
“AI can be also used to predict future disruptions and events due to internal and external factors, how logistics capacity planning in warehousing and transportation needs to respond to any disruptions and events. At operating level, we can relate to the production planning with our daily order picking plan or manpower planning as fulfilment centre acts as a factory, informs Naveen Rawat, Co-Founder Holisol Logistics. A report by Mckinsey stated that the potential value of AI in this industry was around $1.2-2.0 trillion in 2018.
“AI will provide the much needed granular visibility into where and how your cargo is, learn to recommend corrective actions such as route optimisation, intervention to save the condition of cargo and predict accurately the flow of goods through touch points.”
Surendra Lingareddy Director-Asia Pacific, Volteo
AI can enable the analysis of significant volumes of data while doing away with the room for human error. AI/ML can leverage massive data sets to first enable machines to perform various operation related tasks in nearly autonomous ways. For operations, such an approach can be used to streamline and automate the entire production life cycle. Predictive analysis can also be used to identify bottlenecks and problems thereby speeding up the process of resolving operational challenges. Automation can bring about a sea change in efficiency for data tracking, planning, scheduling and rescheduling processes. For instance, the implementation of AI helps ports make accurate predictions on the arrival of container ships. Machine learning can also help identify trends in shipping lanes and ports. “Shipping lines can make decisions on the optimal sailing route and sailing speed; port operators can predict yard utilisation, freight forwarders can use it to accurately predict transit times. A study by McKinsey shows that Logistics industry can save between $1.3 to $ 2 Trillion per year in economic value by integrating AI into manufacturing and supply chain processes,” shares Liji T Nowal, MD, ODeX India Solutions Pvt Ltd.
“The finance industry is employing AI in a lot of their functions requiring forecasting and analytical decision making. However, in logistics, a lot of intuition and guesswork is involved while predicting transit times during ship and air freight movements. We too can utilize the data loads available with us in creating machine learning models that could predict average daily transit time in advance. This would help freight loaders in planning their operations efficiently. Additionally, if we could incorporate parameters like climate variations, traffic fluctuations, etc. in the model, we could do wonders,” suggests Surajit Sarkar, COO, DLDS. Some of the greatest tools in marketing nowadays involves utilizing the customer sentiment harnessed through AI in placing and promoting products. Logistics could too utilize this customer-centric data to ascertain material shortages, demand, supplier status, and other issues.
Another great impact of AI in logistics is the ML assisted predictive analysis. This allows the customers and also the logistics managers to get answers for questions like when, where and why? For logistics managers tracking of drivers and consignment has become easy with the implementation of AI. Taking a cue from AI being used in various industries:
* Ocado, a UK based online grocer, ships an astounding 3.5 million grocery items, within the course of a week, in a preglobal pandemic market. This warehouse is driven by AI fuelled bots. This borg like an ensemble of bots is straight out of a science fiction fantasy. For Ocado, it is humdrum effective reality. The machine algorithm for these bots recognizes frequently accessed items and those rarely ordered. In the warehouse, items are stacked in a manner that offers the utmost efficiency for order fulfilment.
* A fertilizer company is working on what is possibly the world’s first self-driven ship, similar to self-driven cars being used for road transport. Yara International ASA is set to unveil The Yara Birkeland, a 120-teu open hatch container vessel which will be powered by a 7-MWh battery. Similarly, Intel along with Rolls-Royce is also developing self-driving ships.
* Like the use of AI in streamlining manufacturing where it helps streamline maintenance and production. This is usually accomplished with the aid of image recognition and conversational interfaces. In a similar way, AI can also be used to develop smart roads. These roads can use sensors to detect traffic volumes and alert drivers accordingly. They can be used in sync with self-driving ships with the aid of vehicle to vehicle communication, leading to an optimal multi-modal supply chain.
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