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Leveraging AI & ML to streamline logistics and reduce pandemic-induced challenges

The need of the hour has been to drive value while judiciously using precious resources.

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Amidst the global pandemic, efficiency in the logistics industry is affected due to disruptions in various parts of the supply chain. In these uncertain times, there is great value in investing in technology that can help logistics players to better predict demand and optimize decision making. 

At one point in time, the lack of technology management and robust IT infrastructure was considered to be a major performance issue for primarily large-scale enterprises. However, in addition to these basic technological requirements, many enterprises – small, medium, and large - have adopted artificial intelligence solutions to remain competitive and deliver on the ever-increasing consumer experience expectations in recent times. Technological innovation in this manner will continue to aid efficient supply chain management to a wider and larger customer audience in the post-COVID economy. 

Machine Learning with Big Data

Big data modelling in conjunction with machine learning can be used to effectively to optimize supply chain management. Supply chain planning has gone through a period of adversity and the importance of remaining agile in this industry cannot be sufficiently emphasized. Machine learning algorithms can lend a hand in redesigning logistics operations in a manner that supports future growth. For example, by employing machine learning algorithms, an enterprise can improve supply chain planning through better demand forecasting, inventory planning, and route planning. These algorithms function by training large data sets to predict demand and prescribe supply positioning and thereby enhance faster and efficient logistics processes. Data is the new oil. The use of GPS/IoT data collected within organizations helps to select a trusted and reliable supplier for your business. The algorithms will be able to estimate interactions with a particular supplier based on information such as audit reports and credit scores.

The need of the hour has been to drive value while judiciously using precious resources.  Decision making in the logistics industry is also enhanced by machine learning. With the help of extensive computing power, machine learning algorithms can look for patterns and identify problems humans didn’t know existed such as new anomalies/fraud detection for example. As a result, there are tremendous savings on cost when quick and efficient decisions can be taken on carrier selection, routing, and quality control by shippers in the logistics industry. Innovations such as Natural Language Processing by use of autobots/chatbots can completely remove the need for a call centre. These techniques are time-saving and are being used to speed up data entry by auto-populating forms. 

Artificial Intelligence

Machine learning is merely an application of Artificial Intelligence. The umbrella term of Artificial Intelligence refers to a whole gamut of technology that enables machines/devices to carry out tasks that humans consider to be “smart”. The current pandemic has expedited the use of artificial intelligence in logistics to deal with uncertainty. 

One of the main benefits of using AI solutions is to increase the scale of operations and reduce human errors. In the logistics industry, routine warehouse operations such as inventory management and replenishments can be automated. The data collected in these ‘smart warehouses’ can further be used to predict demand for particular products, and in general, plan logistics operations well in advance to reduce transportation costs.

Another use-case of artificial intelligence has been autonomous quality control which makes use of computer vision to scan components at every stage of production and ensure quality control. It would be amiss to discuss artificial intelligence in logistics without surveying the potential offered by driverless vehicles to save time, money and curb accident rates. Granted, the current state of technology does not allow for truly autonomous vehicles as drivers are required to be present behind-the-wheel, but with time and appropriate regulations in place, we will experience autonomous vehicles with no human supervision in the near future.

To summarize, the implementation of machine learning and artificial intelligence in the logistics industry paves the way for high-value generation by optimizing an enterprise’s time and resources. The current crisis has separated the leaders from the laggards. Those who are agile and are able to quickly leverage emerging technology and its applications to streamline their business have been able to cope with adversity while those slow to change have been struggling.  Research by the McKinsey group shows that the potential value to be gained from utilizing artificial intelligence in the supply chain stands at $1.3 trillion to $2 trillion per year. 

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house. Unless otherwise noted, the author is writing in his/her personal capacity. They are not intended and should not be thought to represent official ideas, attitudes, or policies of any agency or institution.


Tags assigned to this article:
artificial intelligence machine learning logistics

Santosh Desai

The author is CTO, Blowhorn – a tech-enabled intra-city logistics provider

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