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Artificial Intelligence

Artificial Intelligence (AI) Renovating Supply Chain Logistics

Thomson Reuters  

· 5 minute read

Thomson Reuters  

· 5 minute read

Artificial Intelligence (AI) is coming to the Shipping industry, but will Carriers/Shippers will be able to take full advantage of the technology?

AI is seeping into every industry. Machines are promising to make better decisions based on massive amounts of data that no mere humans previously were asked to interpret. The sheer volume of data inhibits the ability to provide results quickly without the use of machines. Imagine systems that can shift through huge volumes of information to formulate “smart” recommendations as to routes, scheduling and pricing.  This technology is popping into the shipping industry, however, are carriers flexible enough to use this technology in their operations?

There are some key differences between the terminology used in AI and related technologies and how they apply to supply chain planning.

  1. Artificial intelligence

AI is defined as the use of machines which adopts human intelligence. AI is the processing of data and delivering results with clarification of information and reasoning. So, AI is basically providing an analysis from the volume of data with a speedy analysis and providing various patterns of information which would have been a time-consuming activity if done manually.

But how can AI be used in supply chain planning? AI can be used to help strengthen the value of the current processes with machine-assisted planning by providing real-time recommendations based on historical information, trends and current data analysis.

Another benefit of using AI technology is that it helps improve a supply chains visibility and risk insight by using AI to track and predict possible supply chain disruptions based on inputs and correlations across multiple data sources, including weather forecasts, news and even social media.

  1. Machine learning

Machine Learning is a subset of AI.  It is a method of data analysis where machines use algorithms to detect patterns, learn to make predictions and make recommendations by finding hidden insights in the data, without being explicitly programmed where to look.

How can machine learning be used in supply chain planning? Machine learning can assist with revenue savings by continuously observing, monitoring and correcting out of tolerance lead times for all related products and eventa, based on historical data and slope. In addition, machine learning can help with forecasting customer service levels with analysis of demand behavior for new products using algorithms based on early sell signals to optimize inventory levels and replenishment plans.

  1. Deep learning

Deep Learning is a subset of machine learning.  It leverages neural networks to understand vast amounts of unstructured or unlabeled data in either a supervised, semi-supervised or unsupervised way, drawing conclusions, and learning if those conclusions are correct and then applying that learning to new data sets.

How can deep learning be used in supply chain planning? Deep learning helps in deriving detailed reports on observations and takes corrective actions by sending alerts to the appropriate stakeholders when larger issues arise.

Application of AI within Supply Chain Management (SCM)

Many businesses are aggressively looking for solutions with AI for supply chain visibility and predicting/forecasting features. It has become apparent that companies have realized the benefits of possessing and analyzing the enormous amount of data that is collected by industry, logistics, warehousing and transportation systems.  AI has become a direction that companies view as worth investing in as noted by these examples:

  • Chatbots[1] for Operational Procurement
  • Machine Learning (ML) for Supply Chain Planning (SCP)
  • Machine Learning for Warehouse Management
  • Autonomous Vehicles for Logistics and Shipping
  • Natural Language Processing (NLP) for Data Cleansing and Building Data Robustness
  • ML and Predictive Analytics for Supplier Selection and Supplier Relationship Management (SRM)

Drawback of Implementing AI in Supply Chain Management (SCM)

Accessing large volumes of data utilizing AI features comes with some drawbacks too. Below are a few examples that have been raised:

  • Security Concerns for the IT infrastructure
  • Replacing Jobs – persons currently in roles analyzing data may feel threatened with AI technology as replacing their role.
  • AI learns from the historical data sets. This means that any inaccuracies in the data will be reflected in the results.

[1] A computer program, or AI, designed to simulate conversation with human users, via auditory or textual methods

What is the Role of AI in Global Trade Management as part of the Supply Chain?

AI is playing a big role in Global Trade. 3CE for example has built an expert system that uses AI to read and understand everyday commercial goods descriptions, while reasoning the user through the classification process through attribute selection.

In addition, AI is improving compliance software by reducing the number of false positives and negatives, thus reducing the amount of additional human review and input necessary to maintain compliance. AI is also transforming trade documents into useful documents that can help businesses operate with ease within the parameters of the requirements, and even reduce the risk of legal issues. This type of technology also allows businesses to properly execute contracts and avoid risks without devoting a lot of manpower to contract compliance.

An artificially intelligent supply chain is a proactive supply chain, and one that is incredibly agile and able to alleviate the impact of inevitable disruptions.


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