ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN

 Artificial Intelligence is an intelligence displayed by machines, in which, learning and action-based capabilities mimic autonomy rather than process-oriented intelligence.

The simplest way to understand the potential application of AI is to clearly define it’s potential value-added.

Introduced by Gartner Analyst, Noha Tohamy, at Gartner’s Supply Chain Executive Conference, AI was broken down into two categories:

• “Augmentation: AI, which assists humans with their day-to-day tasks, personally or commercially without having complete control of the output. Such Artificial Intelligence is used in Virtual Assistant, Data analysis, software solutions; where they are mainly used to reduce errors due to human bias.
• Automation: AI, which works completely autonomously in any field without the need for any human intervention. For example, robots performing key process steps in manufacturing plants”

How can AI be applied within SCM activities?

1. Chatbots for Operational Procurement:

Streamlining procurement related tasks through the automation and augmentation of Chabot capability requires access to robust and intelligent data sets, in which, the ‘procuebot’ would be able to access as a frame of reference; or it’s ‘brains’

As for daily tasks, Chatbots could be utilized to:

• Speak to suppliers during trivial conversations.
• Set and send actions to suppliers regarding governance and compliance materials

• Place purchasing requests.
• Research and answer internal questions regarding procurement functionalities or a supplier/supplier set

2. Machine Learning (ML) for Supply Chain Planning (SCP)

ML, applied within SCP could help with forecasting within inventory, demand and supply. If applied correctly through SCM work tools, ML could revolutionize the agility and optimization of supply chain decision-making.

By utilizing ML technology, SCM professionals — responsible for SCP — would be giving best possible scenarios based upon intelligent algorithms and machine-to-machine analysis of big data sets. This kind of capability could optimize the delivery of goods while balancing supply and demand, and wouldn’t require human analysis, but rather action setting for parameters of success.

3. Autonomous Vehicles for Logistics and Shipping

Intelligence in logistics and shipping has become a center-stage kind of focus within supply chain management in the recent years. Faster and more accurate shipping reduces lead times and transportation expenses, adds elements of environmental friendly operations, reduces labor costs, and — most important of all — widens the gap between competitors.

If autonomous vehicles were developed to the potential — that certain business analysts and tech gurus have hypothesized — the impact on logistics optimization would be astronomical.

4. Natural Language Processing (NLP) for Data Cleansing and Building Data Robustness

NLP is an element of AI and Machine Learning, which has staggering potential for deciphering large amounts of foreign language data in a streamlined manner.

NLP, applied through the correct work took, could build data sets regarding suppliers, and decipher untapped information, due to language barrier. From a CSR or Sustainability & Governance perspective, NLP technology could streamline auditing and compliance actions previously unable because of existing language barriers between buyer-supplier bodies