Artificial Intelligence in Supply Chain

 

The potential of AI enhancing everyday business activities and strategies hasn’t just sparked the interest of people and organisations globally, but has initiated rapid implementation.

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” (com 2017).

Enhancing Productivity and Profits.

Understanding these two categories of AI capacities is important for future implementation of AI into business work tools. In particular, the application of AI into Supply Chain related-tasks holds high potential for boosting top-line and bottom-line value.

The graphic below shows a breakdown of the applications of AI in 835 different companies in the past year.

 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 utilised 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.
  • Receiving/filing/documentation of invoices and payments/order requests (Smith 2016).
  1. Machine Learning (ML) for Supply Chain Planning (SCP)

Supply chain planning is a crucial activity within SCM strategy. Having intelligent work tools for building concrete plans is a must in today’s business world.

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

By utilising 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 optimise the delivery of goods while balancing supply and demand, and wouldn’t require human analysis, but rather action setting for parameters of success.

  1. Machine Learning for Warehouse Management

Taking a closer look at the domain of SCP, its success is heavily reliant on proper warehouse and inventory-based management. Regardless of demand forecasting, supply flaws (overstocking or under stocking) can be a disaster for just about any consumer-based company/retailer.

“A forecasting engine with machine learning, just keeps looking to see which combinations of algorithms and data streams have the most predictive power for the different forecasting hierarchies” (forbes.com 2017).

ML provides an endless loop of forecasting, which bears a constantly self-improving output. This kind of capabilities could reshape warehouse management as we know today.

  1. 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 hypothesised — the impact on logistics optimisation would be astronomical.

“Where drivers are restricted by law from driving more than 11 hours per day without taking an 8-hour break, a driverless truck can drive nearly 24 hours per day. That means the technology would effectively double the output of the U.S. transportation network at 25 percent of the cost” (techcrunch.com 2016).

  1. 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 (greenbiz 2017).

 

https://medium.com/@KodiakRating/6-applications-of-artificial-intelligence-for-your-supply-chain-b82e1e7400c8

 

  1. Q) Potential of AI in supply chain
  2. Q) Supply Chain Challenges implementing AI
  3. Q) Potential Threats

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