Supply Chain Management

In the supply chain realm, machine learning is where most of the activity has been focused.

• 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 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.

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.
• Receiving/filing/documentation of invoices and payments/order requests.

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”
• 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. 

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 environmentally 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.
• “Where drivers are restricted by law from driving more than 11 hours per day without taking an 8-hour break, a driver-less 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”

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).
• What every Procurement Team can learn from Marketing Automation.
  Procurement is a function, activity, and role that is increasingly business critical to an organization’s overall value.

ML and Predictive Analytics for Supplier Selection and Supplier Relationship Management (SRM)

• Supplier selection and sourcing from the right suppliers is an increasing concern for enhancing supply chain sustainability, CSR and supply chain ethics. Supplier related risks have become the ball and chain for globally visible brands. One slip-up in the operations of a supplier body, and bad PR is heading right towards your company.
• But what if you had the best possible scenario for supplier selection and risk management, during every single supplier interaction?
• Data sets, generated from SRM actions, such as supplier assessments, audits, and credit scoring provide an important basis for further decisions regarding a supplier.
• With the help of Machine Learning and intelligible algorithms, this (otherwise) passive data gathering could be made active.
• Supplier selection would be more predictive and intelligible than ever before; creating a platform for success from the very first collaborations. All of this information would be easily available for human inspections but generated through machine-to-machine automation; providing multiple ‘best supplier scenarios’ based on whatever parameters, in which, the user desires.

One could hypothesize that SCM is a part of the value chain that would be heavily impacted by AI implementation, for the better and the worst.