AI 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.
Supply Chain break down AI 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”.
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.
• Previous studies, by the Tungsten Network, have suggested that
valuable time and money is wasted on trivial supply chain
related-tasks that are conducted operationally by humans.
• “Businesses estimate they spend on average per week around
55 hours doing manual, paper-based processes and checks; 39
hours chasing invoice exceptions, discrepancies and errors and
23 hours responding to supplier inquiries” (mhlnews.com 2017).
• Companies, even at that enterprise level, have
already begun the implementation of AI tech into
every day supply chain tasks. Tech vendors such as
IBM, Google, and Amazon have released products
that utilize artificial intelligence.
• “McKinsey estimated that tech giants such as Google
and Baidu spent some $20 billion to $30 billion on AI
last year, of which 90% was on research and
development and the rest on acquisitions of
intellectual properties or companies” (asq.org 2017).
Benefits of AI in Supply Chain
1. Accurate Inventory Management
• Accurate inventory management can ensure the right flow of items in
and out of a warehouse. Generally, there are many inventory related
variables like order processing, picking and packing, and this can
become very time-consuming with a high tendency for error. Also,
accurate inventory management can help in preventing overstocking,
inadequate stock and unexpected stock-outs.
• With their ability to handle mass data, AI driven tools can prove to be
highly effective in inventory management. These intelligent systems
can analyze and interpret huge datasets quickly, providing timely
guidance on forecasting supply and demand. These AI systems with
intelligent algorithms can also predict and discover new consumer
habits and forecast seasonal demand. This application of AI helps
anticipate future customer demand trends while minimizing the costs
of overstocking unwanted inventory.
2. Warehouse Efficiency
• An efficient warehouse is an integral part of the supply chain and
automation can assist in the timely retrieval of an item from a warehouse
and ensure a smooth journey to the customer. AI systems can also solve
several warehouse issues, more quickly and accurately than a human can
and also simplify complex procedures and speed up work. Also, along
with saving valuable time, AI-driven automation efforts can significantly
reduce the need for, and cost of, warehouse staff.
Enhanced Safety
• AI-based automated tools can ensure smarter planning and efficient
warehouse management, which can enhance worker and material safety.
AI can also analyze workplace safety data and inform manufacturers
about any possible risks. It can record stocking parameters and update
operations along with necessary feedback loops and proactive
maintenance. This helps manufacturers react swiftly and decisively to
keep warehouses secure and compliant with safety standards.
4. Reduced Operations Costs
This is a big benefit of AI systems for the supply chain. From
customer service to the warehouse, automated intelligent
operations can work error-free for a longer duration, reducing the
number of errors and workplace incidents. Warehouse robots
provide greater speed and accuracy achieving higher levels of
productivity.
5. On-time Delivery
AI systems can help reduce dependency on manual efforts thus
making the entire process faster, safer and smarter. This helps
facilitate timely delivery to the customer as per the commitment.
Automated systems accelerate traditional warehouse procedures,
thus removing operational bottlenecks along the value chain with
minimal effort to achieve delivery targets.
 t

How can AI be applied within SCM activities?

1. Chatbots for Operational Procurement:
Streamlining procurement related tasks through the automation and
augmentation of Chatbot 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
2. 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 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. 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”
4. 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.
Rolls Royce uses AI to safely transport its cargo.
• Rolls Royce recently partnered with Google to create
autonomous ships. Instead of just replacing one driver in a
self-driving car, this technology replaces the jobs of 20-plus
ship crew members. Existing ships use AI algorithms to sense
what is around them in the water and classify items
according to the danger they pose to the ship. In the future,
the technology will include sensors to track ship engine
performance, load and unload cargo and monitor security.
Using AI to help ships be aware of what is around them
makes shipments faster and safer. Ships won’t be lost due to
weather or run into dangerous items, which means goods
can cross oceans faster and more easily.
2. UPS uses AI to create the most efficient routes for its fleet.
• In supply chain deliveries, every minute and mile matters. UPS uses
an AI-powered GPS tool called ORION (On-road Integrated
Optimization and Navigation) to create the most efficient routes for
its fleet. Customers, drivers and vehicles submit data to the
machine, which then uses algorithms to create the most optimal
routes. Instead of back-tracking or getting stuck in traffic, ORION
helps drivers make their deliveries on time and in the most efficient
manner. The routes can even be changed on the go depending on
road conditions and other factors. Optimizing delivery routes has a
huge impact on all areas of UPS’ business, from saving time and
money to reducing emissions and wear and tear on its trucks. With
ORION, UPS estimates it can reduce its delivery miles by 100 million.
Those savings can add up, especially because UPS predicts that for
every mile its drivers cut from their daily routes, the company
saves $50 million a year.
3. Robots deliver medicine, groceries and packages with AI
• Instead of using human couriers, timely items like food and medicine
can now be delivered by robots. Marble, which calls itself the “last-
minute logistics company,” delivers all sorts of items to people
quickly and more efficiently than humans. The robots use LIDAR
technology—the same that is used in autonomous cars—to navigate
city sidewalks and avoid running into people and other hazards.
Marble started as a way to deliver food through the Yelp24 app but
has since expanded to deliver medicine, groceries, packages and
more. The robots track their route and the conditions of the
sidewalks as they go, so that routes are continuously improving. It’s a
faster, more efficient and more affordable way to get goods of all
types around busy urban areas.
5. 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 book, 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.
6. 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.