ArtificialIntelligenceasEmergingTechnologyinSupplyChain

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Inventrax - Supply Chain Management Digital
Transformation IT Services
https//inventrax.com/
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As economies around the world step back from the
financial fringe and begin adjusting to the new
normal in 2020, companies face a completely new
set of supply chain challenges than before. Also,
the globalization of supply chains has led to raw
materials and products travelling across
geographical locations and penetrating through
multiple suppliers, manufacturers, distributors,
carriers and service providers.
Among them are

• Escalating pressure from global competition
• Consumer expectations
• Complementing complex patterns of consumer demand

As companies have been managing their supply
chains over the past years, the hurdles that they
faced and the goals that they have in turn
reflected was mainly a single-minded focus on
weathering and financial crisis. The most
recurring challenge has been the increasing
volatility of customer demand.Often, the supply
chain has limited track over the partners
actually involved, and information about all the
parties in the supply chain. To address such
challenges, Block Chain Technologies could be the
answer.
Majority of the challenges are associated with
below areas

• Counterfeiting
• Visibility/ Traceability
• Efficiency Play

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Hence, the need to manage supply chain
efficiently has been gaining momentum over the
past few years. One of the most popular
technology that is believed to be capable of
achieving this is
Artificial Intelligence
Artificial Intelligence in Supply Chain increases
the productivity and efficiency of the system.
Within various nodes of supply chain, the
connectivity and flow of information and data
between devices and sensors allows for an
abundance of data available. The Key enabler is
able to use these huge volumes of data and
extract relevant useful information, making it
possible to reduce unnecessary costs, optimize
capacity, keep downtime to a minimum. This is
where the recent buzz words machine learning
and data analytics come into play.These
techniques use historical data to find patterns
in them and thereby use these patterns for future
predictions, forecasting. More important is the
fact that once installed, these require the least
human intervention. AI solutions can help in
automating various supply chain processes such as
production planning, demand forecasting,
predictive maintenance etc. Along with automation
comes augmented human decision making, because
the human is then no longer involved in decision
making.
Just as robots can augment human actions, AI and
machine learning solutions can augment human
decision making. By, incorporating such systems
in all the possible areas of the supply chain,
the labor could be divided between human labor
and the AI machines. While the machine can
tirelessly and accurately look at data and build
regular reports from it, a human SME (Subject
Matter Expert) can intervene at the final stage
and perform the final decision making.Based on
the current situation of AI, the safest and the
cost-effective solution to utilize AI engines is
to use AI as an enhancer, not as complete
replacement for human decision making.
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Below are few areas where AI can be applied into
Supply Chain Activity Management

• Chatbots for Operational Procurement

Chatbots can be utilized for below activities

• Speak to suppliers during trivial conversations.
• Set and transform actions to suppliers regarding
  governance and compliance materials.
• Place purchasing requests.
• Answer internal questions related to procurement
  functionalities, supplier sets etc.
• Sending and receiving documentation of Invoice
  requests and Payment Order Requests.

2. Machine Learning solutions for Supply Chain
Planning
The inventory, demand and supply cycles data from
time to time can be analyzed by real time Machine
Learning Systems, through SCM work tools for
forecasting of demand, supply, inventory volumes
or any other Key Performance indicating
dimension. This could revolutionize the agility
and optimization of supply chain decision-
making.By utilizing Machine Learning
Technologies, SCM professional responsible for
Supply Chain Planning would be able to provide
give best possible scenarios based on intelligent
algorithms and machine-machine analysis of big
data sets. This capability has the power to
optimize the delivery of goods while balancing
supply and demand with least human analysis, but
rather action setting for parameters of success.
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3. Machine Learning for Warehouse Management
Supply Chain Planning is heavily reliant on
proper warehouse and inventory management.Regardle
ss of successful demand forecasting systems,
supply flaws can be a disaster for about any
consumer based company or retailer.A Machine
learning based forecasting engine, keeps a
scrutiny to find combination of algorithms and
data streams that could produce the most
predictive power in different forecasting
hierarchies.ML (Machine Learning) provides and
endless loop of forecasting, which bears a
constantly self improving output. This kind of
capabilities could prove to re-shape warehouse
management as we know today.
4. Autonomous Vehicles for Logistics and Shipping
Speedy and more accurate shipping can reduce lead
times and transportation expenses. The can also
add elements of environmental friendly
operations, reduce labor costs, and most
important of all, widen the gap between business
competitors. If autonomous engines are developed
to the capacity such that certain business
analysts and technology experts have
hypothesized, the impact on logistics
optimization would be most revolutionary.
5.Natural Language Processing for  Data Cleansing
and Building Data Robustness
Natural Language Processing (NLP) is an element
to Machine Learning and AI which has the
potential to stratify large amounts of foreign
language data in a streamlines manner.NLP applied
in the right way, could help in building datasets
of suppliers and dig-in untapped information in
foreign languages. From the perspective of
Sustainability and Governance, NLP could
streamline auditing and compliance actions that
were not possible because of language barriers
between buyer- supplier bodies.
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6.Predictive Analytics for Supplier Selection and
Supplier Relationship Management
Supplier related risks have become the Key
Performance Indicators for globally visible
brands. Data sets that are generated from SRM
actions, such as supplier assessments, audits,
and credit scoring have proven to provide further
decisions regarding a supplier.With the help of
intelligent algorithms, the passive data
gathering can be made active.This way, supplier
selection would be more predictive and
intelligible than before, hence creating a sure
shot for success from most of the collaborations.
This kind of information would be readily
available for human inspection but generated via
machine-to-machine interaction, providing the
best supplier scenarios based on the parameter
that the user desires.Hence, the true future of
Supply Chain Management lies within
machine-machine interaction and work which is the
automation of currently human manned-positions.
Inventraxs Falcon MES involves all the features
to enable the above processes, and enables the
optimization and Resource planning for a better
and improved efficiency.
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Contact Us
India


Avya Inventrax Private Limited, Sampath Vinyagur,
D.No. 50-94-14/3,1st 2nd Floor,Santhipuram, Visa
khapatnam, Andhra Pradesh, India- 530016.
Sales 91 630 380 0787 Tel [phone]
6060 Fax [phone]
sales_at_inventrax.com
Artificial Intelligence as Emerging Technology in
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