years, supply chain management has become much more complex as increasingly
complex processes from individual stakeholders need to be constantly
coordinated to ensure that certain goods arrive from the manufacturer to the
end customer. However, various events, such as the COVID-19 pandemic and the
current Ukrainian-Russian crisis, make that task even more difficult or even
previous article, we explained that artificial intelligence (AI) makes it
possible for machines to learn from experience, adjust to new inputs and
perform human-like tasks by combining large amounts of data with fast,
iterative processing and intelligent algorithms. So, let us look at how all
these artificial intelligence features are used in practice in supply chain
systems. What are the differences between companies that take advantage of
artificial intelligence and those that do not?
Optimising supply chain
using Artificial Intelligence
intelligence in the optimisation of supply chain management is becoming more
widespread in various industries. Management of supply chains has become
increasingly complicated in recent years, as physical flows are becoming more
interconnected and market volatility has increased the requirement for agility
and adaptability. The supply chain is a web connecting transportation,
production, acquisition, marketing, sales, and more.
can optimise their earnings with good supply chain management, although
managing these supply chains can become an enormous task without help. The use
of artificial intelligence for supply chain management is one of the ways many
companies are handling increasingly demanding local and global supply chains.
Using the massive amount of data generated by company operations, an organisation can use AI-enabled solutions and teams of data scientists to transform supply chain operations. According to the KD Nuggets website, this can include implementing factory automation, improving quality control, forecasting demand, predictive maintenance and much more.
that successfully use AI-enabled supply chain management have managed to reduce
logistics costs by 15%, inventory by 35% and service levels by 65% compared to
competitors who are not adapting to using artificial intelligence for their supply
chain management. It is becoming increasingly clear to supply chain and
logistics industry leaders that artificial intelligence is more than capable of
handling the complexities of running both local and global logistics networks.
intelligence is changing industries by more efficiently tracking operations,
improving supply chain management and productivity, supplementing business
plans, and even interacting with online customers. Therefore, it is no wonder
that large multinationals, such as IBM and Google, and smaller companies are
taking full advantage of AI for their supply chain management.
example, uses artificial intelligence to create databases that are
self-updating and self-managing that their clients can use and take advantage
of. Coupa is another technology company that uses AI to improve and manage its
supply chain. Coupa has created an entire business structure around helping
businesses manage their supply chains with the help of AI and other deep
logistics industry has almost entirely adopted AI at various supply chain
stages, from how truck drivers are organised to how products are ordered and
Demand forecasting and
companies want to use AI to cut costs by eliminating redundant operations,
mitigate unnecessary risks, improve supply chain forecasts, deliver products
faster and more efficiently, or revitalise their customer service strategies,
AI is becoming critical to supply chain management. One of the primary purposes
of the supply chain is to maintain optimal stock levels to avoid a catastrophe
in the event of a stock shortage or stock overflow.
creating models for demand forecasting, AI can produce reasonably accurate
estimates of future demand against the current stock. For example, an
artificial intelligence program can be used to predict a product’s decline and
end-of-life (EOL) cycle on a sales channel. The program can then create models
for new products that are expected to make breakthroughs into the market,
replacing any product reaching their EOL. Using artificial intelligence for
demand forecasting is helping many companies to increase the lifecycle of a
product in the market significantly.
addition, it is now more important than ever for manufacturers to have total
visibility of their entire supply chain, from start to finish. KD Nuggets
explains that cognitive automated programs driven by AI are being used to
provide data visualisations that can be employed to reveal causes and effects of
supply chain issues, reduce or eliminate bottleneck complications, and identify
opportunities to improve and advance the supply chain.
intelligence can do all this not only by using historical data but also by
taking in and comprehending real-time data across multiple layers of the supply
chain and constantly adapting it.
chain managers may struggle to optimise a supply chain comprehensively. They
cannot see processes in real-time, detect variances, comprehend changes in
consumer demand trends, or stay up to date on unexpected events such as factory
shutdowns and transportation issues. These are complex processes that typically
go through multiple layers of communication before reaching supply chain
artificial intelligence solutions can be integrated with many of these systems
and allow business plans to be integrated across multiple companies and stages
of production. When these business plans and supply chains are coordinated, each
supply chain manager can better grasp their product distribution.
artificial intelligence can integrate business plans across multiple companies,
artificial intelligence programs are also used to generate cognitive
predictions and recommendations to further improve and optimise the supply
chain planning process. This can save a company a tremendous amount of time in
planning through complicated manual business models and reduce the number of
errors during the process.
supply chain software further increases critical factors in the supply chain to
optimise the process from conception to delivered products. This improves the supply
chain’s efficiency by helping manufacturers determine the potential
consequences of various scenarios in terms of time, cost, and revenue.
intelligence can also ensure that material bills and purchase order data are
structured and filed correctly, creating more accurate predictions in
real-time. This allows field operators working with this data to maintain
optimal levels based on current and predicted consumer demand. The ability to
identify and manage these optimal levels is enabled by integrating artificial
intelligence in the supply chain.
already artificial intelligence programs that use computer vision and physical
sensors to monitor and modify processes in the supply chain. This keeps an
accurate and updated spreadsheet of supplies on hand in real-time. Taking it
one step further, some artificial intelligence programs can automatically
detect a need in the supply chain and take the appropriate action to maintain
optimal levels without needing field operators or supply chain managers to do
physical inventory counts on their own before doing so. Such an example is the
process of monitoring products on store shelves and cross-referencing the
remaining product inventory and current demand for the product, followed by
appropriate action if stock is low and demand is high.
companies can save a lot of time, effort, and money by using artificial
intelligence in their supply chain processes.
article is part of joint project of the Wilfried Martens Centre for European
Studies and the Anton Korošec Institute (INAK) Following the path of
digitalization in Slovenia and Europe. This project receives funding from the
information and views set out in this article are those of the author and do
not necessarily reflect the official opinion of the European Union
institutions/Wilfried Martens Centre for European Studies/ Anton Korošec
Institute. Organizations mentioned above assume no responsibility for facts or
opinions expressed in this article or any subsequent use of the information