Following the path of digitalization in Slovenia and Europe: Artificial intelligence dramatically enhances processes within supply chains

In recent 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 impossible.

In a 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

Artificial 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.

Companies 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.

Companies 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.

Artificial 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.

Oracle, for 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 learning programs.

The logistics industry has almost entirely adopted AI at various supply chain stages, from how truck drivers are organised to how products are ordered and scheduled.

Demand forecasting and transparency

Whether 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.

When 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.

In 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.

Artificial 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.

Planning, optimisation, and automation

Supply 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 managers.

Alternatively, 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.

Just as 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.

AI-integrated 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.

Artificial 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.

There are 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.

Therefore, companies can save a lot of time, effort, and money by using artificial intelligence in their supply chain processes.

Author: Rok Žontar

Keywords: AI, supply chain, management, planning, process.

Disclaimer:

This 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 European Parliament. 

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 contained therein.