With the explosion of the e-commerce market and globalization, the need to transport large amounts of goods from one place to another has become an increasing challenge. From containers transported overseas and back again to last-mile couriers delivering individual items to consumers, logistics has many faces and multiple challenges.
According to the Markets and Markets report, the supply chain management market was worth $28.9 billion in 2022 and is expected to grow to a value of $45.2 billion by the end of 2027. This growth was amplified by the pandemic, where consumers were forced to use digital shops instead of brick-and-mortar ones, effectively forcing the adoption of new habits.
Yet growth has its price and shouldn’t be taken for granted. Supply chain management departments and outsourcing companies face challenges in their daily operations.
Challenges in supply chain management
Transporting goods in supply chain management is only the tip of the iceberg – there are challenges related not only to the exact transportation process but also to all accompanying activities.
Starting from IoT and geospatial systems to all the accounting and scheduling – supply chain management is a data-heavy business. This is both a challenge and an opportunity. There are numerous optimization opportunities and potential boosts hidden in piles of data. Yet without proper management, this amount of information can quickly become a burden, with the cost of storing it and no particular advantages gleaned from its existence.
Also, the challenge with the amount of data has another face – the amount of information is way beyond the comprehension of an analyst. Basically, there is no way for a human being to process this number of variables and the full scope of data all at one time. This alone is a good reason to apply artificial intelligence to logistics and supply chain management.
The number of variables mentioned are not only data points. There are also factors that influence the whole logistics process. From unexpected vehicle failures to unpredictable weather conditions, from human-related factors to legal constraints – you name it. The landscape can change several times a day, with flocks of black swans flying over a business.
Weather is a good example. According to a NOAA report, there were a total of 23 billion-dollar weather and climate disasters counting only up to the month of August. And that is only the tip of the iceberg regarding potential weather-related threats – simple, non-disastrous snow can even be a significant disruption.
Interconnections with other processes
Supply chain management is the backbone of multiple other business processes and its performance has an impact on multiple business factors. For example, any delays in delivery have a direct influence on the brand and customer experience. Also, in more complex business environments, a single bottleneck can halt all production processes.
And there is no space for sentiment – only 20% of consumers are willing to forgive a retailer for a delay in delivery, even if the delivery is performed by a separate company and is totally unrelated to the rest of the business!
Moving things from place to place is basically a cornerstone of the economy. In the end, the process includes extremely high costs. There are the wages of drivers, maintenance costs, taxes, and sometimes other, more transport-specific costs, like freight rates.
Also, there are unpredictable aspects in cost structures, for example, oil prices and employment laws, that, even if seemingly small at first glance, can affect a business at scale. The Pandemic has also been a disruptive event when the cost of freight soared beyond levels ever seen before.
Growing customer expectations
Last but not least, supply chain management needs to deal with growing customer expectations. The e-commerce market is imposing higher and higher standards to be met. For example, next-day delivery is switching from a super-luxury and wow effect to an expected level of service.
The same goes for the availability and convenience of services. This contributes to the popularity of pick-up points and pick-up machines. This last-mile challenge drives innovation in the industry, including drones and robotics that tackle daily operations.
The combination of the data-heaviness of the industry with the immense complexity of problems to be dealt with provides a fertile ground for Supply Chain AI-based solutions.
How to use Artificial Intelligence in Supply Chain Management
Artificial Intelligence is yet another way to use technology to support the overall tech-heavy business of Supply Chain Management.
What is Artificial Intelligence (AI)
The field of artificial intelligence (AI) has been around for a while. Starting from myths and fairytales about artificial life and sentient creations of gods and human sages to robots and automatons making decisions by and for themselves.
Modern Artificial Intelligence has come into play since the dawn of cloud computing and machine learning, where computers gained the ability to learn patterns from the data they process, assuming a training dataset is big enough to conduct the training process.
The activities that can be handled by or aided by Artificial Intelligence include summarizations, reasoning, knowledge interpretation, and data analysis, among others. But certainly not limited to only these – in fact, machine learning and artificial intelligence have been boosters for processes rather than separate processes themselves. This is well-represented in the ways AI can support Supply Chain Management processes.
How to tackle challenges with Supply Chain artificial intelligence
The challenges listed above are hard to grasp and impossible to tackle as a single problem to solve – it is rather about the interconnected pains a company suffers from. Yet, with several responses combined, the problem can reach a complex solution, and the business can get a boost in multiple fields.
Demand forecasting is a vital part of supply chain management. Companies that run logistics need to make crucial decisions based on their forecasts, be they regarding supply or demand. Demand forecasting allows a company to predict the amount of goods to move from one place to another, and with the precision of the prediction comes enhanced cost efficiency. According to McKinsey Digital, an AI-based demand forecasting tool can reduce errors in the process by up to 30%.
Precise, AI-supported demand forecasting enables a company to avoid delivering oversupply, cutting the need to pay for gas or freight. Also, the company avoids the risk of undersupplying, which would come with the additional costs of rushing deliveries to meet demand.
Inventory management is the other side of the same coin – it is about managing the resources the company already has. This may include agile stock management, with the ability to harness the “here and now” situation. For example, this can include a swift response to a changing situation and moving certain goods between neighboring warehouses or locations.
According to IBM data, up to 56% of business leaders consider their inventory information to be accurate.
Risk Management deals with all the unpredictable matters mentioned above – from floods and heavy rains to oil price surges and a variety of other risk factors. Risk management is all about predicting the unpredictable and building systems that will mitigate these risks and allow the company to tackle them without overspending on insurance or policies. That’s why risk management is a natural field for artificial intelligence applications in supply chain management.
In the day-to-day struggle, companies sometimes overlook opportunities that can come from optimizing their suppliers in supply chain management. This challenge can be addressed by AI, which spots the points where the company can either negotiate fees or look for a better partner to work with.
Suppliers can influence business operations in multiple ways, starting with the timely delivery of goods for further distribution to influencing margins to put stress on a company with disputable reliance on their source of goods.
Predictive maintenance contributes to risk reduction and management, as mentioned above. With access to IoT devices, companies can now monitor their in-use equipment in search of signs of malfunction or breakdowns.
If spotted early, malfunctions are usually easier to tackle and manage. Also, with the knowledge that there is some time left before a breakdown, the company can respond faster, for example, by ordering spare parts or booking the time of engineering specialists in advance. Considering all these advantages, it is not surprising that the predictive maintenance market is estimated to reach $79.9 billion by the end of 2033.
Trade routes optimization
One of the most popular anecdotes regarding the use of data analytics in trade route optimization is about UPS drivers not being allowed to turn left. Setting trade and delivery routes efficiently is one of the most complicated problems from a mathematical point of view. The traveling salesman problem (or the more modern version – the courier problem) is a perfect challenge to be handled by artificial neural networks.
AI-based systems can estimate the time of arrival of a courier, the optimal route between every point, and the most resource-optimal paths to take.
Last but not least, AI-powered technology can be used to support the last-mile problem – the fact that there are distributed and decentralized sets of locations to reach when delivering goods. This includes not only e-commerce consumer deliveries but also deliveries to retail shops or service points. The World Economic Forum predicts a 78% increase in last-mile delivery in urban areas by 2030.
There are multiple ways AI can contribute to last-mile optimization, in powering drones and autonomous delivery vehicles coming quickly to mind. The startup scene is currently thriving with companies willing to revolutionize the last mile. Also, established companies like Amazon are experimenting to discover the best ways to include robotics and AI to tackle the last-mile problem.
Supply chain AI management is a fertile ground for AI applications, with a data-rich environment and the overwhelming complexity of processes bringing countless opportunities. Considering that, it is not a surprise that there is an increasing number of companies that are either experimenting with or have already implemented Artificial Intelligence and machine learning in supply chain management.