Supply Chain Crisis: How AI Can Mitigate the Effects
Digital technologies have spurred the rise of globalization and complex, interdepend supply chains sometimes relying on dozens of vendors, wholesalers, manufacturers, distributors, logistic service providers and retailers to transform raw materials into products and get them into the hands of consumers. This shift away from local to a global supply chain offers many benefits, but also presents a significant weakness: under the right combination of forces, supply chains can be brittle. This has been exemplified by COVID-19 pandemic, which has caused a global supply chain crisis shortage impacting business, industry, and society around the world.
Why is there a global supply chain crisis?
The recent supply chain crisis is a result of many different factors. In the UK alone, these include the global covid-19 pandemic, a shortage of HGV drivers, a gas/fuel crisis, and border restrictions. For the rest of the world, similar factors have prevented supplies from reaching the demand of businesses. In parts of Europe, such as Germany, Belgium and Netherlands, unprecedented flooding has ground supply chains to a halt. For China, it’s the shortage of containers needed to ship supplies to countries like Singapore. Worldwide, rising infection rates, sporadic lockdowns and sick frontline works and delivery staff have been another obstacle.
These factors have led to many businesses across the world having inventory and staffing issues, leading to panic buying to prevent inventory issues. As a result, the supply chain crisis has been exacerbated by the “bullwhip effect”, which leaves the suppliers of raw materials and manufacturers scrambling to meet demand that is far in excess of their ability to supply.
As we approach winter and Christmas, businesses everywhere are scrambling to prevent further disruption and mitigate disappointment for their customers.
How can AI mitigate the consequences?
With the recent high-profile HGV driver shortage in the UK, the first thought on many people’s minds solving the supply chain issues is self-driving vehicles powered by computer vision and machine learning. Despite the hype, the reality is that mainstream rollout of self-driving lorries is unlikely to happen soon and does not provide a universal solution to all the problems leading to the supply chain crisis. Companies like Waymo, Tesla and even growing startup Arrival are still in the process of researching, testing, and validating this technology. However, incidents caused by Tesla’s Full-Self Driving (FSD) capability have created setbacks.
Luckily, there are applications of AI that are more feasible and can be readily deployed to help businesses confront these challenges. Here are some AI use-cases that should be on your radar if you’re in the supply chain:
Improved Demand Forecasting
Being able to predict how much supply you need to accommodate demand can be a real gamechanger for suppliers and vendors, helping to avoid overstocking or understocking issues. It can also help businesses anticipate and adapt to shifts in customer demand, ensuring that optimum supply levels are managed to minimize loss and maximize profit.
With predictive analytics, enterprises can anticipate customer demand during periods of normality, but also in events of unprecedented disturbances such as the Covid-19 pandemic and peak season such as Christmas. By anticipating customer need using predictive analytics, enterprises can make smart data-driven decisions to manage demand effectively, ensuring that they are prepared to meet customer needs by planning inventory in advance.
Supply chain recommendation systems
A regular, reliably supplier is crucial for stability. But when there is volatility in the market that disrupts this stability, how can businesses adapt if regular suppliers cannot deliver? One way for enterprises to overcome this challenge is by diversifying their supply strategy, which can be augmented by recommendation systems.
Recommendation systems are particularly well known in the B2C space - such as Amazon and Netflix - but also present significant opportunity for B2B supply chain management. By using recommendation systems, businesses can identify a diverse set of products and goods that pose a viable alternative to their traditional range. Specifically, utility-based recommendation systems that create recommendations based on a product’s ‘usefulness’ can help enterprises make data-driven decisions about the best source of supply to use within a constantly changing market, giving them an abundance of choice within their personal supply chain.
These systems can also benefit businesses when it comes to customer interaction. If problems in the supply chain mean that businesses are unable to stock particular items, recommendation systems can help businesses encourage users to consider other items similar to the product that the customer wanted originally, ensuring businesses is not lost and sales are still made.
Delivery schedule optimization
Getting products from point A to B efficiently as possible is one of the main goals of effective supply chain management. Unsurprisingly, delivery logistics is one of the most important aspects of the supply chain, one that has been highlighted recently with the HGV crisis. Effective planning of delivery schedules helps to ensure that businesses receive inventory when they need it and on time, whilst also minimising the costs involved in providing this crucial service. Vast supply chains can make this task complex and unruly, leading to lost productivity and unnecessary waste. Fortunately, AI can optimise delivery schedules and routes through automation.
By combining geolocation and even real-time GPS data with traffic conditions, AI can automate route planning considering factors such as delivery location, expected traffic and delivery times as seen with UPS. This helps ensure that stock is received minimal disruption.
These are just three ways that Artificial Intelligence can help businesses optimise their supply chain operations, mitigate the effects of the current crisis and prevent future issues with managing supply and demand. There is no telling what new situations may arise that shake up and disrupt the supply chain in the future, so businesses must be prepared with all the technological tools required to help fight back and prevent any of the effects from hitting them hard. As with any technology, and AI is no exception, treat these use-cases as thought starters but be mindful that any applications your business pursues should reflect your unique business challenges, priorities and data. If you are interested in further discussing how AI can help your business bounce back stronger from the supply chain crisis, feel free to get in contact by emailing [email protected].
Written by Andrew Modrowski, Joseph Myler and Clayton Black