How AI can transform your supply chainBy Jon Taylor on September 5, 2021 - 10 Minute Read
As most of us will already know, supply chains are the lifeblood of any business.
Gartner predicts that supply chains will become smarter, more automated and more complex within the next five years. This is down to the fact that, with global businesses becoming more intertwined and competition getting fiercer, companies are willing to invest in better technology so that their supply chains are more accurate and make better decisions.
All of this may explain why artificial intelligence (AI) is already proving to be a game-changer for businesses that rely heavily on their supply chains.
Suppliers and customers now expect businesses to move rapidly, making data-based decisions within their supply chain and logistics to keep operations running smoothly. AI is the secret ingredient to making this possible. In this article, we’re going to run through a wide range of topics associated with AI in supply chain, including…
- A quick look at AI in the supply chain
- Will AI take over the supply chain?
- What is AI in logistics?
- How is machine learning used in the supply chain?
- Three real-life examples of AI used in the supply chain
Let’s dive in.
A quick look at AI in supply chain
AI in a supply chain is where key logistical and operational decisions, along with predictions and forecasts, are assisted by using AI software.
We’re talking about the everyday work that gets products from the warehouse to the customer. So, information flows, transportation, shipping, warehouse costs, stock levels – everything that you think a traditional supply chain involves – can also be transformed using AI.
By investing in AI, companies can make their supply chains function more efficiently, avoid stock shortages and maximize efficiency, budgets and profit. AI can influence the supply chain at many different stages, but most notably during:
- Planning: Companies can get real-time updates about product availability and prices and create risk-adjusted margins based on product availability
- Logistics: Automatic optimization of transportation routes, contracting, vessel sharing, reducing costs and reducing environmental impact
- Procurement: Integration and automation of products and suppliers, optimizing ordering lines and assessing prices and availability
Now, we know what you’re thinking – all of these bases are already covered by a company’s operations and logistics team.
Well, yes. However, integrating AI into the supply chain helps companies make more accurate decisions faster while learning from the problems they’ve made in the past. That’s because AI can automate processes and decisions without a human getting involved, so it’s much quicker. And because AI can consume huge data sets within seconds, it can analyze them instantly to give companies an accurate idea of what errors, problems or delays they may have to deal with in their supply chains.
A great example of this is still happening right now. Supply chain issues are still being felt around the globe because of the impact of the COVID-19 pandemic.
The market is volatile, many borders are still closed and worldwide shipping has been thrown into peril. While it may be impossible for a human to keep tabs on every movement within a supply chain and figure out how it’ll impact a company’s logistics, AI can help significantly.
Here’s an example of what happened when COVID-19 hit.
As the pandemic was so new, nobody could possibly know how it would impact global supply chains. But, as AI learns with each new piece of data and uses it to make more accurate predictions, it can create real-time visuals of any potential supply chain issues.
Businesses seemed to have woken up to just how important this kind of technology was to mitigating supply chain disasters during the pandemic. A survey by Insight found that 95% of IT decision makers think that the impact of the pandemic accelerated business transformation priorities. It also discovered that AI, machine learning, high-performance computing, data analytics and digital workplace technologies would impact supply chain tech stacks within the next three years.
So, just how much is AI going to impact the way we manage supply chains? 🤔
Will AI take over the supply chain?
If there was ever a statistic that highlighted how confident businesses are in their supply chains, it’s this – only 4% of leaders in charge of their supply chains feel that they’re ready to deal with the future.
Four per cent.
With that level of preparedness, it’s not surprising that supply chain shortages have impacted everything from transportation to toilet paper over the past two years. And it’s why leaders in charge of procurement, planning and supply chain management have labelled risk management and preparedness as their top priorities.
Companies are already investing in supply chain AI at a staggering rate. AI in the supply chain is expected to be worth about $6.5 billion by 2023, fuelled by company leaders looking at algorithms and machine learning to give them (digital) eyes and ears so they can make split-second decisions. Last year, only 12% of businesses were using AI in their warehouses and supply chains, but that’s expected to grow to over 60% within the next six years.
Businesses taking the plunge and investing in AI may be doing it for several reasons, like being able to:
- Spot future supply blockages, variables and constraints using accurate forecasting
- Analyze and predict product demand across countries, segments and geographics
- Use predictive maintenance, machine learning or data scraping to manage their logistics and procurement
- Adapt to market shocks, interruptions in production and transportation delays
And the biggest reason that AI is disrupting the supply chain? Customers.
In the age of next-day deliveries, automatically-updated product catalogues and algorithms that suggest products and upsells, customer expectations have increased alongside new technology.
It’s now expected that companies will be able to fulfill orders on the same day, have up-to-date inventories, recommend products people will like, and do it instantly.
But how does that work logistically?
What is AI in logistics?
Artificial intelligence helps businesses optimize existing logistical routes by analyzing data, operational challenges and historical seasonal patterns to find the most efficient route.
Think of AI in logistics as a Google Map that analyzes every part of your supply chain. It can show you the fastest (and cheapest) route and what channels are facing delays, ultimately giving you the best supply chain route possible for your products.
It does this in several ways 👇
Better data quality
AI doesn’t just gather data – it learns from it.
Paired with machine learning and natural language processing (NLP), companies can deploy AI in their logistics chains to organize data and make sense of everything happening in their supply chains.
Better data analysis allows companies to avoid risks within their supply chain while maximizing product demands. If a product is in high demand or stock is running low, AI will flag it in your logistics tech stack long before any human realizes.
AI is expected to make logistics operations 40% more productive by 2035.
Companies will need to stay ahead of the curve to hit this productivity increase. Deploying AI into logistics allows companies to link their data sets, so that everything from transportation to product shortages and optimized routes can be forecast well in advance.
Better decision making
Integrating AI into a company’s logistics takes the pressure off of humans to make all of the right decisions.
Even the most experienced logistical managers make mistakes. They miss a data point or let their bias get in the way of making an accurate decision. AI will analyze a company’s logistical channels to improve forecasting and make accurate decisions based on data, not emotions or bias.
How is machine learning used in the supply chain?
A supply chain can be monitored using AI to detect changes in demand and give companies a clear picture of everything, from product demand to supply chain issues.
And it does all of this using machine learning (ML).
ML analyzes every piece of data a company has collected, even the data sets that have sat untouched. These data gold mines can help uncover historical buying behavior, common supply chain issues and demand volatility within a company’s logistics. The technology can also suggest possible solutions to any problems that a company runs into, like supply chain disruptions or transportation blockages.
Companies are now using this technology and deploying it alongside their workforce in warehouses and production lines to get the best of both worlds – humans and computers.
Consumer goods giant Procter & Gamble (P&G) has turned to AI-enabled tech to automate its supply chain, using ML applications to create algorithms to solve some of its toughest logistics questions. The company stores its data inside Google’s cloud infrastructure, and then its AI tools work alongside machine learning libraries to analyze it and ensure operational reliability.
“Increasingly, we turn those predictions into a prescription – so we automate the result of the algorithm and inject them into our transactional and planning systems, so mainstream decisions are automated, freeing up our team to invest time in more complex and unique challenges. The data lake gives us a consistent, unified view of the consumer, and lets us create omni-channel consumer journeys. That means we can serve the right audiences at the right time with the right content on the right channels.”
– Vittorio Cretella, CIO at P&G
But it’s not just P&G turning to AI and machine learning to modernize its supply chains – some of the biggest brands in the world are also investing.
Three real-life examples of AI used in the supply chain
1. Using machine learning, DHL can now predict air freight delays
Delivery drivers have kept the world running over the past two years, but that work came with its fair share of logistical challenges.
DHL’s engineers and solutions designers created the IDEA algorithm to address this possible lack of clarity regarding stock levels and bottlenecks. It analyzes 58 different data points and allows the company to predict delays or speedups in its air freight up to a week in advance.
The algorithm allows DHL to optimize picking routes within its warehouse and cluster orders and routes together for efficiency. Machine learning also analyzes data to optimize warehouse staff workloads and prioritize time-critical packages.
Since DHL started using the IDEA algorithm, warehouse employees have cut the distance they travel by up to 50% and increased productivity at individual locations by 30%.
2. Speedy Hire uses AI to predict seasonal demand
Speedy Hire is one of the largest equipment rental and support services in the United Kingdom, managing 220 depots and a hire fleet of around 3,000 different products.
As its products are expensive, its profit margins depend on holding minimal assets to cut costs while ensuring that product availability is high and their customers are happy. The company decided to invest in AI within its supply chain and utilize an algorithm to:
- Predict seasonal demand to help determine product stock levels based on historical seasonal demand patterns
- Detect anomalies like changes in the demand profile and training data that could falsely impact true product demand
- Set stock levels that are dependent on the different products and locations it operates in
- Substitute unavailable products with similar solutions to ensure the customer still has items to choose from
Using AI in its supply chain has allowed the company to maximize revenue as it can manage leads more efficiently and segment customers based on historical spend and region. AI even reclassifies customers based on their potential opportunity so the company can focus its time on those most likely to maximize revenue.
3. Lineage Logistics uses AI to keep every food delivery fresh
Lineage Logistics has a (not so simple) simple mission: it delivers food to restaurants and grocery stores and ensures it stays cold until it gets there.
The company now uses AI to forecast when the order will leave a warehouse, the path every order will take and when it will arrive at its destination. To achieve this, the company has invested in automation throughout its warehouse and layer picking machines, as well as multiple automated guided vehicles (AGVs) to cut the labor involved in completing an order.
The warehouse is organized so that items that will stay for a long time are further in the back, and those that will move quickly and won’t stay as long are placed more towards the front.
Since using AI in its supply chain, Lineage Logistics has boosted efficiency by 20%.
Supply chain AI: wrapping up
Stabilizing supply chains has never been an easy task, but investing in AI can help you make quicker, more accurate decisions.
With AI, teams can gain deeper insights with greater frequency and granularity than ever before. Using ML and forecasting, companies can spot problems in their supply chains or make smarter decisions based on data analysis.
Not only that, but integrating AI into a supply chain helps companies meet the expectations of their customers. From making sure products are in stock to ensuring quicker deliveries, it’s no surprise that so many businesses are looking at AI as their ticket to giving customers the best experience possible.
Here at Peak, we help companies like yours integrate AI into your tech stack to take advantage of unused data and optimize your supply chains. We do this with a platform built to deliver outcomes in Decision Intelligence – the commercial application of AI to drive profit and growth. You can learn more about the benefits of Decision Intelligence for supply chains here.