How Artificial Intelligence (AI) can be used in Supply Chain Management
- Supplier Management
- Supply Chain Management
If you could go back two years, knowing what you do now, would you do anything differently?
Your answer is probably yes. The cliché that hindsight is 20/20 is cliché for a reason.
The past two years have thrown challenges and disruptions at us that no one could’ve predicted.
Now think about it the other way – if you had a crystal ball and could see into the future, would you use it to avoid disruptions and gain that competitive edge?
Again, your answer is probably yes. Of course, crystal balls don’t exist outside of movies.
But what if I told you that we’re already pretty close to predicting the future?
It might sound farfetched, but it’s not magic, it’s AI in supply chain management.
The Rise of Supply Chain and Supplier Management
Unsurprisingly, supplier management, specifically supplier risk management, is now a top priority for procurement leaders around the globe, trailing only behind cost reduction according to The Hackett Group’s 2021 Key Issues Study.
These events sent a ripple effect through global supply chains, extending across multiple countries, industries, and product lines, with manufacturing and pharmaceuticals particularly hard hit.
Not only that but the White House has now stepped in to review American supply chains, further pushing supply chains and supplier management into the spotlight.
Supplier performance and risk management are front and center in everyone’s mind, and no one is keen on repeating the mistakes of the past. Hindsight is 20/20 after all.
A Path Forward
There is no magic cure that’s going to wipe away all of the underlying supply chain gaps and risks that exist.
To do that is going to take hard work and some creative thinking. However, a step in the right direction to solve the underlying risks does exist.
There are other options you should weigh when evaluating your next steps. Investing in supplier diversity or sustainability are both important avenues to consider.
Both offer numerous advantages and should certainly be included on your roadmap, but pound for pound, nothing can give a boost to your supply chain (and supplier risk) management efforts quite like AI can.
How AI Fits with Supply Chain and Supplier Management
Artificial intelligence is still an evolving technology, with new use cases and developments popping up constantly.
Especially in procurement or supply chain management, AI isn’t quite mainstream yet, but being an early adopter will pay huge dividends down the road by allowing you to iteratively improve internal processes and speed up your ultimate time to ROI.
Today, AI’s main use cases are in these three areas.
Supplier Scorecarding and Performance
One of the best ways to manage suppliers strategically is to run a supplier scorecard.
This will rank them against a number of factors like sustainability, historical performance, strategic value to the business, supplier diversity, and a number of other metrics that allow you to rank and group suppliers to find your “core strategic set”.
It’s no secret that this is an incredibly time-consuming exercise and one that constantly changes.
There is always new data coming in. New laws are passed, new suppliers onboarded, performance or quality issues arise, or maybe your business strategy has changed and requires a completely new scorecard metric.
There is also the problem of data integration, with all of these data points coming from a variety of sources, getting a “single source of truth” can be difficult.
The point is that to accurately score a supplier and then effectively manage performance is a next to impossible task.
But not with AI. By using advanced ML and RPA technologies this supplier data can be captured, scored, and updated on a constant basis.
This allows you to adjust your supplier strategy at a moment’s notice by always having access to updated and reliable supplier profiles in a single dashboard.
For example, the pharmaceutical industry has been in the global spotlight for well over a year now. Vaccine distribution and production have made headlines, and now an increased demand has caused bottlenecks and shortages in many areas.
This has led to some countries pausing vaccine rollout, spacing out shots, and in some extreme cases having to throw out whole batches due to timing and storage issues.
With the right supplier scorecarding and data coming in, this could all have been avoided. Grouping key suppliers together and coming up with contingency plans could’ve been done proactively, while also keeping other supplier options close at hand should the need arise.
Supply Chain Visibility
N-tier supply chain visibility is more important than ever before, but it’s also harder to achieve than ever before.
Up until the 1980s manufacturing plants had offices overlooking the floor to keep an eye on every product line. If one machine went down, it was instantly spotted and adjusted for.
But now we’ve outsourced so much. Five station lines have become five companies, five buildings all over the world. That direct visibility doesn’t exist now, and it’s even further complicated when you have to account for not only your suppliers but their entire supplier ecosystem as well.
Supply chains are global, interconnected webs of activity where it’s hard to get an accurate picture past the second tier of suppliers.
However, by applying AI to an advanced supplier management platform that data becomes accessible.
AI algorithms combined with advanced analytics can model a supply chain to the n-th tier while providing real-time performance updates.
This means that you won’t get caught off guard due to a fire taking out a second-tier suppliers’ factory, or a third-tier supplier going through an ethical workplace investigation.
You’ll achieve a 360° view of your supplier ecosystem that allows you to stay ahead of disruptions while making strategic optimizations to drive increased value.
Predictive and Prescriptive Analytics
One of the most common applications of artificial intelligence is by using it to augment data, specifically in creating predictive and prescriptive analytics.
Predictive analytics is the more common of the two today. This is based on analyzing historical performance data and other external data points to develop predictions of what a likely outcome is.
An example of this in use today is JAGGAER’s On-Time Delivery Predictor (OTD). This solution is able to predict, with up to 95% accuracy, if a shipment will arrive on time or not before you even place the order.
This is an incredibly useful application that drastically cuts down on late shipments causing further supply chain disruptions and bottlenecks.
Prescriptive analytics is the next step of this. By combining enhanced AI with predictive data, the system is then able to give recommendations and solutions to problems.
For example, if you now know that a certain supplier is likely to be late, the system will give you several alternative scenarios that match your timeline, goals, and quality standards.
Quite useful in the case of another Suez Canal blockage, which caused billions of dollars worth of shipments to be late, not to mention the logistics nightmare that ensued. AI could’ve helped solve that in a fraction of the time.
Or even more complex, let’s say the price of oil (or any other commodity) is projected to increase. The system will flag that and then recommend that you renegotiate with certain suppliers or place an order now to beat the price inflation.
These are just two small examples of the potential use cases that prescriptive analytics and AI can unlock in your supply chain management.
How to Get AI Ready for Supply Chain Management
AI, and any advanced digital solution for that matter, can’t happen overnight.
There are a lot of steps that you have to go through in order to prepare your organization.
From building the right data foundation to addressing internal culture and change management you can’t skip steps and hope to have a successful implementation.
Want a step-by-step guide to help you get ready for AI-ready for your supply chain management?