Some level of supply chain disruption is commonplace, even during normal times. But with the current global pandemic, we’ve entered an essentially unknown world where supply chains are being regularly disrupted in previously unseen ways.
There are risks to the supply chain inherent at every turn, especially today. Companies that aren’t ready for supply chain disruptions of the kind we’re seeing now are at real risk of falling behind their competitors and in danger of being disrupted themselves. One of the best ways to mitigate those risks is by leveraging the power of artificial intelligence (AI) and machine learning (ML).
If supply chains are critical to your business, you might be wondering about how to address some of the common—and current—challenges to the supply chain. This post has your answers.
Challenges of preparing for supply chain disruptions
The list of potential disruptions to supply chains is long and varied. Some risks, such as an increase in demand for a product, are easily managed. Others, though, don’t occur with enough regularity for managers to easily get a handle on in order for them to be prepared in advance.
These include natural disasters, suppliers up and down the chain going out of business (or suffering other business interruptions), weather-related slowdowns and diversions, ports closing, political issues, global pandemics, etc.
Despite the best of intentions, many organizations find themselves falling short when they try to plan for potential supply chain disruptions. Most often, that’s because planning for the unknown is a tightrope act—accounting for an unknown future must be balanced with cost efficiencies. It’s money that matters in business, and solutions to mitigate supply chain risk are not always seen as cost saving measures, although they certainly can be in the long run.
Efforts to increase cost efficiencies by finding low-cost suppliers across widely dispersed geographic locations add additional levels of risk to supply chain disruptions, as does reliance on legacy single-source suppliers and centralized inventories. These are known risks, but they may also be challenging to break away from.
The ideal scenario here is one that makes your supply chain resilient, giving it the ability to flex but not break, and to quickly rebound from any shock that happens to the systems wherever it occurs.
How to prepare for supply chain disruptions
So how do you build a resilient supply chain? Let’s consider some of the ways that organizations might get ahead of inevitable supply chain disruptions and avoid any serious long-term impacts, with a special focus on how AI and machine learning can help.
Addressing variable demand
One of the most common ways that supply chains get disrupted—and one that happens regularly, not just during times of crises—is via changes in demand. After all, your customers aren’t going to buy exactly the same thing every single day.
Having a solid sense of what your supply chain looks like, and the various ways that you can manage it to adjust to changing demands, is critical for success. If you want some insight into how AI can help, check out this talk from Ryan Frederick, Manager of Data Science at Dominos that discusses how they used RapidMiner to work through a complex time-series forecasting exercise and uncovered an innovative way to reduce errors and improve runtime speed.
Who’s on first? What’s on second? Having and maintaining an understanding of each and every node along the supply chain allows managers to stay on top of potential detours. Basic questions include: What are the inputs at each stop? What’s supposed to happen there? And what are the expected outputs?
Resilience in this case would mean using the detailed knowledge of processes to, for example, quickly redesign a product to account for the loss of a discrete part. Having plans in place to retool rapidly, if necessary, lessens the risk of shock due to loss of a specific input. Generic, more open underlying designs might accommodate parts from alternative suppliers that can be swapped in if needed.
A digital twin can help with this process, as it allows you to easily make tweaks to a model of your current operation, and see what effects it would have further down the line.
The key to gaining supply chain visibility is data. By collecting the right data at each waypoint, supply chain professionals can start the process of ensuring that their processes are optimized. But ultimately, what are you going to do with that data? Using machine learning to extract insights and serve them up in an understandable and accessible fashion so that leadership can get a picture of what’s happening, at a glance, can help to identify and address problems quickly and effectively.
For example, understanding which items are selling to which customers in which location, and seeing when those change, is important for managers to tune their supply chains to meet demands, wherever those demands may occur. If you don’t have ready access to this information, however, it can’t inform your decision making.
Setting specific key performance indicators (KPIs) for each link in the supply chain allows managers to ‘manage by exception’—that is, increasing the ability to respond quickly by seeing and understanding anomalies along the chain. Machine learning is adept at identifying anomalies, often earlier than humans would be aware of them, so that you can intervene and make the necessary changes to get back on track.
The old, trite saw that ‘you can’t manage what you can’t measure’ certainly applies here. But going beyond that, setting realistic goals for the supply chain, and having some type of dashboard to provide regular insight, or even make changes on the fly, can help mitigate supply chain disruptions.
Making sure that the right materials are in the right place at the right time is an obvious but important factor in ensuring a smoothly operating supply chain. Brick and mortar retailers know all about this aspect of the supply chain.
For example, during the current pandemic, retailers have been upended by the hugely increased demand for toilet paper. Stocks are low, and many shelves are bare. While a robust supply chain, and the ability to quickly retool alternative manufacturers to fill the gaps, have helped to diminish some of the inventory challenges, better management of inventory in the first place would have been a better mitigation strategy.
In addition to managing inventory, executives responsible for the supply chain should make sure that the manufacturing capacity meshes with the available parts inventory.
One way to manage capacity and lessen supply chain disruption is by adapting a make-or-buy approach. This tactic recognizes that in some cases it may be more resilient to produce some products in-house while outsourcing others. Machine learning platforms that support cost-sensitive scoring can help to answer questions about what the most cost-effective scenario would be and the impacts of any changes.
Contracting with additional suppliers of critical parts, before they’re needed, may help to alleviate any potential slowdowns from supply chain disruptions. In most cases it’s simpler, more straightforward, and generally less expensive for enterprises to limit their suppliers to a sole source. But that approach leaves little room for mitigation if that sole supplier suffers from a disruption. A more effective tactic is to line up different suppliers, across geographies if possible, to ensure a smooth operating supply chain.
And there you have it—some of the most common supply chain disruptions and how you can address them with careful planning and the power of machine learning. Supply chains are data-driven and data-rich environments which makes them ideal venues to leveraging data science to predict and manage risks.
Diminishing the impacts of potential supply chain disruptions and optimizing the supply chain are great use cases for RapidMiner. If you’re looking for help to better manage your supply chain risks, find out how we can help with a free AI assessment—we’ll walk through your use cases and help you make a plan for how best to get impact with machine learning.
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