Supply Chain Disruption: Mitigating Threats Effectively with Analysis
Supply chain disruption is never what any organization wants to see.
With COVID-19 causing damage to supply chains around the world, businesses are looking at how they can mitigate the tremendous effects this has had on their companies.
That the virus started and caused enormous issues in China is bad enough for world supply chains, but now, with most nations in lockdown, and individual states here in the US locked down, too, it’s apparent that these chains cannot be taken for granted.
How can affected businesses respond to these drastic supply chain disruptions in a way that mitigates losses? And how can business technology play a role in ensuring that your operations are kept running even in times of crisis?
That’s what we’ll be taking a look at today as we run over how analysis can help you streamline your supply chain.
Digital Management Tools
Digital systems, like enterprise resource planning applications, are used by organizations to help them manage disruptions, whether it’s something as drastic and unexpected as a pandemic, or a trade dispute, or any other factor that can cause issues.
75% of companies report supply chain disruptions in some capacity due to coronavirus-related transportation restrictions, and more than 80% believe that their organization will experience some impact because of COVID-19 disruptions
How exactly can digital tools help you mitigate supply chain disruptions?
In an ideal situation, businesses would be able to predict the exact number of supplies they need for perfect efficiency and no supply chain disruption.
Well, thanks to ERPs, you can get pretty close.
ERP systems utilize data analytics to help predict and manage disruptions. Many companies today make use of big data in order to help streamline their operations and remove any unwanted waste in the supply chain.
Of course, data analytics have always been a part of managing supply chains, but with the sheer volume of data that exists within any given company in 2020, analytics has become a lot more sophisticated.
Over the last two years alone 90% of the data in the world was generated
There are four key areas of analytics that will help you gain the best understanding possible.
Descriptive analysis is the process of assessing historical data in order to identify patterns and trends in your supply chain operations.
This type of analysis helps you gain an understanding of what has happened is the past and is one of the more basic aspects of analytics, but nonetheless an extremely important one when it comes to strategizing.
In descriptive analysis, data is aggregated and mined, in order to establish data sets that can provide clear insight to end users.
Aggregation is used to compile the data, while mining searches data for patterns which can then summarize the characteristics of your data sets.
Examples of descriptive analysis would be identifying key products and customers, as well as breaking down how much of each product is being shipped from particular locations.
Having this information at hand by using an ERP within your business can help you understand if particular areas of your supply chain need more attention, or perhaps areas that warrant less attention in order to minimize supply chain disruption.
If descriptive analysis is about what happened, then diagnostic analysis is about why it happened, as you might have guessed by its giveaway name.
This is the stage where, after your descriptive analysis has shown you areas that might be an issue, you take a deeper dive into understanding the underlying cause of the problem—this is why this process is often known as root cause analysis.
It also differs from descriptive analysis in that diagnostic analysis is typically centered around a single issue, rather than all of your operations.
Rather than just “putting out fires”, this analysis aims to help you put in place processes that stop the same issues occurring again and again in your supply chain.
Diagnostic analysis will usually help you understand the following kinds of issues:
- Why did my revenue fall in a particular sub-segment?
- Why is my stock consistently running low at a particular warehouse?
- Why do I have too much stock leftover?
Just as with descriptive analysis you’re analyzing past data to identify historical patterns and trends in your supply chain, predictive analysis does the same thing—but for your future supply chain.
In short, it tells you what’s likely to happen and allows you the opportunity to understand how you’ll be affected in the future.
Predictive analysis will take your historical data and then use a combination of algorithms and machine learning to establish correlations and likely outcomes.
The number of supply chain professionals who say they’re currently using predictive analytics at their company grew 76% from 2017 to 2019
This kind of forecasting will help you understand how much product you might need and an indication of consumer demand.
It’s worth noting however that predictive analysis is highly dependent on the volume of data sets provided—it can only give back what it receives, and it is an estimate, not a prophecy, so bear that in mind.
Having said that, there’s a reason businesses are adopting predictive analysis techniques on a much wider scale than ever—approximately 30% of organizations used it in 2019, up from 17% two years previously.
Finally, we have prescriptive analysis, which utilizes available data sets to recommend the best course of action for a given scenario.
Prescriptive analysis is a close cousin of descriptive and prescriptive analysis, but rather than just using data to come to a conclusion, is more focused on providing an actionable insight with which a decision maker can enact a proactive course of action.
Because of this, it’s the process that is most closely tied to the decision making aspect of business analytics, and acts as the final step.
Descriptive describes what happened, followed by diagnostic, which describes how it happened, followed by predictive, which describes what might happen, and now prescriptive, which describes the best course of action.
In supply chains, prescriptive analysis can recommend how much stock you should buy and when. It can also aggregate customer data to make recommendations on seasonal changes to demand.
- Analytics using ERPs is extremely useful for businesses trying to determine patterns and trends in their supply chains
- You can learn about past trends, present needs, and what is likely to happen in the future
- SMBs are utilizing business analytics in greater numbers than ever to avoid supply chain disruption
- The more data you have, the better, making it all the more important to digitize your business now
In light of recent events, many organizations have found themselves playing catchup, trying to implement makeshift cloud solutions to make up lost ground while their workforces see drastic transformations.
To find out more about how the cloud can ensure your business is in good shape for the future, download our eBook, “Which Cloud Option Is Right For Your Business?”