Why Supply Chain Analytics Are More Important than Ever
According to one recent study1, people are generating approximately 2.5 quintillion bytes of new data every single day. By the end of 2021, every person will generate about 1.7 megabytes of new information a second, a stunning trend that shows no signs of slowing down anytime soon.
But what’s happening with all of that data — especially when it comes to supply chain management? An IBM report2 shows that while approximately 20% of all supply chain data is structured and can be easily analyzed, the other 80% is unstructured or dark data. With continuing supply chain disruptions from the COVID-19 pandemic, organizations are increasingly looking to advanced analytics3 to uncover key information in their supply chain data and avoid disruptions before they happen.
At its core, supply chain analytics are those tools that organizations can use to gain insight and extract value from the huge volume of information they're working with. This is true not only in terms of procurement data, but also in regard to procurement, manufacturing, distribution, and demand data. Over the years it has become a best practice of supply chain management, and it's one that’s absolutely worth a closer look.
What is supply chain analytics? An overview
Supply chain analytics solutions are those that allow organizational leaders to make more informed and more data-driven decisions, acting less on gut instinct or intuition and more on facts.
They bring together summaries of relevant, high value, and otherwise trusted data all presented in a way that enables organizations to uncover trends and patterns that may have otherwise gone undiscovered.
The sheer volume of data that most organizations are working with would be far too unwieldy for a human to handle.
This is why artificial intelligence (AI), machine learning (ML), and related concepts are often at the foundation of these efforts, making it possible to take advantage of different types of supply chain analytics like:
- Descriptive analytics: Designed to act as a single source of "truth" within the context of a business' supply chain, these analytics bring internal and external data into a central repository to make it as easy to work with.
- Predictive analytics: These are intended to help take current conditions and use them to predict likely future outcomes or scenarios. If you understand what risks you're likely to face in the future, you can put steps in place to mitigate them today.
- Prescriptive analytics: Intended to help businesses solve problems and collaborate with one another across the supply chain, these analytics create the most business value possible. Prescriptive analytics can make recommendations on the best next steps you need to take to reduce time and effort when working with supply chain partners.
- Cognitive analytics: These help business leaders answer complicated questions in natural language. With AI, you can ask: "How can we increase the efficiency of process X?" and get an answer that resembles the way a person or team of people might respond.
Unlocking the power of supply chain analytics
Supply chain analytics not only allow businesses to make the right decisions, but to arrive at the conclusions in faster and in more effective ways.
One of the biggest benefits of supply chain analytics is that they help you better understand and respond to the risks of today and tomorrow. Organizations that can spot trends and patterns all throughout the supply chain have more information to understand the risks (and to potentially avoid them altogether). They can also run what-if scenarios and simulations, enabling them to understand potential outcomes in response to disruptions.
This level of insight offers much greater planning accuracy. By acting as a single source of truth for something like assessing inventory needs to fulfill customer orders per service level commitments, an analytical tool can help segment demand patterns to consistently meet service level targets. You'll be able to optimize inventory and see which products’ inventory levels can be minimized or pre-positioned to accommodate seasonality or capacity constraints, allowing you to reallocate resources elsewhere.
Supply chain analytics can also help break down the data silos that normally exist across an enterprise. Instead of information being segregated to one department, it’s allowed to flow to necessary stakeholders in a way that supports better collaboration through shareable forecasts and similar models. For example, sales and supply chain teams won’t be working from different sets of data. When there’s a promotion tied to available inventory, they’ll all have a clearer picture of what’s on hand and how to promote.
But for most organizations, one of the biggest benefits is that supply chain analytics can help balance profitability, sustainability, and growth. This is key for many supply chain professionals, and arguably more important than just operating as lean as possible.
While the features inherent in a supply chain analytics solution will vary, they typically include functionality like:
- Data visualization: Takes complicated ideas and presents them visually in the form of graphs, charts, and other assets, making data easy to understand and even easier to capitalize on.
- Location intelligence: Applies insights on a location-by-location basis in order to better understand where resources need to be distributed and why.
- Digital twins: A virtual replica of your entire supply chain, which can be manipulated and experimented with to improve both predictive and prescriptive analytics.
Data visualization is a dashboard that can be used to help better understand events that have already happened. They're an efficient way to take a huge amount of historical data and distill it all down to its bare essentials. At a glance, you can see how inventory has changed over the last six months, or what your return has been on a particular investment. This understanding allows you to make even better decisions moving forward.
Digital twins — thought of as a flight simulator for supply chains — offer advantages like a faster time to risk assessment, along with being able to better flow with disruption rather than being knocked off-kilter by it. A digital twin allows organizations to analyze “what-if” possibilities or identify situations most likely to arise. You can see any potential choke points or bottlenecks during the process, including those from potentially unknown sources. In addition to allowing you to make more informed choices in regard to potential risks, it also helps to optimize supply chain nodes, modes, and policies.
Supply chain analytics can also be used to get to the root cause of certain recurring issues. It can help identify why particular shipments are being delayed or not making it to customers, and how you can prevent it from happening again.
Predictive supply chain analytics are an invaluable way to try to see what might happen in the future, all based on what’s happening right now. For example: If the cost of the raw materials you need to make your products is about to increase, predictive supply chain analytics help you better understand the associated consequences, allowing you to mitigate risk as much as possible. While there are some types of world events that could cause the accuracy of these predictions to come into question, like a sudden pandemic that impacts the entire world at once, by and large it's still invaluable insight you would have access to through other means.
The major advantage of these types of supply chain analytics comes down to how they help you better prepare for the future. They help you make sense of the massive volumes of data you're creating and help you extract legitimate value from them. They help you make better decisions at exactly the right moment, allowing you to set your business up for long-term success.
1 "25+ Impressive Big Data Statistics for 2021," Christo Petrov, TechJury, 2 Oct 2021.
2 "What is supply chain analytics?" IBM.
3 "COVID-19: A Call for AI Powered Supply Chain Decision Making," Datamation, 4 Jun 2020.