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9 Min. Read

What is Supply Chain Analytics, and Why is it So Important?

What Is Supply Chain Analytics & Why Is It So Important?

How does a tomato go from a field in Italy to a can of tomato sauce in a Toronto supermarket? It’s something most customers don’t think about. But, supply chains are complex networks.

They need careful planning and involve moving parts beyond a company’s control. These moving parts could range from shipping delays to natural disasters.

As societies grow and global markets expand, supply chain management grows more complicated. So, it’s necessary to put in place a strategy that can incorporate all the generated data.

But it’s not enough to have access to raw data. You need analytical tools to make sense out of them and map out a path to profitability.

There are many advanced supply chain software solutions available. Analytical models will churn out graphs, reports, and visualizations from extensive data sets. They break down the raw inputs into actionable steps.

By using supply chain analytics, businesses can identify key patterns. These data-based insights can help with critical decisions for growth and advancement.

Here’s What We’ll Cover:

Key Supply Chain Analytics Features

Different Types of Supply Chain Analytics

Why Is Supply Chain Analytics So Important?

Key Benefits of Supply Chain Analytics

Limitations of Supply Chain Analytics

How Do You Use Supply Chain Analytics?

Key Takeaways

Key Supply Chain Analytics Features

Supply chains run in the background to keep every business working well. But over time, better supply chain models need to replace the older ones.

Businesses should ensure that their analytics include the following key features. 

The 5 C’s

  • Connected

Data connectivity is essential to any supply chain analytics model. It enables businesses to source information on a global scale. Collection sources can vary, from social media posts, industry reports to weather stations.

  • Collaborative

Cloud-based commerce networks can collaborate. They share data that enhances productivity and mutual advantages.

  • Cyber-aware

Companies should always protect their valuable data from cyber-attacks and network compromises.

  • Cognitive

Over time, supply chain analytics have become more capable of delivering automated solutions. Advances in AI and machine learning continue to improve data collection and interpretation.

  • Comprehensive

Supply chain management requires rapid action to maintain efficiency. Analytical systems need to keep up with the scale and speed of real-time events. 

Different Types of Supply Chain Analytics 

Companies use the following four supply chain analytics. They’re used to pinpoint areas of improvement and generate actionable insights.

Descriptive Analytics

Descriptive analytics look backward. They analyze past actions and give a rundown of historical trends. A company can compare data sets during different periods to see what worked before. These insights can then influence future planning.

 Descriptive analytics is crucial for operations planning. For example, it can identify a supplier that sends late shipments at a higher rate than average. In that case, procurement managers may search for alternative suppliers. Or, they can increase orders from suppliers that descriptive analytics find more reliable. 

Predictive Analytics

Descriptive analytics may offer a picture of the past. But predictive analytics give a glimpse into the future. This type of analytics inspires a proactive approach to supply chain management.

The scenarios created by these predictive models may not always become a reality. Still, it gives a company’s leadership more time to prepare for future risks.

Predictive analytics uses AI and machine learning to assimilate real-world data and events. Then, it comes up with strategic suggestions or can even make automated decisions.

For example, predictive analytics can scan market reports for industry trends. Based on these trends, it can propose ways to take advantage of the current situation. For example, if a specific commodity price is likely to rise in the coming weeks. Analysts can use predictive analytics to prepare for this outcome. 

Prescriptive Analytics 

Prescriptive analytics is the next step in analytical technology. Past, present, and future datasets combine to generate innovative decision-making strategies. It relies on machine learning to process information without human input. As a result, it can quantify millions of data points.

But what is the difference between prescriptive analytics and predictive analytics? Both are capable of guiding decision-making processes. Yet, they do so in two distinct ways. 

Predictive analytics tells us what is likely to happen. In contrast, prescriptive analytics tells us how to make something happen.

When it comes to making high-level business decisions, prescriptive analytics is invaluable. This class of analytics is best suited to looking at many types of inputs and variables. It is not bound by static rules.

Prescriptive analytics can adapt to different business models. It delivers recommendations free from biases or generic methodologies. In this way, companies develop new outcomes that provide measurable benefits.

Cognitive Analytics 

The three previous analytical models offer comprehensive decision-making support. Yet, human analysts still need to interpret the results and refine the suggestions.

Cognitive analytics use AI and machine learning to mimic human thought and behavior.

Over time, cognitive models interact with more data and observe more market behavior. This helps them come up with better efficient tactics. Also, this analytical model limits human error. It minimizes the resources needed to collect and process data.

Why Is Supply Chain Analytics So Important?

Let’s say a company doesn’t use supply chain analytics. If that’s the case, the entire operation can only exist in a reactive state. It would always be the last one to respond to new consumer demands. And by that time, consumers would have moved on, and the company would be stuck with inventory that no one wants.

Instead, a proactive company uses supply chain analytics to adjust production levels immediately. As a result, it improves their bottom line, reduces transport costs, and frees up storage space.

Key Benefits of Supply Chain Analytics

Supply chain analytics can fine-tune operations at every level of a business. Practical benefits of implementing supply chain analytics include: 

  • Reduced Inventory Costs

Supply chain analytics can analyze the market to respond to current consumer demands. This helps to predict future buying trends. 

  • Optimized Production

Supply chain analytics pinpoints inefficiencies within different areas of the supply chain. It can analyze past, present, and future events. As a result, operators can improve supply chains until reaching a better solution.

  • Responsive Logistics

Supply chain analytics allow companies to be proactive instead of reactive. Processes such as shipping routes, production rates, and order fulfillment adjust in real-time.

  • Development of New Workflows

Supply chain analytics can uncover hidden weaknesses that lead to improved efficiency. It can help companies break out of old working methods and develop new ways to approach problems. 

Limitations of Supply Chain Analytics

The wealth of data available to businesses today is overwhelming. A company needs to learn how to ask the right questions and model the correct scenarios. Only then can it reap the benefits of supply chain analytics.

It is not as simple as plugging in a data set and receiving perfect advice that will lead to growth every time. Instead, it requires critical thinking from business analysts and company leaders. They must synthesize the data and decide how to best act on the provided recommendations.

Here are some other limitations to supply chain analytics:

  • Cost

The cost of implementing advanced analytics can be very high and create a barrier to entry. For example, adding temperature sensors to a shipping container is easy. But the sensors won’t provide much value on their own. Instead, they must integrate into an analytics system across the entire supply chain.

  • Scope Limitations

It is not guaranteed that a company will be able to act on the recommendations of an analytics system. For instance, what if it calls for automating a key part of your manufacturing process? Will your business be able to fund and put in place such an action, or will it be a waste of time?

  • Trained Personnel

Analytics is most effective when trained personnel manage and interpret the data. Companies should hire people with a background in data science. This is the best way to extract the most value from supply chain analytics. 

How Do You Use Supply Chain Analytics?

Supply chain analytics is helpful for every aspect of business planning and management. For example, it can determine machine maintenance schedules. Or, it can even suggest when to expand into other markets. Every company should look at its operations to see where to apply supply chain analytics.

Here are some common applications for the different types of supply chain analytics. 

Descriptive Analytics

  • Generate reports and visualizations of performance metrics
  • Helps track progress and manage daily tasks and workflows
  • Used to communicate areas of progress or improvement to shareholders and employees
  • It supports collaboration. The data helps people get on the same page about past and current operations

Predictive Analytics

  • Identifying short-term or medium-term trends
  • Pinpointing future roadblocks to specific areas of the supply chain
  • Supply and demand planning
  • Inventory management
  • Maintenance scheduling
  • Customer retention and attrition
  • Sales trends for individual products

Prescriptive Analytics 

  • Identifying broader trends or patterns across a whole business
  • Strategizing for company-wide improvements
  • Establishing overarching sales strategies or marketing campaigns
  • Planning an expansion into a new market while taking different regulations into account
  • Optimizing supply chains across several markets or regions

Cognitive Analytics 

  • Used to fill gaps in information
  • It's the best way to gather data from unstructured sources. These include images, messages, social media posts, etc.
  • Offers the most human-like approach to predictions and recommendations
  • Constantly learning and improving its models
  • Endless customization options
  • The future of analytics

Key Takeaways

Does your business use analytics to manage its supply chain challenges? If not, it risks falling behind in the rapidly developing world of global commerce.

Supply chain analytics can analyze the past, present, and future. It provides you with a clear picture of where your company is and where it’s heading. 

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