Why You Must Invest in Supply Chain Analytics

Record high inflation. Geopolitical unrest. Prolonged supply chain disruptions. And the possibility of another pandemic wave.

The current business environment is anything but certain.

What businesses need today is real-time data access and end-to-end supply chain visibility.

And when they get this data, they must interpret and analyze it and use the analysis to their advantage.

In this overview, we explore the basics of supply chain analytics, its benefits, types, data analysis, optimization techniques, and the use of AI and machine learning.

What is Supply Chain Analytics?

Supply chain analytics is the process of leveraging data to analyze and optimize the performance of a supply chain. It is a powerful tool that enables organizations to make better decisions, improve efficiency, and reduce costs.

A new GEP and Everest Group whitepaper states: Supply chain analytics brings together data from across applications, infrastructure, third-party sources, and emerging technologies.

With the right data, supply chain analytics can provide insights into the performance of a supply chain, allowing stakeholders to identify areas of improvement and potential cost savings.

Supply chain analytics involves collecting and analyzing data to identify patterns and trends that can be used to improve performance. For example, data can be used to identify areas of inefficiency, such as bottlenecks or delays in the supply chain, as well as opportunities for cost savings.

Types of Supply Chain Analytics

There are different types of supply chain analytics, each with its own advantages and disadvantages. Here are some of the most common types of supply chain analytics:

• Descriptive Analytics: Descriptive analytics analyzes data to identify patterns and trends, such as sales trends or customer preferences.
• Predictive Analytics: Predictive analytics uses data-driven insights to predict future outcomes and make informed decisions. This is especially useful for predicting demand and mitigating risks.
• Prescriptive Analytics: Prescriptive analytics is the process of analyzing data to prescribe solutions. It often uses machine learning algorithms to sift through large amounts of data. It helps formulate strategies and determine a course of action.
• Optimization Analytics: Optimization analytics is the process of analyzing data to optimize a process for improved efficiency and cost savings.

Use Cases of Supply Chain Analytics

While analytics can be used across different processes, it is particularly helpful for specific supply chain functions. Here are some of these:

• Demand planning: Analytics can use data integrated from disparate data sources to accurately forecast demand. It can also help businesses identify changes in demand and customer buying behavior.
• Scenario planning: Businesses can leverage supply chain analytics to determine likely scenarios and adjust plans accordingly. What if scenario planning helps respond quickly to changing market conditions and demand or supply fluctuations.
• Inventory management: Analytics enables businesses to maintain optimum inventory by combining demand and inventory data. It can also provide advance warnings about shortages and slow-moving stock. Additionally, it can also streamline warehouse operations. For example, it can analyze historical data and help businesses store a group of items that are frequently ordered together in the same location.
• Risk management: Businesses are increasingly using analytics to monitor potential risks and disruptions. This has become vital, given the growing uncertainty in the business environment.
• Emissions monitoring: While measuring and reporting carbon emissions is increasingly becoming crucial, most organizations do not currently have a system in place. Analytics can address this challenge and help businesses track relevant data and metrics to report carbon emissions.

Benefits of Supply Chain Analytics

Here are some of the key benefits of supply chain analytics:

1. Improved visibility: Organizations can gain better visibility into their supply chain operations. Increased visibility can help decision-makers evaluate various business scenarios and adjust plans.

2. Cost savings: With data-based insights, organizations can identify opportunities for cost savings and make strategies to reduce costs.

3. Sound decision-making: Organizations can decode data and gain real-time insights to make informed decisions.

4. Improved efficiency: Organizations can identify areas of improvement and take corrective action. For example, organizations can use analytics to identify inefficiencies and delays in the supply chain.

5. Better collaboration: Organizations can also use analytics to improve collaboration between internal departments and external partners.

Using AI and Machine Learning for Supply Chain Analytics

To thrive amid growing uncertainty, businesses must focus on analytics and technology implementation. Advanced technologies such as AI and machine learning can be used to enhance the accuracy and efficiency of supply chain analytics.

These technologies can automate data collection, analysis, and interpretation, enabling organizations to gain insights quickly and accurately. Accurate data analysis can help organizations identify trends and patterns, spot inefficiencies, make better decisions and optimize their supply chain operations.