January 30, 2023 | Supply Chain Software
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.
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.
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:
While analytics can be used across different processes, it is particularly helpful for specific supply chain functions. Here are some of these:
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.
Supply chain analytics use advanced data collection, management, and analytical techniques to optimize supply chain operations. Supply chain analytics use the relevant data gathering from various enterprise systems and external sources, after which the data goes through processes - such as cleansing, statistical modeling, machine learning algorithms, and data visualization tools - helping businesses gain valuable insights. These insights - visible through dashboards, reports, and collaborative analytics tools - enable data-driven decision making to boost forecast accuracy, mitigate risks, control costs, as well as design optimal networks.
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.