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How to get more out of Supply Chain Analytics

supply chain analytics, or Supply Chain Analytics, has become a real revolution for companies in terms of decision making. It is a process that is responsible for collecting and analyzing data to improve something as important as a company’s supply chain, and that encompasses a broad analysis of all the phases that make up this chain. These are planning, facilitation, processing, distribution, inventory, sales analysis, customer satisfaction, and logistics.

Thanks to this analysis, companies can make decisions thanks to in-depth data analysis and more actionable insights. But also capitalize on existing opportunities to improve and grow the company, and work more efficiently. Supply Chain Analytics offers as a final result better products and more satisfied customers.

This is how supply chain analytics works

But to understand supply chain analytics, it’s important to know how it actually works. This analytics uses data from different applications, infrastructures, third party sources and emerging technologies.

With the resulting information, companies can identify trends, optimize processes and make better decisions. Or what is the same, to achieve a holistic view of the supply chain that, in addition, allows problems to be identified and measures to be taken to solve them.

Analysts use various methods, including data mining, predictive modeling, and statistical analysis, to find patterns and opportunities. With this information they can make decisions about the best strategy for the supply chain, and implement actions and initiatives to improve it.

Identifying the error in Supply Chain Analytics

Supply chain analytics greatly helps companies identify where errors and inefficiencies exist and how to improve them. Also to know how to reduce costs and improve, finally, the image of the brand with respect to the final customer.

Apart from identify errors, also reduces downtime through predictive maintenance; data-driven decision making; better understand customer behavior; and greater operational flexibility

Types of supply chain analysis

There are four different types of supply chain analytics: descriptive, predictive, prescriptive, and cognitive.

The descriptive analytics use historical data to analyze how supply chains are performing and what needs improvement. For example, to assess inventory and determine if you have enough products or determine how many products are shipped daily.

The predictive analytics uses analytical models that attempt to predict potential outcomes or forecast potential problems with the operation of a business. To do this, it uses machine learning and advanced statistics.

The prescriptive analytics collects historical data to identify trends and patterns. It later uses mathematical models to find the best solution to a problem and identify potential opportunities to improve business performance as well as customer satisfaction.

Lastly, the cognitive analytics mimics human thought and behavior to synthesize information from various sources. It uses advanced machine learning, natural language processing, text mining, and other technologies to analyze large data sets.

According to a report by Grand View Consultancy, a huge growth is expected in the supply chain analytics market, with an annual growth rate of 17.6% from 2022 to 2030. The reason lies, of course, in the overwhelming need for companies to optimize their supply chains with data-driven insights that only supply chain analytics tools can provide.

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