Supply chain analytics has become increasingly important. By analyzing data and leveraging technology, businesses can gain insights into their supply chain operations and make more informed decisions. In the supply chain and logistics industry, data is a critical component in the tracking of shipments at all levels. It is necessary to optimize inventory, accurately estimate delivery times, forecast customer preferences, manage demand, and improve overall speed.

In essence, data is the key to unlocking success in the industry. Customer analytics and customer data are also essential tools for improving organizational KPIs in the supply chain. Unfortunately, many current logistics businesses lack the infrastructure necessary to effectively manage data, which hinders their ability to fully leverage the power of data. The following statistics highlight this issue:

• Only 22% of companies have a proactive supply chain network.
• 43% of small businesses do not track their inventory.
• Only 6% of companies report full visibility on their supply chain. 69% of companies do not have total visibility.
• On average, retail operations have a supply chain accuracy of only 63%.
• 67.4% of supply chain managers use Excel spreadsheets as a management tool.

In a 2021 report, the World Economic Forum estimated that the average cost of a supply chain disruption is $1.5 million per hour.  In a 2018 report, the CSCMP estimated that the cost of poor supply chain visibility is $1.2 trillion per year. 

These statistics demonstrate the need for increased focus on supply chain analytics to drive improvements in the industry. You must also read about other challenges faced by Global supply chain networks.. With the right infrastructure and tools in place, supply chain managers can make better, data-driven decisions to optimize their operations and ultimately deliver better results.

What is Supply chain Analytics?

Supply chain analytics is the use of big data analytics to improve the supply chain process. By analyzing the data collected in the supply chain management process through various tools, businesses can draw real-time insights that can help enhance logistics and the supply chain environment. It has three core components, which are data analytics, data visualization, and a tech platform.

Data analytics enables businesses to draw valuable insights, while data visualization allows for a better understanding of these insights through graphical representations. These processes are facilitated by a tech platform that captures, stores, and analyzes supply chain transactions.

Since supply chains generate massive amounts of data, supply chain analytics helps businesses to make sense of this data. It uncovers patterns and generates insights that can improve supply chain processes. Additionally, supply chain analytics is also the foundation for applying cognitive technologies, such as artificial intelligence (AI), to the supply chain process. Cognitive technologies can understand, reason, learn, and interact like humans, but at an enormous capacity and speed.

This advanced form of supply chain analytics is ushering in a new era of supply chain optimization. It can automatically sift through large amounts of data to help organizations improve forecasting, identify inefficiencies, respond better to customer needs, drive innovation, and pursue breakthrough ideas. With these benefits, it’s clear that supply chain analytics is a critical tool for any business looking to optimize its supply chain process.

Types of supply chain analytics

There are several types of supply chain analytics that organizations can use to improve their supply chain operations. Here are some of the most common types:

1. Descriptive Analytics: This type of analytics involves the analysis of historical data to gain insights into past supply chain performance. Descriptive analytics provides information on what happened in the past and what is currently happening in the supply chain.
2. Predictive Analytics: Predictive analytics involves the use of statistical algorithms and machine learning techniques to analyze historical data and make predictions about future supply chain performance. This type of analytics can help organizations identify potential supply chain disruptions and take preventive measures to mitigate them.
3. Prescriptive Analytics: Prescriptive analytics involves using historical data and predictive analytics to make recommendations on how to optimize the supply chain. This type of analytics helps organizations make informed decisions on how to improve their supply chain operations.
4. Network Analytics: Network analytics involves analyzing the relationships between suppliers, distributors, and other supply chain partners to identify areas for optimization. This type of analytics helps organizations identify potential bottlenecks and inefficiencies in their supply chain network.
5. Inventory Analytics: Inventory analytics involves analyzing inventory levels and patterns to optimize inventory management. This type of analytics helps organizations reduce inventory costs while ensuring that they have the right amount of inventory to meet customer demand.
6. Transportation Analytics: Transportation analytics involves analyzing transportation data to optimize transportation operations. This type of analytics helps organizations reduce transportation costs and improve delivery times.

Overall, organizations can benefit from using a combination of these types of analytics to improve their supply chain operations and gain a competitive advantage.

How customer data can transform the Supply chain Industry?  

In any industry, it is critical to understand customer requirements, perform targeted marketing and enhance customer interaction. Without an extensive database, it becomes really difficult to reach the target audience and tailor the services to meet the customer needs & expectations.  

In addition to this, we are living in a technology-driven era, where the customer is used to getting everything at the click of a button. This makes the customer expectations really high. The customer desires flexibility in delivery location and timing and doesn’t want to be worried about not being home for the delivery. 

So, for logistic companies, it is very critical to get the last-mile delivery right as it is directly related to customer satisfaction. Some of the factors that diminish customer satisfaction in logistics include limited global coverage and late deliveries.  Forecasting customer preferences and demand management are other key areas in logistics required for optimizing your working capital to customer satisfaction ratio.  

Customer analytics and customer data are increasingly playing an important role in the logistics and supply chain industry. Here are some ways that customer analytics and customer data are helping:

1. Demand forecasting: Customer data can be used to predict customer demand, which is essential for inventory management and supply chain planning. By analyzing historical sales data, businesses can better predict customer demand and adjust their supply chain accordingly.
2. Route optimization: By analyzing customer data, businesses can identify patterns in customer behavior, such as delivery locations and preferred delivery times. This information can be used to optimize delivery routes, reduce transportation costs and improve delivery times.
3. Customer segmentation: Customer data can be used to segment customers based on factors such as purchasing behavior, demographics, and geographic location. This information can help businesses tailor their supply chain and logistics operations to better meet the needs of different customer segments.
4. Personalization: By analyzing customer data, businesses can offer personalized experiences to customers, such as customized product recommendations and targeted marketing messages. This can improve customer satisfaction and increase customer loyalty.
5. Returns management: Customer data can be used to identify patterns in returns and product defects. By analyzing this data, businesses can improve their returns management processes, reducing costs and improving customer satisfaction.

Overall, customer analytics and customer data can help businesses better understand their customers and improve their supply chain and logistics operations. By leveraging customer data, businesses can improve efficiency, reduce costs, and improve customer satisfaction.

Also Read: How to achieve excellence in Supply chain

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Also, read Knowledge Graphs and uses in Supply chain and Real time analytics in Supply chain