Supply Chain Analytics: Features, Benefits and its Types

Businesses have a number of difficulties with supply chain management (SCM), including a lack of transparency, production schedules, and team synchronization. Intelligent data analytics are used by businesses in the manufacturing, retail, and Fast-Moving Consumer Goods (FMCG) sectors to address these issues. Supply chain analytics is the phrase for this. Businesses can use it to determine consumer preferences and forecast future product or service demand. Let's examine supply chain analytics in more detail.

 

Also Read | IoT In Inventory Management: Impact and Benefits

 

What Is Supply Chain Analytics?

 

Supply chain analytics is the study of data that businesses extract from a variety of applications related to their supply chain, such as supply chain execution systems for order management, inventory management, warehouse management, and fulfillment (including shipping). Each step in a supply chain has an impact on the one behind it, and any problems at any level could ultimately undermine the company's ability to satisfy customer expectations.

 

Each of the aforementioned software programs may have its own reporting features that provide information on that particular stage of the supply chain, such as anticipated lead times for suppliers, current safety stock levels in the warehouse, or orders filled per hour, as examples. 

 

But when all of these systems are connected, typically through an Enterprise Resource Planning (ERP) system, supply chain analytics are at their most effective. Then, through dashboards or reports, the ERP itself or a different application can show and illustrate data from all around your global supply chain.

 

Employees are given a thorough understanding of this logistical network as well as the ability to comprehend the upstream and downstream repercussions of a particular interruption. They can then act swiftly to minimize the problem to the greatest extent possible. Some systems, for instance, can instantly evaluate the data and inform users of potential problems before they escalate.

 

Also Read | Top 5 Supply Chain Management (SCM) Tools You Should Know

 

Features of Supply Chain Analytics

 

The following are some essential aspects of supply chain analytics:

 

• Data collection and integration: 

 

Gathering and integrating data the capacity to gather and integrate data from various sources, including sensor data, logistics providers, and ERP systems, in order to get a full picture of supply chain operations.

 

• Data visualization: 

 

Visualizing data having the capacity to show data in a way that is easy to understand and use, as through dashboards and charts, to aid in decision-making

 

• Predictive analytics: 

 

The capacity to forecast future events, like demand, inventory levels, and logistics routes, using past data and machine learning algorithms.

 

• Optimization: 

 

A supply chain operation's capacity to employ optimization techniques, such as linear programming, to find the most practical and affordable solutions.

 

• Real-time monitoring: 

 

The capacity to keep an eye on supply chain processes in real-time, enabling early problem detection and prompt action.

 

• Collaboration and communication: 

 

The capacity to exchange data and work together with partners, such as vendors and logistics service providers, to enhance the efficiency of the supply chain.

 

• Reporting and analytics: 

 

The capacity to produce reports and carry out sophisticated analytics, like statistical modeling and machine learning, to gather more information about supply chain processes.

 

• Scalability: 

 

The capacity to manage massive data volumes and expand to meet the requirements of extensive, intricate supply chains

 

• Privacy and security: 

 

The capacity to safeguard sensitive data, such as monetary and personal data, using encryption and other security methods.

 

• Cloud and mobile capabilities: 

 

The capacity to store data in the cloud for simple access and collaboration, as well as the ability to access and analyze data from any device and location.

 

These characteristics of a supply chain analytics solution give the business a comprehensive perspective of the supply chain activities, making it easier to spot problems, increase efficiency, cut costs, and improve performance.

 

Also Read | Applications of Data visualization

 

Benefits of Supply Chain Analytics

 

Using supply chain analytics has a number of advantages, including:

 

• Enhanced efficiency: Businesses can locate bottlenecks and inefficiencies in their supply chain and take action to fix them by evaluating data on sales, inventory, logistics, and manufacturing.

 

• Cost reduction: Businesses can cut their overall costs by identifying areas where costs can be decreased, such as through improved supplier negotiations or more effective logistics.

 

• Greater operational visibility: It is one benefit of using supply chain analytics, which enables businesses to better comprehend and manage their supply chains.

 

• Risk management: Businesses can identify and reduce supply chain risks, such as potential disruptions to their supply of raw materials or completed items, by evaluating data on suppliers, logistics, and other factors.

 

• Better decision-making: Businesses may make better data-driven decisions regarding their supply chain operations by leveraging data and analytics to guide their decision-making.

 

• Improved inventory management: Predictive analytics and forecasting can assist in optimizing inventory levels to match demand and prevent stockouts or overstocking.

 

• Better customer service: Using supply chain analytics, delivery schedules, and inventory levels may be optimized to better meet customer demand.

 

What is the process of Supply Chain Analytics?

 

The practice of employing data, statistical and quantitative analysis, and other analytical techniques to help guide and make decisions on the management of a company's supply chain is known as supply chain analytics. In order to increase productivity and cut costs, this can involve reviewing data on sales, inventories, transportation, and production. It can also involve detecting and minimizing supply chain risks.

 

Data collection from multiple sources, including enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, transportation management systems (TMS), warehouse management systems (WMS), and other data sources, is the initial stage in supply chain analytics. To get a more complete picture of the supply chain, data may also be gathered from outside sources like the weather, economic indicators, and social media.

 

Data is cleaned, processed, and incorporated into a data warehouse after it has been gathered. This is the process of combining data from various sources, transforming it into a standardized format, and integrating it into a single location for analysis. Large amounts of data can be stored via data warehousing, which enables analysis of past data to spot trends, patterns, and linkages.

 

The data is then examined using different analytical techniques, including statistical analysis, data mining, and machine learning. These instruments make it possible to find patterns and connections in the data, which can then be utilized to guide choices and enhance supply chain operations. For instance, data mining and statistical analysis can be used to find patterns in consumer behavior and the main demand drivers, respectively. Based on past data, machine learning can forecast future demand.

 

Having a more thorough understanding of the supply chain is one of the main advantages of supply chain analytics. This includes being aware of the connections between and interactions between various supply chain components. For instance, a business can spot supply chain bottlenecks and address them by evaluating data on sales, inventory, and logistics.

 

Cost savings are another crucial advantage of supply chain analytics. Companies can discover areas where costs can be lowered by improving supplier negotiations or improving logistics by evaluating data on suppliers, logistics, and other factors. By anticipating demand and optimizing inventory levels to prevent stockouts or overstocking, analytics can also aid in inventory management.

 

Supply chain analytics can aid in risk management in addition to cost-cutting and inventory control. Companies can identify potential supply chain risks, such as potential disruptions to their supply of raw materials or completed goods, by evaluating data on suppliers, logistics, and other factors. Using this knowledge, solutions can subsequently be created to lessen these risks and guarantee business continuity.

 

The last benefit of supply chain analytics is that it can enhance customer service. Companies can streamline their operations to satisfy customers and meet demand by examining data on customer demand, delivery timetables, and inventory levels. This can be done by giving more precise delivery estimates and making sure the appropriate goods are available when needed.

 

Also Read | How is Blockchain used in Supply Chain?

 

What types of supply chain analytics are there?

 

Various supply chain analytics models exist, such as:

 

1. Descriptive analytics: 

 

Analysis that is descriptive Past data is summarised and described using this kind of analytics. To find patterns and trends, such as changes in sales patterns over time or changes in inventory levels, includes evaluating historical data.

 

2. Diagnostic analytics: 

 

These analyses are used to determine the causes and effects of various data elements. It is used to pinpoint the source of a problem, such as a supplier issue or a bottleneck in the supply chain. It can be applied to anticipate inventory levels, detect potential supply chain hazards, and forecast demand.

 

3. Prescriptive analytics: 

 

Prescriptive analytics is used to produce recommendations and advise courses of action. Determining the most economical logistics routes or the most effective inventory levels can be used to optimize the supply chain.

 

4. Real-time analytics: 

 

By analyzing data in real time, this sort of analytics enables prompt decision-making. It can be used to track the effectiveness of the supply chain, spot possible problems, and swiftly address them.

 

5. Text Analytics: 

 

Using text analytics, it is possible to glean insights from unstructured data, including emails, posts on social media, and customer reviews. It can be used to learn more about customer mood and spot potential supply chain problems.

 

6. Advanced analytics: 

 

This category of analytics uses machine learning, artificial intelligence, and statistical modeling methods to extract insights from large, complicated data sets.

 

7. Big data analytics: 

 

This category of analytics uses methods like data warehousing, data mining, and data visualization to extract insights from vast amounts of data from many sources.

 

These various supply chain analytics techniques can be applied to the supply chain management process to get various insights and guide various decisions. To make data-driven decisions and improve the performance of the supply chain, a combination of these approaches may be utilized, depending on the goals of the firm.

 

Also Read | Data Analytics in FMCG Industries

 

Challenges with Supply Chain Analytics

 

Companies may encounter a number of difficulties while using supply chain analytics, including:

 

• Data availability and quality: It can be challenging to acquire precise insights and make wise decisions when dealing with supply chain data that is inconsistent, partial, or wrong.

 

• Data integration: Integration of data from several sources can be challenging because businesses frequently employ various systems and programs for various supply chain operations.

 

• Lack of expertise: Businesses may not have the know-how to efficiently gather, store, and analyze data, which can prevent them from utilizing the supply chain analytics insights to their maximum potential.

 

• Supply chain complexity: Because the supply chain is dynamic and complicated, it can be challenging to spot trends and patterns.

 

• Lack of uniformity: It may be challenging to compare the effectiveness of various suppliers or logistics providers due to the lack of standardization in data gathering and analysis.

 

• Security and privacy: Companies must make sure they have proper security and privacy safeguards in place because supply chain analytics frequently involves sensitive data, such as financial or personal data.

 

• Change Management: Change management is a challenge when implementing analytics solutions in the supply chain because they frequently call for modifications to roles, workflows, and other procedures.

 

• Limited scalability: The amount of data produced by huge supply chains may be too much for some analytics tools to handle, which can affect both the scalability and usability of those solutions.

 

It takes a mix of the appropriate technology, knowledge, and processes to overcome these obstacles. To efficiently collect, store, and analyze data, businesses need to invest in the appropriate tools and resources. In order to effectively capitalize on the insights gleaned through supply chain analytics, businesses must also make sure that the proper people and procedures are in place.

 

Also Read | How does Blockchain Benefit Supply Chain Management?

 

Future of Supply Chain Analytics

 

The aims of many firms for increased supply chain visibility and transparency will be realized with the help of analytics. By 2025, the supply chain analytics industry is expected to be worth more than $10 billion, with a cumulative annual growth rate (CAGR) of 16%.

 

Prescriptive and cognitive analytics are still out of reach for some smaller businesses because of the necessary human and financial resources, but this is already changing and will continue to do so in the near future. Leading suppliers of supply chain software are already integrating AI into their platforms to provide emerging and midmarket companies with the same benefits as larger corporations.

 

As businesses continue to digitize their business processes and incorporate IoT devices in factories, warehouses, trucks, and other locations, supply chain analytics will draw on an increasingly larger data set in the years to come. And technology suppliers will make use of a variety of AI-related tools to transform that constantly expanding body of data into easily understandable insights. It's the only way for businesses to truly profit from the vast amount of data coming from their supply networks.