Types of Analytics in Supply Chain Management

 


The global supply chain management ensures seamless functioning, enhanced efficiency and productivity, and increased profit margin. It is only possible through proper management and insights into every activity in the supply chain. Analytics in supply chain management is utilised to skim through data insights to filter, address, evaluate, and identify solutions to potential challenges and loopholes in the supply chain. It helps the supply chain management figure out newer strategies and optimise operations to tackle supply chain inefficiencies. It immensely benefits supply chain operations as it finds the exact problems, gives solutions, and can test the potential solutions to identify their efficiency in practical life. Let us look at the different types of supply chain analytics and how they manage supply chain operations.


What is the need for analytics in supply chain management?

Analytics is indispensable in supply chain management due to several compelling reasons. Firstly, the supply chain encompasses a multitude of activities and functions performed at various levels. Industries involved in trade, such as shipping, logistics, transportation, and container sectors, generate vast volumes of data on a daily basis. This data encompasses transactional records, customer information, stakeholder details, shipping company data, and infrastructure-related data. The significant and diverse data generated within the supply chain is not merely collected but is instead harnessed for analytics purposes.

 

Data analytics involves the process of collecting raw data and transforming it into valuable insights. By analyzing this data, hidden trends and patterns can be discovered, leading to informed decision-making and appropriate conclusions. Supply chain analytics assumes a critical role in supply chain management as it oversees multiple operations and strives to enhance productivity and efficiency throughout the supply chain.

 

Through the application of analytics, supply chain management gains valuable insights into various aspects of the supply chain. It enables businesses to optimize inventory management, streamline transportation and logistics operations, identify cost-saving opportunities, improve supplier performance, enhance demand forecasting accuracy, and mitigate risks effectively. By leveraging data analytics, companies can make data-driven decisions, improve operational processes, and ultimately achieve a competitive advantage in the dynamic business landscape.


Overall, the need for analytics in supply chain management arises from the immense volume of data generated within the supply chain and the desire to extract meaningful insights from this data. By harnessing analytics, supply chain management can make informed decisions, drive productivity, and enhance overall efficiency within the supply chain ecosystem.


Benefits of Analytics in Supply Chain Management 

Analytics in supply chain management provides numerous benefits that aid in the efficient and effective management of the supply chain. The following are some key advantages:

 

  1. Risk Identification: Supply chain analytics helps identify potential risks and vulnerabilities that could lead to disruptions within the supply chain. By analyzing historical data, patterns, and market trends, potential risks can be detected, allowing proactive measures to be taken to mitigate them.

  2. Problem Solving: Analytics provides insights and probable solutions to identified supply chain problems. By analyzing data, supply chain management can make informed decisions and choose the most suitable solution to address issues and optimize operations.

  3. Efficient Planning: Supply chain analytics enables better planning and management of supply chain operations. By utilizing data-driven forecasting and prediction techniques, organizations can optimize inventory levels, streamline logistics processes, and improve overall operational efficiency.

  4. Collaboration and Performance: Analytics promotes proactive collaboration among various industries involved in the supply chain. It facilitates improved delivery times, reduces investment requirements, and enhances overall supply chain management by optimizing coordination and performance across stakeholders.

  5. Market Trends and Real-time Planning: Supply chain analytics helps organizations stay updated with evolving market trends. By continuously analyzing market data and real-time information, companies can adapt their strategies and plans to align with market demands, enhancing productivity and competitiveness.


Types of Analytics in Supply Chain Management
 

  1. Descriptive supply chain analytics: Raw data is provided to draw more insights regarding market trends, relationships, and challenges. It is the first stage of analytics and helps in culling out current patterns and productivity in the supply chain and whatever loopholes lie within. Through this, descriptive analytics proves beneficial in enhancing sales and supply chain efficiency.

  2. Predictive supply chain analytics: Advanced algorithms align with filtered data from descriptive analytics to help forecast by spotting historical trends and patterns. It is advantageous in preparing the supply chain operations for the influx of demands, tackling any incoming breakdowns or disruptions, and managing the seamless trade flow.

  3. Prescriptive supply chain analytics: It tests various solutions to evaluate their working and efficiency in the supply chain. Digital twins in virtual reality enable the testing of multiple strategies and solutions, identifying potential outcomes, and analysing data to determine success or failure. It helps in proactive decision-making and solving supply chain inefficiencies.

  4. Diagnostic supply chain analytics: It helps analyse the 'why' of supply chain operations. The filtered data insights point out the possible failures in the supply chain. Industries may face many challenges in carrying out activities, and previous disruptions in the supply chain also need to figure out an answer as to why they happened. Diagnostic analytics helps in determining the reasons for poor performance.

  5. Cognitive supply chain analytics: Industries are shifting towards automation and use cognitive analytics for that purpose. It helps integrate large and diverse data sets into software and machines for automation. By minimising human interference, automated operating systems use cognitive analysis as their resource to carry out supply chain tasks.


These different types of analytics in supply chain management help enhance supply chain efficiency, productivity, and profitability.


LOTUS Containers is a prominent shipping container service provider that sells and leases new and used shipping containers across the globe to keep trade businesses running.


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