Explaining Supply Chain Analytics & Its Importance

Managing a global supply chain is a complex task that involves coordinating operations between various parties, including suppliers, vendors, buyers, carriers, agents, and business partners. The ultimate goal is to deliver a superior customer experience. However, the many moving parts, disruptions, and data can make this difficult. As a result, a substantial amount of data is generated. Supply chain leaders understand the importance of analytics in managing this data. According to Gartner, around 79% of supply chain leaders plan to train their teams in advanced analytics. This article will discuss why supply chain analytics is crucial for success and how to use supply chain data effectively.

Explaining Supply Chain Analytics

Supply chain analytics is the process of analyzing data and using insights gained to optimize the performance of a company’s supply chain. The supply chain includes all the processes involved in delivering and producing a product or service, from getting raw materials to the final delivery of the end product to the end customer.

Supply chain analytics aims to improve the efficiency and effectiveness of the supply chain by identifying zones for improvement and implementing changes to optimize performance. This is accomplished by gathering and analyzing data related to various aspects of the supply chain, such as inventory levels, transportation costs, delivery times, and supplier performance.

Supply chain analytics can also help companies identify potential risks in their supply chain, such as supply shortages or disruptions, and develop strategies to mitigate those risks. This is especially important in today’s globalized economy, where companies rely on suppliers worldwide.

The data used in supply chain analytics can come from various sources, including internal data from the company’s systems and external data from suppliers, logistics providers, and other sources. This data is typically analyzed using advanced analytics techniques such as predictive modelling and machine learning, which can help identify patterns and trends that might not be apparent using traditional analysis methods.

Supply chain analytics is essential for companies looking to optimize their supply chain and improve their overall business performance. By leveraging data and analytics, companies can make better decisions about their supply chain operations, reduce costs, and improve customer satisfaction.

The Importance of Supply Chain Analytics

Supply chain analytics is becoming increasingly important in today’s fast-paced, globalized economy. By leveraging data and analytics, organizations can gain valued insights into their supply chain operations and make better-informed decisions about optimizing their supply chain performance.

Here are some of the critical benefits of supply chain analytics:

Improved efficiency

Supply chain analytics can help companies identify inefficiencies in their supply chain operations, such as excess inventory, delays in transportation, or production bottlenecks. Companies can reduce costs, increase productivity, and improve overall efficiency by addressing these inefficiencies.

Better risk management

Supply chain analytics can help companies identify potential risks in their supply chain, such as supply shortages or disruptions, and develop strategies to mitigate those risks. This is particularly important in today’s globalized economy, where companies rely on suppliers worldwide.

Enhanced customer satisfaction

By improving the efficiency and reliability of their supply chain operations, companies can better meet the needs of their customers, which can lead to amplified customer satisfaction and loyalty.

Increased profitability

Supply chain analytics can help companies increase productivity and gain competitive benefits by reducing costs and improving efficiency.

Improved collaboration

Supply chain analytics can help companies collaborate more effectively with their suppliers and other partners in the supply chain. By sharing data and insights, companies can work together to optimize their supply chain performance and achieve mutual benefits.

Types of Supply Chain Analytics

Companies can use several types of supply chain analytics to gain insights into their supply chain operations and improve performance.

Descriptive analytics

This type uses historical data to gain insights into past performance. Descriptive analytics can identify trends, patterns, and anomalies in the supply chain, which can help companies understand how their supply chain is performing.

Diagnostic analytics

This type of analytics involves analyzing data to control the root cause of a particular problem or issue in the supply chain. Diagnostic analytics can identify why specific supply chain metrics are not meeting expectations, such as delays in transportation or low inventory levels.

Predictive analytics

This type involves using statistical models and machine learning algorithms to predict future outcomes based on historical data. Predictive analytics can forecast demand, anticipate supply chain disruptions, and optimize inventory levels.

Prescriptive analytics

It involves optimizing models and algorithms to recommend specific actions to improve supply chain performance. Prescriptive analytics can optimize production schedules, reduce transportation costs, and improve inventory management.

Network analytics

This type of analytics involves analyzing data to gain insights into the relationships and interactions between different entities in the supply chain, such as suppliers, manufacturers, and distributors. Network analytics can identify opportunities for collaboration and optimization within the supply chain.

These types of supply chain analytics can help companies gain valuable insights into their supply chain operations and make data-driven decisions to improve performance and achieve better business outcomes.

Challenges of Supply Chain Analytics

While supply chain analytics can provide valuable insights into a company’s supply chain operations, there are also numerous challenges that companies may face when implementing a supply chain analytics program.

Data quality

One of the biggest challenges in supply chain analytics is ensuring that the data used is accurate, complete, and consistent. This can be especially difficult in complex supply chains that involve multiple partners and systems.

Data integration

Supply chain data often comes from multiple sources, such as ERP systems, transportation management systems, and warehouse management systems. Integrating this data can be a complex and time-consuming process.

Lack of expertise

Analyzing supply chain data requires specialized skills and knowledge in statistics, machine learning, and optimization. Companies may struggle to find and retain employees with the necessary expertise.

Cost

Implementing a supply chain analytics program can be expensive, requiring investments in hardware, software, and personnel. The benefits of supply chain analytics may not be immediately apparent, making it difficult to justify the costs.

Resistance to change

Supply chain analytics may require changes to present processes and systems, which can be met with resistance from employees and partners in the supply chain.

Limited data availability

Some companies may not have access to the data they need to achieve effective supply chain analytics, mainly if they rely on suppliers or partners unwilling or unable to share data.

Despite these challenges, companies that can overcome them can benefit significantly from supply chain analytics, including improved efficiency, better risk management, and increased profitability.

Why Choose INFOFISCUS Supply Chain Analytics?

INFOFISCUS offers Supply Chain Analytics services that can help companies transform overwhelming amounts of supply chain data into easily understandable dashboards, reports, and visualizations. These insights can guide crucial decisions and improve business outcomes. Clients can access important Key Performance Indicators (KPIs) such as Booking Amount, Quantity, Shipment Amount, Manufacturing Cost, Gross Margin, On-Time Delivery, Booking vs Shipment, and more. These KPIs are crucial in the competitive landscape of supply chain management. INFOFISCUS Supply Chain Analytics is developed using out-of-the-box ETL/ELT technology from Informatica/Matillion with a Data Cloud from Snowflake by leveraging industry-leading Visualization Tools like Tableau, PowerBI, and Looker.

• 70% Reduction in Shipment Backlogs.
• 60% Production Cost Decreased
• 91% Reduction in Stockouts
• Integrates with any On-prem/Cloud Apps
• Comprehensive analysis of Sales Orders vs Shipments.
• On-Time Delivery Analysis.
• Pre-Built KPIS on Manufacturing Cost, Sales, Gross Margin etc.
• Supports Bring Your Own License (BYOL) model.
• Pre-packaged highly configurable ETL/ETL mappings.
• Bring Efficiency to Logistics and Transportation.
• Better Warehouse Operations
• Eliminate Wastage and Improve Gross Margins

Conclusion

Supply chain analytics allows businesses to integrate and analyze all the data related to their supply chains to understand better what is going on and how it can be improved to drive better efficiencies. However, this process requires a robust analytical framework that can integrate different data sources, aggregate data using secure pipelines, and create collaborative reports and visualizations. INFOFISCUS provides a solution to power supply chain analytics efforts. To experience this, businesses can try a free trial of INFOFISCUS.