Expert Column Data Analysis in Supply Chain Management

Data analysis has become an integral part of supply chain management as companies seek to gain insights into their operations and make data-driven decisions. With the vast amounts of data generated across global supply chains, leveraging this data through analysis techniques can lead to improved efficiency, cost savings, and better customer service. This article will explore how data analysis is transforming supply chain management.

Measuring Supply Chain Performance

One of the key applications of data analysis in supply chain management is measuring performance. By tracking key metrics across the supply chain, companies can identify areas for improvement.

Key Performance Indicators

Some important supply chain KPIs that can be measured through data analysis include:

• On-time delivery - The percentage of orders delivered on or before the requested delivery date. This measures reliability of fulfillment.

• Order cycle time - The time it takes from when an order is placed to when it is delivered. Faster cycle times can improve customer satisfaction.

• Inventory turnover - How quickly inventory moves through the supply chain. Higher turnover indicates efficient use of inventory.

• Forecast accuracy - How close demand forecasts match actual demand. Higher accuracy allows for better planning.

• Transportation costs - The costs associated with moving goods through the supply chain. Lower costs improve profit margins.

Data Sources

Data to calculate supply chain KPIs can come from:

• ERP systems - Capture order, inventory, and fulfillment data

• WMS systems - Provide inventory levels and order tracking data

• Transportation management systems - Give transportation costs and delivery status

• Point of sale systems - Reveal demand data at the retail level

• Supplier portals - Share order and delivery information between partners

Analytics Techniques

Data analysis techniques like descriptive statistics, data visualization, and dashboards allow supply chain analysts to monitor KPIs, identify trends and outliers, and share insights with stakeholders. Advanced analytics using machine learning can also help predict future performance.

Digitizing the Supply Chain

Digitizing supply chain processes through IoT sensors, cloud platforms, and data integration enables the collection and analysis of vast amounts of data.

IoT Sensors

IoT sensors placed on products, vehicles, and equipment can track locations, conditions, and events in real-time across the supply chain, generating data for analysis.

Cloud Platforms

Cloud-based supply chain platforms allow partners to share data in real-time. Integrated data from multiple sources enables advanced analytics.

Data Integration

APIs and data pipelines integrate data from legacy systems across the supply chain into cloud platforms for consolidated analytics.

Benefits

End-to-end visibility from digitized data improves planning, coordination, and decision making between supply chain partners. Automation based on analytics findings also increases efficiency.

Creating a Resilient Supply Chain

Data analysis plays a key role in identifying and mitigating supply chain risks to create a resilient supply chain that can withstand disruptions.

Risk Identification

Advanced analytics techniques can analyze data to identify potential risks and vulnerabilities in the supply chain, such as:

• Supply risks - Insufficient suppliers, supplier quality issues

• Demand risks - Forecasting errors, sales volatility

• Process risks - Production downtime, warehouse automation failures

• External risks - Natural disasters, trade wars

Mitigation Strategies

Once risks are identified, mitigation strategies can be implemented, such as:

• Increasing inventory buffers for high risk products or regions

• Securing additional suppliers to diversify sourcing

• Improving demand forecasting accuracy with predictive analytics

• Automating production and warehouse processes to reduce downtime

Conclusion

Data analysis is transforming supply chain management by enabling data-driven decision making. Leveraging the data flowing through modern digital supply chains allows companies to measure performance, increase visibility, mitigate risks, and operate more efficiently. Companies that effectively utilize data analytics will gain a competitive advantage in their industry. With data as a strategic asset, supply chains become smarter, more agile, and more resilient.

About Cello Square

Cello Square is a digital freight forwarding service, developed by Samsung SDS. The platform provides end-to-end visibility and optimal routing of logistics transport with a simple booking process. Features range from real-time quotations, chat functions to dashboards reflecting operation insights. Cello Square is used by leading companies from various industries to optimize supply chain stages and enhance system capability. 

Samsung SDS is a provider of Digital Logistic Services, establishing strong logistical capabilities across the world. Our global network spans across 36 countries with 53 international branches. We provide comprehensive IT-enabled services optimized to respective areas of Global Logistics, reliably handling the world’s 10th largest volume of air and ocean cargo. Speak with us today to optimize your supply chain operations.

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