Supply Chain Logistics Data

Introduction

Understanding the intricacies of supply chain logistics, especially in the context of large big-box retailers and shopping malls, has historically been a complex and opaque process. Before the digital age, firms relied on manual counts, paper-based records, and anecdotal evidence to gauge supply chain health and efficiency. This often meant operating in the dark, with significant delays in recognizing shifts in supply volumes or assessing the health of tenants based on resupply patterns. Traditional methods such as direct observation, manual inventory checks, and reliance on sporadic supplier reports were the norm. The advent of sensors, the internet, and connected devices, alongside the proliferation of software and database technologies, has revolutionized data collection and analysis in this field.

Today, the importance of data in understanding supply chain logistics cannot be overstated. Real-time data collection and analysis have replaced weeks or months of waiting for insights. This transformation allows businesses to respond swiftly to changes, optimizing their operations and improving decision-making processes. The evolution from antiquated methods to modern data-driven approaches highlights the critical role of data in shedding light on supply chain dynamics.

Geolocation Data

Geolocation data has emerged as a pivotal tool in analyzing supply chain logistics. Historically, the ability to track and analyze the movement of trucks and freight was limited by technology and data availability. However, advancements in GPS technology and the development of extensive Points of Interest (POI) databases have enabled precise tracking of trucking routes, volumes, and patterns.

Industries ranging from retail to manufacturing have historically utilized geolocation data to optimize supply chains. The advent of real-time and historical truck trip data, capable of delineating truck classes and covering both commercial and passenger vehicles, has provided unprecedented insights into supply chain logistics. This data is instrumental in assessing tenant health, understanding seasonal variations in trucking supply, and differentiating between owner-operated and leased trucking fleets.

The acceleration in the amount of geolocation data available has been remarkable. Providers now offer insights into trucking traffic and mobility patterns with granularity down to 15-minute intervals. This wealth of data supports a wide range of applications:

• Real-time tracking of truck movements to and from big-box retailers and shopping malls.
• Seasonal analysis of supply volume changes, aiding in inventory management and planning.
• Tenant health assessment through analysis of resupply patterns and trucking volumes.
• Operational optimization for logistics and supply chain managers by identifying bottlenecks and improving efficiency.

Examples of successful application of geolocation data include optimizing delivery schedules, reducing idle times for trucks, and enhancing overall supply chain visibility.

Automotive Data

Automotive data, particularly from connected vehicles, has become another crucial source of insights for supply chain logistics. The emergence of connected vehicle technology has provided a wealth of data on vehicle movements, including commercial and freight vehicles. This data offers a complementary perspective to traditional geolocation data, with a focus on the vehicles themselves.

Roles in logistics management, fleet operations, and retail supply chain coordination have historically leveraged automotive data to gain insights into vehicle health, traffic patterns, and operational efficiency. The technology advances in vehicle connectivity and data collection have led to an explosion in the volume of automotive data available.

The specifics of how automotive data can be used in supply chain logistics include:

• Traffic flow analysis to reduce congestion and optimize delivery routes.
• Vehicle health monitoring to prevent breakdowns and reduce maintenance downtime.
• Efficiency improvements in fleet operations through data-driven decision-making.
• Enhanced safety measures by analyzing driving patterns and identifying risky behaviors.

Examples of automotive data application range from improving delivery timeliness to enhancing fuel efficiency and reducing operational costs.

Conclusion

The transformation of supply chain logistics through data is undeniable. Access to diverse data types, such as geolocation and automotive data, has empowered business professionals to gain deeper insights into supply chain dynamics, enabling better decision-making and operational optimization. The importance of becoming more data-driven in today's competitive landscape cannot be overstated, as data discovery becomes increasingly critical to organizational success.

As corporations continue to explore ways to monetize the valuable data they have been generating, the future of supply chain logistics looks promising. New data types and analytical techniques, potentially leveraging artificial intelligence, hold the promise of unlocking even deeper insights into supply chain logistics, driving efficiency, and fostering innovation.

Appendix

Industries and roles that stand to benefit significantly from advancements in data collection and analysis in supply chain logistics include investors, consultants, insurance companies, market researchers, and logistics managers. The challenges these industries face, such as optimizing supply chains, reducing operational costs, and improving tenant health assessments, are being transformed by data.

The future of supply chain logistics, powered by data and AI, may unlock value hidden in decades-old documents or modernize the analysis of government filings, further revolutionizing the field.