The prevailing challenges of supply chain management have triggered the demand for logistics analytics.
Since the pandemic, many logistics brands turned towards big data analytics solutions to manage their operations for the market demands and changes. Noting that the global logistic sector faced significant issues with overstocking goods due to COVID-19 restrictions, the role of data analytics was sighted.
The constant disruptions in supply chain operations and management methods also contributed to the dominance of big data in logistics operations.
As per a report by GlobeNewsWire, the global supply chain analytics (SCA) market will reach USD 38.78 billion by 2032. That also conveys the importance of data analytics in logistics, involving numerous factors to favor the same.
If you’re new to logistics data analytics, navigate this blog section to discover more about its benefits for the supply chain.
What is Logistics Analytics?
The union of logistics and big data has transformed the way supply chains operate and manage shipments.
With the adoption of advanced business intelligence (BI) tools and robotic process automation (RPA) in the supply chain, the global logistics sector has eventually become a data-driven enterprise. Logistics companies are all set to hire certified data engineers to analyze their data for insightful gains.
No wonder logistics analytics is one of the critical supply chain risk management steps to take against these challenges:
- Lags in internal supply chain operations, including production, demand, delivery, transport, and payments.
- Operational issues emerge due to inefficient workforce or manual methods.
- High transport costs due to peak pricing for last-minute supply demands.
- Inadequate inventory resources, volume, real-time stock monitoring, and management needs.
- Unorganized shipment and delivery of goods leads to delays and customer dissatisfaction.
- Irregular system maintenance and support causing increased downtime and last-mile hassles.
- Complexities in integrating, managing, securing, and analyzing data for logistics decisions and risk prevention.
Source by: https://www.bluent.net/blog/logistics-analytics-in-supply-chain/
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