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Predictive Analysis in Logistics and Supply Chain: How to Apply

3PL Links

Predictive Analysis in Logistics and Supply Chain: How to Apply | Image source: Pexels In logistics, predictive analysis is simply the process of identifying and forecasting patterns, trends, and behaviors in both human and machine learning approaches, data, and algorithms.

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Top Transportation Technology Trends

Logistics Viewpoints

However, their predictions are based, in part, on a survey of more than 250 global shippers and logistic service providers. The survey covered what technologies they are currently using as well as their planned investments. In transportation, digital freight procurement and asset tracking & data mining are in broad use.

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Data key to tackling forecasting challenges

FreightWaves SONAR

Businesses are prioritizing the speed of data propagation within their supply chains. According to Gold, NRF members have made significant investments in data analysis to improve their ability to predict consumer demand to prevent bloated inventory levels that many large retailers experienced around this time in 2022.

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Scaling the Supply Chain: A Comparative Analysis of Logistics in SMEs and Startups

Logistics at MPEPS at UPV

To provide a comprehensive overview, the author draws from various academic studies, reports, and surveys to shed light on the latest trends and best practices in supply chain management. A recent survey by Accenture (2021) revealed that 72% of SMEs and 78% of startups are actively investing in digital supply chain solutions.

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Digital Transformation in Logistics

Logistics Viewpoints

ARC Advisory Group, where I work, publishes an analysis of the 25 manufacturers with the most mature digital transformations. This survey-based research gathers quantitative data as well as information on practices or performance drivers. From a maturity standpoint, the majority of respondents are digitizing data and processes.

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Demand Forecasting: How It Enhances Ecommerce Operations

Ship Monk

The process usually includes analyzing historical data for seasonal trends and product performance, as well as gathering current data on competitors, marketplace trends, future marketing plans and promotions. All of them rely on data, whether you’re using historical data or new findings gathered from consumer research.

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Autonomous Planning in Supply Chain: 4 Must-Haves for Next-Generation Platforms

Logistics Viewpoints

According to data from a recent research survey, the following were on top of the supply chain headaches not addressed by their current systems: Supply shortages due to supplier’s inability to meet expected performance targets. Network cost modeling. Data cleansing and data robustness. Response to disruptions.