Warehouse and logistics workers using technology


Part 3: Key Strategies to Improve Logistics Forecasting

Discover proven strategies to enhance logistics forecasting speed and accuracy. Focus on process, data, and technology improvements to optimize your supply chain.

This post is part of our series on logistics forecasting and demand planning in the supply chain. Visit our introduction page for an overview of what we're covering and links to each part of our research.

Key takeaways


  • Effective logistics forecasting requires a focus on process, data, and technology improvements
  • Integrating internal and external data sources is crucial for accurate, actionable forecasting
  • Advanced technologies like machine learning can significantly enhance forecasting accuracy and flexibility
  • Collaboration and communication with supply chain partners are essential for end-to-end optimization

There are three main strategies for improving your logistics forecasting: process, data, and technology

Now that we understand the key challenges to reliable logistics forecasting, we can explore the common strategies and steps that supply chain managers can use to significantly enhance forecasting speed and accuracy.


Logistics forecasting requires intuitive, robust computer modeling. To achieve this, it’s prudent to focus efforts on three main areas: 


  • Process-based forecasting improvements
  • Data-based forecasting improvements
  • Technology-based forecasting improvements


Each area has many contributing factors, depending on your sector, industry, and marketplace. We’ve provided some helpful starting points below.


Process-based logistics forecasting strategies

Process-based improvements focus on creating efficient communication channels and information-sharing protocols throughout the supply chain. This ensures logistics forecasting takes all internal and external factors into account.


These factors feed into a logistics forecasting model, which will generate scenarios representing a range of real-world changes and disruptors. Process-based forecasting strategies include:


  • Integrate with internal teams like sales and marketing, operations, and product development to get early sight of promotional and other activities that could influence demand for particular items
  • Engage with risk management teams to understand planning for black swan events and other supply chain disruptors
  • Establish what contingencies and fallbacks are already in place with your supply chain partners like suppliers, manufacturers, and transportation providers
  • Understand strategic imperatives for adding or changing distribution channels, like eCommerce, third-party marketplaces, or omnichannel
  • Build reliable information-sharing and reporting channels with supply chain partners for the efficient collection of data
  • Understand possible supply chain constraints and their impact on forecasting (for example, physical space to store products or ocean freight transit times)
  • Work through any other key factors that can affect supply, demand, or capacity management for your industry and accommodate them in your forecasting (for example, crop yields in the food supply chain)
  • Prioritize a range of forecasting and modeling scenarios based on promotional activities, operational needs, risk analysis, contingencies, supply chain constraints, and other factors
  • Agree on reporting and decision-making criteria with internal and external stakeholders to make logistics forecasts actionable
  • Design and provide a forecasting framework based on an organization’s supply chain resilience, aligned with risk management priorities
Warehouse shelving and products

Data-based logistics forecasting strategies

Data-based improvements focus on measuring, sharing, and using accurate and timely data to ensure you have high-quality inputs for forecasting, which lead to high-quality outputs. Data-based forecasting strategies include:


  • Understand and audit existing supply chain monitoring of stock levels, lead times, delivery times, and other areas to ensure you’re accurately measuring the right things
  • Build marketplace research data into demand planning so you have early sight of changing consumer behaviors and preferences, and the impact of those changes on demand for particular products
  • Map historic and seasonal trend data into your forecasting model to allow for predictable changes over time
  • Establish contractual (agreed) and actual (real-world) lead times for ordering, supplying, and manufacturing products with supply chain partners so you understand delays between ordering and receiving products

Don't let inefficient supply chain forecasting hold your business back. Get in touch with GEODIS to discover how our data-driven approach can boost your performance.

Technology-based logistics forecasting strategies

Technology-based improvements focus on the applications and systems you use to generate logistics forecasts. This ensures you have proper integration, centralized data, and actionable scenarios.


  • Make use of a centralized platform for gathering, analyzing, rationalizing, and reporting on data from all supply chain partners—this gives you “one source of the truth” that creates robust inputs and outputs for logistics forecasting.
  • Integrate with data sources from internal partners like sales and marketing, product development, operations planning, and other areas
  • Integrate with data sources from all external partners across the supply chain, including disparate, siloed, and legacy systems
  • Take advantage of the latest machine learning models to create accurate forecasting algorithms that can be tested and refined against real-world data
  • Create multiple forecasting scenarios and outputs based on operational needs, promotional activities, product development, risk analysis and contingencies, supply chain constraints, and other factors
  • Build reactivity and flexibility into forecasting models to allow rapid recalculation and prediction based on fast-changing real-world data
  • Test and refine forecasting outputs to ensure accurate predictions that stakeholders can act on
  • Use available data to create descriptive, predictive, and prescriptive analytics to refine existing supply chain processes


Read the previous post in our series: the challenges of logistics forecasting, or the next post in our series: the benefits of better logistics forecasting

How GEODIS can help

Unlock the full potential of your supply chain with our advanced logistics forecasting solutions. From cutting-edge technologies to proven processes and global expertise, we have everything you need to optimize your operations and drive long-term success. Contact us today to learn more.

Paul Maplesden

Paul Maplesden

Lead Content Strategist

Paul deeply researches logistics and supply chain topics to create helpful, informative content for our US audience. Read Paul's work in the GEODIS blog, our in-depth GEODIS Insights reports, and our case studies and white papers.