Warehouse and logistics workers using technology


Part 5: How to Improve Your Logistics Forecasting

Harness the power of data-driven insights to streamline your logistics operations and delight your customers. GEODIS can help.

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 integration of internal and external supply chain data
  • Generating multiple supply chain scenarios helps identify risks and opportunities
  • Choosing the right forecasting tool and continuously refining the model are critical for accuracy
  • Acting on forecasting insights through procurement and ordering optimizes the supply chain

A practical framework for improving your supply chain forecasting

Now that we’ve explored the challenges, strategies, and benefits of logistics forecasting, let's dig into how you can use these findings. We’ll suggest a framework for improving supply, demand, and capacity in your supply chain. You can use this framework as a starting point for implementing better forecasting capabilities into your business. 


We’ve built the framework around several key areas:


  • Internal business integration for demand management
  • External supply chain stakeholder and supply management
  • Scenario and forecasting development and testing
  • Taking action on forecasting and scenario predictions


Calculations, reporting, and analytics

Principles of the logistics forecasting framework

It’s important to understand the principles, caveats, and assumptions of this framework, specifically:


  • This framework is deliberately technology-agnostic so it can be applied across multiple partners, stakeholders, and others without relying on specific applications or platforms
  • Despite being technology-agnostic, logistics forecasting benefits enormously from centralized data, integration across multiple systems and apps, and a probabilistic approach for modeling and trends, so seek out tools that can support these concepts
  • This framework is designed to be applicable across a wide variety of industries, logistics, supply chains, and partnerships
  • Consult with supply chain experts to tailor this framework into an effective, high-ROI program to enhance your logistics forecasting
  • This framework does not take into account areas like establishing a business case, getting stakeholder buy-in, program and project management, or employee training as these criteria vary widely between businesses


Internal business integration for demand management and logistics forecasting

Effective forecasting relies on the right policies, processes, data gathering, and other areas across the supply chain. This starts with your business’s internal teams, who you should engage with to incorporate them into your forecasting model.


Identify the internal teams that affect logistics forecasting

Speak to each department in your business to identify how their projects, operations, and initiatives influence supply and demand management, for example:


  • Product development to understand new goods coming through the pipeline and the impact they will have on sourcing and manufacturing products
  • Marketing to establish promotions for new and existing products that will increase consumer awareness and desire
  • Market research to identify shifts in marketplaces and consumer demand
  • Sales to get awareness of existing demand and upcoming deals or partnerships that will increase demand
  • Operations to understand how orders and goods are currently moving through the supply chain
  • Risk management to establish the possible impact of disruptive incidents


Establish policies, agreements, and data sharing with internal teams

For each team you’ve identified:


  • Get policies and agreements in place that ensure they provide high-quality, timely, and accurate inputs into the forecasting model
  • Identify the key data provided by each team and the impact it has on forecasting, then integrate it into your model
  • Test, audit, and tweak internal team data to ensure it’s properly represented and incorporated by your forecasting algorithms


Engage with risk management to create contingency and fallback scenarios

Factoring in likely and unlikely risk scenarios will make your forecasting more robust:


  • Engage with your internal risk management team to understand the likelihood and impact of specific, disruptive risks on the supply chain
  • Establish key internal and external legal, compliance, governance, environmental, and other factors that influence supply chain risks
  • Understand the contingencies that are already in place, both internally and with external supply chain partners, that will impact forecasting in the event of disruption
  • Build these risk factors and contingencies into your logistics forecasting models and scenarios


Understand changing consumer trends and behaviors through market research

Your market research department can provide essential, early information on marketplace and consumer demand to tweak your forecasting models:


  • Understand the consumer data and trends that are available to understand current and future demand
  • Analyze competitor behavior that may influence consumer choices
  • Identify how other changes to each marketplace are influencing availability needs
Warehouse and logistics workers using technology

External stakeholder and supply management for logistics forecasting

Global supply chains require extremely robust stakeholder management for all partners, from raw material providers, through to manufacturers, third-party logistics (3PL), transportation, and distribution. Here’s how to integrate third-party agreements and data to ensure flexible and realistic forecasts.


Establish contractual terms and agreements for product quality and timeliness

The service level agreements (SLAs) and contracts that you have in place with supply chain partners are vital to understanding lead times for ordering and product availability:


  • Map out your existing supply chain to identify all suppliers, manufacturers, logistics services, and other partners responsible for producing, storing, and transporting goods
  • Review the SLAs and contractual terms in place with all partners to build a comprehensive model of ordering, processing, manufacturing, delivery, and other timescales
  • Establish lead times for discrete parts of the supply chain together with complete, end-to-end times between order placement and delivery


Understand and gather real-world measures and third-party data in the supply chain

SLAs are just a starting point. Establishing good data collection practices and ensuring data accuracy provide vital supply management input for your forecasting:


  • Engage with supply chain partners to understand all of the data they’re collecting that could impact the timeliness, quality, and availability of products
  • Integrate with third-party systems and information to collect and rationalize data from across the supply chain and centralize it in one place
  • Audit the quality of external supply chain data to ensure it accurately reflects real-world experiences


Engage with third-party logistics providers to ensure accurate capacity and distribution measurements

One of the most important factors for reliable forecasting is your logistics network, from intermodal transport and consolidation to storage and distribution. Incorporating that data will provide a better understanding of capacity management:


  • Work with your logistics providers to get an accurate understanding of any constraints in collecting, consolidating, deconsolidating, transporting, and storing stock
  • Establish common transit times, especially over large distances
  • Incorporate different types of freight transport (rail, road, air, sea) into various forecasting scenarios


Scenario and forecasting development and testing

The previous steps have helped us to develop high-quality inputs. It’s now time to bring those inputs together to start building logistics forecasting scenarios that accurately represent real-world supply, demand, and capacity management, and their impact on orders, product processing, and logistics.


Decide on a reliable, fit-for-purpose logistics forecasting tool

You have a wide variety of options when it comes to choosing a logistics forecasting platform. When you’re deciding which tool to go with, here are some areas to consider:


  • Make sure the forecasting tool is right-sized for your current and future business needs—the capabilities and costs of such tools vary widely, so review your business growth plans and supply chain management strategies to understand the best options
  • A Software as a Service (SaaS) or Platform as a Service (PaaS) forecasting tool will almost certainly be less expensive and faster and easier to integrate and maintain, so you may want to consider a cloud-based tool versus a local install
  • Talk to your supply chain partners and third-party logistics providers about their recommendations for a good logistics forecasting tool
  • Look for tools that make excellent use of machine learning algorithms to rapidly and reliably generate realistic scenarios
  • Test out a variety of forecasting tools to see which ones perform well for your industry and how useful the outputs are for business decisions


Ensure high-quality, reactive, flexible interactions with data from internal and external partners

Accurately connecting your logistics forecasting system to all of your data inputs is critical. Whether that’s through a centralized data storage warehouse, or via individual connections to disparate systems, you need to ensure that all connections are:


  • Complete: The sum of data allows the forecasting tool to work as expected
  • Rationalized: The data is provided in a standard, easily parsed and processed way
  • Accurate: The data properly represents the situation in the real world
  • Timely: Data should be as up-to-date as possible


You should carry out periodic audits on data quality, accuracy, speed, and reliability to ensure everything is aligned and your forecasting tool continues to receive good inputs.


Map historic and seasonal trend data into your forecasting model

A good logistics forecasting tool incorporates historic contexts and trends to predict likely future needs. Gather trend data from internal and external partners and incorporate it into your tool.


Develop multiple supply chain forecasting scenarios that take account of real-world operations and events

The value of your logistics forecasting comes from generating scenarios that represent likely future supply and demand, based on various business-as-usual (BAU) operations or incorporating different levels of risk:


  • Engage with internal teams and business decision makers to understand the most common BAU supply chain circumstances
  • Incorporate the effects of promotional activities, operational needs, risk analysis, contingencies, supply chain constraints, and other factors
  • Plan in unusual scenarios based on discussions with risk management around disruptive events
  • Look back at historic data for BAU and disruptive periods to understand the previous impacts on the speed, quality, and cost of producing and distributing goods
  • Speak with supply chain decision makers on the breadth and depth of forecasting predictions they need to make informed choices
  • Create and prioritize lists of scenarios for algorithmic modeling and forecasting
  • Run scenarios based on historic and current data—test and audit these forecasts to see how accurate they are and refine your forecasting models as needed
  • Build reactivity and flexibility into forecasting models to allow rapid recalculation and prediction based on fast-changing real-world data


Taking action on forecasting and scenario predictions

Finally, you need to turn forecasts into actions. This brings together all of your data inputs, algorithms, processing, outputs, and scenarios and turns them into actionable data and business intelligence to optimize the supply chain.


Create multiple sample outputs and discuss them with supply chain stakeholders

Once you have a solid forecasting model, use sample data to generate multiple scenarios. Go through each one and review with your internal and external stakeholders, especially procurement and order management. Get their feedback on the accuracy and relevance of the forecasts and incorporate that back into your model.


Agree final reporting and decision-making criteria

Get to a final end state for your forecasting model, verify this with stakeholders, and incorporate these outputs into ordering, procurement, and business intelligence tools. Test and refine these integrations until you have a high level of confidence that everything is working as intended.


Build a continual review and refinement process for speed and flexibility

Every supply chain process can benefit from continual improvement, and forecasting is no exception. Create strong review activities incorporating stakeholders, data, processes, and your modeling tools. Ensure that your forecasting system is reactive and flexible, allowing for recalculation and updated predictions based on rapidly changing underlying factors.


Go-live: act on forecasting outputs

It’s time for go-live. Implement your logistics forecasting system and use it as key decision making data to optimize your supply chain. Continue to iterate your processes, data, and changing circumstances to keep forecasting aligned with your business needs.



We hope you’ve found this exploration of the necessities, challenges, benefits, and enablers of logistics forecasting helpful. Rethinking your entire forecasting model, and implementing the program of work isn’t for the faint-hearted, but for those businesses ready to embrace the challenge, there are enormous benefits.


As competition for consumers and suppliers increases, building supply chain data into the DNA of your operations allows you to execute your business strategies. You’ll have the confidence that you’re making the strongest, evidence-based decisions based on ever-changing circumstances.


Bringing your internal, external, and logistics partners into your forecasting embeds this discipline throughout your supply chain, ultimately resulting in a winning combination of supply, demand, and capacity management.


Read the previous post in our series: benefits of better logistics forecasting.

How GEODIS can help

Ready to unlock the full potential of your supply chain? Our advanced logistics forecasting solutions integrate your data, optimize your operations, and deliver exceptional customer experiences. Contact us now to start your journey towards supply chain excellence with a trusted global partner.

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.