Freight Predictive Modelling

What is Freight Predictive Modelling?

Introduction

Before we have Freight Predictive Modelling, we first need to have set up a Freight Business Intelligence solution. We need within that system a decent amount of freight data, displayed via well designed Business Intelligence Dashboards, that we can then put to use in Freight Analytics.

Freight Analytics in the 2020s is no longer a nice to have, it is imperative. It is having the biggest impact on the industry this decade.

Predictive Modelling uses historical and transactional data to identify patterns of both risk and opportunity in your business in order to guide you in your decision making around future actions for optimising your business: strategies and tactics.

This is not particularly new; the old saying ‘the best predictor of future behaviour is past behaviour’ has its roots in this type of analysis. But today, with the advent of digitalisation, we finally have the right tools to conduct this analysis more broadly, and so industry leaders have taken to this capability to drive competitive advantage.

Freight Predictive Modelling for Carrier Distribution Solutions

Freight Analytics can be used to predict capacity, service requirements or it can be used for predicting cost. In this article we are focusing on freight cost reductions; we are interested in exploring the predictive modelling that can be used for reviewing your freight to improve your carrier distribution network.

Along with the ‘rear view mirror’ data – historical freight data – required, you need first to profile the freight, then benchmark to assess for strengths and opportunities. Once you’ve done this you can collate the right new freight data inputs to compare with the historical freight data run some predictive modelling reports and analyses. The various data sets: old and new will be used building ‘what if’ scenarios.

Pragmatically, what we are explaining here from a freight perspective, is selecting the right carriers and services that match the profile, plug the gaps the benchmarking process finds within the distribution network, to produce the predictive modelling reports on how to implement the improvements from the insights provided.

Agile Freight Analysis

Freight Predictive Modelling provides agility, resilience, certainty and improve cost efficiencies for your supply chain business. All of which has gained importance since the pandemic.

The ability to react quickly to the need for a new supplier, (carrier in the instance of freight), or proactively source, review, and setup a new carrier in anticipation to upcoming need has become increasingly important. Therefore, having automated systems with imbedded algorithms to greatly reduce the time it takes to review freight rates – data – and to trust the outcomes from the analysis, is key to this.

Resilience

Agility improves your resilience as you’ve achieved the ability to change suppliers rapidly and so you can react quickly to any unforeseen disruptions, whether at your organisation, your customers, suppliers, or your industry, or even globally, and can mitigate the risk that the disruption has created.

Certainty

Certainty, control, is more important than ever. No one can afford costly mistakes. Knowing your profile, benchmarking that against industry standards for service and cost efficiencies, and then filling any gaps via freight predictive modelling provides a level of certainty previously unheard of.

Cost Efficiency

Agility also implies cost efficiency. It takes less resources; is automated and therefore you reduce the manhours and soft costs involved in assessing and implementing the changes highlighted from analytical reporting. Technology plus people: expert data analysts with logistics experience is what is needed to make the most of this type of analysis.

Freight Business Intelligence

As stated above, Freight Predictive Modelling is a component of Freight Business Intelligence. Post conducting this type of analysis for designing an optimised Carrier Distribution Solution for your business, you need to confirm the outcomes of the Freight Review project. To do this, you need to assess key freight KPIs such as volume, average cost per consignment etc., within your Business Intelligence Dashboard & Reporting system to analyse the impact on your business and also to see what might have changed within your freight profile since the solution was implement. You can learn a bit more about that process here.

Conclusion

Freight Predictive Modelling is a type of Freight Analytics that is a highly useful tool, within a suite of Business Intelligence tools, that will provide you with the agility to respond to the ever-evolving needs inherent in distribution management, the ability to improve resilience and predict outcomes (certainty) from the automated, efficient redesign of your carrier distribution network.

At Freight Controller we have bespoke Freight Technology systems built for this type of analysis which means that we can confidently estimate the outcomes of our Freight Review services. If you are missing these tools via 3rd party or internal software systems, please contact us to learn more about how we can put our Freight Predictive Modelling to good use for your business.

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