Data Driven
The first step for any company that thinks about its emissions reduction roadmap for freight and logistics, is to understand the emission footprint. In addition, companies need to be able to report their footprint and their reduction progress. Freight and logistics may not be the largest source of (scope 3) emissions for multinational cargo owners, but the puzzle of how to decrease it is a complex one. It will require high-quality data to assess progress and impact of reduction initiatives undertaken globally.
In recent years, a lot of progress has been made in ensuring a sufficient quality level for freight and logistics emissions calculations. The GLEC Framework facilitates a harmonized reporting of the GHG footprint. At the same time, many companies still struggle with collecting good data from their third-party contractors and understanding if the data and calculation approaches are comparable between them. This is important for companies when assessing concrete actions for the reduction roadmap and understanding emission hotspots, emissions profiles, etc. We believe it is time that multinational cargo owners with an emission reduction target in logistics take a more strategic approach to their emission data maturity.
Many of the companies we have engaged with roughly break down their actions into three categories across a short, medium and long-term horizon. Short-term actions focus predominantly on logistics optimization and intermodal opportunities and medium/long-term horizons include alternative fuels and introduction and scaling of technologies (e.g. medium-haul EV, hydrogen).
To measure the possible impact of initiatives, footprint calculations increasingly need to step away from using default assumptions for emission factors, load factor, and so on. Footprint calculations need to reflect the impact from optimization initiatives more correctly, for example to support the business case of investments that may be needed to achieve them. For many companies this requires a major step-up in the data-quality, data collection and calculation approaches.
As the graphic above shows, we suggest that companies start with assessing their current state of data maturity and design a roadmap to improve it over time, understanding what data is needed, what processes must be in place, and so on. The data maturity needs to align with the business strategy and the changing needs over time. Companies should therefore have a clear data strategy: how will data become an integrated part of day-to-day business, who is responsible for what, what technology is needed?
As the analytical needs become more advanced, higher data maturity is required. At this moment, most companies focus on being able to report their footprint with some basic insights into its structure (descriptive analytics), often based on basic activity data (weight and distance) and default assumptions on emission factors. To take the next step to diagnostic analytics, that is understanding why the footprint changes, more granular and better data is needed. For example, initiatives to optimize logistics will not only reduce ton-km, which is relatively easy to collect, it may also require a refinement and differentiation in emission factors applied in the calculations as utilization (vehicle or container) increases or the number of empty-runs decreases. Identifying opportunities for emission reduction and assessing the possible impact of optimization initiatives or technology roll-out may also require more predictive analytics (what will happen if we change the order cycle?) or prescriptive analytics (how can we make it happen?).
However, data maturity is not just about data quality and granularity for emission calculations. To make the business more aware of the GHG impact of different shipment options, it will become necessary to integrate this information into your TMS. Similarly, to track impact of specific initiatives and/or to have better insights into emission performance, you may want to consider how to collect more real-time data from your partners (e.g. API interfaces and engine data integrations).
As part of the change management that needs to take place to reduce logistics emissions, dashboards and storytelling through data are crucial. Data will increasingly need to become accessible to business users. Storytelling through data is important to effectively communicate insights to different stakeholders within your organization. Storytelling through dashboards is more than just a visual. We often find that people struggle to make the dashboard and story match their targeted audience, both internal and external. All of this requires being very clear on what the important metrics are, focus on user capabilities and ensuring intuitive design.
Many business practitioners are aware of the general trend of continuously growing data-driven decision making. At AllChiefs we believe that this will become increasingly important in logistics to successfully implement emission reduction initiatives and change stakeholder behaviors within the organizations.
Do you know where you stand on your data maturity?
