Data at a Deeper Level: Leveraging Analytics for Sustainable Logistics
Companies today are more tuned in to the potential environmental impacts of their decisions, not to mention the impact that more sustainable decisions can have on their brand and customers’ perception of their business. We know that for most businesses, Scope 3 (indirect) emissions make up the majority of their carbon emissions, in some cases upwards of 80%. We also know that by targeting this category, they are often able to realize the most progress for their efforts.
However, reducing Scope 3 emissions is not a single, one-size-fits-all process. It requires detailed data as a critical element, along with a thorough understanding of the data. Companies can then use these insights to develop a customized and dynamic plan for decarbonization.
This is the power of data analytics at work. It informs and directs in the way sustainability efforts should be channeled for the best results. Applied to the category of sustainable logistics, analytics offers significant potential for companies to elevate their decision-making processes, whether for freight procurement or route optimization.
The Data Revolution in Logistics
The logistics industry, once characterized by decisions led by experience and intuition, is now in the middle of a data revolution. Companies are more informed than ever before, leveraging data to navigate the complexities of global supply chains and using data analytics for strategic decision-making. With more data, we can derive more and better insights, and specialized technology solutions are emerging to help companies with their analytics, typically falling into one of four different categories.
Descriptive Analytics – This form of analytics examines past data to provide an understanding of what has already happened. A company might analyze its freight data from the previous year to identify which routes had the most delays or which products had the highest shipping costs. This retrospective analysis serves as a starting point in the data-driven decision-making process.
Diagnostic Analytics – This dives deeper into the data to understand why something occurred. If the company noticed a spike in shipping costs in the third quarter, diagnostic analytics might reveal that a major port strike during that period led to rerouted shipments, incurring additional expenses.
Predictive Analytics – By analyzing past patterns and trends, this type of analytics forecasts what is likely to happen in the future. For example, a company might use predictive models to forecast demand for a product, allowing them to optimize inventory levels and reduce storage costs.
Prescriptive Analytics – Prescriptive analytics offers recommendations on actions to take based on the data. If a predictive model forecasts a spike in demand in a particular region, prescriptive analytics might suggest reallocating resources, ramping up production, or pre-emptively increasing freight capacities for that area.
Overall, analytics provides insights that can help drive efficiency, reduce costs, and improve customer satisfaction. It enables companies to be proactive rather than reactive so they can foresee challenges and capitalize on opportunities. In an industry where margins can be thin and the environment is dynamic, data analytics offers a strategic advantage.
The Nexus of Data Analytics and Sustainable Logistics
When it comes to sustainability, the data revolution has moved at a slower pace, mainly because companies have only recently realized the pressing need for sustainability. As environmental concerns have become more pronounced in recent years, industries are now waking up to the importance of data in driving sustainable initiatives.
Central to sustainable logistics is the need for accurate and precise carbon emissions data. Before companies can get into advanced data analytics to optimize sustainability, they must first have a clear understanding of their carbon footprint. Accurate carbon emissions data is the foundation for building sustainable strategies. Without this layer of information, efforts to reduce environmental impact can be misguided, inefficient, or, at worst, counterproductive.
Driving Freight Procurement Decisions
One application of data analytics contributing toward sustainable logistics is freight procurement. With data analytics, companies can analyze and make decisions based on the carbon emissions of different carriers, routes, and modes of transportation. This allows them to choose their balance between optimized costs and environmental impacts. By leveraging data-driven insights, businesses can identify the most eco-friendly carriers, optimized routes for reduced fuel consumption, and the biggest sources of waste or inefficiencies.
They can also compare different options and see where there are big tradeoffs between cost and sustainability and where the tradeoffs are minimal. In this way, data analytics becomes a tool enabling greater control over each procurement decision. When companies are working toward specific sustainability targets, procurement managers can decide where exactly their carbon emissions can be reduced.
Granular-Level Data Analytics: The Key to Sustainability in Logistics
Having data means a company knows its carbon emissions output. Having data analytics means the company knows clearly how to reach its emissions goals. There is depth to the information, which allows managers to prescribe a course of action.
However, there is one common problem that gets in the way of this—a lack of data on a granular level and therefore a lack of analytics on a granular level. Many companies look to default data to fill out the information they need. Default data, which is based on historical averages derived from primary sources, can provide a good approximation, but it cannot fully support sustainable logistics goals. Why? It does not take into account the details of each situation it is describing.
Default Data
The Smart Freight Centre released the globally recognized GLEC framework to standardize the use of default data. Companies get convenience with this method, but there are also drawbacks. Default data is inherently less precise than its primary counterpart, as it is composed of averages of average values. It’s possible that these averages might happen to align well with specific scenarios, but it also opens the door to misrepresentations, either understating or overstating actual emissions.
The goal is to ensure consistency in measuring emissions and informing sustainability efforts. Standardization theoretically leads to better comparative analyses, but unfortunately, default data can reduce accuracy to the point where the data is not completely helpful. What companies need is to base their carbon emission measurements on granular data like actual distance, actual speed, and vessel-level details. This enables accurate baseline measurements and accurate representations of the emissions of different options, ensuring that analytics outcomes are both relevant and actionable. Essentially, the more detailed the data, the clearer the insights derived from analytics.
Modeled Data
While a granular level of primary data would be ideal in every scenario, the fact is, it is not always available. This is where Searoutes offers modeled data as a more accurate and precise alternative to default data. This method leverages primary data and supplementary data like AIS and specific services and equipment types and aligns the data model closely with the details of individual shipments.
How Does Searoutes’ Modeled Methodology and Analytics Enable Sustainable Logistics?
Searoutes’ modeled data ensures that the context of each scenario is accurately represented for precise conclusions once analytics is applied. This can help shippers and vessel owners in the following ways.
1 Helping Shippers Optimize Freight Procurement Decisions for Carbon Footprint Reduction
Data analytics provides shippers with detailed insights into the carbon emissions of various freight options. By analyzing factors like mode of transportation, route efficiency, fleet efficiency, and carriers’ sustainability practices, shippers can make informed decisions that prioritize environmentally friendly choices and actively reduce their logistics (Scope 3) carbon footprints.
2 Helping Vessel Owners Increase Fleet Efficiency with Data-Driven Insights
Vessel owners can use Searoutes’ data analytics to monitor and optimize fleet performance. Insights derived from their fuel consumption within the context of their routing needs, factoring in traffic separation schemes, SECA/ECA zones, piracy zones, canals, and port entries, can lead to increased fuel efficiency, reduced emissions, as well as reduced costs.
3 Helping Companies of All Types Access Real-Time Monitoring and Adaptation for Sustainability
With Searoutes’ modeled data and analytics, companies can employ monitoring tools that track their sustainability metrics accurately and continuously. They can detect weaknesses and make changes fast, for a level of responsiveness that ensures consistent alignment with sustainability goals and helps in identifying areas for further improvement.
Searoutes is Paving the Way in Sustainable Logistics
Searoutes exists to make sustainable decisions easier, through access to the highest quality sustainability data and analytics, delivered through easy-to-implement APIs and dashboards. With our proprietary modeled methodology, shippers, vessel owners, and BCOs can reach their goals for sustainable logistics, one data-driven decision at a time.