How Data Analytics Is Enabling Successful Supply Chain Sustainability Initiatives

Analyst Insight: The evolving use of data analytics is helping companies achieve supply chain sustainability objectives in the areas of forecasting, transportation and packaging.

Sustainability continues to be a key focus for consumers and businesses across industries, and supply chains play a vital role in achieving sustainability goals. Incorporating sustainable practices into your supply chain not only benefits the environment but can also lead to long-term economic gains, improved brand reputation, and compliance with regulatory requirements. 

The technologies, tools and resources designed to help companies achieve sustainability success across the supply chain have continued to evolve. A prime example of this is the evolving role data analytics is playing in helping companies achieve supply chain sustainability objectives.

Advanced data analytics have already established a foothold in supply chain management, thanks largely to third-party logistics providers that have integrated systems across the supply chain while also employing powerful business intelligence and visualization tools. 

The increased connectivity brought by the digitalization of the supply chain continues to create new opportunities for companies to collect, analyze and utilize data for sustainability initiatives.  

Forecasting

Supply chain forecasting plays a pivotal role in enhancing sustainability by improving efficiency, reducing waste, and ensuring resource optimization. When companies predict demand and supply with accuracy, they can make informed decisions that benefit both their bottom line and the environment.

The greater use of data analytics is instrumental in helping companies proactively predict and prepare for peak demand. This leads to improved inventory management to meet customer demand, improving efficient resource utilization and reducing the need for stockpiling. The company, its customers, and the environment win with optimized inventory management. 

For example, there are machine learning models that leverage past and current peak seasons’ data to enable companies to make decisions more effectively on inventory inside the warehouse. The models can incorporate a variety of internal and external factors, such as inventory levels, product demands, the rate of incoming inventory, buying patterns, economic forecasts and consumer trends, so companies can better position inventory to meet customer demands as they change or intensify.

Transportation

As transportation can often be a significant contributor to greenhouse gas emissions and other environmental impacts, transforming how goods are moved globally is essential. Data analytics is helping companies achieve more sustainable transportation within their supply chain by enabling them to optimize transportation routes and consolidate shipments to ensure more efficient full truckloads. Less frequent, more efficient shipments reduce the environmental impact of transportation, making the entire supply chain more sustainable.

Another way data analytics is being utilized to bring greater sustainability to transportation is through the use of the Environmental Protection Agency’s SmartWay program. The program provides a comprehensive system for tracking, documenting and sharing information about fuel use and freight emissions across supply chains. By ranking carriers on their emissions and environmental impact, the program helps companies identify and select more efficient freight carriers, transport modes, equipment and operational strategies to improve supply chain sustainability and lower costs from goods movement. 

Some 3PLs have even developed and use proprietary algorithms using SmartWay data to drive more freight to better-performing carriers. They run predictive scenarios to show how much it would cost — and how much CO2 is saved — to transition to a SmartWay-rated carrier. 

Packaging

Finally, data analytics is helping companies overcome the challenge of delivering more sustainable packaging while balancing performance, cost and other convenience requirements crucial to the customer experience. 

A prime example of this is right-sized packaging, using packaging that better fits the product, the carton and the pallet. Several carton optimization tools reduce waste and minimize empty space from packaging by allowing companies to select the appropriate box mix. They use advanced algorithms, along with item-level dimensions such as weight, size and shape, to identify a minimum number of standard-size cartons required to maximize carton and pallet utilization at a facility. Some can even select the ideal carton and filler amount for each order in near real-time, as well as determine how products should be placed within cartons.

As supply chain digitalization continues, companies will be able to collect and organize even more data that provides the business insight to identify additional opportunities to bring sustainability into the supply chain.