Latest News

SCT Blog

Timely insights and updates from SCT Software.

Powering Dynamic Cartonization with Data-Driven Machine Learning

For high volume packing and shipping operations, even a small reduction in cost per order can deliver significant savings directly to the bottom line. The continued adoption of new, forward-leaning technologies is key to continually raising the bar for cost effectiveness and efficiency in the shipping environment.

One such technology is machine learning, a strategic application of artificial intelligence that has started to change the face of operational processes across sectors. According to a recent study, 64% of C-level executives will make at least some investment in machine learning this year. Applied to shipping, machine learning relies on the constant evolution of sophisticated algorithms that take into account vast amounts of data about items, cartons, dunnage, shipping modes, and business realities. These algorithms allow solutions to make optimized decisions autonomously, while continuing to learn and improve over time.

Unpacking Machine Learning
Machine learning is rooted in the theory that computers can learn without being programmed. In order to “learn,” advanced machine learning systems rely on pattern recognition and the resulting data. Machine learning essentially builds data-rich algorithms, which evolve continuously based on large volumes of information to which they are exposed. Machine learning algorithms themselves are not a new development, but their ability to apply complex mathematical calculations to immense data sets, then make that data actionable in various environments, is a recent improvement that has resulted in more widespread adoption.

Developments in machine learning, growing volumes of available data, more efficient computational processing, and affordable data storage have contributed to a resurgence of innovation in applying machine learning in a variety of industries. Modern machine learning algorithms make it possible to quickly and automatically construct models that can analyze bigger, more complex data and deliver faster, more accurate results in a scalable way. Organizations across all industries can benefit from utilizing precise models to help optimize and accelerate all kinds of functional tasks.

Machine Learning Meets Shipping Cartonization
Machine learning is a powerful breakthrough when applied to intelligent cartonization, which empowers shippers to consider more variables, make more effective configurations, streamline processes, and maximize savings on each and every item they ship. The potential savings are even greater in the wake of dimensional weight shipping (DIM), which added carton dimension to the mix alongside weight when calculating shipping costs. It’s estimated that DIM pricing increases shipping costs nearly 17%.

In order to fully appreciate the next-gen advantages of machine learning for dynamic cartonization logic, first consider a traditional, non-technical approach to picking and packing. In this model, shippers must pack items by trial and error until they reach a configuration that is acceptable—not necessarily one that is optimized for the lowest possible DIM pricing. Not only does this process require multiple experimental carton and dunnage combinations for each package, but it also relies upon tribal worker knowledge and configuration guesswork.

Comparatively, intelligent cartonization powered by machine learning is able to optimize carton configurations the first time. In this smart alternative, an automated algorithm rapidly accounts for the unique variables and business rules associated with each item, then immediately calculates the carton and dunnage needed for the most efficient packing configuration possible.

It Keeps Getting Better
Dynamic cartonization logic doesn’t just “shut off” after a configuration has been calculated. Because it harnesses the power of machine learning, its algorithm is always doing just that—learning to improve calculations over time via historical data and observations about a shipper’s unique preferences and practices. This results in faster recognition of frequent configurations, enhanced carton and dunnage recommendations, and even the incorporation of new carton recommendations. All of these improvements aggregate over time, saving your business more time and money the longer the machine learning solution is in use.

Bring Machine Learning to Your Packing Processes with Carton Space Optimizer (CSO)
As a premiere next gen packing optimization tool, CSO harnesses machine learning to dynamically configure items for both cartons and pallets. A comprehensive, intelligent solution leveraging real-time dimension and freight calculation, CSO’s logic eliminates guesswork with one click by recommending the most cost-effective configuration, every time.

Leveraging data about each item, carton, dunnage, shipping parameters, and individual business requirements, the algorithm autonomously identifies best, most cost effective fill configuration, allowing the items to be more efficiently packed. This data-driven approach to packing not only reduces shipping costs today by achieving the optimal fill rate, but it also it ensures your packing practices will become more efficient as time goes on, empowering you to deliver in today’s customer-centric shipping environment.

Download our informative infographic today to learn more about how CSO from SCT Software applies data and machine learning to the cartonization process to pack smarter.