With an increasing need for efficiency and sustainability in the industry, it is becoming more evident how crucial the role of machine learning in packaging companies will become.
In the dynamic landscape of modern order fulfillment and ecommerce, packaging plays a pivotal role in ensuring the smooth flow of goods from manufacturing to consumer hands. Conventional packaging approaches are frequently linked to inefficiencies, surplus waste, and heightened environmental impact. Recognizing the pressing need for transformative solutions, forward-thinking packaging companies are now turning to artificial intelligence (AI), with a particular emphasis on machine learning to improve business outcomes.
Machine learning is well-suited for process and flow optimization, predictive maintenance, quality control and demand forecasting tasks in the packaging industry. These applications leverage patterns and insights from historical and real-time data, allowing packaging companies to enhance efficiency, reduce waste, provide real-time decision-making and deliver personalized experiences to consumers. This article delves into the significance of machine learning in packaging and its potential to reshape the industry.
Traditional packaging methods rely on standardized box sizes, which can result in excess wasted space, materials, increased shipping and logistics costs, increased risk of product damage and a greater environmental impact. However, right-sized advanced cubing algorithms can analyze vast amounts of data in real-time to tailor packaging solutions for each product.
Adapting packaging sizes based on product dimensions, weight, fragility and shipping destination allows packaging companies to optimize their processes for both the contents and the consumer. This reduces waste and streamlines operations, leading to faster and more cost-effective packaging.
Right-sized packaging also empowers transportation cost savings. For example: converting a shipment of packages from wasteful, air-filled, and packing peanut-filled boxes to right-sized packaging has been shown to save companies as much as 25% in freight costs. Imagine a truck filled with packages that have excess air space in them vs a truck filled with right-sized packages. 25% more packages in that truck means 25% savings on fuel costs and vehicle maintenance and can allow a smaller sized fleet to ship the same number of packages.
Customary packaging often involves using excess materials, such as bubble wrap, foam peanuts, and oversized boxes, which contribute to environmental degradation. However, right-sized packaging algorithms minimize material consumption by creating boxes that perfectly accommodate the size of the product, eliminating the need for unnecessary fillers and inefficient transportation, which contributes to a significantly reduced freight and shipping related carbon footprint. Machine learning in packaging addresses this issue by optimizing box sizes, reducing material usage, and optimizing loading and transportation efficiency. Also, packaging tailored to a specific products or consumer’s needs reflects attention to detail, care and quality standards, strengthening the overall brand image.
Right-sized machine learning solutions create boxes precisely when needed, minimizing the storage space required for pre-made packaging materials. Packaging companies can significantly lower their environmental impact by reducing wasted space and materials. This waste reduction aligns with many businesses’ sustainability goals and meets consumers’ increasing demand for environmentally friendly practices. Machine learning, therefore, becomes a powerful tool in helping packaging companies embrace sustainable practices and contribute to a greener supply chain.
The fast-paced nature of today’s markets demands agility and adaptability. With their fixed configurations and slow decision-making processes, traditional packaging methods struggle to keep up with the dynamic demands of e-commerce and retail, where consumer preferences and consumption trends change rapidly. Machine learning in packaging addresses this challenge by providing real-time decision-making capabilities.
Analyzing data and making packaging decisions on the fly enables companies to meet tight deadlines, especially in industries where fast shipping is crucial. Real-time decision-making also allows companies to respond instantly, create a package that is precisely made to the exact product dimensions (even in multi-order applications), and respond to emerging market demands, supply chain disruptions or new shipping requirements. These ensure a crucial competitive edge in addressing consumer demands.
Examples of real-time decision-making’s value could include a data-informed, automated decision determining whether placing the entirety of a multi-item order in one box vs. splitting the order into multiple boxes is the most efficient option, for variables including weight, damage reduction or shipping costs. From a productivity standpoint, when faced with the dilemma of more incoming orders than are possible to fulfill in one shift, prioritization may be determined in the context of limited staffing or resources.
For any technological innovation to make a significant impact, it must be accessible and easy to integrate into existing workflows. Machine learning solutions in packaging must be designed to be user-friendly and adaptable with existing warehouse and logistics automation solutions that allow packaging companies to seamlessly adopt these technologies without disrupting their current operations.
The integration of machine learning into packaging processes can be tailored to meet the specific needs of each company or even each product within a company. Regardless of the size of the enterprise, this adaptability ensures that all businesses harness the power of machine learning to enhance their packaging operations
