In warehouse operations, managers are on a never-ending quest to drive greater productivity and throughput in the most efficient manner possible. Especially in lower-margin e-commerce categories such as fast-fashion apparel, electronics and books or media, the need to continually achieve process optimization is paramount. Increasing labor costs and low unemployment, fluctuating demand, rising customer expectations and supply chain disruptions have created challenging conditions that tax the limits of traditional warehouse management system (WMS) technology.
A lack of real-time data analytics constrains adaptability in order processing and tasking as conditions change, often throughout the day. Companies need technology that not only improves order fulfillment metrics, but also helps them retain workers by making it easier for those workers to hit productivity targets, be accurate while doing so, and stay engaged with the task at hand. The WMS “capability gap” has created a growing need to evolve traditional warehouse operations into self-optimizing, intelligent systems.
Using artificial intelligence and machine learning, innovative warehouse optimization augments the performance of a legacy WMS, putting it on steroids. Companies can boost fulfillment efficiency through things like voice-directed processes, improved labor utilization, smarter tasking and mobile work execution. Warehouse optimization technology also allows managers to create a dynamic fulfillment environmen.
