In traditional warehouses, mechanical issues, fluctuating weather, pick-up delays, and traffic can make accurate scheduling and staff planning a complex guessing game, causing long turn times and increased costs for facilities due to overstaffing and fines.
Covid-19 has exposed the inefficiency of supply chains and the importance of building their resilience for the future. Now, the demand driven by e-commerce retailers means that traditional warehouses are being replaced by next-generation warehouses that can deliver predictive, cost-effective, and innovative data-driven services.
Tracking transportation metrics and data patterns has not been exploited to its full potential, for example, to better predict incoming truck arrivals. But truck turnaround times can be improved and supply chains streamlined by predicting carrier appointments using Artificial Intelligence (AI) and machine learning (ML).
Shippers, logistics, and supply chain leaders looking at data-driven applications to help modernize and automate warehouse operations while improving customer satisfaction should consider these three powerful data science applications.
Demand and Supply Optimization
To effectively plan the staffing capacity on any given day, warehouses must forecast the demand of pickups to assign enough warehouse pickers to carry out the operational activities.
As staffing capacity is based on the number of hours per picker and the number of pickers, supervisors traditionally tweak these levers manually based on prior experience. Often, pickups are impacted due to fluctuations in warehouse demand and variability in staffing capacity. This disrupts customer operations
A data-driven solution forecasts the demand ahead of time, plans staffing capacity with a high degree of accuracy, and optimally tweaks the levers to achieve operational feasibility.
From our experience, a low-code platform as a service (PaaS) allows warehouse supervisors to get daily alerts about the forecasted demand and staffing capacity. Supervisors can use the tool to adjust the number of pickers and the number of hours available to optimize staffing capacity, removing operational bottlenecks and improving customer satisfaction.
Intelligent Appointment Schedulers
Logistics companies must identify and prioritze the data science use cases to pursue by screening them based on likely business benefits and implementation feasibility.
United States Cold Storage (USCS), for example, chose to prioritize improving the prediction of truck turn times and built a machine-learning-based intelligent appointment scheduler.
With ML capabilities, it evaluates and analyzes order complexity, warehouse load, and the expected delay at the carrier’s end to make smart recommendations and automatically schedule appointments.
Built with the power of low-code, PaaS data platforms can help rapidly build such customized data science applications using pre-built components and microservices. Some platforms have zero learning curves because of easy drag-and-drop visual features that allow non-developers to build and create enterprise-grade data applications to quickly deliver business impact.
By 2030, the global low-code market is forecasted to generate abou $187 billion in revenue.
Automated Task Planning and Worker Allocation
Planning the picking tasks to ensure the job is completed before the truck arrives at the warehouse for pickup is typically carried out manually. Companies often see delays in task completion due to planning inefficiencies which lead to longer wait times for trucks.
Data science solutions can automatically plan complete sequences of tasks and allocate the best-suited worker for every task based on factors such as workers’ past performance, and suitability for a task. Human supervisors can review, accept, or edit the allocation.
A task allocation solution also dynamically reassigns tasks throughout the day as it gains additional intelligence about employee location, availability of other employees, and updated appointment schedules, among other factors.
Black box warehouses that need minimal human intervention are the future, and it is time to be ahead of the curve with the latest data-driven solutions. As Arthur C. Clarke once said: “Any sufficiently advanced technology is indistinguishable from magic.” Data science will redefine and reinvent the logistics industry.
