Rising consumer expectations for order fulfillment further complicates the equation, creating a situation where success seems close but still out of reach. To flip the script, warehouse operators must embrace tech-driven solutions like warehouse management systems (WMS) to help automate repetitive and time consuming tasks.
This article explores in detail how a WMS uses data analytics, AI, and workflow automation to drive efficiency, demonstrating the transformative power of these technologies in optimizing warehouse labor productivity.
- Understanding warehouse management systems (WMS)
- Combating labor shortages with fulfillment automation
- Optimizing warehouse efficiency with labor standards
- Leveraging AI and predictive analytics for labor planning
- How WMS use data analytics and AI
- Strategies for successful implementation of WMS automation processes
- Trends in warehouse automation
- Optimize your warehouse labor with Logiwa WMS automation
- FAQs on enhancing warehouse labor productivity
Understanding warehouse management systems (WMS)
A warehouse management system (WMS) is a software solution designed to optimize warehouse operations. It manages inventory, processes orders efficiently, tracks shipments, and automates various tasks.
Like warehouse operations themselves, WMS have undergone a significant evolution. Traditionally these systems were paper-based and human-driven. Inventory tracking meant endless ledgers, manual picking, counting, and a high margin for human error.
The introduction of computer technology in the 1970s marked the first significant turning point. Suddenly, these systems could automate basic tasks like inventory tracking. Their integration with enterprise resource planning (ERP) systems in the 1990s allowed warehouses to connect their management systems with other critical business functions like finance and customer relationship management.
However, when ecommerce exploded in the early 2000s, the complexity of order fulfillment increased tenfold, demanding greater warehouse visibility and control. Thankfully, the ecommerce boom occurred just as cloud technology became a thing. Adding cloud technology into WMS allowed fulfillment centers real-time visibility into operations, unlocking significant operational flexibility.
Fast forward to recent years, where rapidly advancing technology has ushered in a dramatic evolution of WMS. Advanced WMS platforms like Logiwa IO now integrate with cutting-edge technologies like AI, machine learning, and robotics. By incorporating such technologies, a fulfillment management system (FMS) can transform warehouses from static storage facilities into flexible and intelligent operational hubs.
Combating labor shortages with fulfillment automation
Labor shortages in warehouses have been incredibly rampant in recent years. These shortages are driven by an aging workforce, shifting employment preferences, and the surge in ecommerce activities. Unfortunately, they contribute to delayed order processing and increased error rates, leading to customer dissatisfaction, costly returns, and lost revenue.
On the bright side, warehouse operators can do something about this type of situation. Warehouse labor automation is a strategic solution. By deploying next-gen fulfillment management systems (FMS) like Logiwa, warehouse operators can ease the stress on manual labor.
Logiwa leverages AI and robotics, allowing warehouses to automate and delegate repetitive tasks like picking and sorting. This warehouse automation benefits labor and frees up human workers to focus on complex, strategic activities that require advanced decision-making skills.
Optimizing warehouse efficiency with labor standards
Labor standards in warehousing refer to the time a trained employee takes under normal operating conditions to complete a specific task. When well-defined, employees clearly understand their roles, responsibilities, and performance expectations. This prevents confusion, optimizing warehouse efficiency with labor standards. With all staff on the same page, warehouses achieve consistent workflows and a positive, supportive workplace culture as everyone cooperates.
These standards also serve as a benchmark for productivity. They enable managers to understand precisely how long specific tasks should take and identify areas for improvement. Logiwa IO uses data analytics to take this to another level. It enables team leaders to estimate each employee’s capabilities and strengths based on past performance and develop targeted training strategies.
This platform also uses AI-driven predictive analytics to compare and predict productivity across workers and teams. This empowers managers to create transparent, measurable performance expectations for each team and effective workforce development plans, thus cultivating a well-organized and motivating work environment.
Leveraging AI and predictive analytics for labor planning
AI in warehouse operations transforms labor planning from a guesswork-driven process to an exact, data-powered strategy. For example, thanks to AI-driven predictive analytics, Logiwa allows warehouse managers to forecast their labor needs accurately and optimize employee shifts accurately.
They can review incoming orders, seasonal data, and existing labor standards. They can then use this data to estimate and schedule employees for different tasks based on their best capabilities. This ensures warehouses are adequately staffed, preventing delays and errors. It also fuels productivity as each staff member is assigned tasks they can efficiently complete. By creating clear labor standards and performance metrics managers can plan labor fairly and purposefully.
How WMS use data analytics and AI
Seamless communication between WMS and data analytics platforms allows real-time inventory tracking, order-fulfillment rates, and other crucial operational metrics. Meanwhile, AI integration enables WMS to transform raw data into strategic insights. With both approaches combined, they greatly enhance fulfillment performance.
For instance, by combining AI and data analytics, WMS can transform raw data into actionable recommendations for workflow automations and resource allocation. This improves operational efficiency as warehouse managers identify and resolve bottlenecks like stockouts or delays in real-time. WMS integration with AI and data analytics allows warehouses and fulfillment centers to adapt quickly to evolving market demands, achieving scalability and flexibility.
Strategies for successful implementation of WMS automation processes
While AI-powered WMS like Logiwa can completely transform warehouse operations for the better, how impactful they become depends on their implementation. To fully maximize the power of these advanced fulfillment systems, warehouse managers should:
Thoroughly assess current warehouse operations
A comprehensive assessment of warehouse operations before implementing advanced WMS like Logiwa enables warehouse operators to identify areas where automation will have the most impact. This assessment should include:
- An analysis of current technology, such as determining whether legacy systems are inefficient, how well different systems integrate, and if the current data is accurate and reliable.
- A warehouse layout and design evaluation addressing questions like: Is the warehouse space optimized? Are current storage methods effective?
- An assessment of existing KPIs such as order accuracy, fulfillment, inventory turnover, and labor productivity.
Develop a comprehensive implementation plan
A well-structured implementation plan ensures a smooth transition from the old system to the new WMS, minimizing disruptions to daily operations. It also facilitates effective resource allocation and timely execution. That said, this plan should at least include:
- Employee training
- Pilot testing
- Change management processes
- Contingency plan
- Post-implementation support
- Labor standards for evaluating automation effectiveness
Trends in warehouse automation
The future of workflow automation for warehouses is very promising. Here are some trends currently defining and set to continue shaping automation:
- Artificial intelligence and machine learning: Advancements in artificial intelligence and machine learning are taking the power of predictive analytics to a whole new level. By analyzing vast amounts of data, these technologies enable predictive analytics that optimize warehouse management, from demand forecasting, automated guided vehicles, and cobots to preventive maintenance.
- Sophisticated collaborative robots (cobots): Cobots are becoming more agile and capable of handling delicate items and performing complex tasks. They also have advanced safety systems such as force and torque sensing, ensuring they can work safely alongside humans.
- Sustainable and energy-efficient automation solutions: Sustainability is becoming increasingly important, and warehouse automation is evolving to align with this need. For example, some manufacturers now use eco-friendly materials to construct robots and other automation equipment. They’re also designing these solutions to consume less energy, thus reducing operational costs and carbon footprint.
Optimize your warehouse labor with Logiwa WMS automation
Warehouse management systems have come a long way and continue to evolve. These systems now integrate AI and machine learning, greatly enhancing the potential of predictive analytics to a whole new level. They use past and present data to generate recommendations that allow warehouse operators to scale DTC fulfillment operations with labor management and optimize productivity.
Schedule a call with one of Logiwa’s fulfillment experts to see how our advanced WMS can optimize your labor productivity. We’ll show you how Logiwa IO can become your competitive advantage.
