The retail industry is facing exceptional challenges in this post-pandemic era. Customers are more demanding, less loyal and more diverse than ever before. Add concerns such as labor shortages and the quest for greater profitability and the odds begin to look more than just demanding. To stay ahead of the curve, a lot depends on warehouse logistics and distribution.
So how can retailers survive and thrive in this new normal? To overcome these hurdles, artificial intelligence (AI), automation and robotics have emerged as game-changing solutions that are driving the retail industry forward. However, achieving full automation in the retail space is often not feasible or desirable. According to McKinsey, only 5% of jobs can be fully automated. As a result, the industry is witnessing the swift ascent of partially automated systems such as Autonomous Guided Vehicles (AGVs), Autonomous Mobile Robots (AMRs) and Collaborative Robots (Cobots).
The buzz around human-machine collaboration is growing as retailers aim to strike a balance between automation and human expertise. This is where collaborative automation, or ‘CollaboMation,’ comes to the fore, offering seamless technological integration for successful deployment.
Bringing the Bottom-Up Perspective into Focus
CollaboMation strives to connect human workers with shop floor technology, creating a smooth interface that harnesses shop floor analytics and bridges any existing gaps that may disrupt operations. Traditionally, retailers use enterprise applications like enterprise resource planning (ERP) or warehouse management systems (WMS) to gain a virtual bird’s-eye view over how and when items move through the warehouse. However, these solutions provide only a top-down perspective, missing the on-the-ground reality.
Likewise, many retailers attempt to pinpoint inefficiencies through third-party audits, with auditors manually collecting data during site visits. This often results in unreliable data, rendering it ineffective for actionable insights. To fill this void, retailers are better off using tools that offer a more accurate understanding of what’s happening in-store and in the warehouse or distribution center.
Painting a Complete Picture
Many retailers require more detailed data and the ability to break down information by individual workstations, processes or initiatives. Traditional enterprise systems often fall short in highlighting these specifics, especially regarding recurring issues. Their critical shortcoming is the lack of valuable metadata, including data collected from barcode scans, pedometer readings, timing and location.
Such data holds immense value for retail analytics, enabling retailers to assess and optimize process quality and efficiency. It can reveal patterns like excessive employee walking distances, inefficient workstations or recurring scanning issues with specific products. Armed with these insights, retailers can enhance efficiency, reduce employee workloads and streamline operations.
Enhancing Order Picking
CollaboMation can significantly impact one crucial area: order picking. This process often consumes a substantial amount of labor, time and cost, accounting for 50%-75% of total operating expenses in a typical retail store. The average travel time and distance for warehouse workers depend on factors such as warehouse size, the type of work performed and the variability in travel between employees. Different methods exist for measuring this travel, and the most suitable approach depends on the specific circumstances. However, leveraging metadata can bring much-needed clarity and accuracy to determining optimal routes and travel times.
Through CollaboMation practices that augment human capabilities, retailers can save precious time on repetitive tasks by harnessing data insights. Order picking is a prime example of a workflow involving countless iterations. It typically includes order placement, inventory picking, sorting, packing and shipping. By implementing CollaboMation, retailers can cut travel and process times, leading to significant productivity gains. Experts estimate that productivity can surge by around 30%, easing the burden on store employees.
Moreover, CollaboMation boosts efficiency while contributing to sustainability and ergonomic improvements in retail operations. With a potential 30% reduction in error rates, workflows become more streamlined, resulting in improved sustainability and ergonomic conditions. The increased throughput enables retailers to handle higher order volumes, meeting customer demands more effectively.
As we continually advance our technology, AI is poised to play an increasingly pivotal role in the future of CollaboMation. Artificial intelligence will assist retail decision-makers by recognizing patterns and identifying previously unnoticed best practices.
By analyzing vast amounts of data, AI algorithms can uncover optimization opportunities that would otherwise remain hidden. For example, Nike uses AI that the company acquired through two predictive analytics companies, Zodiac and Celent, to personalize their customer experience and better predict purchasing decisions. Just as AI delivers deeper insight into consumer habits, it could similarly utilize shop floor metadata to detect and predict purchasing trends and ultimately enhance retail efficiency.
This shift from human estimation to data-driven insights will transform how we address productivity challenges. Ultimately, CollaboMation’s ultimate goal is to boost productivity by equipping decision-makers with accurate, actionable information.
Reimagining Retail Productivity
With a significant workforce shortage and rapidly rising costs, collaborative automation, or CollaboMation, offers a potent solution for retailers looking to enhance efficiency and productivity in their stores. By bridging the gap between human workers and retail technology, CollaboMation facilitates seamless integration and offers valuable insights through detailed data analytics.
When implemented strategically, CollaboMation can transform workflows, optimize order-picking processes, and save time and money. Furthermore, by arming decision-makers with accurate, actionable information, CollaboMation simplifies how we approach productivity itself, ultimately driving success in an ever-evolving retail landscape.
Stefan Lampa is CEO of ProGlove, wearable scanner solutions for innovating human-centered productivity. Lampa has a 30-year history in robotics and manufacturing, with previously held executive management positions at ABB, KUKA Roboter and Cargotec.