In an evolving era of digitalization, the warehousing industry has undergone a foundational shift toward advanced technology adoption. Over the past decade we’ve seen traditional and manually operated warehouses and supply chains revolutionized by innovative technologies. Artificial Intelligence (AI) and machine learning have enabled warehouses to streamline workflows and boost efficiency. Advanced forecasting models have made it easier to weather sharp demand swings and market volatility while robotic automation has given warehouse operators more on-floor support than ever before. And don’t forget real-time location services (RTLS) solutions, which have provided unprecedented visibility into supply chains of all sizes.
Each of these digital tools generates an extensive amount of analytical data. Not just any data — data that can be leveraged for sustained growth and pathways to profitability. Although without the combined use of predictive and prescriptive analytics, warehouse data isn’t serving its full purpose. It’s time to consider predictive and prescriptive analytics as invaluable assets that can empower warehouses to bridge interoperability gaps, optimize inventory and maximize staff-wide productivity (robots included) — all by acting on their data.
Predictive vs. Prescriptive in Warehouse Settings
The roles and basic functions of predictive and prescriptive analytics are different; however, both solutions are equally important to any organization’s data analytics strategy. Predictive analytics integrates advanced forecasts and statistical models to measure the probability of future outcomes and obstacles, thus enabling an “outside-in” view of supply-chain management and planning. Through automated demand sensing, a predictive analytics solution flags potential supply chain disruptions and determines their ripple effect on inventory levels and model stock to prevent bottlenecks and out of stocks, improving the level of service to the customers. It also calculates how labor structures, fulfillment costs and operational budgets will impact future profit margins — indicating when and where spending and/or risks are either too high or too low. However, it still falls on the warehouse and other supply chain operators to analyze the probability data and take corrective action based on their own points of view and calculations — sometimes through sophisticated sales and operations planning processes, always reliant on practice and knowledge.
The beauty of prescriptive analytics is that it automates that analysis process for these workers, leveraging AI to build upon basic reporting and produce simplified directives that employees can follow to optimize outcomes. By reducing the potential for human error, prescriptive analytics helps ensure warehouses make effective changes with a proactive, data-driven approach to inventory management, financial planning and daily workflows.
The Crucial Need for Both
A warehouse environment is a system of constant moving parts. When goods arrive from suppliers, employees (or machines) are tasked with transporting them to designated storage areas (“slots”), organizing and preparing them for shipping, loading them onto delivery vehicles and then reporting items that left the facility. Cross dock-type warehouses are supposed to end the day with zero inventories, which reflects their function of connecting incoming trucks, breaking down their loads and shipping them on an outbound truck within the day. The fast-paced nature of warehouse workflows makes it exceedingly difficult to maintain workplace safety and operational efficiency, especially amid periods of high demand.
A prescriptive analytics solution helps warehouses alleviate those challenges with smart, impactful data analysis in real time, optimizing the outcome KPIs. With mobile computer and tablet compatibility, it grants employees immediate access to actionable insights from anywhere inside the facility. The corrective instructions streamline their decision=making process, reducing the risk of human error by shifting their focus to completing high level tasks and following safety compliance. In turn, warehouses become proactive instead of reactive, preventing workflow disruptions and workplace accidents before they happen. Even at warehouses where most work is done by cobots, autonomous fork trucks and/or other robots, you should always expect the unexpected and have the right tools in place to optimize outcomes when both positive and negative events occur.
The Path Ahead
Welcome to the future of warehouse digitalization, where advanced analytics solutions are serving as the ultimate competitive edge. As the rate of digital transformation in warehousing continues to rise, organizations will need the ability to capitalize on hidden opportunities that lie within their data. From effective planning and inventory management to operational efficiency and supply chain flexibility, the benefits of predictive and prescriptive analytics give warehouses the power to turn basic data reporting into valuable insights.