Today, nine out of ten companies are stuck in their ability to power results at the intersection of operating margin and inventory turns. With the coalescence of new technologies, new processes are evolving, offering promise to drive unprecedented improvements. The biggest barrier is company’s abilities to digest new ways of working. To take advantage of these new approaches, there is a need to learn and challenge the status quo, test-and-learn on the possibilities, and partner with new solution providers to define new capabilities. Here we share research on the future and four case studies of leaders that accomplished this mission.
As business leaders move forward towards Supply Chain 2030, the focus is on Autonomous vehicles, Blockchain, machine learning, the Internet of Things (IoT), the collaborative economy and robotics. While investments today are focused on driving improvements through data visualization, improved visibility, big data analytics, mobility and demand sensing, the shift is to a more automated supply chain fueled by insights from real-time data and cognitive computing. This transformation will change the fundamentals of supply chain processes as we know them today. As a result, we must learn from the past, to unlearn, to relearn. The challenge for all is unlearning.
For example, if we apply these concepts in the chart to the area of supply chain planning. How will it change by 2030? It will not be as simple as transforming today’s supply chain planning systems with cognitive computing. This will not be an evolution. Instead, it will be a step change. The future will be defined by systems that learn as we sleep and resolve supply chain issues by the time that we get coffee in the morning. Instead of a list of exceptions to resolve, the system will answer many questions and provide a prioritized listing for the planner on what to resolve.
Digital manufacturing will combine wearables, robotics, and the Internet of Things to reduce labor and make manufacturing teams more productive. Maintenance will be based on sensing and streaming data of potential equipment failure, as opposed to maintenance based on mean-time failure, production planning will be based on actual line speeds, and production schedules based on data-driven planning. Additive manufacturing will redefine service parts and the sourcing of many of them.
Logistics will be more automated through the combination of drones, autonomous vehicles, GPS, telematics, and blockchain. Visibility will be redefined through streaming data and prescriptive analytics.