Data-driven strategies can optimize your supply chain, with benefits such as gaining immediate insight into your shipment status.
By Rob Stevens
In the world of manufacturing, data-driven optimization tools have been commonplace for decades. But when it comes to supply chain, this hasn’t always been the case. A lack of data has made it difficult to apply manufacturing-style optimization to the supply chain. Fortunately, new IoT tools are giving supply chain managers access to new sources of data, enabling companies to apply the same data-driven methods to their supply chains that they have used for decades in their manufacturing processes.
Specifically, with real-time IoT-powered tracking across a global supply chain, managers can monitor key metrics, including damage rates and delays, for each step along a shipment’s journey. With compact cellular-connected trackers attached to each shipment sending real-time location and condition data to a cloud-based software platform, supply chain managers can gain immediate insight into the status of their shipments. This newfound access to data makes it possible to adapt analytical tools from manufacturing to bring a new level of optimization to the supply chain, from end to end.
Made famous by the Japanese automotive giant Toyota, lean is a comprehensive framework for systematically reducing waste in a process. Though its most widespread application is in manufacturing, IoT tools are making it possible to apply similar strategies for optimization and waste reduction in the supply chain.
Specifically, insight into exactly where shipments are at any given time enables predictive analytics and improved, lean inventory management. If a certain route always experiences damages or late arrivals, managers can plan around that uncertainty and move safety stock to different locations based on where variability is highest. In addition, greater visibility into supply chain data makes it possible to reduce uncertainty throughout the system, eliminating excess and ensuring resources such as labor hours, facility space and transportation are all set up in an optimal configuration.
Six Sigma Framework
In some ways a counterpoint to Lean, Six Sigma is a set of tools focused on improving quality and reducing risk within any given process. With IoT tools, managers gain access to real-time data for all shipments, making it possible to develop success metrics and ensure quality levels are maintained along every step of the supply chain. Six Sigma can be applied not just to improving the literal quality of the end product through damage rate reduction, but also to risk reduction for other key variables such as early or late arrivals.
With a Six Sigma mentality, managers can use IoT data to optimize every aspect of a shipping process, and minimize delays, damages or other issues to within tolerable thresholds.
Statistical Process Control
One of the most commonly used optimization tools is SPC, or Statistical Process Control, a method for measuring and controlling variation within any given process. In general, SPC requires the use of a Control Chart: a graph that documents data for a particular variable over time and compares current data to the average, upper limit and lower limit for historical data.
Traditionally, SPC has been used to optimize manufacturing processes, such as drilling a hole to a particular diameter, or cutting a part to a particular length. But with access to supply chain data, it becomes possible to use SPC methods to track supply chain variables such as damage rates, late or early arrivals, and more. For example, managers can track real-time damages or delays and use Control Charts to distinguish normal variation from potential issues as soon as they occur.
Another classic manufacturing optimization methodology is DMAIC, which stands for Define, Measure, Analyze, Improve and Control. With IoT data, managers can not only define challenges and success conditions quantitatively, they also can measure their current operations, analyze those data sets and implement procedures to improve problem areas and control for variation going forward.
For example, a supply chain manager may notice that shipments often arrive early or late, disrupting downstream operations and causing high levels of waste and excess costs. With an IoT-powered DMAIC approach, the manager can define the quantitative costs of the delays, pinpoint exactly when and where issues are originating, and work with carriers to develop new, optimized processes that use real data instead of guesswork.
With these data-driven strategies, managers can bring the optimization of manufacturing to the world of supply chain. From tools such as SPC and DMAIC to the broad lean and Six Sigma frameworks, manufacturing has a lot to offer to supply chain. When IoT tools and technologies complement these quantitative methodologies, a new level of supply chain analysis and optimization becomes possible. With a data-driven supply chain, firms can reduce damage and delay rates, reduce waste, improve customer experience and increase company revenues. It’s an exciting time to be in supply chain.
Rob Stevens is co-founder and chief revenue officer at Tive, provider of sensor-driven tracking solutions to deliver full visibility into products as they move through the supply chain.