Turning Cold Chain Data Into Smart Decisions
Supply chain companies have the data they need to optimize performance, but they lack the ability to turn that data into actionable insights. Here’s how they can put that data to use.
Companies in the supply chain need to be able to make informed decisions on the fly. They might have to change or optimize routes to avoid delays, for instance. Or they may have to adjust inventory management to account for shifting demands. A company that can make those real-time decisions can ensure that products—particularly perishable goods such as food and pharmaceuticals—arrive on time and in good condition, giving that company a competitive advantage.
That ability depends on data, which can be a more confounding problem than it might appear at first glance.
The problem isn’t a lack of data. Supply chain companies have massive amounts, generated by sensors, data loggers, and other tools that detail everything from location and temperature to shock impacts incurred during transport.
Warehouse and transportation management systems aggregate data from across the enterprise’s supply chain. The problem, however, is knowing what to do with the data—many companies lack the ability to operationalize data visibility to produce actionable insights. They have the ingredients but don’t have the recipe.
Data Overload in the Cold Chain
Supply chain companies foster data-rich environments. Companies have invested in active data loggers and sensors that collect data on goods in their supply chains and cold chains.
Active data loggers, connected via GPS or cellular signals, can transmit real-time data on factors ranging from the location of a shipment to temperature and humidity variations that could affect the quality of perishable goods, or the light exposure, vibrations, and shocks that can affect certain products.
Sensor devices that collect but don’t transmit data in real time gather precise data on similar factors, such as temperature, humidity, and shock that can be used to identify patterns to establish the most efficient shipping routes, performance baselines, and areas that need to be improved.
But putting that data together to deliver actionable insights is where companies fall short. They may have the data but lack the visibility that would tell them what actions need to be taken immediately. And failure to act on that data puts products at risk, reducing operational efficiencies, and resulting in unhappy customers. In the process, it also reduces the ROI on their investment into data gathering and visibility.
Optimizing the Value of Your Cold Chain Data
Supply chain companies can make their active and passive sensor data work for them by understanding how to use that data and implementing a system that allows them to contextualize insights that will drive intelligent decision-making.
Start by considering the volume of data. Trying to manually sift through all that information in search of patterns, trends, and insights is neither practical nor scalable. The process must be automated—and it must be self-learning.
A platform enabled by artificial intelligence (AI) and machine learning (ML) can deliver real-time insights to help guide decision-making, automate sensor management, and predict on-time in-full (OTIF) performance and where adverse conditions or disruptions in the supply chain could occur.
Understanding the different uses of real-time and sensor data also is important. Real-time data sent by active data loggers is essential to tracking shipments and being able to react to events that could affect the quality of the products. But these tools are expensive. Companies may not want to deploy them throughout the enterprise, but only where they are most needed.
Sensor data collected passively, meanwhile, is a cost-effective way of gathering valuable insights by, for example, recording environmental data to help determine optimal future routes. The data also can be used to establish baselines for performance.
When data-driven insights fuel decision making, companies can reduce supply chain risk, increase efficiency, and make their customers happy. Supply chain companies already have the data. They just need a platform that lets them make the best use of it.