End-to-end temperature and product movement monitoring.
Vehicle condition and location monitoring of assets.
Remote temperature monitoring for warehouses and cooling units.
Cold chain management
There are numerous data loggers available in the market for temperature controlled shipments of perishable products. It’s hard to keep track of them all. Many boast “real-time” data monitoring capabilities, yet their technology falls short and impedes businesses from gaining the full visibility, scalability, and agility needed to fuel their growth.
Temperature data loggers (also referred to as data loggers or temperature loggers) have been around for decades, and were one of the first sensor revolutions in the supply chain. They provided shippers with temperature audit trails of shipments in transit and at warehouses, and enabled receivers to determine whether the products they’ve received are fit for consumption.
The current status quo requires significant manual input — starting and stopping the data tracking through a data logger (or else shipment data isn’t collected), plugging in a data logger at the end of shipment and uploading data, and ad hoc shipment review — which increases the likelihood of human error occurring along multiple supply chain touchpoints and delays the review process.
The market’s focus has been on loggers and hardware and not on their potential data output and what that means. Status quo technologies aren’t empowering businesses to fully leverage the opportunities that the collection of real-time data presents, including predictive and prescriptive analytics and proactive response to prevent excursions from happening at all. This data enables businesses operating in the cold chain to gain control over their supply chain, prevent wastage of products and resources, and drive impactful decision making.
A few short years ago, businesses believed it wasn’t realistic to expect end-to-end visibility into shipment locations and statuses across an entire supply chain. Today, it’s not only possible and readily available, but there are cost-effective ways to achieve it.
The strength of any cold chain lies both in its individual links and in all of the connections between them. As manufacturers, wholesalers, distributors, and retailers increasingly require environmental and product movement visibility, truly real-time visibility is a necessity.
For instance, if you consider that more than 75 percent of all cargo in North America — more than 10.5 billion tons of freight annually — is hauled by trucks, the ability to meet freight visibility requirements from a growing number of shipping companies can mean the difference between securing their business or losing out on a significant number of freight hauling opportunities.
There’s a perception that real-time data loggers are prohibitively expensive and therefore, can only be used for a portion of the supply chain — either for routes that have historically posed challenges, such as products moving into Brazil or Russia, or only for high-value shipments.
Most data loggers available on the market are expensive because they require upfront investments and purchasing before use. Even then, they fall short in providing real-time visibility over these important shipments, including of environmental conditions (temperature, humidity, light) and product location.
Due to these cost concerns, companies resist pursuing “real-time” loggers as an option to connect all shipments across the supply chain — air, ocean, rail, or road — and as a result, visibility is limited to projects or shipments routes only. Thus, businesses aren’t able to connect data points across their global supply chain to optimize logistics and performance, maintain provenance and agility, proactively reduce waste, drive down costs, and generate ROI.
The fast-paced, constantly changing supply chain can be impacted at any moment by a variety of factors. To satisfy delivery needs, businesses and their logistics partners need to monitor the on-time delivery of products while in transit and at rest. If they don’t, the process slows down, leading to costly consequences. A late or damaged load has downstream effects that can lead to medicines and vaccines not reaching patients, or to food supply shortages that can negatively impact consumer brand loyalty.
Most data loggers are all about physically collecting data after a trip’s completion. They’ve got a storage memory that tracks temperature data captured at regular intervals, typically every 15, 30, or 60 minutes. After a shipment’s journey, data is extracted by manually plugging in a USB cable to the logger, which reads and plots temperature (and possibly humidity) data installed on the receiver’s system, calculating parameters like Mean Kinetic Temperature, (MKT) to notify the receiver as to whether the products have met the required quality and compliance requirements.
Extracting data from loggers can take between 15 to 30 minutes per logger and is a highly manual process. The process not only makes it time consuming, but it can place the products at risk while verification is being completed. If the data shows a possible excursion, the products enter into a quality review process, which may take up to 24 business hours or longer to verify whether they’re safe to use.
Without real-time data, businesses aren’t able to prevent products from being spoiled — both in transit or at rest in a warehouse. Visibility is ad hoc, or worse: it’s incomplete.
At the end of a shipment, loggers need to be discarded or returned, and, even if they are meant to be returned, without logistics processes in place, they may be discarded anyway, adding to the total waste attributable to a shipment.
Today, cold chain businesses are requiring more than 100 percent visibility. They are needing the ability to focus on late and off-schedule loads that threaten to disrupt their operations and their business and brand. Managing loads by exception using predictive analytics to drive proactive management practices leads to more informed decision making that ensures efficiency and productivity across the supply chain.
A predictive analytics capability, which utilizes data collected throughout a shipment’s duration, means that businesses and logistics partners only have to address issues with a small percentage of their loads instead of spending management time on 100% of the loads in their supply chains.
Yet, most of the analog data logger solutions on the market do not support this capability due to latency and the inability of their technology to provide up-to-the-minute information. This means that both products and employee resources are at risk of wastage.
Without having truly real-time visibility over their entire supply chain, businesses are at a significant disadvantage. In a recent Logipharma benchmark report, the majority of respondents, including 100 top decision makers in the pharmaceutical industry, cited innovating their distribution channels to remain competitive and improving end-to-end visibility as among their key challenges, and view Big Data — machine learning, artificial intelligence, and advanced analytics — as likely having a considerable impact on the supply chain in the next five years. Traditional data loggers will not get them there.
To offset the challenges that threaten to disrupt supply chains, manufacturers, wholesalers, distributors, and other partners are requiring visibility into freight location and status. The market has changed over the last couple of years. Previous track-and-trace methods are no longer viewed as providing sufficient end-to-end supply chain visibility or moving the supply chain from a cost center to a core competitive differentiator.
Instead of embracing the status quo, businesses and their logistics partners should consider where they will want to be in five or ten years. Rather than simply looking at IoT data loggers, companies should actively look to the solutions they can offer in building a smarter, cost-effective, and resilient cold chain. It’s already possible.
Building an efficient and proactive cold chain goes beyond monitoring your assets in real-time. It requires doing so in a wireless, touchless manner by leveraging the Internet of Things (IoT). It means the ability to foresee what will go wrong with the condition of your goods — like temperature, humidity, and tampering — where it will go wrong (location), and what you’ll need to do if and when something does indeed go wrong.
To be proactive with a shipment, businesses need to leverage the power of IoT and data, not only to know how their shipment or goods are doing in real-time, but also to ascertain what it is that could put them at risk of spoilage, contamination, or theft — the root cause of a potential excursion.
An IoT and cloud solution connects the physical and digital worlds together. It is the outcome of utilizing the right technologies to enable things to talk to one another and to us. You can piece together your smart cold chain by utilizing IoT and wireless temperature, humidity, and light sensors, which track the conditions of your package. Wireless, GPS, and geofencing — instead of the status quo passive RFID technology — provides the ability to automatically drive data into the cloud without manual input. If a logger’s start button isn’t pressed, data is still collected, and shipment updates automatically register.
Connectivity becomes important in relaying data promptly for decision making. When choosing a cold chain environmental and product movement monitoring solution, the most important aspect is the data analytics that it offers. It is about making sense of data, and making sense of it at an extremely granular level while still telling you how to run your cold chain broadly. It involves the analysis of your past cold chain logistics patterns and tying them together with external data streams to inform you on the action to take in a given situation.
The right cold chain monitoring solution with truly real-time data analytics enables businesses to streamline operational efficiency, improve visibility and collaboration throughout the supply chain, save shipment loads, optimize route performance, and manage by exception. If temperatures within a truck shipping palettes of fresh produce traveling through the Mojave desert are deviating, knowing that the temperatures are going up is necessary but it isn’t sufficient. To proactively take action to save the load, you’ll need to get to the root cause. Predictive and prescriptive analytics do just that — but they can also prevent the riskier shipment route from happening at all through accurate forecasting.
A number of data logger companies on the market talk about where they’re heading with the cloud, real-time data, Big Data, and analytics. Yet, smart cold chain solutions are already available — with the added caveat that they’re cost-effective, scalable, fully validated, and provide immediate visibility across the entire supply chain.
Real-time data and analytics are necessary but they aren’t sufficient for a smart cold chain. Effective businesses transporting temperature-controlled products are turning to a new type of partner — a service provider who delivers accurate, real-time information through an established and proven visibility platform, and who can also automate the cold chain data visibility and logistics process. This entails:
Managed and professional services offerings, including reverse device logistics to maintain loggers in their circulation pool, 24/7 monitoring of shipment data and response and troubleshooting, and technical integration support
Build a smarter cold chain today, with Controlant’s Cold Chain as a Service (CHaaS) solution.