Supply chains have been evolving alongside business processes and customer demands. As such, processes are getting more complicated to deliver products to their destinations faster in the highest quality possible. 

Unfortunately, as workflows become more complicated, things can easily fall through the cracks. 

Who has not experienced losing cargo and items in shipping? How about receiving products in questionable conditions? Or spending a fortune on shipping without noticing any improvements in delivery times?

Businesses are suffering significant financial losses due to inefficiency in supply chains. 

In fact, supply chain professionals' top priorities are overall performance management, service quality, and data analytics. 

These are motivated by the desire to optimize processes within the supply chain. And, at its core are technologies such as IoT devices such as data loggers, and dedicated analytics tools for consolidating signals and transforming those into insights. 

This article takes a quick peek at how data loggers contribute to the overall optimization of a business's supply chain.  

Improving Supply Chain Visibility

Mishandled assets, misplaced products, and late deliveries are some of the daily challenges businesses face in managing their supply chain. 

Preventing these pitfalls from happening requires a significant amount of visibility into various processes in the supply chain. However, manually collecting information through various stages in the supply chain can be daunting, especially given how many different vendors are often involved.

Additionally, infrastructure, personnel and equipment are required to provide decent process visibility. This can quickly make for extra costs, time and effort. However, a data logger simplifies the collection of information. Consequently, it eliminates the need for expensive, labor-intensive solutions. 

The IoT device can be installed on a package before it enters the supply chain. Throughout its journey through different phases, it collects valuable data to provide a snapshot of the products' condition at any given point. It can track GPS coordinates to determine delivery times and performance. Additionally, advanced data loggers show the shipment's temperature, shocks, tilts and even light exposure. 

This allows businesses to see what's happening during transit, storage, and handoff. Transparency, in this regard, allows businesses to make timely decisions such as ordering more supplies, reporting mishandling of cargo, and more.

Gathering Valuable Insights for Savings

Information gathered by in-shipment data loggers provides essential insights about the supply chain. Businesses can use these to evaluate the performance of service providers. They can also implement new strategies to improve efficiency and costs. 

Combining IoT with other technologies can further improve savings in time and money. For example, the food industry uses IoT and Blockchain to confront significant challenges in its supply chain. 

Statistics gathered by data loggers can minimize food wastage, which is common during transit and storage. It also allows for fewer recalls, which generate significant savings. 

These insights are also accessible to various stakeholders. Consistent information sharing decreases inherent risks while improving traceability and accountability. These are critical, especially for global food supply chains. For instance, a location history creates opportunities to redesign routes that can lower the delivery time, which is crucial in maintaining the quality of food products. 

While blockchain and IoT are still in their early forms, these innovations are expected to save the food industry around $31 billion by 2025. Similarly, big data and cloud technology are used to understand the complexities in supply chains. 

The enormous amount of information from data loggers such as cargo transfers, traffic patterns, and weather conditions can be consolidated in cloud storage solutions. Studying and identifying links and patterns within varying pools of data can enhance efficiency and streamline operations. 

According to Alexander Rinke of Celonis, companies are beginning to incorporate big data analytics into their supply chain management to fully understand its complex processes. Since supply chains are driven by key performance indicators (KPIs), intelligence from big data makes it easy for companies to make evidence-based decisions. 

Machine learning and artificial intelligence are then used to provide complete transparency of processes and how they perform in real-world scenarios. 

For instance, the Genetic Algorithm and simulations and machine learning were used to study New York streets' complexities and their effects on delivery. The algorithm produced an optimal delivery route that includes 11 drop-off points across a large area by analyzing geospatial data, traffic trends, and other information. 

Enhancing Efficiency Through Intelligent Tracking

Data loggers were originally designed to track the location of cargo at a given time. These early prototypes have evolved into customizable devices equipped with various connectivity technologies and smart sensors.

Various data from the sensors are necessary to ensure that products maintain their quality, delivery schedules are met, and damages due to mishandling are addressed.

Intelligent tracking also identifies opportunities for improvement in various stages of the supply chain. For instance, food and medicine require numerous data to ensure that they are shipped as fast as possible. Handlers need to maintain specific conditions to keep the quality of these products.

Data loggers with relative humidity and temperature sensors tags monitor the temperature, humidity, and even dew point. Alarms in these devices can alert the product handlers to prevent spoilage or expirations. If the log history shows that the temperature rose beyond the product's safe threshold at some point, they can identify which ones are not safe for consumption without checking each package. 

This ensures that quick decisions can be made and problematic processes, infrastructures, and equipment are identified. Some data loggers are even developed to withstand extreme conditions while monitoring the slightest change in the environment. They are often used for transporting medicine and chemicals across the globe.  

Additionally, information logs allow businesses to determine if the products are still up to their standard without extensive quality control. More expensive loggers can send this information to data warehouses in a constant stream, using cellular connections, whereas more affordable options use radio or bluetooth transmissions to update a central transmitter. Newer logger devices display dynamic QR codes on LCD screens so that anyone with a smartphone can scan and sync.

Without a record of temperature and humidity during transit and storage, companies have no guarantee that guidelines were followed throughout the supply chain. 

Conclusion

By collecting numerous data at all points in the supply chain, in-shipment data loggers reveal critical insights about these processes and workflows. Businesses can leverage these IoT devices to improve goods' safety, optimize operations and logistics, and reduce overall supply chain costs.

Furthermore, the data collected can be analyzed to create an overview of various KPIs, highlight opportunities for optimization, and manage risks. Then, valuable insights can be used to maximize business assets and infrastructure, ensure compliance with various standards, and minimize profit loss.