How IoT Analytics Are Transforming Cold Chain Efficiency

Some industries, such as pharmaceuticals, chemicals, and food, have temperature-sensitive products. 

A slight temperature change can greatly increase the chances of spoilage. In such industries, cold chain management holds considerable importance.

However, the challenges for the products of these industries are diverse. If there is a challenge of temperature fluctuation on one side, there is also the challenge of complex logistics on the other.

IoT-based predictive analytics has greatly relieved these temperature-sensitive industries by strongly attacking these diverse challenges. By using this technology, these industries can not only prevent product damage but also manage their resources more efficiently.

Let's discuss how IoT-based predictive analytics can help these industries by transforming cold chain efficiency.

The Rise of Predictive Analytics in Cold Chain Management

The reports show that the cold chain logistics market is expected to grow substantially in the coming years.

The worldwide cold chain logistics industry was worth $342.8 billion in 2023, as reported by Precedence Group. Forecasts indicate that it will reach over $1,242 billion by 2033, expanding at a CAGR (compound annual growth rate) of 13.9 percent.

Another Allied market research report says that the global predictive analytics market was valued at $10.2 billion in 2023. This market is expected to grow at a CAGR of 22.4 percent from 2024 to 2032 and reach $63.3 billion by 2032.

Predictive analysis provides information about equipment's past and present data, which can be used to determine when a particular piece of equipment is more likely to break down and what effect this will have on transportation.

However, with the help of IoT and advanced analytics platforms, businesses can get real-time information about their equipment, predict problems, and take steps ahead of time to ensure operations keep running smoothly.

Predictive Analytics & Cold Chain Efficiency

Strengthens Cold Chain Monitoring

IoT platforms play a crucial role in enabling predictive analytics for cold chain monitoring. These platforms are highly capable of collecting data. 

They collect data from different IoT devices, such as temperature sensors, humidity monitors, and GPS trackers. This data is analyzed with the help of machine learning algorithms.

This analysis observes the working patterns of the equipment used in the process to determine if any signal of potential failure is showing.

In the food industry, the biggest reason for product spoilage is temperature fluctuations. It is estimated that 20 percent of temperature-sensitive products become damaged during transportation due to improper temperature control.

Here predictive analysis proves to be a boon for these industries. It alerts operators whenever there are temperature fluctuations so that any equipment issues can be prevented before they occur, and the product can be saved from damage.

Reduces Downtime and Maintenance Costs 

Reducing equipment downtime along with reducing repair costs through predictive maintenance is one of the most significant advantages of predictive analysis in cold chain logistics. 

As per the industry reports, predictive maintenance can reduce unplanned equipment downtime by up to 50 percent and lower repair costs by 10-20 percent.

Maximizes Energy Efficiency

The refrigeration unit is the most energy-intensive unit in the cold chain system. It consumes the most energy, which has caused the overall costs to increase significantly. On average, refrigeration accounts for around 70 percent of total energy consumption in cold storage facilities.

Predictive analytics is no less than a boon here. It can optimize energy consumption by identifying inefficiencies in cooling systems, like using them too much during off-peak hours or using broken equipment continuously. Overall, these factors collectively increase the energy used to run the equipment.

The International Energy Agency (IEA) says that cold storage systems can reduce their energy usage by 10-30 percent by using IoT-based analytics. This not only helps businesses cut costs but also helps them reach their sustainability goals.

For example, predictive analytics can help a cold storage facility determine that a compressor uses 20 percent more energy than usual. By finding and fixing the problem quickly, the facility stops more energy waste and makes the equipment last longer.

Prevents Product Spoilage

As discussed above, product spoilage is a huge issue in cold chain logistics, especially in industries like food and pharmaceuticals. Let us tell you that approximately 40 percent of global food is wasted annually due to poor cold chain monitoring.

Now, with real-time monitoring of IoT-based predictive analysis, crucial factors such as the ratio of temperature and humidity of the particular equipment can be detected. This allows for early detection before any issues arise.

With predictive analytics, companies can optimize transportation routes, avoid delays, and ensure that temperature and humidity are maintained.

Major Changes

IoT-based predictive analytics is bringing many bigger changes in the cold chain industry. Earlier equipment failure was only identified after a breakdown. Today, actionable data provides information about the health of the equipment in advance. 

This proactive approach helps industries prevent potential failures. By taking action on time using this real-time data, product damage can be prevented, and repair costs can be reduced.

The importance of cold chain logistics in the global supply chain is increasing. More and more industries are now using an IoT platform to optimize their logistics with reduced costs. 

Businesses that maintain industry standards and want to safely transport their goods from one place to another are investing in IoT platforms.

Companies are getting stronger in the logistics industries by putting money into new technologies. They are doing this so that it will be easy for them to handle problems that might come up in the future.