What are the best IoT technologies for supply chain management?

Monitoring air conditions in storage locations using sensors is a good practice, especially for specialty papers that require specific temperature and humidity levels to preserve their fundamental structure and properties. But directly controlling the air conditioning in large storage areas might be economically not feasible. So, installing sensors allows for continuous monitoring and data collection. This data enables us to identify variations over time. By having these insights, precautions can be taken during the production process to mitigate potential issues, ensuring the quality and integrity of specialty papers. This proactive approach helps in minimizing the risk of undesirable outcomes due to fluctuations in storage conditions.

In the pulp and paper industry, many raw materials, often in liquid form, are stored in tanks equipped with level and flow transmitters. These sensors collect valuable data that can be seamlessly integrated into production quantity calculations, facilitating accurate Bill of Materials (BOM) balancing. Moreover, leveraging this data for prediction and forecasting enables proactive optimization of the supply chain by anticipating the required raw materials.

Within the pulp and paper supply chain, many chemicals such as AKD and ASA, demand precise storage conditions to maintain their properties and ensure consistent production quality. Even minor variations will impact the properties of the chemicals and heavily affect their performance during production. To address this, IoT sensors are integrated into the supply chain to monitor the storage conditions of these chemicals. The data collected can be used for proactive analysis of chemical performance and correlation with any production issues.

The most important is to consided a hybrid solution stack to meet specific use cases. However, if cost in not an issue I would start with IOT. This means we are collecting data from devices we can be very accurate to make real time decisions. For end to end supply chain visibility IoT works for both modern and old infrastructure and gives you lot more autonomy. After that considering event driven architecture on cloud would be my best approach to for data sharing. To fine tune decisions and learn from large algorithm already available I'll apply machine learning when I forecasting delivery dates and volumes. I'll also like to use ML to optimize loads and routes. For accuracy of location I'll still use GPS.