The immediate gains of early adopters seem to outweigh the risks
The Internet of Things (IoT), Big Data, Block Chain, Machine Learning and Artificial Intelligence (AI) are terms we are hearing more frequently across a variety of sectors. The logistics industry seems to be slowly grasping the potential and ultimate inevitability of these technologies. However, do we really understand how to extract all the efficiencies that these innovations can provide?
Covering the creation and implementation of a technology eco-system to fully optimise a supply chain would likely require a whole series of textbooks. In order to keep this brief, let’s stick to the basics.
Just remember – data is everything. Most systems rely on accurate and real-time data. This has always been the case but now most things are going towards being digital rather than on paper.
IoT devices facilitate the collection of data by measuring a feature or recording data and communicating the information to a database or other devices wirelessly. This allows a package or pallet or container’s location, type, dimension and even temperature to be monitored in real-time by anyone in the supply chain. For this reason, IoT could be considered key in creating supply chain visibility and the foundation for a technology eco-system which automates and optimises.
The amount of data collected by these devices can be overwhelming to process and understand. Firstly, it is important to determine what exactly should be measured in order to decrease the collection of less useful or repeated data. And secondly, it is important to create a procedure to record and store the data collected to make it easier to access and analyse.
Another important aspect to consider is data security. Organisations should design cyber security measures to avoid meddling from parties with malicious intent.
Then, all that is left is making the most of the data collected. Anyone could manually look through the vast amount of data collected and identify at least some inefficiencies to improve. Luckily, many data science and machine learning techniques have been developed to help discover, visualise and then act on all the insights available, with less effort.
When it comes to machine learning, models for predictive analytics or operational improvement are only as good as the data the model is trained on. This once again highlights the importance of data collection and pre-processing.
According to Gartner, these technologies are still far away from reaching market maturity. For example, IoT technologies are estimated to reach maturity in 5 to 10 years. For this reason, many in the logistics industry are right to approach them with caution. Enterprises are further discouraged by the difficulty and costs of planning, creating and implementing a full technological eco-system in their existing operations. However, in most cases, the immediate gains of early adopters seem to outweigh the risks.
Furthermore, as the adoption and development of these technologies continues to accelerate, businesses and customers will come to expect the visibility, efficiency and analytical capabilities which new systems can provide. Enterprises which have not already started to adjust and digitalise their supply chain will struggle to compete with such systems in the future.
Source: Transport Intelligence