Recent changes in consumer demands are reshaping supply chains and the way companies operate.

Efficient logistics is the heart of the supply chain and the engine of commerce and the economy. If it is true that consumers demand faster shipments, lower costs, and an optimal customer experience, then logistics must adapt, continuously innovate, and leverage data-driven solutions that provide complete supply chain visibility.

Companies that provide international item-level tracking services need to implement innovative and flexible technology solutions to gain a competitive advantage and ensure an optimal customer experience. With the increasing deployment of these solutions, the logistics industry can move toward a digital transition that will bring added value to all players.

New technologies in aid of the supply chain

Innovative solutions for supply chain visibility, and thus for efficient logistics that can track products at the item level, contribute greatly to the progressive digitalization process of companies.

This process relies on a number of crucial innovative technologies, including the Internet of Things (IoT), Big Data Analytics, Artificial Intelligence (AI) and Machine Learning (ML), which are the scaffolding that underpins software for visibility and dynamic supply chain management – the landing place to which companies wishing to innovate their processes and operations should strive.

In the case of IoT, we are talking about all those field devices that enable shipment tracking, possibly in real time, by transmitting a range of data and information essential – including location, temperature, humidity, etc. – to the smooth flow of product in the supply chain.

When we talk about Big Data Analytics, we need to analyze the issue piece by piece: Big Data is the set of technologies and methodologies for analyzing massive data, which have the ability to mine, analyze, and relate a huge amount of heterogeneous, structured and unstructured data to discover links between different phenomena and predict future ones; Analytics are algorithms, technologies, and software implemented for studying and finding connections and correlations between data. Big Data without Analytics is just a huge amorphous amount of data; Analytics without Big Data is just a statistical tool. It is the combination of Big Data and analytics that creates a completely different tool with enormous potential, Big Data Analytics. These are technologies and software applied to the study and research of connections and relationships between Big Data, and capable of extracting new information and creating new forms of value.

Finally, bringing the topic of Artificial Intelligence down to the world of logistics, we can see applications that, thanks to different Machine Learning models, bring greater autonomy to processes that used to be carried out manually: in fact, there is software that can automate data management and sharing processes, manage unpredictable phenomena such as traffic intensity at a time after analysis, establish the optimal timing for an unloading operation or the ideal quantity of warehouse stock.

The coordinated use of these technologies enables companies to implement a data-driven approach.

Data-driven approach: what it means and how to use it to improve the logistics industry

The frontier of using data to make decisions has expanded dramatically: many high-performing companies today are building their competitive strategies based on information derived from in-depth analysis of vast amounts of data, capable of generating excellent business results. This is exactly what the data-driven approach is all about: proactively using innovative data collection and analysis technologies throughout the supply chain to make informed decisions, not based on subjective feelings, and anchored as much as possible in the data itself. Thus, the crucial importance of data emerges:

“It has been repeated for years that data is the new oil. In fact, I believe they are destined to become like the new water: simply a critical resource for our businesses and our lives.”
Joel Gurin, founder and author of OpenDataNow

Importing the data-driven approach into supply chain companies means being able to set up efficient logistics at all stages of the chain.

We can take the example of a tracking solution for a product in transit in the supply chain: usually companies use a detached, non-integrated platform procured by the supplier of the devices or systems used internally within the logistics company. In these cases, the item-level product tracking data is not linked to the company's operations: it may happen, for example, that I know, yes, the temperature at which my product is, but in order to see whether it is in the charge of the logistics service provider or whether it is in the charge of someone else, I have to go and check another system or make phone calls, exchange emails. In short, this is a pretty common case history, but it serves to underscore how the optimal customer experience is not easy to guarantee, can result in time-consuming actions, and definitely depends on the level of integration that enterprise systems have.

Back to the data-driven approach, its implementation ensures that one can coordinate supply and demand, be able to manage inventory stocks optimally, be able to monitor all moving carriers, and generally have timely, real-time information on all operations. This level of knowledge granularity provides companies with unprecedented control over products in transit through the supply chain and, when used to track products at the item level, empowers companies to provide consumers with a high-level, brand-strengthening customer experience that realizes time and resource savings. This is because the data-driven approach is a key step in the direction of full supply chain visibility.

Visibility in tracking, how to improve?

Those who work in the supply chain know that having visibility throughout the chain is a necessary requirement for setting up efficient logistics. The path to supply chain visibility is intricate and surrounded by several challenges, but it is not impossible. We have gathered some key steps that will be able to help structure 100% operational supply chain visibility.

“The first goal should be to build the two basic pillars of supply chain visibility: traceability and transparency.”

In other words, there is a need to collect granular information about products and processes, and to identify what information should be shared, with whom, and when. Let's look at 4 steps to achieve this goal:

• Digitalize data: without digitalizing all paper records and manual processes, it is not possible to begin the journey toward visibility and collect accurate information about operations. Digital records management will allow a large amount of data to be stored and processed, and will lead to transparency by sharing the information collected.
• Gather reliable data and breaking down silos: you need to have a clear understanding of all the players in your supply chain, so you know who has what information and activate a mechanism to share it. However, getting the information is not always easy: many companies, for example, receive notification only when the shipment reaches control points (such as a port or distribution center), but they do not know the location and condition of their products during transit. The solution is to find the right connectivity platform that can integrate regardless of the technologies or legacy systems in use. It must be able to connect all key players and ensure that all stakeholders can easily access the data.
• Leverage data: interpreting the data collected will enable proactive management of exceptions before they occur, saving the company time and money and maintaining a high level of customer satisfaction. Data analysis, Artificial Intelligence and Machine Learning are the enabling technologies for this phase.
  Build an extended supply chain network: once the data has been digitalized, all reliable data brought together, and leveraged for reliable insights that lead to data-driven decisions, an extended supply chain network should be developed to move forward in the groove of a collaborative approach to supply chain management.

Conclusion

Consumers today expect to have products delivered as quickly as possible, and for logistics companies to share information about the product from the first moments it is in transit through the supply chain. There is great pressure on supply chains to meet these flexible consumer expectations, and supply chain managers must ensure that the right products are in the right place at the right time.

Efficient logistics, and thus complete supply chain visibility, is only possible through innovative data-driven technologies: the future of supply chains hinges on the ability to collect granular information about both products and processes, sharing this information with the right stakeholders at the right time, making meaning out of the information collected, and, finally, building an extended supply chain network.