Which tools to use to access data quickly?

Although some bottlenecks are easing^1, the global supply chain system is under severe stress: inflationary pressures, continuing lockdowns in China, geopolitical tensions and the resulting spillover effects on energy markets lead to the conclusion that companies need to make decisions quickly to steer all their chain processes in a direction of higher productivity and cost containment. 

When it comes to making decisions, we must rely on data. It is impossible for organizations to make accurate decisions without using data. Since a large amount of data is continuously being produced at all stages of the supply chain, including but not limited to through IoT devices – such as data loggers – this data can be collected in real time and then turned over to the various big data analysis tools. The more these tools are collaborative and centralized on a single platform, the more companies can increase efficiency, decision-making speed and profits.

Real-time data management

The fact that supply chains produce large amounts of data (also called big data) that are collected by different IoT devices contributes to unintended consequences: it can often be the case that supply chain data generated by data loggers used to track the location and condition of shipments (e.g., temperature, humidity, shocks), manage warehouse inventories, or monitor operations are fragmented across different platforms.
They are often stored in different applications and in silos, making it very difficult to simultaneously leverage data generated from different sources and slowing down the harmonious operation of big data analysis tools.

Consider a company that ships temperature-controlled products to foreign countries, and receives shipment status data from one application and temperature data from another. It also handles purchase orders with its ERP system. 

Accessing data quickly is crucial to gaining as much visibility as possible in as little time as possible – and consequently to gaining a competitive advantage and better positioning of the quality of service offered by the brand – but under such conditions it becomes difficult to make decisions quickly.
This is a very clear example of data fragmentation.

Another theme, however, emerges: considering the volume, velocity and diversity of big data, classical methods of data collection are no longer suitable for generating valuable insights for the company because the results of processing always come after the event has already occurred, after the process has already finished or after the delivery has taken place. It is for this reason that today, analysis occurs in real time and results are presented almost instantaneously. Yet, most supply chain or logistics managers, even those with a technical background, have little or no experience with this type of data analysis.

"To extract value from big data, companies need to adopt appropriate technologies and methods."

Overcoming data fragmentation

Organizations often find that traceability data is fragmented across different platforms resulting in what are called data silos, that is collections of data held by one group that are not easily or fully accessible by other groups. There are three important and commonly used concepts that are often heard when talking about breaking down data silos: 

• Aggregation, which in the supply chain context means gathering supply chain data from multiple data loggers or data sources and placing them into well-defined groups;
• Integration, which refers to the process of collecting results from multiple data sets; 
• Interoperability, which in the supply chain context means that systems from different trading partners can communicate and understand each other.

To maximize the performance of big data analytics tools and overcome silos, it is therefore necessary to act on these three concepts.

Companies that aim to gain insights from big data in order to make the right decisions need to understand how to effectively collect data through aggregation practices and analyze it through integration processes. These operations can be applied to both internally and externally generated data, and with different methodologies.

This aspect highlights the importance of data interoperability functions in a context of exchanging data and information between companies, or when dealing with a multi-enterprise supply chain business network.

“Companies that want to gain insights from big data to make the right decisions need to collect data through aggregation and analyze it through integration.”

There is great potential for optimization if a company can store, aggregate and combine data and then feed the results to big data analysis tools. The main problem for management is how to choose the right technologies and approach to create data-driven solutions efficiently and then make data-driven decisions quickly.

Supply chain and collaborative platforms for fast data analysis

We have seen that if the goal of managers is to be able to make decisions quickly and based on real-time data streams, then the need to access data quickly becomes all the more pressing. There are many technologies today that make it possible to get to this goal and also to have supply chain data in a single platform, to have the evidence of the data in real time. Contrary to popular belief, these solutions – similarly to data loggers and IoT devices – are not only accessible to technology companies or large multinational conglomerates.

Opportunities for supply chain analytics can be found in all stages of the supply chain, such as planning, production, warehouse, transportation, point of sale and consumer.

Advanced analytics solutions, Artificial Intelligence and Machine Learning technologies can be deployed on a single collaborative platform, which can then be used by companies operating in all sectors of the supply chain: from food to pharma, companies are already using GPS technologies to reduce wait times by assigning warehouse bays in real time, and are implementing route optimization technologies by selecting the best routes for deliveries.

Other organizations are using these technologies in internal processes to digitize and automate document reading, ensuring significant savings in paper and time that resources previously spent on repetitive, low-value-added tasks.
All of this helps reduce unnecessary travel, reduce wasted time, cut costs, and reduce the impact of deforestation and emissions of CO2 and other greenhouse gasses.

Conclusions

In conclusion, tools for analyzing supply chain big data are already a crucial element for business strategies and making decisions quickly. In an increasingly innovation-driven marketplace, being able to move these processes into a single collaborative platform-thereby breaking down silos and carrying out a time & cost-saving operation-and flanking them with the more advanced technologies just mentioned can be a key competitive advantage for companies.