In addition to real-time monitoring and reporting, deep analytics provides the opportunity to understand patterns and trends to forecast future outcomes.
Our deep analytics system goes beyond leveraging the collected data, such as temperature fluctuation for example, to anticipate and optimise future performance. It also suggests solutions on how to take advantage of a future opportunity or mitigate a future risk, demonstrating the implications of each decision option.
Make analytically driven decisions knowing the implication of each outcome.
When it comes to logistics operations, more questions can be asked and, most importantly, more questions can be answered by means of employing all possible data collection opportunities. Deeper analysis, provides a separate repository of collected data, specifically designed around a specific logistics operation. This repository allows for deeper querying of huge amounts of data which would otherwise be unachievable, especially if only real-time monitoring was used.
Collecting data and metrics facilitates a better understanding of current operations. Data can be combined and analysed to identify patterns, trends, co-relationships and influential outliers. In this context, predictive models that make use of patterns drawing on collected data are created to identify risks and opportunities.
Predictive models identify relationships among several factors in order to facilitate risk or opportunity assessment with regard to a particular set of conditions, thus assisting the decision-making process.
The variety of goods requires different handling methods during their transport. For example, pharmaceutical and edible goods are highly temperature-sensitive whereas scientific machinery may be very sensitive to handling. Keeping this in mind, we personalise the collection and analysis of data aiming to render the optimal outcome.