Based on a sufficiently representative history of operational data, we will be able to apply algorithms to identify operational inefficiencies both at a granular level, of specific operationally inefficient delivery/collection points, and at an aggregated level, of operationally inefficient zones, together with recommendations to improve these inefficiencies that will allow us to redesign the operation and gain in profitability and operational capacity.

In transportation and distribution scenarios where route planning solutions must work with time and distance matrices generated from different mapping and traffic data providers, cases of non-compliance with the triangular inequality may occur, which if unnoticed could lead to incorrect or inefficient routes.

The triangular inequality or Minkowski inequality is a Euclidean geometry theorem that states that in any triangle the sum of the lengths of any two sides is always greater than the length of the remaining side.

By applying the algorithms developed for this purpose for the analysis and detection of these cases we will be able to manage and resolve these anomalies in the data and ensure the correct and efficient route planning we need.

In operational scenarios with a large number of points to manage, and in which time-windows of certain requirements are also established (very short windows, very different between nearby nodes, etc.), there may well be cases in which two points very close to each other are not compatible on the same route, since it is not feasible to comply with the time-window of both nodes and complete the movement from one to the other within the times returned by the matrix.

By applying the model for detecting these cases, we will be able to identify a list of priorities on which to work from the management team, to ensure their compatibility on the same route, which will result in greater efficiency and profitability of our operation.

This analysis provides us with a list of pairs of incompatible nodes, the direction of their incompatibility (it can be single or bi-directional), and the time ranges that should be extended in one or the other node, or between both, to ensure their compatibility.

In operational scenarios with a large number of nodes, it is very common that the degree of cleanliness of the data associated with the location of the node is erroneously recorded, whether it is the address, the ZIP, the locality, the district, and of course, the geolocation (latitude, longitude). Sometimes even other particular entities of our operation, such as Distribution Zones, or any other way of aggregation or classification of the nodes of our network, are involved in this problem.

To ensure the maximum quality of the master data with which we will work within oga.Logistics, we can apply our master data analysis models and automated anomaly detection in the ZIP, Locality, District or any other entity of our own use. As a result, we will obtain a list of nodes with high probabilities of error in the registration of any of these data, for its review and cleaning, critical for the best quality of the proposed planning solutions.

The oga.Logistics roadmap is constantly growing and incorporates new analysis functionalities on a recurring basis. Both from the analysis base requested by our customers that are eventually incorporated into the analysis pool that we can offer openly, we can offer specific analysis of any entity or sub-process associated with the data model of the solution and of interest to the customer.