Any decisions a company made is base on the information they have in their possession. If these data are not enriched, the decisions will be of low quality and will have an impact on the efficiency of the business.
Location Intelligence (LI) seeks to give a new dimension: so the results can be much more positive than before thanks to answering questions that were not even formulated before. Next, we explain how LI specifically benefits the distribution sector.
For a long time, Business Intelligence (BI) and Customer Relationship Management (CRM) have been part of the vital functions of modern business. The first seeks to obtain information in-depth on structures, trends and potential opportunities that allow better decision-making through the evaluation of relevant data.
Meanwhile, the CRM focuses on building a documented base for business with an eminent orientation towards its customers through systems and processes that strengthen B2C relationships.
These tools have been consolidated in recent years, Geographic Information Systems (GIS) and other solutions such as geomarketing, which base their work on geolocated information, joined them. Its implementation is not so settled, but its success has been widely demonstrated in any sector or specialty.
In addition, in the Big Data and the Internet of Things (IoT) era, Location Intelligence comes to converge information with time and the space of it. The use of new technologies not only demands a constant flow of information with a temporal context, but must also be spatially located on the map.
New questions and answers thanks to Location Intelligence
Traditionally, BI solutions have given answers to questions about who, what and how many. However, where was never answered or even asked, and this has associated other issues such as who, when and how much. The more answers they get, the more information the company will have to work on and make decisions.
Within the distribution sector, it is also important this enrichment of data that is achieved through geolocation. Before, the work was done with conventional databases, composed of numbers and letters (for example, simple addresses), being impossible to perform complex tasks with them.
The integration of LI allows them to enrich all this data with the daily work in the sector: sales areas, customer locations, risk areas, supply relationships, and so on. The visualization and analysis of all the above is impossible with the BI tools.
The implementation of Location Intelligence in the distribution
The integration process is done in three steps: geocoding, analysis and optimization. The first step is the labeling of all the elements so that they have a spatial reference to work later.
Once these are located, thanks to the intersection of other available data, it is possible that they inherit certain attributes, such as purchasing availability, affinity of products or sociodemographic indicators, making the information much more complete. This process is known as data refining.
Already in the analysis part, LI benefits greatly from the fact that it works with data on location, so it is essential to capture them on a map, with the advantages that this visualization entails. From a simple glance, on a map you can discover disparities, trends or potential strategies improvement that can’t be achieved by observing an alphanumeric database.
This is achieved simply by visualization, but LI and GIS go further and allow to combine and analyze different layers of information in which problems difficult to see in another way and search for a later solution with the same tools.
Thanks to the capacity of analysis and processing of these tools and the enrichment of the data, this type of incidents can be solved in a way that a person could not or would cost too much time, improving the general functioning of the distribution system.
Distribution routes are associated with a large number of factors (number of sale points, vehicle capacity and variable costs according to itinerary …) and it is very complex to take them all into account if it is not with the artificial intelligence of a program.
That two sale points are close do not necessarily have to be on the same route, since they may have different needs or replenishment frequency. Also, there are other issues that must be taken into account such as seasonal variations.
After visualization and analysis, the next step is prediction, which is what enables a company to act before something negative happens. With all the information collected, an LI tool is able to search for trends and create predictive models. For example, transport vehicles that pass through certain roads tend to get damaged more or take longer to reach their destination.
If this event is repeated several times, you can find out that there is a tendency and that perhaps in some way the route has to be modified to avoid certain places. Thus, action is taken to prevent harmful future events that have a high probability of happening.
In summary, the integration of Location Intelligence within the distribution sector directly reverts to the return on investment (ROI) thanks to the optimization of basic resources through the analysis of routes and the discovery of potential problems and the application of solutions in accordance.
Data enrichment, traditionally only alphanumeric, through geolocation, increases the value and quality of these, which affects more substantiated decisions made in less time. At the end of the day, there is an improvement in efficiency in all senses and areas, enhancing what is established by Business Intelligence.