Benefits of Big Data for logistics companies
Big Data algorithms allow you to update analytical information in near real time. This is necessary so that supervisors or managers can quickly identify problems and resolve them on the spot. Information is displayed on a dashboard with cards. They can reflect the rating of timely delivery of goods, the percentage of accepted orders, the completion of tasks, and other parameters. The point of these features is not to wait for the compilation and delivery of reports, but to see the real picture of what is happening and react immediately.
The cards can reflect both the absolute values of metrics and KPIs, as well as the average figures necessary to assess the overall performance of the fleet. This data helps to make informed management decisions about the strategy for further work. In particular, to build more “heapy” routes and utilize vehicles by 100%, getting rid of extra units. Or another example: depending on the load of the transport, Relog BI generates recommendations on weight and dimensions for the purchase of new units in the future.
Our data scientists keep statistics on the demand for Relog BI analytics from clients. And in the first place – reports on drivers. With their help, you can identify both leading drivers and outsiders for any period (from one day to a year). Graphs are built automatically.
Here are some examples of data analytics:
a complete picture for each driver: the date of creation of the request for delivery, the time of actual service of each order, the sequence of execution, the total weight of the goods in the car and their gross amount.
comparison of specific drivers with each other.
identification of the “champion” by check among couriers in different periods.
ranking couriers by undelivered goods (a backlog is maintained that records the client’s refusal to deliver the goods due to the driver’s fault).
All this data forms KPI drivers in real time. And they, in turn, can be taken into account when calculating salaries.
In general, the main benefit of working with Big Data for a logistics company is a deep understanding of its business and predictive capabilities. Data analytics, in particular, allows you to identify external factors of influence such as seasonality (summer-winter) or a weekly sales cycle in retail outlets.
Big Data + geoanalytics: together more efficiently
We have been using Leafnet based on raster maps for a long time, and this year we have added more modern technology – Mapbox vector maps. They work with binary codes, and their speed is higher. However, both types have their own advantages.
The “tiles” of raster maps are very detailed, and therefore better suited for conveying information about objects. At the same time, they take up more space than the description of an object using a binary code. Vector maps load faster – this is their advantage with an unstable connection. There are also disadvantages: the success of the same Mapbox is limited in working with small maps or projects, strict data standardization also creates difficulties. Despite all this, Mapbox excels at working with large datasets.
Based on vector data about objects in Mapbox (shape, height, category, purpose, etc.), you can create 3D maps by coloring a certain selection of objects (shops, cars, warehouses). This allows for visual analysis. For example:
estimate the number of target trade objects in the area or city;
estimate the population near the outlet in a 5-minute walking distance;
compare the density of orders in city districts highlighted on the map in different colors;
see the location of various types of transport and the direction of its movement, etc.
All this data helps the logistics company or the retailer’s delivery service evaluate the effectiveness of route allocation.
How is the integration going?
The Relog BI analytics product part only works with data that comes from other ERP systems (be it 1C or SAP), as for other reports, we use several data sources: PostgresSQL, MongoDB, GoogleBigQuery, ClickHouse.
The main rule of integration is that API documentation is written simply enough so that a developer of a client company can quickly figure it out and send their first requests to a personal account. By the way, for our part, we also provide integration services through the standard “RELOG (Data Exchange)” subsystem, which is designed to work with any 1C 8.2 and higher systems, both on managed forms and on ordinary ones.
When integrators connect “1C” of the client with Relog, they implement our module in ERP. Through it there is a two-way data transfer. Relog receives information about orders and builds optimal routes for them, passing them to the dispatch service. In addition, our system promptly updates the statuses of orders (completed, late, refusals) in the client’s 1C. The lack of integration means that the operator has to manually change the status of each order at the end of the working day. And the almost inevitable mistakes and inefficient use of employees’ time.
Paradoxically, it is the fear of making mistakes that keeps customers from automating. Often it is perceived as an even greater evil. After all, for example, an incorrect date or delivery address for an order automatically turns into an incorrectly constructed route. This leads to increased logistics costs, delivery delays and even loss of direct profit. Therefore, integration with 1C, which keeps a comprehensive record of most of the company’s business processes, requires a scrupulous approach.
Understanding the “pains” of our clients, we try not only to implement a solution based on Big Data, but to adapt it so that the technology really starts to bring benefits. The potential effects are worth it. The mega-array of data contained in ERP systems allows you to do important and deep analytics in Microsoft Power BI.
Studying the client’s business processes based on his data gives an understanding of what tools need to be applied in the Relog system and what they are best used for. This approach allows you to tailor the implementation of the product as much as possible. We also monitor the interaction of the client with the Relog system and how fully he uses all the functionality of the program.
To sum up, using the power of big data, logistics companies will be able to make accurate forecasts and improve their efficiency. Technologies allow timely identification of problem points in business processes, keeping records of logistics volumes and optimizing the use of resources.