You might have thought that predicting the future is science fiction. It’s not quite so anymore, there are ways and means to get insights and knowledge of what will happen. With the help of machine learning and historical data we can more consciously manage our resources, optimize supply chains in companies, improve financial results and increase satisfaction among customers.
The main purpose of predictive analytics in logistics networks is to reduce the costs of deliveries by finding the most optimal patterns of deliveries, reducing distance covered per driver and increasing the efficiency of finalized transactions by finding the most suitable timings for the deliveries. In our use case we have used a database of almost 50,000,000 historical deliveries coupled with over 80,000,000 transaction records. The machine learning models created from this, with big-data-driven technology with GIS standards (e.g. historical traffic patterns), have reduced daily driven distances up to 5-10% (depending on the region), while simultaneously increasing the success rate of deliveries.
Applying web scraping also enabled us to update a company’s customer database with more reliable opening hours. It was then possible to account for apparently random effects related to external factors such as weather conditions or calendar events categorized for a special group of customers (e.g. sports events, tourism, national holidays and so on).
Thanks to all of the above, the higher ratio of successful deliveries not only significantly improved monetary efficiency but also contributed to reduced risk among drivers.
Applying machine learning based on predictive analytics in supply chains also creates new opportunities for further development of a company’s sales logistics. It may give hints, for example, around creating or optimizing plans for a new area of deliveries. If you don’t know the opening hours of new customers or the best way to add a new point on your tracking plan, the GIS + ML based microsimulation based on the data framework just described will tell you how to deal with this new situation.
Moreover, this kind of approach, once fine tuned for effects related to such a wide variety of factors, can be used to model useful information in a broad range on transport issues, including freight, public transport and other areas. The key here is in the work being done to consume and make use of data such as weather patterns, work environment data and so on.
