The logistics function has traditionally been regarded as particularly sensitive to unforeseeable external factors. A 2022 UK article for the pallet industry points out how “unpredictable situations arise all the time causing the logistics team to go through a lot of trouble to find solutions to various problems they have no control over and that tend to come with all kinds of excuses such as bad road conditions, technical problems, family emergencies or the driver's lack of experience, among others.”
Customers and supply chain partners become frustrated when they perceive that these external factors are being used as an excuse for logistics performance shortfalls. For example, ‘unavoidable’ delays due to bad weather may provoke responses along the lines of “Couldn’t they have predicted it would snow in January, in Europe?”
When we talk about unforeseeable external factors in logistics, we are generally referring to data that could be captured, analyzed, modelled, and considered in logistics planning, given the right access, data management, analytics and planning systems capabilities.
Using, e.g., Google Maps to work out the quickest route right now for a delivery is a small step forward, but it is a manual process that only uses a tiny part of the potential of the huge range of data sources and analytics capabilities that exist now.
SAP Data Intelligence, with SAP SAC, BW and Datasphere, enables the multitude of heterogenous factors that can impact logistics to be organized into data streams, orchestrated, analyzed, modelled, and plugged back into intelligent, ML-based planning in real-time.
To take a single potential use case, it enables optimization of delivery schedules and routes for multiple customers on the day using ML-based models that have a comprehensive view of historical and current data on road conditions, weather, staffing levels, system loads and all the other factors that can impact delivery.