Data has always played an important role in logistics, but the rise of big data has driven a strong and rather sudden need for a way to effectively filter, aggregate and validate it in order to glean meaningful insights.
In particular, data analytics is now being used in logistics to drive informed decision making in real time and help predict potential situations and incidents by extrapolating existing data as well as current trends.
Data-enabled Decision Making
When data is used to make timely decisions, it fulfills its true purpose. Take the example of a truck that is carrying a vital consignment and suddenly breaks down along its pre-defined route. Should this incident go unreported, all stakeholders in the supply chain related to that particular consignment are facing losses. If the relevant decision makers do hear about the incident, their decisions could be akin to taking a stab in the dark, and the result could be chaos.
However, when the data is analyzed and used to provide insight in terms of the breakdown’s location, cause, and implications, true progress can be made. For example, the chance of fixing the problem quickly can be determined, as can the resources that would be needed to get job done and the other alternatives that are available. When decision makers have access to the most highly recommended option based on similar occurrences in the past, it enables them to find the best solution for any disruption.
When intelligence is added to the analyzed data via predictive analytics, everyone can prepare to deal with situations that might crop up in the future. This can help to forecast market demand and plan for resources correctly. It’s also useful for streamlining warehouse planning. Efficient driver rostering can be achieved easily, whether it entails reviewing drivers’ performances or ensuring compliance with OSHA regulations. It can also help to plan vehicle selection, monitor performance, and determine optimum service schedules.
This blog post was based off of an article from Ramco. View the original here.