COVID-19 and climate change have directly impacted the supply chains of the sectors and industries that generate the most economic output.
Unfortunately, fiction has become reality, and a global pandemic coupled with sudden climate changes have increased these problems worldwide, also due to unforeseen events in logistics routes and the exponential increase in online shopping, forcing industries to increase the load of transportation, vehicles, staff, and resources in general.
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Not surprisingly, industries and business sectors have problems or stoppers within their supply chain processes. Over time, increasingly innovative strategies have been generated to implement solutions that maximize and optimize shipments, pickups, transportation routes, among others.
According to Business Insider magazine: “In an interview with CNBC, Tim Huxley, chairman of shipping company Mandarin Shipping, said floods had damaged railway lines used to deliver goods — “another body blow” to the supply chain. Delays and shortages are likely to have an impact on Black Friday deals and discounts because retailers will raise prices to cover extra costs, Pawan Joshi, executive vice president of supply chain software firm E2open” Read the full article here.
These types of problems can be minimized and even avoided, with the use of technological tools such as location intelligence based on spatial data and predictive models, as they can identify and evaluate patterns that infer a problem in logistics and improve processes in their respective supply chains and trade relationships around the world.
These tools incorporate concepts such as weather, time on delays, or unexpected repairs, thanks to a large amount of Big Data that can be obtained through sensors, GPS, and other traffic management systems.
With this type of intelligence and using the data available in various records, system parameters, and supply statistics, it is possible to optimize commercial routes, inputs such as gasoline, transportation maintenance, employee management, merchandise, among others. In this way, losses are minimized and any factor affecting the supply chain can be predicted.
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With real-time machine learning models, companies can now:
- Integrate a global productivity command control for logistics and operations, identifying sources of inefficiency.
- Implement a traffic management system optimization algorithm that enables crane and equipment scheduling, improving terminal performance and productivity.
- Adapt a predictive maintenance system based on the measurement, tracking and monitoring of equipment sensors, achieving a minimization of downtime and breakdowns.
In PREDIK Data-Driven we help the sectors to find the most efficient distribution routes, to optimize expenses within their operations, and maximize their income, foreseeing future problems and providing solutions in real-time.
Need to implement intelligent solutions within your distribution routes? Contact us!
