Route optimization in healthcare and pharmaceutical logistics: An artificial intelligence-based approach.

Designed by pikisuperstar Freepik - logística sanitaria

La logística desempeña un papel fundamental en el sector sanitario y farmacéutico, donde la entrega puntual de medicamentos y suministros médicos es crucial. Con el avance de la inteligencia artificial, la combinación de la optimización de rutas dinámicas y estáticas se ha convertido en una solución eficiente para mejorar la eficiencia y la precisión en … Read more

How IoT and AI is making a big impact on the logistics sector

Entrega autónoma concepto abstracto ilustración vectorial - Cómo se usa el IOT y la IA en la logística vector

For several years now, IoT (Internet of Things) technology has been increasingly known and applied in different fields thanks to its increasingly reliable and complete technology. Undoubtedly, among the industrial sectors in which the use of IoT technologies is trending, the logistics sector stands out due to the growth it has experienced in recent years and the needs it entails. 

It is well known that all logistics operations require rigorous monitoring and agility to run efficiently, from supply chain management to last mile distribution. For this, one of the most popular solutions is IoT and digitization of certain operations in the logistics department.

In this case, in which logistics operations is IoT technology used specifically? If you want to find out how IoT and AI are used in logistics, read on:

Predictive analytics

It is one of the best known tools where both AI technology and IoT are used. It consists of using previously collected data to forecast future results and, to do so, it organizes the data obtained, preprocesses them and develops predictive models so that the results can be validated later.

The advantages of applying predictive analytics to the logistics sector are varied. Some of its most frequent uses are:

  • Demand forecasting: We analyze the sales history together with the market and estimate the sales that would be generated on the products.
  • Supply chain planning and optimization: Thanks to a prior analysis of the demand, the right quantity and timing of the production of the goods can be calculated.
  • Inventory optimization: Demand estimates help in making decisions about optimal stock levels for each type of inventory.
  • Improved resource management and savings: By applying current data taking into account constraints and other characteristics, it is possible to optimize resources and reduce operating costs.

In general, predictive analytics act as a guide for decision making and to give us a more accurate estimate of future activities. In this way, a clear view of the current and future situation of the supply chain and company is obtained.

Real-time shipment tracking

The most important and essential thing that customers ask for today is transparency in reporting the status of the order in real time to ensure that it arrives at its destination at the agreed time without incident. Today it is possible to receive all the data thanks to geolocation algorithms received by sensors via wireless networks.

The advantages of order tracking focus on estimating delivery times and detecting and solving problems. For example, if an order is not going to arrive on time or has been lost due to a wrong address or accident, the incident can be solved as soon as possible or an alternative can be offered to the customer.

Mujer joven tomando fotografías de cajas de entrega - Cómo se usa el IOT y la IA en la logística foto

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What are the logistics trends for 2023?

Sistema de transporte global concepto abstracto ilustración vectorial. tendencias de logística para el 2023

Cuando se está finalizando el año, es hora de dar un repaso a las tendencias de logística para el 2023 y observar qué le depara a este sector tan desafiante pero imprescindible. Entre ellas se suele incluir desde nuevos avances tecnológicos que optimizan las operaciones logísticas hasta nuevas prácticas que buscan una mejor concienciación por … Read more

How has AI been successfully applied in the supply chain?

examples of artificial intelligence in vector logistics

Artificial Intelligence is known to be a growing trend in many industries around the world. And it is no mystery when logistics and transportation are looking for ways to apply such technology for the improvement of all their operations within the supply chain. From back office solutions to IoT control solutions, there are several examples of artificial intelligence in logistics.

However, for people who are not deeply involved in the industry, it is not very clear to them what specific operations AI is involved in. What are the current supply chain issues for the supply chain to be optimized with AI? And what advantages does this technology confer over the competition?

If you want to learn how AI is being applied in supply chain management with these examples of artificial intelligence in logistics, take a look!

Warehouse automation

Sectors such as e-commerce have gained much relevance in recent years, so the increase in demand and the need for a more efficient storage management call for a solution. For this reason, there has been a notable trend in warehouse automation as one of the notable examples of artificial intelligence in logistics.

Warehouse automation encompasses various operations, from the application of automated storage systems with stacker cranes to warehouse management software. Within it, they use data analysis and demand forecasting to coordinate various processes in the warehouse.

And not only do they perform this step, but they synchronize the tasks of both the operators and the automated systems. In this way, personnel can focus on other tasks.

Inventory control

This operation usually goes hand in hand with warehouse automation and, it seems, is going to be an essential element for companies. Currently, it is known that the volume of shipments is dropping considerably due to inflation, as a result of which stock is accumulating and will take longer than desired to accumulate in the warehouse.

First, the warehouse is stocked with the right amount of inventory according to demand prediction based on previous sales, market analysis, among others. Then, automation systems would be unified with catalog control to improve stock visibility by alerting on low stock levels and detecting defective products and packaging before they are shipped via AI cameras or sensors.

Thanks to one of the examples of artificial intelligence in logistics, it is possible to reduce error margins and time spent on tasks, obtaining high customer satisfaction and competitive advantage over other companies where logistics plays a crucial role.

Primer plano de un ordenador portátil que ejecuta un algoritmo que analiza el código en el escritorio frente a los programadores que trabajan en equipo - ejemplos de inteligencia artificial en la logística foto

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4 Logistical decisions to be made by Artificial Intelligence

Ilustración del concepto de cara de robot. decisiones logísticas que tomará la inteligencia artificial vector

Whether in the digitization of processes or digital transformation, artificial intelligence is increasingly present among companies in any sector. But logistics stands out, as companies today seek to streamline their digital operations and/or change their strategy to meet current industry demands, looking for the best logistics decisions that Artificial Intelligence will make.

There are currently many options and tools with AI on the market that, rather than offering a magic solution to all problems, help in the management of the most complicated processes and assist in making decisions in the face of difficult challenges to solve. Some of them have already been heard a lot about due to their recent use in large companies, but others are not so well known.

The following are the types of logistics decisions that Artificial Intelligence will make in both operations and strategy.

Demand forecasting

It is no mystery to know the most popular peak demand times such as Christmas or Black Friday, when most industries are affected. However, each sector and, in particular, each company knows its own order increases in more or less detail depending on the times and data from previous years, among other aspects.

Despite this, the optimal number of stock is not always right in order not to suffer losses due to excess or broken stock due to its incredible complexity determined by both external and internal factors. That is where AI comes in to plan demand based on algorithms that calculate previous years, marketing campaigns and even taking into account some external circumstances such as changes in laws.

Reverse logistics costs

Reverse logistics is known to be one of the biggest challenges for a company running a supply chain. This is because the cost of a return is much higher than that of a shipment because it is not only a matter of bringing the product back to the warehouse. It is not only a matter of bringing the product back to the warehouse, but also a rigorous administrative management to reintroduce it to the active stock, repair it if necessary, or destroy it if the previous steps cannot be applied.

At this point is when Artificial Intelligence comes in to make decisions based on several criteria, but whose main objective is to assess whether to process the return of the order or not. To do this, the AI takes into account such obvious factors as the cost of the refund, but also other not so obvious ones such as the condition of the product to consider its resale and even the buyer's history to analyze his behavior with the brand.

Once these facts have been analyzed, the AI can make two decisions: either not to process the return, but to pay the customer if the costs are not profitable, or to process it when the losses are not so serious for the company's economy. Not forgetting that the dissatisfied consumer is also analyzed in terms of his brand loyalty and whether he will be penalized for processing too many returns.

Hombre usando un asistente digital en su tableta. decisiones logísticas que tomará la inteligencia artificial

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4 Smart City initiatives that drive sustainable mobility

Ilustración vectorial del concepto de mensajero autónomo. movilidad sostenible Smart City

Currently, one of the issues we are most aware of and concerned about is global warming and measures to reduce its impact. Whether it is minimizing carbon emissions, eliminating plastic and opting for renewable energies, and among them is to achieve sustainable mobility.

One of the most important initiatives is to promote smart cities in cities. It consists of managing all the usual things that are taken into account in a city but adding the plus of sustainability.

Here are some examples of how to promote sustainable mobility applied to logistics:

Electric vehicles

Given the increasing traffic restrictions on distribution vehicles in terms of number allowed, length, capacity, vehicle model, level of congestion and other aspects, it is becoming increasingly common for companies to renew their vehicle fleet to pursue an ideal level of sustainable mobility.

Among the most notable changes are electric or hybrid trucks that significantly reduce CO₂ emissions and minimize noise pollution.

Some last mile companies have also opted for more unusual vehicles such as scooters and electric bicycles because of their ability to access difficult urban areas that even a van could not enter, thus considerably reducing the impact of the carbon footprint.

DUM monitoring and optimization

Given the increasing difficulties faced by last-mile distribution (LMP) due to the growth of large cities, the increase in traffic restrictions and the great momentum of e-commerce, solutions to these problems are becoming increasingly complex.

Among the initiatives to reduce these inconveniences, deliveries are made during off-peak hours to avoid congestion.

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What are the trends in logistics for 2022?

tendencias en la logística para el 2022

In a world that is changing more and more rapidly, it is essential to be aware of new technologies, methodologies or changes that are occurring in the field, especially if they come from a gradually more demanding sector such as logistics. To this end, companies directly or indirectly related to the industry are attentive to the trends in logistics for 2022.

It is known that the pandemic has greatly affected the logistics sector, either because of the problems with transport vehicles or because of the constant growth of e-commerce due to mobility restrictions and confinement. Here are the most important trends in logistics for 2022:

Green or sustainable logistics

One of the biggest concerns in recent years is the great environmental impact that logistics has on CO₂ emissions, as they account for 27% of total emissions in Spain (MITECO, 2020).

To this end, they have mobilized to use technological tools to help them reduce carbon dioxide emissions and more environmentally friendly materials such as the following:

More and more companies are looking to the future and opting for tools that help them to be more sustainable and reduce emissions that harm the planet.

Blockchain

Along with the high demands on delivery times, it is also very important to maintain the security of logistical data. Blockchain, or block data, is used to keep all the numbers in one place.

Apart from that, the blockchain makes sure to keep a real-time tracking of all logistic operations in order to plan processes in warehouses and vehicles.

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Optimizing Delivery Routes: 6 Strategies for Successful Delivery

Last mile distribution

One of the most important and costly processes for companies is optimizing product delivery during the last mile

With an increasingly demanding customer and the constant advancement of technology, a simple mistake in delivery can have serious consequences for the company.

The focus of organizations is now on the end customer. A satisfied customer is a sure value. 

If we want to provide a last-mile service in line with demanding customers, we must consider the following strategies:

  • Implement the use of artificial intelligence. Both AI and machine learning son dos herramientas fundamentales en la gestión de las plataformas de logística. Con ellas la mejora de precisión en el pronóstico de envíos es de hasta un 90%. En temporada alta, ya sea Navidad o fechas señaladas como el Black Friday, las ventas pueden aumentar hasta un 300% y tanto los procesos como los equipos deben agilizarse más que nunca. Gracias a las previsiones inteligentes se puede mejorar la organización de las rutas, la optimización de los vehículos y qué repartidores hay disponibles. Invertir en Inteligencia Artificial merece la pena, ya que también su uso conlleva una reducción de los costes.

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