Artificial intelligence (AI) software development has been a boon in a variety of verticals for many years. It has allowed many impressive innovations, from autonomous vehicles to predictive analysis.
AI has also been beneficial in helping companies make and save more money, improve user experiences, and create more efficient workflows. In the logistics industry, AI has proved to be a modern marvel.
As you will find out in this article, the impact that AI has had on the logistics industry has been tremendous. In particular, here are the areas where AI has provided an effective solution.
Warehouse automation has been an important part of the logistics industry for several years. While many logistics companies have used technologies like radio-frequency identification tags and barcodes to digitize processes in their warehouses, that was just the initial step towards a more automated environment.
More recently, AI has made further strides into warehouse automation. This is evident from the fact that Amazon uses robots packed with AI algorithms in numerous locations across its warehouses, which include Seattle (U.S.), San Marcos (Mexico), and Russelsheim (Germany).
When considering how to automate warehousing operations at scale through AI, it’s not just about choosing what technology you will use but also making sure it can handle massive workflows.
Amazon, the largest online retailer in the world, has a documented history of using AI in their warehouses to much success. It won’t be long before other companies follow suit.
One of the most talked-about applications of AI is in autonomous vehicles. In fact, many experts predict that autonomous trucks will be a disruptive force in the logistics industry over the next decade or so.
The use of autonomous trucks has been tried for years and has faced regulatory challenges due to safety concerns. However, one can argue that it remains the freight sector’s Holy Grail; an intelligent truck could handle almost all aspects of shipping goods and could also improve efficiency substantially.
For example, self-driving trucking companies don’t have to provide breaks for their drivers – they simply make their loads lighter (by using less cargo capacity) and thus save tons on fuel costs.
There are several advantages that autonomous vehicles provide. For example, they can reduce traffic congestion by as much as 50%. The safety advantage is also tremendous: self-driving vehicles don’t get distracted and are thus more likely to avoid accidents. This will benefit both drivers and those riding in them.
As autonomous vehicles gain traction in the logistics industry, there will be a need to manage their increasing numbers on our roads. To that end, AI companies like Veoneer have created software solutions that improve road efficiency by giving insights into how well truckers are driving while using connected vehicle technology such as ADAS (Advanced Driver Assistance Systems) or 360-degree cameras.
One of the biggest issues with trucking is fuel consumption due to inefficient driving routines. In fact, many freight companies actively seek to reduce this expenditure every year.
AI can reduce this waste. For example, AI can prompt truckers to drive more efficiently by taking curves slower or keeping speeds within speed limits. This will lead to increased fuel efficiencies and also reduced traffic on our roads.
This is one reason why companies like Veoneer are investing in technologies that improve efficiency for the logistics industry by helping drivers save time and money through better road driving.
One of the biggest problems with traditional inventory management is its inability to forecast good from bad products. In a large retail business model, sales forecasting often takes into account customer demand trends at an aggregate level (i.e., if we sell X product category Y amount of times in region Z, then it’s safe).
Through predictive analysis, AI can easily predict stockpile levels and help coordinators plan ahead. It is much easier to plan ahead for large stores with thousands of products than it is for small businesses with just a few.
For example, if we know how many units of each product we sell in a month, then the AI will be able to predict when we will run out of stock if sales continue at their current levels. This means that inventory coordinators can easily plan ahead and order more goods before they run low on inventory.
The same logic applies even better when you introduce other variables such as seasonality into the equation. For instance, many companies usually see higher demand for certain products during Christmas time or around Valentine’s Day. With data science, AI can determine this trend and also forecast future stock-related challenges due to holiday product surges.
Another major area where AI can impact logistics is in improving customer experience. Traditionally, companies have been limited to distributing a few products in different regions. However, with the help of predictive analysis and inventory data from previous years, AI can help companies determine which products are likely to be most profitable or what type of product will sell well in an upcoming season.
For example, Amazon uses machine learning as a business intelligence solution to predict consumer demand by forecasting future stock levels based on seasonality and price changes over time. Using this information, customers can find goods that they want quicker than before and also get them at lower prices through better supply chain management algorithms.
It won’t be long before other corporations are using AI and other technologies like robotic process automation (RPA) for the sole purpose of improving their customer experiences.
AI and machine learning are both considered to be a part of cognitive computing, or simply the ability for machines to think and operate as humans. At Dev.co, we specialize in embedding the principles of AI into real-world applications, including logistics.
Ryan is the VP of Operations for DEV.co. He brings over a decade of experience in managing custom website and software development projects for clients small and large, managing internal and external teams on meeting and exceeding client expectations–delivering projects on-time and within budget requirements. Ryan is based in El Paso, Texas.