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Logistics Management: Top Tech Aids Shipping Forecast

Today’s business realities seem to mean that logistics professionals must be equipped with a crystal ball to accurate forecast the demand on shipping volumes. However,  magical combination of artificial intelligence, machine learning, and predictive analytics promises a similar effect.

The importance of demand forecasting

Demand forecasting plays a paramount role in corporate-wide planning activities within the organization. It is also important in a customer-centric supply chain. Predicting product demand leads to shipping enough product to fulfill customer orders, which in turn fulfills customer satisfaction.

Demand forecasting also helps having less inventory just sitting on a shelf waiting to be sold. In this case, a JIT system helps in precisely syncing purchases with orders that need to be fulfilled.

One of the main goals of demand forecasting is doing the same amount of shipping using less trucks. Not only this benefits the logistics industry making it more efficient but also contributes to the reduction of carbon emissions.

Challenges logistics and cargo transport service providers face

Transmetrics, a company that has developed a software that uses Artificial Intelligence (AI), data mining, predictive analytics, and computer optimization has found a successful way to optimize transportation capacity. The company is helping logistics and cargo transport service providers saving significant costs in transportation and operations. The first step is to identify the main challenges organizations face: 

  • Data is not clean to allow process transparency
  • Data generated manually at multiple points makes it difficult to measure efficiency gains
  • Data quality cannot be improved at source

Asparuh Koev

Asparuh Koev

According to Asparuh Koev, CEO and Co-Founder of Transmetrics, There are four main steps that technology can support to better plan for optimal transportation capacity: 

  • Data cleansing and enrichment using AI and advanced statistical methods: AI algorithms analyze the data identifying the problems. The data quality is then improved in order to allow transparency.
  • Forecasting : Instead of focusing on historical performance, Koev advises to focus on future performance building forecasts for both demand and supply for the next few weeks. Visualizing the problem areas while they are in the future allows organizations to take a proactive action and correct the problem before it happens.
  • Optimization : AI and complex stochastic optimization algorithms help making decisions based on forecasted information; this helps to increase or decrease capacity as needed.
  • Execution and control : Monitoring the optimized plan.
  • Reaping the benefits of AI, machine learning, & predictive analytics  

A company can dramatically reduce truck departures by 25% by forecasting expected shipping volumes one week in advance and by optimizing the number of trucks that the organization needs to have ready in order to deliver all the shipments.

Once this is done, there is an eight to nine percent total cost savings. A company can improve pricing accuracy by measuring the delivery cost of each parcel as well as identifying low or negative margin customers, adjusting their pricing accordingly, according to Koev.

Before implementing AI and predictive analytics, Koev advices to improve the data quality. Automation is the best, fastest, and most accurate way to do this.

Implemented properly the benefits are visible, says Koev. ”If your average truck utilization was 75% before and now it’s 80%, you have to decide whether to give the credit to the AI software, or to the team that achieved the savings. It’s often much better to praise your team. If you are trying to say that the AI improved your company, then you’re creating distrust in the people who will start thinking again in the paradigm of AI taking their jobs, and that’s not the goal,” he said. 

Koev’s approach suggests to keep a balance between the AI solution and the human team who used the tool and worked hard to achieve to goal. At the end, it’s the synced collaboration between AI and humans what really works in benefit of the company.

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