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Special Delivery: How Logistics Providers are Slashing Inefficiencies with Machine Learning & IoT

We’re living through a unique period in history – a time where massively powerful technologies, like artificial intelligence (AI), computer processing power, Internet of Things (IoT), and blockchain are all maturing at the same time, and dramatically changing the world at large. Each has its own application, but there’s no denying that collectively all these advancements are significantly impacting business models of enterprises in every industry.

As recent as 10 years ago, logistics companies struggled with the management of fleet operations. Many found themselves unable to proactively answer essential questions like where a delivery vehicle was located in its journey, and whether there might be a delay. That, in turn, alienated the customer in the process.

When we look at transportation industries today, we see a very different picture, with many of these tools already being put to good use. For example, UPS is using IoT technology to streamline the logistics involved with shipping and help its customers better match demand with supply. Similarly, other companies are pulling in data to make operational decisions that were not possible before.

Yet, there remains exciting opportunities to change how the business performs. Leveraging things like GPS, big data, and IoT sensor technology – in conjunction with traditional logistics business processes – allows logistics providers and their customers to identify problems before they arise, and mitigate them. In turn, this helps create an ecosystem where reduced costs in logistics, improved on-time delivery rates, and a superior customer experience aren’t merely organizational goals, but a measurable reality. 

Making big data actionable with an IoT-enabled network

Just about every industry in the world has its eyes on big data, including transportation and logistics. But, data collected in isolation from other business operations is potential value lost – every day. Logistics companies must take advantage of their fleets of vehicles and equipment and combine them with IoT sensors to truly harness the power of data. Companies like ARI have done just that, and seen measurable improvements.

The first step for logistics providers is to deploy networks of sensors to each vehicle, container, or piece of equipment in the supply chain, which will connect to the internet and enable the use of geospatial tracking creating a “digital twin” of the operation. In doing so, these companies can combine this data with existing operational data and processes to make better informed and timely decisions at key levels such as diagnostics optimization, driver-behavior, and delivery execution to name a few. 

Technology helps to meet the demand

PwC estimates that the volume of traded goods will reach $18 trillion by 2030, nearly double that of 2014. At the same time, logistics providers are still struggling with inefficiencies like wasted fuel, inventory, and even simple back-office processing. Many of these everyday operations can benefit from the addition of machine learning (ML). 

For example, invoice information often varies with each order, and can be described in many different ways – particularly when it comes to specifying delivery locations (zip, street address, dock door, etc.). Not only that, these invoices often contain thousands of line items to scan through. When human intervention is needed, that is when costs soar.  One of the best applications for ML comes in pattern recognition and predictive analytics, which, when applied to invoices, would help match items to make processing run quicker and smoother. 

Incorporating ML also allows transportation and logistics companies to make better, more informed decisions about things like routing and fueling, thus making deliveries faster and more efficient. With its ability to analyze petabytes of data collected from IoT-enabled equipment and vehicles, ML helps optimize the planning phase of logistics well ahead of time. 

A superior customer experience

Both IoT and ML combine to significantly impact the customer. In the past, the supply chain lacked transparency, and customers were left unsatisfied with their knowledge of when to expect a delivery. Today, customers as well as all participants in the chain demand more information. With the ability to track everything from flights to shipments quickly online, logistics providers have a responsibility to meet this demand with greater visibility. When properly utilized, these technologies allow customers to track their shipments with confidence, and receive notifications of not only delivery, but any deviations as well.

 

Companies like Trenitalia are also a good example of how companies can enjoy the benefits that predictive analytics provide. By adopting new technology and approaches that leverage their existing technology investments, this leading rail operator is able to analyze the life remaining on parts and equipment, and receive notification before a potential repair or failure. This circumvents unplanned downtime, and saves significant money 

The ability to anticipate a customer’s needs before they realize it themselves while simultaneously delivering better services experiences at lower costs, dramatically improves not only the way customers interact with their providers, but also brand loyalty. Customers are much more likely to choose transportation providers who understand their frustrations and pain points and proactively work to reduce them.

Achieving success

The goal of any logistics provider is to safely deliver goods and to keep customers informed along the way. While on-time delivery is a priority, we often see the need for greater transparency and monitoring due to external threats such as theft.

To achieve this, logistics companies need to have real time control and exception management for their complete logistics chain, and they must begin utilizing IoT and ML to save time and make their data actionable. It’s essential to provide customers with predictable and functional insight into any exceptions in the pickup or delivery of their shipments.

Thanks to a digital approach, these companies are in a better position than ever to deliver on those promises.

Kevin Schock, vice president of Solution Management, Transportation and Logistics Services Industries at SAP co-authored this article. 

1 comment on “Special Delivery: How Logistics Providers are Slashing Inefficiencies with Machine Learning & IoT

  1. skype
    June 4, 2018

    Machine learning has lots of advantages over simple programming languages. In nowadays peoples are applying machine learning in business. This makes them reducing cost as well as getting optimization. To know more visit skype support

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