How to implement digitization and automation in legacy industries such as logistics

Stroll the picturesque trails all around Felixstowea port city on the heather-dotted coast of southern England, it’s hard to imagine that such a peaceful-looking place has played host to events that have disrupted the global logistics industry.

The connected and interdependent nature of our modern economy means that when dock workers in Felixstowe went on strike for eight days in September 2022, it caused major problems around the world. In addition, once-in-a-generation events are becoming the new normal, creating even more uproar and raising the question, “Can supply chain technology come to our rescue?”

The past three years have redefined what global supply chain disruption means. The COVID-19 pandemic, the blockade of the Suez Canal and congestion in the ports have caused chaos in logistics operations for many companies. Studies show that these events are catalysing investments in innovations such as digitization and automation in supply chains across many sectors.

It is likely that we will continue to see industrial actions in ports, railways and freight transport impacting logistics in the coming years, and other global events such as the pandemic are also possible. However, new supply chain management technologies will help navigate this uncertain landscape.

Accurate visibility, timely response

The first step to increasing industry agility and reducing execution risk involves moving away from outdated and usually highly manual practices and processes. For example, companies have struggled for years to track and collect data about cargo as it travels from point A to point B.

This opacity is addressed by a large number of track-and-trace solutions, which provide brands and forwarders with accurate insight and a timely response to disruptions.

If we continue with this, companies will now have access to ‘ready data’ for purchase orders via digital platforms. This can help them predict inventory levels or suggest alternative freight methods to avoid delays or reduce costs.

To make it more concrete, consider origin dwell, a metric used in freight management: the sum of all delays between goods ready to ship and when they actually leave for the destination (i.e., time on the factory floor, en route, in the harbour). The digitization of the complex manual processes allows this benchmark to be lowered, much to the delight of anxious supply chain professionals.

Undoubtedly, there is no industry as macro and complex as global supply chain logistics. It is an atlas-spanning industry that influences every aspect of the modern consumer economy. As mentioned, it’s also plagued by poor visibility and an almost never-ending list of things that can go wrong.

Digital twinning, a technology that is rapidly gaining popularity, is now helping to address these issues.

What are Digital Twins?

A digital twin is a virtual representation of a physical object or system. This gives organizations the ability to understand and predict behavior. Weather forecasting and air traffic control are two of the most common examples of the technology. When applied to supply chains, it can provide accurate visibility and in-depth knowledge of all aspects related to the movement of goods around the world, reflecting reality in all its messy glory.

This advanced capability facilitates the collection or generation (via simulations) of datasets that can then be used to predict the behavior of logistics systems to support decisions by understanding their impact. The more accurate the digital twin, the more it can help manage the cost, inventory and environmental impact of supply chains and how best to respond to issues as they arise.

This technology has enormous potential, a potential that will provide supply chain professionals new levels of understanding of the almost infinite complexity of their domain, and it is likely to become a driving force for further digitization in the logistics sector.

The role of AI in logistics

AI has been making headlines for some time now and could provide the predictive capabilities that make digital twins even more valuable. It is clear that AI and its incarnation, machine learning (ML), will be able to revolutionize the world of logistics through decision support and automation.

However, ML is a difficult feature to integrate and adopt, requiring extensive training and expertise within an organization looking to incorporate it into its suite of tools. In addition, one of the key elements to the success of ML models is the quality of the datasets used to train them, as well as having the right people on a team to manipulate them.

Digital twinning can combine the intricacies of the real world with the power of AI. Improving the quality of datasets used as inputs can vastly improve the utility of ML, potentially resulting in unprecedented inventory optimization, carbon footprint, and cost reduction. This can also increase employee and customer satisfaction.

Ultimately, these technologies will transform supply chain management from reactive and incredibly stressful to more proactive and competitive.

Increase employees, not replace them

The deployment of new technologies, especially those that drive automation and reduce human labor, usually raise concerns about job cuts. However, as history has shown, jobs do not disappear, they change. This is likely to be the case with supply chain digital twin capabilities.

By transferring some of the more mundane tasks and decisions to machines and providing insight and scenarios for human consideration, logistics professionals can use their experience, knowledge and cognitive abilities to drive strategies and deliver value to the organization.

Instead of having to constantly put out fires and manually process huge amounts of (mostly) bad data, they will not only have to worry about human error, but will also be free to see the bigger picture and do their best. Hopefully, as this becomes more and more widespread, some of the concerns raised by executives will be addressed recent surveys will also be softened.

Adoption, trust-oriented models

Adoption is a key success factor in delivering value from new technologies. Success usually depends on the cognitive load the product places on users and how it is introduced into the organization.

Making the solution easy to use through a well-designed user experience (UX) and providing documentation, tutorials, and other resources can help reduce the former. And the use of change management processes, especially for large teams and companies, can make all the difference for the latter.

It is also important to consider the complex landscape of supply chains and the fact that many different partners must work together to move goods around the world. These companies and individuals usually have different needs, levels of technical knowledge, languages ​​and cultures. Therefore, a trust-oriented model of dealing with them must rely on awareness, understanding and adaptation of the diversity of the ecosystem.

It is becoming clear that further digitization and automation of supply chains through the popularization of digital twins and other technologies is inevitable. There is a growing body of information, including studies, surveys and analysis, indicating that the logistics world is poised to take advantage of these new opportunities to deliver more efficient, environmentally friendly and value-driven global supply chains.

But as with any change, it will take commitment, investment and collaboration to become a reality.

Tamir Strauss is chief product and technology officer at Zen cargo.