Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More
Strolling down the picturesque paths surrounding Felixstowe, a port town on the heath-speckled coast of southern England, it’s hard to imagine that such a peaceful-looking place has played host to events that disrupted the global logistics industry.
The connected and interdependent nature of our modern economy means that when port workers in Felixstowe went on an eight-day strike in September 2022, it caused major issues around the world. What’s more, “once-in-a-generation” events are becoming the new normal, producing even greater upheaval and begging the question: “Can supply chain technology come to the rescue?”
The last three years have redefined what global supply chain disruption means. The COVID-19 pandemic, Suez Canal blockage and port congestion have brought chaos to many companies’ logistics operations. Studies show that these events are serving as a catalyst for investments in innovations such as digitization and automation in supply chains across many industries.
It’s likely that we will continue to see industrial actions across ports, railways and trucking impacting logistics in the coming years, and other global events like the pandemic are also possible. However, new supply chain management technologies are set to help navigate this landscape of uncertainty.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
Accurate visibility, timely response
The first step towards increasing the industry’s agility and reducing execution risk entails moving away from outdated and usually highly manual practices and processes. As an example, for years, companies have struggled with tracking and collecting data about freight as it travels from point A to point B.
This opaqueness is being addressed by a host of track-and-trace solutions, which give brands and freight forwarders accurate visibility and the ability to timely respond to disruptions.
Taking this further, companies can now access, through digital platforms, ‘ready dates’ for purchase orders. This can help them forecast inventory levels or suggestions for 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 the delays between goods being ready to be shipped and when they actually leave for the destination (that is, time on the factory floor, in transit, at port). Digitization of the complex manual processes can see this metric slashed, to the delight of anxious supply chain professionals.
Arguably, there is no sector more macro and complex than global supply chain logistics. It is an atlas-spanning industry that impacts every aspect of modern consumer economies. As mentioned, it is also plagued with poor visibility and an almost infinite list of things that can go wrong.
Digital twinning, a technology rapidly growing in popularity, is now helping address these issues.
What are digital twins?
A digital twin is a virtual representation of a physical object or system. This provides organizations with 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 relating to the movement of goods across the world, reflecting reality in all its messy glory.
This advanced capability facilitates collating or generating (through simulations) datasets which 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 when they occur.
This technology has huge potential, one that is set to grant supply chain professionals new levels of understanding of the near-infinite complexities of their domain, and it is likely to become a driving force for further digitization in the logistics industry.
AI’s role in logistics
AI has been making headlines for some time now and can deliver the predictive capabilities that make digital twins even more valuable. It’s 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 function to integrate and adopt, requiring extensive training and expertise within an organization that wants to incorporate it in its suite of tools. In addition, one of the key elements for the success of ML models is the quality of the datasets used to train them, as well as having the right people in a team to manipulate them.
Digital twinning can combine the intricacies of the real world with the power of AI. By improving the quality of datasets used as input, one can vastly improve the utility of ML, potentially resulting in unprecedented inventory optimization, carbon footprint and cost reduction. This can also enhance employee and customer satisfaction.
Ultimately, these technologies are set to move supply chain management from being reactive and incredibly stressful to more proactive and competitive.
Augmenting, not replacing, workers
The deployment of new technologies, especially ones that drive automation and reduce human labor, usually introduces concerns about eliminating jobs. However, as history has shown, jobs tend not to disappear, but change. This will likely be the case with supply chain digital twin capabilities.
By offloading some of the more mundane tasks and decisions to machines and providing visibility and scenarios for human consideration, logistics professionals will be able to leverage their experience, knowledge and cognitive powers to drive strategies and deliver value to the organization.
Instead of having to constantly put out fires and manually deal with huge quantities of (usually) bad data, in addition to worrying about human errors, they will be free to consider the bigger picture and do their best work. Hopefully, as this becomes more and more widespread, some of the concerns raised by executives in recent surveys will also be mitigated.
Adoption, trust-centric models
Adoption is a key success factor for delivering value from new technologies. Its success usually depends on the cognitive load placed on users by the product and the method by which it is introduced in the organization.
Making the solution easy to use through a well-designed user experience (UX) and providing documentation, tutorials and other aids can help reduce the former. And deploying 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 need to come together to move goods around the world. These companies and individuals usually have varying needs, tech-literacy levels, languages and cultures. Therefore, a trust-centric model for engaging with them should rely on awareness, understanding and accommodation of the ecosystem’s diversity.
It’s becoming clear that further digitization and automation of supply chains through the popularization of digital twins and other technologies is inevitable. There’s a growing body of information, including studies, surveys and analysis, that suggests that the world of logistics is set to leverage these new capabilities to deliver more efficient, environmentally friendly and value-led global supply chains.
However, as with every change, this will require commitment, investment and collaboration to become a reality.
Tamir Strauss is chief product and technology officer at Zencargo.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!