Leaders in logistics are increasingly adopting advanced digital technologies, with most planning to implement at least ten new use cases within the next three years. A survey by McKinsey, involving over 260 respondents from both shippers and providers, highlights high levels of digital adoption, strong investment plans, and growing interest in advanced generative AI (gen AI).
However, challenges persist despite this enthusiasm. The logistics tech landscape remains fragmented, requiring companies to integrate multiple solutions for effective operations. While over 85% of respondents report that digital projects have added value, many still face issues such as delays in results due to poor data quality, system integration difficulties, and challenges in managing organizational change.
Source: Digital logistics: Into the express lane, McKinsey & Company
To successfully harness the potential of digital transformation in logistics, it’s essential for companies to understand the value it can bring and establish a clear vision. This includes defining how processes and technologies align with specific activities and objectives to extract value. A clear strategy will guide how to utilize data, integrate systems, and, most importantly, how to adapt the organization’s working methods. Overcoming long-standing analog challenges, such as skill shortages and resistance to change, is crucial to unlocking the full value of digitalization
Source: Digital logistics: Into the express lane, McKinsey & Company
Digital technologies are being widely applied by both shippers and service providers across various aspects of logistics, including planning, sourcing, execution, and performance management. The survey examined the adoption of 28 digital use cases, ranging from demand forecasting to warehouse automation and asset maintenance.
This year, the list expanded to include around a dozen generative AI (gen AI) use cases, which leverage large language models for automating tasks like scenario analysis and document generation. For larger companies with revenues exceeding $500 million, the story is one of rapid growth in digital adoption, building on an already strong foundation. On the other hand, smaller companies with lower revenues tend to have fewer digital and AI deployments due to limited resources and a more cautious investment approach.
Large enterprises, both shippers and service providers, demonstrate a strong ambition in adopting gen AI. Fifty-five percent of these enterprises have already implemented at least two gen AI use cases, and they aim to adopt at least seven within the next three years.
The technologies used by shippers vary depending on their industries. Shippers in the energy, industrials, and materials sectors lead in adopting digital use cases, while companies in advanced industries slightly outpace others in generative AI adoption. Healthcare companies, however, lag behind with the fewest digital and gen AI deployments and the lowest expectations for implementing new technologies over the next three years. Smaller companies, those with revenues under $500 million, generally trail their larger counterparts in both current and planned use case deployments.
This gap is often due to limited resources and a more cautious approach to investment. Among smaller companies, those in consumer and healthcare sectors show higher levels of digital maturity, likely because of their strong focus on serving end consumers.
When focusing on digital tools that are already operational, the survey revealed notable differences in adoption rates and perceived value between traditional digital technologies and generative AI use cases. This highlights that advanced generative AI technologies are still in their early stages. Approximately half of the companies that have started using generative AI believe that implementing these tools is fundamentally different from previous digital efforts.
Nearly 60% say that generative AI transformations are more complex. Despite the added complexity, companies are optimistic about the potential rewards. Around 60% are more enthusiastic about generative AI transformations than conventional digitization, and 65% expect these new technologies to deliver greater business value.
Successful digital transformations require more than just advanced technology. Companies recognize that effective implementations also depend on skilled personnel, strong change management processes, and efficient strategies to scale and integrate new workflows. According to the survey, the most common obstacles in digital projects are technology-related, including data quality issues, lack of available data, and integration challenges. People-related issues, such as skills shortages and difficulties with change management, follow closely behind.
This year, concerns about data quality were cited more frequently compared to the previous year, likely because advanced digital and AI tools demand higher-quality data. Companies may also be starting new projects with less-than-perfect data as they scale their use cases across the organization. Process-related challenges, such as scalability and regulatory compliance, were also flagged by many respondents, especially in the context of generative AI use cases. While only one in eight companies cited difficulties in achieving a return on digital investment, this was the most common reason given by companies that recently postponed or canceled their digitization plans.
Article by: Asst. Prof. Suwan Juntiwasarakij, Ph.D., Senior Editor & MEGA Tech Facebook Twitter Pinterest