However, adoption of cognitive technology is still maturing. In fact, only 40% of survey respondents said they are actively using AI, which signifies that adoption of cognitive technologies still has a long way to go. Figure 3 shows that those players who adopt cognitive technology are looking to find value across all supply chain functions and capabilities. The top function (supply chain tracking) is 10% more likely to generate value for players than the bottom function (cold-chain optimization).
Drive enhanced analytics: Enabled by IT modernization/cloud, cyber risk management, and employee retraining and upskilling
Advanced analytics and real-time decision support are seldom turn-key solutions. Driving enhanced analytics capabilities requires coordination to modernize siloed organizations, legacy IT architecture, cyber safeguards, and talent requirements (retraining and upskilling).
For example, once a company implements and engages a state-of-the-art AI network, it can generate volumes of data that should be stored in the cloud. This, in turn, can expose the company to cyber risk. Also, new technology implementations will require staff training in these new technologies. Our research reveals that companies are already thinking about potential challenges. Just over four in 10 survey respondents cited investing in legacy IT to improve insights/analytics capabilities. Some organizations are leveraging significant analytical and computing capabilities in unique ways, including National Air Traffic Services (NATS) in the United Kingdom. NATS collaborated with Deloitte to develop Performance Optimizer, a fast simulation and predictive analytics tool for airspace analysis. The tool enables NATS to gain a deeper understanding of the capacity constraints and the likely outcome of a range of flow management measures available to the operations team. The tool leverages cutting-edge simulation engineering and data science capabilities to inform postoperations analysis. This enables users to quickly simulate “what-if” scenarios and evaluate the impacts of alternatives.
Our data also showed that 38% of respondents are looking to the cloud as datasets grow in complexity, and similarly, 38% said they are taking steps to safeguard new data streams to prepare for cyber risks. From a talent perspective, organizations recognize the need to work with employees to prepare for looming changes. Some 81% are currently investing in—or believe they should invest in—employee training and upskilling to prepare to work with new analytics platforms.
IoT automation requires that all company resources, infrastructure, and people are part of a coordinated approach such that the full organization is tuned into the success factors of holistic decision-making. Enhancing analytics is especially important for organizations with large geographical footprints or those that have acquired companies. For example, advanced analytics and real-time decision-making cannot be limited to one link in the supply chain or to one area of the organization. So, widespread company geographies require a maturity scale to create a coherent strategy going forward. And upgrades should be handled companywide.
There are two potential roadblocks for organizations embracing this success factor: prioritization of resources and change management. First, from an organizational perspective, prioritizing resources, though vital, is potentially difficult for companies to overcome. Especially when it comes to driving enhanced analytics, organizations should align their efforts across the board—from IT updates to HR upskilling. Second, change management can be challenging. Because following this success factor will change how a company conducts business—from the physical to the digital. Empowering a company to adopt real-time decision-making AI and ML is not easy. So, managing change will likely be vital to success from an organizational perspective.
Conclusion: Harnessing and harmonizing data to continuously learn and predict
When it comes to overall capabilities, organizations should employ the right sets of tools, hire the right people, and promote the right leadership. Prioritization and planning are key and can help organizations embrace change.
Our survey found that nearly 80% of those executives we surveyed are currently investing in or planning to invest in holistic decision-making capabilities. However, we know that there is no one-size-fits-all solution—or even a set of solutions—that works for every organization. A lot depends on the size of the business and where an organization plays within the overall transportation supply chain ecosystem. As such, large integrated players will have different critical needs than logistics providers.
While digitalization is accelerating, we are still witnessing a growing digital divide. According to our research, 50% of large, integrated players are digitalizing the value chain and looking to new data sources for insights. This signals a growing maturity in this pillar. However, adoption falls significantly (13%) among smaller-sized players. In the end, as companies advance their operations toward a new normal, we believe they should consider connected community and holistic decision-making—as well as intelligent automation.
In our next article, we will investigate our final pillar, intelligent automation, where we will discuss how automation can create new opportunities for humans and machines to work together to help achieve the maximum value from holistic decision-making.
Companies should take the right approach to suit their organizational priorities. While the maturity of companies across each strategic pillar varies in the transportation ecosystem, it is imperative that the entire industry continues to harness and harmonize new and traditional data to continuously learn and predict what comes next.