After all, the future of work encompasses changes in work, the workforce—and the workplace. First, organizations should understand what aspects of work can be automated, taking into consideration contractual obligations between trading partners. Second, organizations should leverage the continuum of talent (traditional full-time employees, managed services and outsourcing, contractors, gig workers, etc.) to delineate tasks associated with each type of talent to optimize organizational benefits. And finally, with new combinations of collaborative, teaming, and digital reality technologies, organizations should rethink how the workplace is reshaping where and when work is done, and fostering culture and team connections to maximize innovation and business results.
Shifting from process automation to smarter automation
Across the movement of goods value chain, “automation” is usually synonymous with robotic process automation or digital bots that perform repetitive tasks to create efficiencies and reduce costs. Approximately 80% of those surveyed leverage or plan to leverage process robotics to automate repetitive digital tasks.
Artificial intelligence (AI), for example, is helping to drive more value from rules-based automation. Among survey respondents, 78% use AI to drive more value from rules-based automation (or are planning to do so in the future). Among manufacturers, this figure jumps to 93%. For example, Walmart and Procter & Gamble have collaborated to create an automated re-ordering system. Walmart utilizes satellite communications, which are then sent to Procter & Gamble whenever an item is needed. Procter & Gamble then fulfills the order and delivers the item. This helps Walmart form more accurate forecasts and react more efficiently to customer needs.9
Companies can utilize AI for new automations wherever they have role-based manual processes. Employees can leverage the cloud to support automation of workflows and then leverage blockchain smart contracts to automate the process across different stakeholders. Blockchain, however, is still lagging, with only 35% of respondents noting that they leverage blockchain smart contracts to automate existing processes. For example, Coca-Cola adopted an enterprise Ethereum blockchain platform to streamline the interactions between franchised bottling companies to make cross-organization supply chain transactions more efficient.10
As the holistic decision-making pillar aligns with intelligent automation, the power of automation can be applied to high-value decision-making tasks—shifting the automation application from “cheaper” to “smarter.” Businesses thriving in this rapidly evolving environment will likely increasingly feed cognitive technologies and predictive insights into a growing robotic network (both digital and physical). This can create intelligent supply chain solutions that can not only identify potential bottlenecks, but also circumvent them altogether. Organizations following this path should align on several important steps, including implementing AI to drive more value from rules-based automation, integrating cloud applications to support the growing automation of workflows, and adopting process robotics to automate repetitive digital tasks.
Conclusion: Utilizing the right human or machine for work
Our survey data reveals that the intelligent automation pillar, albeit lagging in adoption currently, is attracting the most investments. Approximately 43% of respondents are planning to implement intelligent automation capabilities over the next year, compared to 35% for connected community, and 40% for holistic decision-making. The lead time for intelligent automation, as compared to the other two pillars, can be protracted. Therefore, we believe the industry today is far from mature when it comes to the future of movement of goods.
Maximizing the potential of intelligent automation to take full advantage of a constant flow will likely require organizations to change their existing logistics systems to maximize the value that comes from a touchless network, enhance their operating models to delineate between internal talent and machines, and feed cognitive technologies and predictive insights into the robotic network. The increasing power and capability of machines can ultimately transform work, evolving skills and roles required from employees.
Evolutionary changes that we are seeing in the movement of goods may not happen overnight. But as the Hillwood case demonstrates, a change is coming, and organizations should not only have an awareness of success factors that make up intelligent automation, but also should strive to embrace them. The future of movement of goods might be coming faster than we once envisioned.