Barcelona, May 24, 2022.- Author: Pierfrancesco Manenti is Research Vice President for the Gartner Supply Chain Strategy team. Mr. Manenti provides insights and advisory support to CSCOs and Head of Strategy of global manufacturing and retail corporations into the future trends and key challenges affecting end-to-end supply chain strategy. He focuses on strategic transformation, digitalization, agility and design for profitability.
Over the past few years, we have experienced an unprecedented time, with a steady increase in supply chain disruptions caused by the pandemic, geopolitical events and the effects of climate change. All of these events have changed enterprise expectations for supply chains. According to Gartner research, nearly 80% of organizations expect supply chains must be able to make faster, more accurate and consistent decisions in real-time, in such an increasingly volatile and fast-evolving market.
With this expectation in mind, CSCOs need to design a new supply chain operating model that is geared around real-time data availability and people enablement for better decision making. Digitalization is the critical enabler, not only because it helps automate tasks originally requiring some form of human judgment or action. Technology also helps unleash employees’ trapped talent by freeing up their time from nonvalue-added tasks and by augmenting their decision-making capability.
These steps are already being taken by leading supply chains. For example, Intel’s autonomous planning uses machine learning (ML) to analyze results from the planning engine and explain plan changes cycle over cycle, including what drove particular changes. With this knowledge, it can identify the need for a new plan and then start an autorun. If the scenario meets all stated goals, it can autonomously publish it as the plan of record for the company. It’s also able to create a knowledge base that gets stronger over time by accumulating supply chain knowledge and expertise.
Another example is Nestlé which is deploying technology to make its order-to-cash process more automated and intelligent. The company is exploring ML technologies that can predict different customer ordering patterns, estimate risks, forecast short-term customer orders, propose different allocation scenarios, and autonomously make real-time adjustments to the allocation.
Shifting to hyperautomation
The consensus among most CSCOs from global supply chain organizations is that over the next 10 years the most advanced global supply chains will be leveraging hyperautomation — a combination of technologies including robotic process automation (RPA), ML and many others — to become more autonomous.
Business-driven hyperautomation is defined by Gartner as a disciplined approach to rapidly identify, vet and automate as many business processes as possible. As a result, organizations cannot only reduce the breadth and expense of business processes that require humans to perform manual tasks and mundane decisions. Automating nonvalue-adding tasks also means freeing up people time, providing opportunities to unleash their inherent knowledge and talent.
Additionally, supply chains can tap into the massive and growing amounts of data that exists in global supply chains that can only be understood and acted upon with the assistance of technology.
Charting a path to supply chain autonomy
The journey begins with CSCOs creating a multiyear, integrated digital supply chain strategy and roadmap to experiment, pilot and roll out hyperautomation. CSCOs should push for more process automation through RPA, while combining it with ML to also automate more complex decision making.
The path is determined by how fast technologies will mature and when they’ll become mainstream. Gartner’s Hype Cycle for Supply Chain Strategy Research provides essential information:
By 2025, you must have completed your supply chain process automation initiative by leveraging RPA. This technology is expected to become mainstream in supply chain application in only two to five years. If you haven’t completed this by 2025, you are lagging in respect to competitors.
By 2030, you must have achieved decision-making augmentation through the use of ML. This technology is expected to reach mainstream adoption in supply chains in just five to 10 years. This means you must develop your machine learning strategy now, within your current supply chain strategy planning cycle.
Although it’ll be a 10-year journey, it’s recommended that CSCOs start working on this transformation now. Technology is developing really quickly, and there is no time to wait and see.
While the final step leads your organization towards supply chain autonomy, most CSCOs do not foresee a supply chain of the future that is void of people. Hyperautomation is an opportunity to free up people’s time for the value-added work that only humans can perform. The ingenuity and empathy of the human brain can’t easily be replicated. Defining supply chain strategy, driving innovation, taking care of customers and controlling data biases and autonomous decision making are areas that will always require a human touch.