As intelligent automation and AI have evolved to achieve more, the learning curve and time required to get started have also been shortened. However, companies should proactively debate how to shape their programs upfront. Here are some action items to begin the dialogue and then deliver on the possibilities.
1. Establish your “North Star”
What are you trying to achieve with your intelligent automation/AI program? What supply chain problems are you looking to solve, and how will you measure if you’ve been successful? Is your focus to cut costs and capture greater efficiencies or enable greater resiliency and agility? Or are your efforts centered more on fulfilling customer needs? Intelligent automation is a mechanism for doing all the above, but setting priorities up front will define the rest of your journey.
2. Take inventory and assess gaps across the value chain
By themselves, technologies don’t solve business problems — they enable strategies to solve those problems. Work back from your priorities to determine which foundational elements you lack; for example, is your data in a place to make your dream a reality? Or is it trapped in one system, in a format that makes it more difficult to leverage and integrate elsewhere? Most large companies rely on a complex web of systems and applications, as well as third parties with their own systems and applications. Intelligent automation and AI can fill these gaps, but defining problem areas and how to address them are foundational elements of your approach — including governance, responsibility assignment matrixes, playbooks, guiding ethical principles and more.
3. Find use cases to achieve your program goals
As noted, intelligent automation and AI are inherently flexible, with applications from one end of the supply chain to another. In recent years, we’ve seen a significant rise in technological capabilities that take the guesswork out of discovering and designing processes, such as through process or task mining. Rather than solely interviewing professionals about how they do their jobs, you or your advisors can use this software to create solutions that are more science than art, without guesswork and endless iterations, as well as accurately monitor how value is realized.
4. Build with best practices and reusability in mind
Setting up IA with AI requires a level of investment that is just a fraction of what’s needed for other technologies, and when you get the foundational pieces correct, you can leverage economies of scale for broader use cases across the supply chain. Developing your IA and AI playbooks and center of excellence, upskilling your people, and pinpointing the proper governance are all actions that are just as valid for planning as they are for manufacturing and delivering. Mapping and integrating systems with other platforms can leverage the same connectors internally (like for planning) as externally (with your suppliers in delivering). Data storage, cleansing and analytics fuel demand sensing and planning into inventory management and beyond. Storing and reusing libraries of components also serves as an accelerator for launching projects that immediately address tasks properly.
5. Leverage organizational change management and clear communications
Equipped with IA solutions, your employees can achieve so much more — but you need to communicate that narrative to effectively market the program and its objectives. This marketing plan and the necessary change management are crucial for turning plans into repeatable action. What is your supply chain automation story, and how are you communicating it to align stakeholders? You could have a landing page in your intranet, with videos, to show what you’re doing and how it’s connected to your strategy, priorities and ultimately your bottom line. This is an opportunity to maximize and showcase the benefits of the technology, and your company should cultivate relationships and ideas from within the supply chain and operations and across other business lines for greater success in future projects.
