Automation is a familiar term in supply chain circles, conjuring up thoughts of long-established use cases such as sortation or automated replenishment. But those barely scratch the surface of what’s possible today. Thanks to advanced analytics and technologies such as artificial intelligence, machine learning, knowledge graphs, and natural language processing, automation has rapidly evolved to address significantly more complex supply chain management processes—and it’s just getting started. 

Industry analysts are still feeling their way toward what exactly to call more advanced uses of automation to make key business decisions: terms include intelligent automation, hyperautomation, cognitive automation, and decision automation. But beyond this debate, there is widespread consensus that this advanced capability is essential for dealing with the ‘drink-from-a-fire-hose’ level of data supply chains must take in, make sense of, explore, and predict in real-time to keep up with the pace of today’s market—and make more informed, nuanced decisions.  

Advanced supply chain automation leverages best-in class algorithms and analytics processes to continually interrogate a torrent of disparate data, combine it with current knowledge, strategy, and goals, and make increasingly automated adjustments to the supply chain as the system learns and adapts to changes in data or business operations and learns from additional insights over time. The goal is to assist, augment people resources, and relieve them of the tedious tasks so they can take on the more value-added ones, such as defining strategy, innovating, interacting with customers and partners, and controlling for biases in the data.  

Analysts agree that supply chain operators need to rethink the way they make decisions to put automation at the center. Gartner, for example, says: 

“By 2030, you must have achieved decision-making augmentation through the use of machine learning. This technology is expected to reach mainstream adoption in supply chains in just five to ten years. This means you must develop your machine learning strategy now, within your current supply chain strategy planning cycle.” 

Benefits of Advanced Automation

Advanced, decision-making automation isn’t just a way to process and act on data faster. Some of the benefits that are already clear from early adopters include:

  • Reduced costs. Deloitte found organizations which have successfully scaled their decision-making automation have already achieved a 27% reduction in costs on average from their implementations.
  • Cleaner, more reliable data. Automated data cleansing leverages expert systems to clean and normalize data, and it gets better at it over time, a key capability to make data actionable.
  • Inventory optimization. Applying machine learning to identify data patterns and automate optimizing inventory targets and levels throughout the supply chain network.
  • Better decisions. Advanced automation can account for more variables to make more informed, data-driven decisions than was possible in the past such as demand sensing.
  • Context-aware outputs. A form of advanced automation called automation intelligence can make recommendations that take both immediate and long-term needs in mind to arrive at the most optimal decisions.
  • Elevating the job. By taking away routine tasks, advanced automation frees supply chain folks to spend more time on the interesting parts of their jobs, increasing satisfaction and most likely, retention.

Perhaps most important, advanced automation transforms how supply chain decisions are made, putting the decision itself at the center of the process, rather than the various functions it spans.

It takes time to reach the most sophisticated levels of advanced automation, so it’s important to get started now. The right partner can help you evolve through the four key stages—awareness, augmented, automated, and autonomous—in each part of your supply chain operations.

John Galt’s powerful Atlas Planning Platform leverages a full range of advanced analytics, AI and machine learning techniques in combination with a team of supply chain experts to help you develop a cohesive technology roadmap to unlock the right supply chain planning automation strategy to solve today’s critical business needs and deliver future transformational outcomes. Check out this white paper to learn more.