There’s always a better way. It’s a similar refrain, no matter the industry you work in. There are few jobs where you can confidently state, “we reached our pinnacle here, nothing more to do!” Alas, many of us might welcome such a thought, but there’s always more to do. 

The last two and a half years have been challenging for supply chains. COVID-19 is the principal driver of labor shortages across global chains, while the chain as a whole has become much more stretched. This means that anything from a storm in Texas to a fire in Taiwan ends up affecting folks both near and far from Texas and Taiwan. 

Volatility, ambiguity, complexity, and uncertainty are all operating at peak levels. Large firms had been considering automation for decades. But the current challenges are accelerating this consideration and intelligent automation (IA) is now playing a well-deserved, protagonist role.   

IA is the combination of artificial intelligence (AI) and robotic process automation (RPA). The end goal is to improve efficiency and cut costs. Companies or industries with humans working on repetitive tasks are prime IA customers. As detailed In “Unlocking the true potential of supply chains with intelligent automation,” Reuters Events and Blue Prism present some compelling case studies as to how different industries are driving value via IA in their supply chain processes. 

There are a host of disruptive technologies altering how firms conduct their business. Everything from 5G to the Internet of Things, cloud computing, and of course blockchain are in play. But a Reuters survey of logistics professionals found that AI was at the top of the list in terms of technologies that will have the biggest impact on their industry over the coming years. The journey towards full-scale IA is highly dependent on the successful integration of AI and RPA. Yet, the same embrace that automation received on the factory floor has not permeated into the white-collar (supply management) suites. Thankfully, a supply chain disaster just might be the impetus that was needed. 

Reuters Events and Blue Prism found that some of the clearest examples of where IA can make an immediate impact are in forecasting, demand planning, data transformation, document digitization, invoice management, record handling, regulatory compliance, freight management, and automated purchase ordering. One of the paper’s case studies focused on Boeing. Through a newly implemented standardized system for managing and automating purchase order releases, the supply chain team estimated that 1 million-plus order changes have now been automated which equates to 15,000 labor hours saved. 

Unilever might be the most advanced, having handed over the “keys” of their baseline demand forecasts to a predictive, machine learning model. According to employees, the machine is outperforming humans when it comes to arriving at baseline calculations. Departments provide inputs to the system (store closings, new products, etc) that naturally change future calculations, but the baseline itself is fixed and is completed without the need of dozens of teams pouring over historical data and hashing out their prognostications. 

The Unilever example is the best in terms of communicating the collaborative nature of IA and human workforces. IA is not here to take anyone’s job. Rather, process-driven tasks are freed up and employees can then focus on more complex projects. Supply chains are late to the automation game, but this game doesn’t have an ending. There is always time to find a better way.