Since the onset of the technology age, we have experienced the trend of people being ‘replaced’ by automation. The question of “to what extent CAN people be replaced by systems?” has been at the forefront of U.S. labor. This is especially relevant in a warehouse environment, where state-of-the-art automation has reduced the number of FTEs required – automated sorting, pick-to-light, and even forklifts that drive themselves.
So, will Big Data eventually replace Industrial Engineers in the world of Labor Management? The short answer is “no” – but Big Data will be help many companies to improve their labor management. But Industrial Engineers will always be necessary to get to absolute, best-practice 100th percentile.
For better or worse, many companies are still in their infancy with labor management, without even have basic labor measurements in place. We believe these companies sit at the So how can the “30-40th percentile” group advance to the next tier in labor management? Traditional models using software consultants often mean a 2 year project at an investment of $2 million – a lofty expenditure and leap-of-faith for many warehousers.
However, using a technology-driven, data regression model can get customers to the 80-90th percentile of Labor Management very quickly – often within a month. This data-driven approach is often just 5% of the cost of a traditional model (~$100K per project), and offers a payback within 3 months. Using a regression model for LM presents a different, more innovative way of thinking about labor standards – launching companies to the 90th percentile of Labor Management.
Once within the 90th percentile in Labor Management, Industrial Engineers can be brought in to “polish the apple” – leading the operation to best-practice of 100%. Data regression models are intended to support the engineers – not replace them. Quite simply, the data provides Engineers with valuable insights as to how to improve the operation, enabling them to make smarter, more efficient decisions.
The bottom line is like many industries, labor management is unlikely to ever be fully-automated. We can get close to best-practice in labor standards with Big Data alone, but without the human touch of a trained Industrial Engineer, companies cannot reach the best-in-class 100 percentile.
