A 2016 U.S. Dept. of Commerce survey of 80 U.S. manufacturers and vendors reported that “Smart” manufacturing would provide $57 billion in annual cost reductions – or approximately 3.2% lower shop-floor costs. Artificial Intelligence is already being used to improve safety, quality, maintenance, scheduling, and product design, but many companies do not have the necessary culture in place to benefit.
Smart manufacturing depends critically on information governance: Rules concerning the collection, flow, and analysis of performance information, most often in digital form. If your business isn’t good at these things — if it doesn’t already possess a culture of curiosity, effective data gathering, and use of data in decision-making and problem-solving — it won’t suddenly get good at these things upon installing AI.
A past client of Rick’s experienced a good bit of unplanned downtime on its extruders. The organization possessed considerable data on unplanned downtime, which operators had gathered manually.
Nonetheless, managers and engineers weren’t interested in tackling the problem. They didn’t trust the data being gathered, so the downtime continued.
Organizations that use performance metrics simply to monitor and hold workers accountable also will have trouble implementing AI effectively. Those businesses will use AI to create resentment and fear among workers even more quickly.
An article we read had this to say about the promise of AI: “After a company deposits enough big data from the workflows, a manager can query the average time it takes for a field worker to perform routine maintenance. When a particular worker goes out to complete the task, the manager can tell him, ‘The average time to complete this is two hours, and you’ve been taking three hours.’” If AI is used to enhance a “fix the blame” culture, it will fail in all instances.
All of this shows that a mismatch between culture and technology will lead to a failure of the technology. We’ve already seen this with Enterprise Resource Planning (ERP) initiatives over the past 30 years. Many millions of dollars have been spent on ERP, which offered increases in productivity through better business process orchestration within and across business functions. Studies show that somewhere between 55% and 75% of ERP implementations failed to meet expectations or failed outright.
Several years ago, Brandon managed a large machining plant that had made a large investment in technology that promised an end to operating inefficiency, and was touted as an easy integration with current systems.
Brandon was concerned that his team had just started using data to understand the plant’s operations better and to solve problems. Fundamental data analysis tools weren’t well understood and were little used. Other basic tactics like bill of materials (BoM) and router accuracy audits were not deployed to ensure ERP systems data integrity.
The software vendor promised a turnkey solution to those challenges. The reality was far different.
Because a culture of good data gathering and analysis wasn’t already in place, there was no disciplined approach for formal systems management and problem solving. When the system turned on, the plant was treated to a display of red, yellow, and green blinking dashboards that made little sense to operators. They grew frustrated with the ongoing false alarms the system generated due to bad information fed into the new system. Over time, everyone simply quit using the new technology.
Manager’s illusions were replaced with the realization that such technology fails when it is implemented on a weak foundation of data management, gathering, and problem-solving fundamentals. Company leaders eventually accepted the fact that they would first have to journey into problem-solving proficiency the old-fashioned way.
Ironically, companies who seem to need AI the least are probably in the best position to take advantage of it, because they are already so good at identifying and addressing problems. Ron once worked for a candy company where he led his associates in an effort to get better data. As they gathered more information, the best approaches became clearer. They were able to identify the sources of the problem and correct them. In other words, a culture of good data governance provided a foundation for effective problem-solving without AI.
Such a culture is established only by investing time, financial resources and, most important, the attention of leadership. Rick’s client could have taken steps to improve its capacity to solve the unplanned downtime problem. Leaders could have had conversations with operators about the manner in which downtime data was collected and how it could be made more effective.
Leaders could have had conversations with operators about their experience with unplanned downtime and what hypotheses they had with respect to the issue.
Leaders could have spent more time on the plant floor observing operations to develop their own hypotheses, which they’d then share and discuss with operators. The company could have trained leaders and operators in good data-gathering and problem-solving skills and made sure that their use was reinforced.
There will be plenty of AI technology vendors who will promise their “plug and play” solution will overcome those cultural shortcomings. They’re wrong. Before you invest in AI, you need to invest in creating a strong problem-solving culture.
Rick Bohan is principal with Chagrin River Consulting LLC; Brandon Davis is a distressed-asset facilities turnaround leader; and Ron Jacques is a Certified Lean Practitioner and consultant.