Due to the COVID-19 pandemic, many construction jobsites and infrastructure projects were shut down for extended periods of time. Many of these sites operated at far less than full staff since spring, and without normal operations and inspections there are well-founded concerns that under-utilized equipment will be unsafe when business returns to something like normal.
The reason: Machines and facilities that aren’t used and maintained regularly tend to break. But with fewer employees available—due to social distancing guidelines, forced headcount reduction or other reasons—predictive maintenance and regular inspections have dwindled substantially.
Issues that would not be a factor in normal times, such as stagnant water, uninspected equipment or unused elevators, are now potential threats to health and safety. Complicating this are the twin facts that workers don’t know what conditions they will return to and that the post-pandemic world will likely require new standards for worksite sanitation and safety.
In short, operations managers have significant challenges before them as their jobsites return to “normal.”
Using Technology to be Smarter Going Forward
As always in challenging times, though, there are opportunities to rethink how routine business is done. More and more companies are turning to predictive AI and other technologies to give their workers a 360-degree view of their machines, buildings and infrastructure, making them much better prepared to handle unexpected challenges.
A survey published earlier this year shows a growing willingness to adopt AI for all kinds of purposes—30% of the companies surveyed reported using it, up from 4% two years ago.
And with the right AI, companies can not only do more with less, they have an opportunity to rethink longstanding processes that may be insufficient, outdated or simply designed around obsolete problems.
There are several ways AI can help companies be better prepared for unforeseen future challenges.
Detecting, Diagnosing and Responding to System Anomalies
In addition to being able to augment the efforts of existing maintenance workers, AI can detect process anomalies in real time, determine how serious the anomaly is and propose a response. This helps maintenance workers anticipate when equipment is likely to break well in advance of it becoming an actual emergency.
Be a Repository of Institutional Knowledge About Critical Infrastructure
Workers come and go, either through job mobility or retirement, and when they go, they often take important knowledge about critical infrastructure with them. Over several technological generations, the potential loss of that accumulated knowledge—often stored, if anywhere at all, in places as diverse as old notebooks to obsolete floppy discs—can become a serious risk. Centralizing that in AI ensures that understanding of critical machinery isn’t lost.
Know the Repair and Llife Cycles of Machines
Having a transparent record of a machine’s repair history can go a long way towards predicting the duration of its life. When this history is recorded and presented as a searchable database, workers have a much more detailed picture of when a machine should be fixed versus replaced.
Manage Staff Under Conditions of Reduced Capacity
In the early post-pandemic days, there will likely be limits on how many workers can be onsite at any given time. Even in the absence of regulatory or legal requirements, it’s important that employees feel safe in returning to work, and limiting the number of people allowed in will go a long way towards that assurance. AI can help manage the number of people on the jobsite at any given time, while still maintaining the necessary safe social distance.
Separate Good Data From Bad Data
Not all indicators are created equal. For example, a glassware company used a series of alarms to alert workers to potential problems with machines or processes. The alarms went off so frequently that workers ended up ignoring them most of the time—thus defeating the purpose of installing them to begin with. AI is much better at separating false alarms from real ones, and can in the process save millions of dollars in downtime.
Workers and managers are right to be apprehensive about the condition of the sites they will be returning to in the near future. With predictive and analytical AI, companies can ensure both their safety going forward and become better prepared for unforeseen future disruptions.
