Safety is often touted by companies as their number one priority, but it is not an easy thing to improve and maintain. People, by their nature, have free will and are prone to making decisions or mistakes that affect their safety and the safety of others.
People are also naturally prone to injury. After all, we are a soft-bodied organism. Injuries have been part and parcel of the human condition throughout evolution, however in the modern age and with corporate and social responsibilities, it’s not cool to injure your employees.
Unfortunately, many companies attempt to maintain safety with administrative controls, and anyone who knows the Hierarchy of Controls knows administrative controls (trying to influence how people work) is only above personal protective equipment (e.g. gloves, glasses, hardhats) as the least effective ways of controlling hazards in the workplace.
As for better controls on the hierarchy, there are only so many engineering or elimination controls we can put in place to protect workers, so influencing how people work and think is very important.
There’s a couple of safety measures I want to share right here to give some context before I continue with the story. Workplace safety is measured in many ways including Lost Time Injury Frequency Rates (LTIFR) and Total Recordable Injury Frequency Rates (TRIFR).
The total recordable injury frequency rate is the number of fatalities, lost time injuries, cases of substitute work and other injuries requiring medical treatment by a medical professional per million hours worked. What a mouthful. TRIFR is the focus of my story. Notably, improving it by influencing how people work and think. This is actually a major part of how a mature Lean business culture sustains growth and efficiency in a business.
Some time ago I was asked by a manager of a major mining equipment fleet to help him improve his TRIFR. The mining company he worked for was very happy with his performance, in fact, his department often performed better safety wise than any other department. But, he was having trouble identifying how he could get the TRIFR below the company’s targets.
To make it harder, TRIFR was measured on a rolling 12 months, so that cut finger back in December was still haunting him and his team in June with a poor TRIFR result. He needed to see a sustained improvement for more than a year to see his TRIFR drop significantly.
We sat down and discussed if there was something he could tell me from the safety data he had recorded in the safety system. He had a lot of data, but most of it was more relatable to forming a Pareto of the type of injury classifications, and these were only telling us the symptoms of the problem, not the root causes.
We looked at injury by the time of the shift, and found injuries spiked after lunch on day shifts. This was the first curious thing, as anyone would logically think that the night shift was a more dangerous period. We looked at the amount of time someone had been with the company and noted that employees whose tenure was under 12 months were more at risk. Again strangely, their prior years of experience made no difference, 10-year veterans were as much at risk as first-year apprentices in their first year working at this site.
So next I decided to see if a regression analysis on several factors I suspected of having a relationship to the incidents would yield anything. I suspected that how long the employees had been on roster might be a contributing factor (i.e. fatigue). I found that regression indicated a strong relationship with factors such as the amount of supervision, and if the crew was short people due to absenteeism or annual leave. Of note, new employees on the roster appeared to have a positive effect – possibly due to hyper vigilance of supervision and other employees.
I looked at the data in other ways too, also trying to pinpoint a trend in the time the incident occurred but found a weak relationship. I performed an Anderson Darling Normality Test and found a clear indication that there were 3-4 people off shift when an incident occurred. This absenteeism often related to supervision as well.
Drawing Our Conclusions
When we sat down and discussed all this data, it did make sense to us that supervision would be a major factor, but how? Why would anything change for the administrative controls that are clearly shown as being in place?
Hazard reporting and Take 5 quotas were high and consistent. So, we could only guess that maybe quality was not there. Then the project manager mentioned that when a supervisor, a desk-bound job, was off shift, the team leader was the only one who could step into the office role and do all the computer work. This was an immediate red flag.
Team leaders are hands-on leaders and not familiar with computer work. We discussed this some more. Supervisors tended to work with team leaders in enforcing and coaching the safety, with the team leader out with the employees and the supervisor predominantly in the office but every other waking minute coaching and guiding safety. But when the supervisor was off, the team leader stepped into the office, tried to concentrate on his computer work, and the employees were left largely without the close coaching of two people.
So, the supervisor not being on shift was in effect like having two safety coaches off the shift. Add that to the lower numbers, increased pressure and it was a real possibility that this was increasing the risks.
We were very pleased that we were able to use statistics to identify the importance of good leadership to safety. It made my day too because it showed meeting quotas of hazard reporting and take 5’s was more valuable if the effort behind them was guided and coached and therefore more meaningful.
Given that at the foundation of Lean methodology there is respect for people, I’d work on a project regarding safety in a heartbeat if it meant using Statistics, Lean Strategy and Lean Leadership to prove how we could make peoples work safer.
Read More: Makoto Lean Business Consulting