As the world around us becomes more and more connected and data-driven, companies have the potential to harness valuable information to benefit their businesses and workers. This session will discuss how wearable safety technology, machine learning, AI and cloud computing are helping safety leaders gain valuable insight into understanding workplace risks, keeping workers safe, and mitigating hazards before incidents and claims occur. Case studies from deployments of wearables in a variety of industrial environments will be examined and examples provided of how the data gathered from wearables has led to; work changes and process improvements, environmental hazards detected, uncovering harmful human motion, and positively impacting safety culture while respecting employee privacy.

Key Takeaways:

1. How wearable technology provides a unique means of gathering relevant EHS data while respecting employee privacy.

2. How machine learning & AI are being used to identify high-risk safety trends.

3. How analytics intelligence can be provided to safety leaders in an easy-to-understand format with actionable advice before incidents happen.

4. How T.R.U.E. Leading Indicators to Hazards & Risk help companies reduce risks and workers’ compensation claims.

5. Case studies with findings and insights around; process improvements, environmental hazards detected, uncovering harmful human motion, and positively impacting safety culture.

[Webinar Transcription]


Tiffany:Hello, and a warm welcome to everybody! We would like to wish everyone a good morning, a good afternoon or a good evening, depending on where you are in the world. My name is Tiffany, and I'm a part of Safeopedia. Safeopedia’s mission is to support the EHS professionals, operational folks, and any safety-minded individuals with free safety information, tools and education.

I'd like to extend a huge thank you to those dedicated professionals for the great work they do on a daily basis. Just a reminder, the webinar is being recorded, and we’ll send out a link to the recording to everybody in a few days. This webinar is for you, the audience, so we'll keep it interactive. Get your questions into the GoToWebinar console as we go, and we'll get to them at the end of the presentation.

Today, we are proud to present “How Workforce Wearable Data is Impacting Worker Safety Today”, presented by MakuSafe. MakuSafe is an award-winning safety data and analytics company based in America's heartland. Their mission is to improve worker health, safety and productivity while reducing worker compensation claims and mitigating workplace hazards and risk exposures. You can learn more at MakuSafe.com/news.

It is now absolutely my pleasure to introduce to you today's presenter, Tom West. Tom West is the Director of Strategic Relations at MakuSafe, as well as the a SHRM and HRCI senior certified human resources professional. Over the years, Tom has held executive leadership roles with many companies providing learning and development tools, technology and services. In 2003, he founded Agility Learning Group and served clients large and small for 14 years. Tom has also been a professor at Des Moines Area Community College for 28 years, teaching management, marketing and small business entrepreneurship.

I'm very grateful to have you sit back, relax and enjoy this webinar. With that, Tom, please take it away.

Tom:Thanks very much, Tiffany. I appreciate that, and we're very happy to do this presentation in partnership with Safeopedia, talk about how we're using wearable technology and some of the evolutions in technology that may prove to be an extremely valuable tool to safety leaders in sending more people home safely at the end of each day. So, with that said, let's talk a little bit about startling reality.

I believe that there is a safety gap that exists. And that would be a gap between where we are today and where we would like to be, ensuring that the large number of preventable incidents and accidents that occur are mitigated and remediated before people have accidents and injuries in the workplace. That's evidenced by some startling statistics.

I'll start with the fact that around the world, more than a thousand people go to work each day and don't get to go home to their loved ones. Globally, in excess of 5,000 people die daily as a result of work-related illnesses or diseases. And that statistic on the right is a little closer to home. In the United States alone, more than $1.2 billion is paid out in workers’ compensation claims every week.

Some of you may have seen this before. It goes further to the scope of the problem. Every year, a large workers’ compensation insurer, Liberty Mutual, puts out what they call their top 10 offenders list, documenting the top 10 types of injuries and incidents that they're paying work comp claims on. And if you take a closer look, you'll see that large number of these are the type that are preventable. And when looking even closer, several of these are somewhat simple end of the spectrum, things like slips, trips and falls. We've taken a close look at this and realized that even just slips, trips and falls are a $19 billion a year problem in the United States alone.

So, in order to begin to close that gap, I believe leaders in every facility, in any kind of environment, need to become in tune with the practice of identifying clues, these indicators or signals that exist, hazards and risks that are present in their facilities. These are the kinds of things that give safety leaders a path on hazards to be addressed and will help safety leaders prevent, and even predict, accidents and injuries in their facilities, things like motion injuries, things like environmental hazards that may impact worker safety and health. So, we'll talk a little bit further about that. But for a moment, let me jump topics.

I'm sure many of you may own some sort of wearable device these days, whether it be a Fitbit or an Apple Watch. This is not a new concept, actually. The origin of our company was a result of asking how we might be able to use technology that is growing in acceptance and prevalence in order to be able to collect data that can be used to predict and prevent accidents and injuries in the workplace before people got hurt. This is not a new concept, wearable technology, but certainly is growing in popularity, prevalence and acceptance. Certainly, Fitbit, Apple Watch and others, versions of wearables are widely known and used today.

But some of the data projections on wearables are, and some of the study that's been done is quite remarkable. I'll point you to the fact that 71% of young people in the workforce want wearable technology. That's very interesting and probably didn't exist a few years ago. Wearables are being credited with increasing productivity and job satisfaction. Certainly connected devices and the rapid growth of connected devices that send data to the internet is probably something that everyone is familiar with. But specific to wearables, the projections are that by next year, nearly a billion connected wearable devices will exist. And we believe a lot of those will be used in industrial environments.

A problem is present though, and that is that even though they're growing in acceptance and popularity, they can be perceived as Big Brother-ish, maybe even a little bit creepy. I think Amazon may have learned that lesson about a year, year-and-a-half ago. They received a patent for an idea that they had which was wearable wristbands and weren’t reviewed favorably when word got out that they wanted to use those wristbands to direct the movement of workers in their jobs by using haptic feedback and vibrating that could guide a worker’s hands to the most efficient and productive way to go about their jobs. Certainly, that's a risk, and I think people are aware of privacy, don't want to be intruded upon. And this kind of haptic feedback can give the impression that people are being watched, and certainly nobody likes that.

So, since 2016, we at MakuSafe have been working on this concept. How do we use technology that is emerging and developing to create safer workplaces? How can we gather data that can be utilized by safety managers to do their jobs more effectively and efficiently? And how can we provide insights that make sure more people go home safely at the end of each day?

I believe that there's great application and fixed workplaces, like manufacturing settings, logistics facilities, job sites, even construction. And excited to have the opportunity today to talk to you about how we are proving that, and I'm going to walk you through a little bit of detail about what we're doing and how we're doing it so that I'm able to share with you some case studies and findings and insights that have come from our deployments of wearable technology in a variety of environments and the kinds of incidents that we're detecting, indicators that we're collecting, how data is being used and how we're impacting worker safety today.

So, with that said, it's necessary for me to describe a little bit of our technology and talk to you about our approach and how we’re using it. This is a look at what I would describe is our MakuSafe technology ecosystem. You can almost think of this in two halves to a total solution. There's our hardware half or components, and there's our software or cloud based platform component.

A little bit of a closer look, we're using a wearable device in the form of an armband. The actual core device is separate from an armband worn holster, and it’s worn on the upper arm of industrial employees. That core device carries with it a large number of IoT enabled sensors that look outward into the environment around a worker and gather indicators of the actual working conditions that a worker is operating in.

The image in the center there is a little bit closer look, you can see that there are some holes or ports. Those are where the sensors reside in the core wearable device. That wearable is actually about the size and shape of a typical matchbox. It doesn't weigh very much, comfortable to wear. And then on the right, there's also an image of what we refer to as our base station, which is a wall mounted kiosk. Kind of looks like a time clock by design that hangs on a wall.

And you could picture a worker walking into a facility. At the beginning of their shifts, they walk up to the base station and enter a unique personal identifier code, maybe it's an employee ID number. And a core device is assigned to them when a bay in that base station flashes. That base station houses 20 wearable devices. And they remove one simply sliding into that armband worn holster and go about their day or their job.

I'll point out it's kind of a compliment in our eyes whenever we're told, you know, I forgot to take that off at the end of the day, or I just put that and put my coat on right over it and went out to lunch, forgetting that I had it on. That kind of thing happens from time to time, and we chalk that up to the fact that it's not an encumbrance, and it's comfortable to wear.

So, you're probably asked what exactly we are collecting in terms of data. Here's a little bit of a close look that has, you know, sensors pointed out, labeled here. Some of the sensors that are on board, I kind of like to describe this though in terms of the four categories at the bottom of this page.

So, first category would be we're using sensors to monitor environmental conditions around a worker. We're looking outward, and that would include some simple things like light levels, temperature, humidity, even air pressure. Also, complex things like the fact that we've got a full noise dosimeter built into our device. Using that microphone, we're monitoring sound in eight octaves, according to OSHA guidelines, and calculating time weighted average.

We've got air quality sensors built into the device. They may seem kind of simple. They're difficult to pull off effectively. And I'm sure as many of you know, these are the kinds of things that may only be studied, monitored, tested for once a year, if at all, and certainly have an impact on worker health, productivity, even impact fatigue to get real-time data on, you know, why two workers who are in close proximity to a piece of heavy machinery may be experiencing higher temperatures and may need to, you know, have heat protocol enacted, so they get more frequent breaks if thermometer, you know, on the wall just may not accurately reflect that all of the time for everybody. So. that's a bit about environmental conditions.

Second category of data would be potentially hazardous human motion, and that's forceful motion. So, we're using accelerometers on board our device that detect motion that is coupled with force in any of three axes. And I'll give you a look at what that data looks like when we capture it, and we can talk a little bit about that. Certainly, though we've done an awful lot of study on slips and trips and falls and the kinds of motion that lead to ergonomic strains and injuries. And we are using machine learning to begin to identify motion in real time when it's occurring.

Third category would be location. In a facility, when either in environmental abnormality is detected or motion signatures captured with force, we then identify the location of where those things are occurring in a facility. You know, I'll even point out here that so often, if somebody slips and gets injured in an operation, when that report is made to the safety manager, oftentimes they hear from the rest of the department or the rest of the employees, “Oh, yeah, that happened to me last week. I was meaning to tell somebody.” In fact, when an employee has one of those near misses, probably the first thing that runs through their mind is, “Wow, that was stupid. I shouldn't do that again, or you know, I should learn to be more careful. I hope nobody saw me.” Not that it needs to be reported because it may be part of a larger trend that's been occurring for the past few weeks in a location. So, when we're detecting these indicators, we identify the location of where they're occurring within a facility. We believe that's important. And I'll show you how we display that in just a moment.

And last, but certainly not least, there's a button in the middle of the device that we've labeled as manual submit. We hear all the time that the holy grail to keeping people safe may be ensuring that more near misses or observations from the front lines get reported. Yet, virtually everyone agrees that the vast majority of these huge percentages probably never get reported to safety leaders. And the reason for that maybe that people don't want to interrupt their work, take time away from their job, to go fill out paperwork or report something that didn't really happen. And we believe that it's key to reduce the barriers that exist in trying to get more of those reports.

So, that manual submit button enables a worker to press and hold it, speak into the microphone and actually record a voice memo up to 20 seconds. And we hear things all the time, like, “I'm in a dark corner of the warehouse, and there's a stack of pallets that looks like it may fall over.” We've had workers push that button and report that, “Maintenance may have repainted the floors in my area last night, and I'm kind of wondering if they use the correct paint. It seems awfully slick here,” only to find out that safety leaders may not have been aware that those things were occurring. In the case of that paint story, there actually was no friction element, no sand added to the paint, and it was done incorrectly.

So, those are the four categories of data that that we're collecting on our device: environmental conditions, forceful motion, the location of where those things are occurring, and we're enabling real time reporting of near misses and observations on that voice recorder, that manual submit button as well.

I will point out that we're not collecting anything that is personal or biometric. We're not constantly monitoring the worker, instead, we're collecting data that can be used, and we send that to the other half of our solution we call our cloud-based software platform MakuSmart. And here, we're displaying the data that comes in from the front lines in real-time or near real-time. We're making that available to safety leaders in order to enable evidence-based decision making to help them do their jobs more effectively and efficiently.

So, we're trying to make this as visual and understandable and actionable as possible so that at a glance on any device, safety leaders have data that can help them investigate happenings in their facility, you know, be in tune with what's occurring right now on the factory floor, go have conversations with their employees. And you don't have to be a data scientist in order to be able to understand what's going on.

So, here's kind of a closer look at an example of how we're displaying data. And I'll start there at the top left. I mentioned, we're capturing motion of force in three axes. This actually happens to be a stereotypical slip, and this is how we display and gather motion data. So, on the left there, you can see somebody is walking. Then there's motion of force that's a very pronounced motion, and then they go back to walking. We gather motion like that. We send that to our cloud platform, and we attempt to categorize and classify what type of motion it is.

We've, as I mentioned, did a lot of study on slips and trips and falls. And over time, understanding the motion in a facility will be able to identify repetitive motion, twisting, turning, and those kinds of things. Even somebody climbing an access ladder to work at height and their boot slips off a rung on the ladder. That would be the kind of motion that we would capture. If somebody is on a forklift to fork truck and backs into a racking system, stops abruptly, takes a corner. If they're wearing one of our wearable devices, that would be the kind of motion that we would detect.

On the top right, you see that we're displaying other indicators that are detected by the sensors in real-time. So, it might be really interesting to see that there’s an increased number of slips or trips. And at the same time, noise levels have risen, and temperatures have risen on a particular, you know, day part or shift, and we can see that maybe the location of where those are occurring is out in the loading dock area on that facility floor plan at the bottom of the screen. That's where we're overlaying the frequency of indicators that are being detected into a facility floor plan so that we can begin to understand the concentration where they are and understand trends that may be occurring.

And that's how we're trying to gather data and make it immediately actionable, meaningful intelligence for frontline safety leaders. We're not sending any haptic feedback to the employee. We're not constantly, you know, setting off buzzers or whistles and indicating that they're doing something incorrectly. Instead, we're passively looking into the environment, not at the person gathering data that may be useful intelligence to safety leaders in order to help them do their job more effectively and efficiently.

And I will point out that we're using machine learning and artificial intelligence to also look for correlations between these types of data points and others. You know, I'm in the Midwest United States, and this time of year, when, you know, the sun comes out, as it's right now, it may cause dramatic shifts in temperature that might bleed to something like, you know, condensation on the floor. And couple that with some fatigue impacting environmental conditions, and that may lead to more frequent indicators being detected more risk of incidents and injuries actually occurring in a particular location.

So, one last look here at the way that I describe the layout of our software tool for frontline safety leaders is that we're displaying safety analytics, and we're enabling quick, easy, intuitive filtering so that you can search by any metric, you know, period of time, date range. You can look at multiple locations and begin to drill in and really get an understanding of what's happening in your facility in real-time.

But we're also raising that to a higher level which I would call risk intelligence or safety intelligence, in that we're using the power of cloud computing and artificial intelligence to begin to identify trends and display those trends generate alerts for frontline safety leaders, and at the same time, on any device in the software, maybe begin to get recommended action steps, remediation suggestions that may come from the company, may come from generally accepted sources of advice, OSHA and others.

In some cases, even workers compensation insurance companies have an interest in engaging with their policy holders to prevent accidents and injuries before they happen, rather than just paying for claims as they come in. So, they've got a considerable number of resources in the form of risk improvement departments, loss prevention, even specialists, industrial hygienists, and ergonomists and such that they want to deploy and make available to their policy holders before people get hurt. And we're able to do that in our software platform.

So, with a bit of an understanding there about what we're doing and how we're doing and our real goal here is to gather what I would refer to as true leading indicators of hazards and risk. There's a lot of debate these days about leading indicators and how they should be used versus lagging indicators. Lagging indicators are our metrics that indicate frequency of occurrences after they have happened. Probably the most well-known is recordable rate or dart rate. Instead, leading indicators are predictive, proactive metrics. And all too often, I believe I hear discussions about leading indicators that lead to a feeling of it being really very difficult to start gathering leading indicator data and use it. You may have to, you know, set up a software tool and begin mining existing databases and monitoring all kinds of things.

So, I believe wearable technology that is using sensors to gather data in real time, even on day one, provides timely data, leading indicator data. It's happening real time. It takes about 30 to 45 seconds actually for something that we've indicated to be transmitted and show up in our cloud-based software platform. Certainly, this is relevant data. We're monitoring things that have an impact on employee health, safety, productivity, preventing fatigue. These are unique things that aren't often monitored or measured. They're certainly useful to the safety leader.

And maybe most important, I would say that this has all been done in our eyes with an attempt to make it as easy as possible to set up and deploy, and very, very economical. And I believe regulatory bodies are talking about how to implement leading indicator standards and characteristics of leading indicators that should be monitored, but not very often do I hear enough that it really should be easy to pull off and shouldn't take rooms full of people and be very costly.

So, I would say we're also well in tune with the safety management process that should exist, whatever, you know number of steps or however you view this, identifying hazards. Certainly those that are our sensors are able to detect, but also enabling safety leaders inside of a software tool to easily initiate hazards, document them so that they can be analyzed. Controls that are implemented and then engineered need to be documented so that they can be tracked and results evaluated so that you can close that loop. That's something we're, again very in tune with and trying to do so that we're not making the safety leaders job harder. But, you know, a good example may be that one day, a safety leader wearing one of our wearable devices may be able to push the button on the device and begin to take notes of checklist items, or to do's, that they want to assign to other people. Just by using their voice and speaking into a wearable device, it may make them much more productive and may enable this process to have more documentation, more tracking and therefore lead to better results.

So, with that said, a little bit about what we're doing at MakuSafe, I'd like to talk to you about some of the things that we're learning in the field. We've just recently conducted what I would refer to as our final round of pilots. We've gone through many different iterations, many generations of our tool in order to prepare to get it to market, and we're just finishing up a round of pilot deployments with strategic partners. Those pilots lasted about 120 days.

And here's a quick punch list of some of the revelations, some of the outcomes, of those pilots. We certainly were in dramatically varied environments, everything from, you know, steel manufacturing, to product, consumer product manufacturing. Some were extremely demanding. We'll talk a little bit about that. Both end user industrial company as well as some work comp, insurer policy holders. Three of those were in labor union sites, organized labor. We were running concurrently in six states. And just with a limited number of workers, we had limited hardware available at our disposal.

What 140 workers wore our wearable devices for about four months. We gathered 15 million data points from those workers wearing our technology for 75,000 manhours. And we began to in that pool of data that was gathered, identify things that were outside the norm. We would call those indicators so a few hundred thousand of those were things that were abnormal. And we talked closely with our partners so that they would and could investigate and confirm what our technology was picking up, and document those things that were unknown to them and label them is near misses. These are the kinds of things that we've been discussing here. Some were environmental issues, human motion slips and trips, near misses and observations being reported from the front lines.

And in the eyes of the partners that we worked with, because we asked, “If this resulted in an incident, what do you think it would have cost you?” they began to assign dollar value to the potential claims that were saved, and it's hundreds of thousands of dollars. We're interested in proving that our solution definitely has a return. And it was so great that it even if they derive no further value out of it for the rest of the subscription period, it was beginning to look like many thousands of percent in ROI.

And here's an example of one of those case studies. So, in an industrial cleaning facility, the indicator that was detected was forceful, repetitive motion. And it was shown by multiple employees in a specific area, right in one workstation. The hazard that was identified actually was a result of material being jammed at the end of a process in a machine over and over and over again, whenever that process was being run. And the workers were bending over in a precarious position and using their legs and back and shoulders to push and pull the dislodged, this material. So, when we showed that to safety leaders, they immediately began to consider a process change or modifying the machinery to keep workers from having to perform that motion. And it may seem obvious, but it was not known to safety leaders who were very active and effective on the floor, but it was known to the employees. Further, when they observed the employees performing this motion, they also noticed that they were using the light curtain which is intended to be the last line of defense to shut down the machine instead of following procedure, proper procedure to do so.

So, we uncovered something here that was important. Could that have resulted in repetitive motion injury to the shoulder, back or worse, in the eyes of the partners that we were working with? They believed so and began to conservatively assign a dollar value to that. What would that have cost typically in an operation like yours, if that were occurring? Occasionally we get, you know, answers that are a little above, a little below. If it happened to numerous people, it may be dramatically higher. Certainly, if it resulted in a suit, it would be dramatically higher, but we're trying to be as conservative here as possible.

This is something you probably have seen before. Many of you often referred to as Heinrich experiment, Heinrich and Bird Safety Triangle. Whatever you call it, a lot of companies have actually put their name on this, and it's occasionally controversial, hotly contested, because many believe that there's nothing we can do to really eliminate the unpredictable, serious injuries and fatalities at the top of that pyramid.

However, I believe there's a lot of common sense here. If we're interested in remediating mitigating, eliminating the preventable, then there are an awful lot of clues. There are a lot of indicators, behaviors and near misses, that we can pay attention to, that we can use to make better decisions and significantly impact the top of the triangle. So, I don't want to get into the controversy of all of this, but I think that the type of data that is being gathered, and if you're in tune with and cultivating those near misses and reports from the front lines, can certainly have an impact.

Here's another example. At a consumer product manufacturer, actually a global name, a partner of ours, we detected an indicator. Again, this was forceful motion. And interestingly, this was an employee who was at a workstation working right alongside a couple of others that are doing exactly the same job that he's doing, except that we were detecting a very, very concerning high force motion from one worker, and not the others.

We incurred safety leaders, and in fact, talked with the employee ourselves because this was a close partner. And it was not known that this employee had nagging shoulder injury, not known to safety leadership that that was the case. He was a long-term employee that had been there for an awfully long time. And then that led to a question about why the motion signature seem to be dramatically different later in the week as opposed to early in the week, there was almost nothing. And then a worker revealed that usually over the weekend, he went to see a chiropractor. So, on Monday and Tuesday, he was feeling pretty good. Late in the week, it seemed to get more and more aggravated, and probably impacted how he was moving.

I'll point out that it was interesting for us to hear that the worker was impressed that anybody cared and asked about it. The safety leaders immediately recognized that this was probably an injury waiting to happen, and asked if the employee when his shoulder was bothering him would just like to rotate to a different workstation. Further actions may be taken, but that was a simple step to take and again, something that was unknown and could certainly have led to an expensive claim.

Where do we get these numbers on typical claim costs? Well, if you haven't seen this, I would encourage you to take a look. The National Safety Council usually this time of year puts out a report that documents some of the average costs for typical claim types take a look at falls and slips there in the middle of that model. The average work comp claim for a slip and fall that results in a day away from work is an excess of $46,000. And take a look at the total cost of occupational injuries in the US, billions and billions of dollars.

So, one more example, this was in a heavy manufacturing metal fabrication facility that we were at work in. The indicator that was detected was high noise levels, and when we were looking at the facility as a whole, the entire work team, there wasn't a problem. Safety leaders believe that they had this under control and we're well in tune with what was happening. It was shocking to them to see that a couple of workers, one in particular, in the first couple hours of his shift was achieving 200% of his allowable sound dosage for the day.

So, again, understanding the actual working conditions around a worker led them to look at ratings on hearing protection and rotate that job so that this worker wouldn't be subjected to what could cause significant hearing impairment or loss. And what would that cost in your facility? We've conservatively estimated that for our purposes here.

So, again, are these the kinds of leading indicators that would be helpful in understanding the real hazards and risks that are present in your facility? Hopefully, I've given a couple of good examples. I've got one more here, but you know, are these timely, relevant, unique and useful if they, you know, without much work, if you're able to gather these through the use of a wearable device? That's not an encumbrance, and it's inexpensive. I think those are some of the characteristics hallmarks that should be kept in mind when contemplating this kind of tool. But there certainly are benefits.

Here's one more example, our last example. We're at work in a steel foundry, very demanding, 3,000-degree temperatures in furnaces that run all day, every day. And MakuSafe wearable devices detected a forceful motion. Our cloud software identified that that was a slip. Actually, this was just the launch of a pilot. So, the shift supervisor and the safety director went to talk with the employee because the they received an alert and asked what happened. And the employee shared with them that this was happening a lot. It was repetitive, and it was probably more of a trip because I think I lunged forward when this occurred, but it was the ergonomic mat that was recently purchased and installed in my workstation.

So, our software enabled the safety manager to easily reclassify that. And I point that out, because every time we're able to do that and then in further trains our machine learning, our AI models in the cloud. It can be done from a smartphone in the facility on the fly. And they documented a fix. They got an ergonomic mat. Excuse me, that was a better fit for the location, and we're reevaluating others as well. The best of intentions may have led to trip, slip, leading to sprains, broken bones. Certainly, those are extremely costly when they become claims.

So, my last point before we get to questions, Tiffany, if there are any coming in, would be this that, I think in every organization, anything that we can do to ensure that there's a constant ongoing conversation taking place between leadership, supervisors and the frontline employees, is what it takes to truly build a positive safety, culture change, influence, safety mindfulness mindset. And the impact of that is extraordinarily important.

There’s no magic bullets, but I believe that having data or that leads to questions, not about, you know, what have you done wrong today? Or do you think the company cares about you? Nobody likes to answer those questions, but rather, what's going on around your workspace and how's your job going? Is there anything that we need to do to support you better and make it easier for you to be successful in your job? That is an important culture building conversation that all too often doesn't occur.

For those of you haven't heard this story, I love this example. I would highly recommend this book. It's not your typical safety book, maybe, but called It's Your Ship. If you haven't heard this story, Captain Abrashoff on the day that he finally was promoted to command of his own vessel in the US Navy, an admiral looked at him and said, “Congratulations, son, you just inherited the worst ship in the Navy.” That didn't sit very well with him. And the book documents his quest to go from the worst to the best ship in the Navy, which he did. But I would point to those bullet points there.

I've spent my career talking to leaders and supervisors about what really makes a difference, and asking and listening, what's working, what’s not working around here, and what needs to be changed in order for you to be successful? May sound cliché, but is the single most important thing that frontline safety leaders can do.

So, I would leave you with that tip. And I think occasionally, it's hard to know what to ask your people about, but having data, not about productivity and you know, not constantly talking to people about what they're doing wrong, but again, asking them what's going on in your workstation and what do you need for me in order to do a better job. That kind of conversation helps shift safety culture to be more positive. And there’s lots and lots of evidence that's mounting out there as a result of studies that show that stronger cultures and more safety, mindfulness, increases worker engagement and actually leads to 14 times better or more safe workplaces is what I have noted here, but certainly the impact on productivity, on quality, the impact on cost savings as a result of reducing incidents and accidents before they occur is dramatic and can help you be more effective and efficient in your job.

So, Tiffany with that said, I'll pass it over to you and see if we have any questions.

Tiffany:Great presentation. Thank you, Tom. Yes, we've had a few questions come through. This is a quick reminder to get your questions in as we will now be starting the Q&A section. We'll keep it brief to finish on the hour, but we'll start with some questions that came through during the registration.

So, we have a question here from Jamie, who asks, “How do workers respond to wearing the armbands? What kind of feedback have you gotten from their perspective?”

Tom:Yeah, great question. Thanks for asking. I will point out a couple of things that I alluded to in the presentation. One, even when we hold orientation and launch or kickoff something with a new team, the fact that we're not collecting anything that's personal and we're not, you know, nothing biometric, not like heart rate or stress level or anything like that. We're not constantly monitoring employees or tracking people. We don't care, you know, how many times you've gone to the restroom today or how long you've been in the break room. Instead, once people understand that we're using sensor technology to look at the environment around them, even in unionized environments, that is what everybody is interested in: monitoring hazards and actual working conditions that people are faced with.

And also, I mentioned it's kind of a compliment to us when people forget that they have it on which usually happens after the first you know, 15, 20 minutes, 30 minutes. So, I wear one at the office and routinely walk out to my car and forget to take it off, and that happens from time to time with employees as well.

Tiffany:Great, thank you, Tom. I have a question here from Cody who asks, “How would these be useful with frequently changing manpower?”

Tom:Well, that's a terrific question I think. I assume you're alluding to maybe high turnover. I will say that we're focused on fixed workplaces where people go to work at a facility or a job site, and the reason is because of how we communicate data. So, we're not currently addressing remote workers or field workers. But if you're referring to, you know, workers who change jobs with facility often rotate through positions, or maybe the fact that you have high turnover, I would say that this lends itself well to helping with your, with the cause you're battling against. Anything that can be done to demonstrate care and concern for the health and well-being of employees that can be communicated to them, I think aids retention, and maybe even attraction, of employees and certainly that's a big concern today when we definitely have a war for talent going on and shortages.

I think there’s, you know, a mindset shift that takes place when people put on our armband that may sound a little out there, but just the fact that they have it on causes them to think more about their job and to be present in thinking about safety, and also that the company cares. So, in short, I think all of those things may help.

Tiffany:Yeah, absolutely. Thank you, Tom. I have a last question here just from Alex, who asked, “What metrics are being used to determine success?”

Tom:Well, success for us is helping, again, organizations predict and prevent accidents and injuries by understanding what's really happening and mitigating those risks before people get hurt. Tracking some of those stories that have come out from come out of use of our tool and technology is what we've been doing here. And it's nice to finally have some evidence to be able to point to. I think over time, this is new technology. Over time, you know, we'll be able to see that an organization has reduced its costs or improved its e-mod score, modification score.

The organization itself, I think, will be able to more effectively document hazards that are identified and more frequent documentation of those so that they can be tracked within the organization, whether it be, you know, air quality concerns, sound concerns, you know, eliminating contributing factors that have led to slips and trips and, you know, ergonomic concerns within an organization.

And I think, the for a frontline safety leader or for a safety director to be able to use a tool like ours that shows the type of data that's being gathered, the type of hazards that are being identified as a result of this data and what we're doing about it. The fact that we can track and document our actions, and that results are improving, I think that being able to show that is probably where the world of safety is, is headed in the future. No longer is it adequate to just track, you know, reportable, recordables or days away those lagging indicators of things that have already occurred.

So, hopefully that's an adequate answer, and I would welcome any additional questions, inquiries or, you know, thoughts feedback input that the community has. I’m certainly easy to find at our arc website. I've actually put the URL for a report that was written a short while ago that may be interesting to you: makusafe.com/nearmiss. You can download that there. But certainly send me an email: [email protected], and I'd be happy to answer any further questions.

Tiffany, I don't know if we have any remaining or any time left?

Tiffany:Yes, we have several questions, but unfortunately we are out of time. If we didn't get to your question today, I will be sure to pass those along to Tom, so he's able to reach out and answer those for you.

Tom, yeah, I was going to ask if you have any last words, but I think you just covered it. I personally, or no, go for it.

Tom:No, thank you everyone for making time.

Tiffany:Yeah, I'd like to thank everyone for attending today's webinar. I'd also like to thank Tom for a great presentation and MakuSafe for putting it on.

Just a reminder, we will be sending out a link to the recording and the presentation slides in a few days. So, thanks again. Take care and stay safe.