Data as the Future of Safety, Quality, and Productivity

The fast-paced, high-risk, and opportunity-filled reality of today’s businesses is making the balance between environmental health and safety (EHS), quality, and productivity more important than ever. Given that these pillars are a key part of the future of businesses, it is no longer possible to pay more attention to one over the others. Doing so would be detrimental to the mandate and goals of the organization. At the center of this balance, the revolutions in data analysis, machine learning (ML), the Internet of Things (IoT), and artificial intelligence (AI) have put data in the spotlight. The tools to find performance trends, predict and manage risks, and evaluate the effectiveness of EHS and quality (EHS&Q) policies will help you set higher quality standards, safeguard employees, and guarantee customer satisfaction and retention.

Passing on the opportunity of using data and analytics for EHS&Q and productivity to increase an organization’s standards can come at a high cost. According to the American Society of Safety Professionals (ASSP), injuries in the workforce cost society and companies $128 billion in losses in 2019. Most of these costs will come in the form of indirect costs, including, training, compensations, repairing damaged property; accident investigation, low employee morale, and poor customer, among many others. This demonstrates the need for higher standards across all industries and shows why companies should adopt new tools, especially those involving big data, ML, and AI capabilities, for EHS&Q and productivity assurance.

In this blog, business owners, EHS&Q leaders, and operational managers will be introduced to the importance of data in the future of EHS, quality, and productivity. You will gain insight into current and future data collection methods and good practices for moving forward.

The Need for More Data

EHS&Q and productivity assurance teams are in constant need to collect, review, and analyze data in multiple areas. Using this data to achieve high levels of safety in the workplace leads to improved regulatory compliance, improved quality of life for workers, increases in quality, and the reduction of costs. The collection of data comes in a wide range of formats, from pen and paper to safety software systems. With all these options, we could assume that there is plenty of data available for EHS&Q and productivity professionals to confidently formulate policies to reach higher standards. Unfortunately, there is a shortage of accessible digital EHS&Q records across most industries. This makes it hard to make informed decisions on EHS&Q policy.

Increased collection of data and a better understanding of processes will also help your company to increase its productivity. An LNS Research survey report showed that real-time visibility of EHS&Q metrics can lead manufacturing companies to a 21% improvement in overall equipment effectiveness (OEE) over those that don’t. 

Developing an understanding of an organization’s EHS&Q and productivity performance indicators is just as important as understanding its key financial or service delivery performance indicators. As such, a company needs to define the “what”, “why”, “when”, “where” and “who” of data collection in order to get value out of it.

“What” data is needed?


Collecting data to improve the performance of EHS&Q programs and increase overall productivity requires thoughtful leadership and careful planning. Knowing specifically which data to collect and in what format it should be collected may not be intuitive unless EHS&Q leaders and managers have an understanding of both what data will be significant, as well as how that data will be fed into and interpreted by AI and ML tools.

When it comes to what data is valuable for predicting and preventing incidents using leading indicators, a number of data sources probably spring to mind. The findings from equipment inspections and jobsite walkthroughs, the tracking and analysis of near misses, and information from equipment sensors and computer vision systems all represent excellent sources for proactive EHS data collection. Likewise, lagging indicator data like incident records and quality nonconformance records are essential for informing management on the effectiveness of the business’ EHS&Q program.

However, in order for any of this data to be made into actionable recommendations, it needs to be accurate, complete, and in a format that is easily interpreted and analyzed by AI and ML systems. This is the reason for the shift from paper-based safety programs to digital systems. With carefully designed and integrated digital solutions, EHS&Q and productivity data can simultaneously be collected passively through IoT devices and actively through well-structured digital forms, both of which can be fed seamlessly into AI software in real-time.

The collection and analysis of this safety data digitally thereby leads to faster, more accurate, and more insightful conclusions.

“Why” is data needed?


There are many reasons why we should collect EHS&Q data. From a managerial perspective, the need to measure EHS&Q indicators is just as important as measuring financial, production, or service delivery ones. The data collected provides key information about the effectiveness and progress of safety and quality assurance strategies. In addition, data can inform decision-making processes by answering questions and providing additional information about safety and quality policies, such as by quantifying different aspects of a safety program in order to determine if new safety measures will be efficient. Additionally, a worthwhile incentive for increasing data collection and moving toward “smart” analysis is the elimination of lags between a particular indicator and the implementation of a targetted response by the EHS&Q program, ideally before an incident can occur.

“Where” to collect and store data?


Data should be collected from employees, external inspectors, and IoT safety devices alike to provide the most all-encompassing sets of data. These will take advantage of human insights and creative thinking, as well as the unbiased, continuously-operating observations of digital safety systems.

Additionally, the storage and analysis of EHS&Q data should be organized and centralized in a single location. This location is often a database system local or external to the organization. Doing this avoids physical silos within the organization and allows information to be validated more easily. Just as important as the place where data is stored, database systems, when coupled with analysis, become the place where historic trends, key metrics, and actionable suggestions come from.

“When” is safety data needed?


There is not a single right answer about when data collection is needed or even possible. Should it be collected at regular intervals, at a frequency that meets the requirements of legislation, or continuously? In an Industry 4.0 setting, data can be collected and analyzed in real-time, then used to generate immediate notifications and actionable suggestions. While this rapid turnaround may not be an essential aspect of a facility’s EHS&Q program, it certainly has its benefits. For example, if AI systems trained for computer vision applications spot an overlooked hazard or unsafe behavior, they can provide instant feedback to local EHS&Q teams and site supervisors, who can then take action before an incident occurs.

While this presents an obvious benefit, there is also the potential for EHS&Q leaders and managers to receive an unrelenting influx of notifications. As such, it is important that the safety software being used is well-designed to benefit your team.

“Who” needs this data?


Every level of management in an organization has a different degree of responsibility to collect health, safety, quality, and productivity data. In particular, senior managers must portray a participatory, empowering, and inclusive culture of health and safety. While the role of EHS&Q professionals is central to data collection and to the allocation of responsibilities inside an organization, their role must also be supported by all levels of management.

At all levels, managers should be given responsibility for participating in and making progress toward the achievement of EHS&Q plans and objectives, including monitoring compliance with safety initiatives within their teams. The flow of information around safety, performance, and productivity should be tailored to suit each member of the company so that EHS&Q policies and goals can be implemented effectively. Ideally, this flow of information would match the organization’s structure as managers communicate with their teams and vice versa.

Data Collection Methods

The effectiveness of an organization’s EHS&Q data collection practices will ultimately be measured by how its program performs, both in terms of how much the data collection costs and what value the collected data brings. The main methods of data collection can be classified into:

  • Manually collected from internal sources, such as internal incident reports and behavior-based safety observations.
  • Manually collected by externals sources, such as inspector audits.
  • Autonomously collected by IoT devices, such as equipment sensors and video analytics systems.

At times, data collection can be a time-consuming and dynamic process, especially when tools for these tasks are constantly changing. Regardless of whether it is stored in a physical notebook or a spreadsheet, companies will eventually face the need to index and centralize data. Today, it is more effective for EHS&Q teams to use applications and software so that data is made digital directly. Just because data is entered manually by human workers does not mean that it cannot be digitally accessible. This saves resources and reduces errors in the long run as the transfer of paper-based records is no longer required.

The transition to digital data collection can be fast and effective. As reported in EHSToday, safety managers and directors found actionable insight from safety programs powered by digital data in as little as four weeks.

Importantly, data collection does not only come from EHS&Q professionals. Employee engagement and empowerment are key to the success of new policies. For this, mobile and website applications are great tools, as employees can access them via mobile phones for an easy way to input data which can then be immediately stored and analyzed.

Additional tools like cameras, contactless cards, and quick response (QR) codes can also help identify employees and allow them to track the information they submit to EHS&Q systems.

The techniques and methods developed for data collection should be inclusive and easy to use by everyone. Moving from traditional methods will likely face resistance from employees if the tools developed are hard to use. In fact, an independent study showed that 55% of EHS professionals thought that their team required more data science expertise.

This can be addressed in two ways. Firstly, it will likely be useful to provide more training to EHS personnel on the use of advanced tools and how they can take advantage of big data. Secondly, we can demand more of our safety tools, taking advantage of the most modern AI solutions to provide more important safety insights with less human involvement. For example, video analytics systems can analyze live or recorded files to inform EHS&Q professionals of potential safety threats, such as employees not wearing personal protective equipment or entering restricted areas.

It is worth noting that the quality and characteristics of the data collected can drastically change the quality of the results from its analysis. For instance, something as simple as the record of incidents can become misleading if the total number of personnel or the production levels are not considered. For example, finding out that most of the incidents in your plant happen in September is both unhelpful and nonactionable unless you also consider that there are more personnel on-site and production is at an all-time high. As such, we know that a clear understanding of safety performance can only happen if a complete and accurate set of data is collected and analyzed.

The Past, Present, & Future of EHS&Q Data Analysis

Today’s Industry 4.0 revolution has come with new opportunities and challenges as companies venture into a new era of big data featuring levels of automation and connectivity that would have never been possible before. These changes have sent ripple effects through EHS&Q, particularly when it comes to how data is collected, managed, analyzed, and more importantly, the actionable conclusions that can be drawn from the data. To have a better understanding of how EHS&Q got to this point, we can explore the evolution of data collection and analysis over time.

The past


This is essentially data collection with pen and paper, paired with basic statistics and the generation of short-sighted or incomplete graphs and plots. In a rather reactive fashion, incidents and quality issues would only be recorded and acted on when something went wrong.

As an EHS&Q strategy, this is no longer standard practice for the majority of modern companies. Unfortunately, this practice can still occur unintentionally, even in companies taking advantage of modern EHS&Q tools, if some areas of operations are left out of EHS&Q development scopes.

While the “ratio of accidents”, sometimes called “Heinrich’s Law” or the “Heinrich ratio”, has been modeled in the past and found to be relatively constant, over time and across companies, it has also been well documented that the severity, cause, and frequency of these accidents can vary widely between industries, companies, and even departments. This clearly indicates a need for an increased understanding of safety trends, which points to the need for improved EHS&Q data collection and analysis across all industries.

The present


With the growth of ML and AI, new and powerful tools have become increasingly available to EHS&Q professionals. The use of more complex statistical methods and larger data sets means AI systems are increasingly capable of identifying key insights for improving safety performance. Autonomous data collection can be used to generate live graphs that lead to actionable recommendations by EHS&Q leaders and management. The additional data and analysis allow management to better plan safety events and initiatives, like training updates and internal quality inspections.

Unfortunately, current EHS&Q data collection can still be inexact, pushing safety programs in the wrong direction or missing important insights. This can potentially lead to initiatives with minimal impact benefits for their cost, or the potential for overlooked weaknesses in safety, quality, and productivity programs that persist and decrease efficiency.

The future


The accelerated development of AI and ML, coupled with the interconnectivity of Industry 4.0, will bring data analytics for EHS&Q into a new area of optimization and performance. This is possible when IoT networks integrate all enterprise assets into a live ecosystem. The vast amount of data from collection tools like smart sensors and video cameras could be fed into advanced data mining, AI, and ML methods to generate live actionable recommendations and automated EHS&Q responses. In the future, businesses with advanced EHS&Q programs will be preferred by clients because of their advanced capabilities, enhanced efficiency, and improved EHS&Q track record.

It should be noted that EHS&Q professionals will not be out of their jobs. In fact, they will still be needed to guide AI and ML solutions, applying data science expertise to help optimize EHS&Q programs. The future will also bring the application of EHS&Q standards to AI and ML systems, which then would feed right into updating international standards like the ISO 9000s. EHS&Q professionals will be essential in the conversations surrounding how to develop advanced software solutions for workplace safety, quality, and productivity.

The Best Way to Collect & Manage Your EHS&Q Data

With the future of EHS&Q and productivity on the horizon, companies will need to adapt soon. This adaptation will come to businesses as great opportunities for improvement and growth. Some of the first steps to move forward into the future are:

Data collection


The times when one-fits-all assumptions and rules of thumb were used to define EHS&Q and productivity policies are long gone. In the future, as much as in the present, your organization should find the most suitable and effective data collection methods for your industry. Before collecting data, you should make sure that the data is centralized, clean, normalized, and part of your organization’s context. The data collection itself should then be standardized, monitored, part of a culture of inclusion, and done with a clear purpose.

Integration of new technologies


The possibilities for integrating technologies like AI, ML, and IoT seem endless. This applies to EHS&Q as much as it does to other parts of your organization and can result in distinct improvements in EHS&Q performance. That said, the integration of any new solutions should be planned carefully to make sure it is worth the investment and to foster improved adoption of the tech by workers and management.

Actionable recommendations


Get the most out of new technology. Regularly monitor and assess EHS&Q and productivity performance indicators to ensure that the results from the data collection and analysis lead to meaningful, actionable recommendations for improvement.

EHS at the core of your business


New data collection and analysis technologies are showing their worth across a wide range of industries, improving business financials and operational efficiency. The EHS&Q sector is no different, and with the increasingly widespread adoption of a safety culture and initiatives like the “Zero Accident Vision”, EHS&Q continues to prove itself as an essential component of a sustainable and successful business.

About Us


Knowella AI Inc. offers an industry-leading digital solution to managing EHS&Q. We provide workers, supervisors, EHS&Q professionals, and top-level management the tools they need to improve workplace safety. We use AI-powered data analysis to optimize your approach to EHS&Q, resulting in lower operating costs and quantifiable improvements to your safety performance.

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