Machine Learning is an Investment Priority for QHSE Users

Best-in-Class organizations lead in Quality, Health, Safety, and Environmental (QHSE) management. QHSE users lead in emerging technology investment in machine learning.

Aberdeen’s recent survey of EH&S leaders helped us uncover the top Fourth Industrial Revolution (4IR) emerging technologies companies plan to incorporate in their EH&S technology toolbox in 2019. These are not based on hype, but rather self-reported insights shared by worldwide EH&S leaders in companies of all sizes and across all industries.

QHSE is Best Practice

Quality, Health, Safety and Environment Management (QHSE) generally refers to a management operation mode which integrates the common elements of three standards:

• ISO9001 (quality management),
• OHSAS18001* (occupational health and safety) and
• ISO14001 (environmental management).

*NOTE: ISO 45001 is the standard eventually replacing OHSAS 18001. With nearly 70 countries involved in its development, ISO 45001 will replace OHSAS 18001, the existing management system standard for Occupational Health & Safety (OH&S).

Establishing a QHSE system is an effective means of protecting employees’ health and safety, as well as the environment, and doing it in a cost-effective and well-planned manner. Best-in-Class organizations are 71% more likely than All Others to automate QHSE with software or SaaS.

QHSE Users Lead in 4IR Emerging Technology Usage

Aberdeen’s EH&S study investigates a dozen top emerging technologies pursued by respondents, and QHSE user investment plans for all 12 are significantly higher than the typical respondent. Let’s use this post to take a closer look at the QHSE user leadership in machine learning technology (Figures 1 and 2).

 Figure 1: Machine Learning is an Investment Priority for QHSE Users

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in an autonomous fashion, by feeding them data and information in the form of observations and real-world interactions. Although still in the early phases of adoption, QHSE users are leaders in implementing or evaluating artificial intelligence via machine learning.

In QHSE, where the goal is reducing risk, helping the environment, and saving lives, these users are 50% more likely than the typical user to invest in machine learning. As time goes on, Aberdeen believes there will be more predictive modeling based on machine learning.

Machine learning helps companies use software to analyze vast volumes of data with greater accuracy to find hidden trends and correlations. QHSE leaders can analyze historical and real-time data to determine patterns driving increased or decreased organizational risk, gaining valuable insights through predictive analytics. In the end, the practical result of machine learning is more informed decisions that alter organizational practices to reduce risk. With this insight, we will see a large reduction in incidents, injuries, and deaths on the job.

The Numbers: Manufacturer Buyer Intent in Machine Learning

Aberdeen tracks buyer behavior and content consumption across millions of websites to connect web-based search activity to discernible purchase intent signals. In addition to those intent signals, this data also reveals macro-level trends in the relative interest level for AI technology. To this end, Aberdeen maintains several industry-focused indices that demonstrate sector-level fluctuation in research activity across hundreds of technology categories. One such index contains thousands of organizations in the manufacturing sector.

If we take a six-month view of aggregate activity for this index, focused on technology categories related to manufacturing analytics and big data, the findings reveal a discernible bias toward more sophisticated and cutting-edge technologies (Figure 2).

Figure 2: Machine Learning Buyer Intent in Manufacturing

In aggregate, this Manufacturing index is showing a sustained buyer intent signal in machine learning: Manufacturing providers are looking to bring new and sophisticated analyses to the abundant data they maintain. A major push in the manufacturing is the strong desire to convert this data into focused predictive insights. The results show that machine learning is a means to accomplish that goal.

Aberdeen’s intent data provides a macro-level view of how Manufacturing and large groups of companies are consuming content, and where their interests lie. However, this data also provides a more granular, company-level view, down to the device ID. This provides unprecedented clarity into how specific organizations are consuming content, and where they might be making near-term investments.