Celent Study: Benefits of Connecting Manufacturers to Insurers

By connecting to manufacturers, insurers and their clients can realize the kinds of advantages in underwriting and risk mitigation resulting from increased data collection from automobiles during the past decade. 

(Image source: Drixit, created of Spotter, which provides collision avoidance alerts.)

Underwriting a risk requires understanding it, and that understanding potentially increases along with an increase in availability of relevant data. Such an increase provided an opportunity to auto insurers during the last decade, and now a similar opportunity exists for insurers of manufacturers, according to a new Celent study, “Connecting Manufacturers to Insurers,” authored by Donald Light, Director of the Boston-based research and advisory firm’s North America Property/Casualty Practice. As with automobiles, there is a great deal of data that can—or could be—gathered by connecting to manufacturing equipment itself or the instrumentation that governs it. And just like data produced by automobiles produce, data from manufacturing equipment has the potential to provide valuable guidance for pricing and underwriting—and also to mitigate risks through newly precise forms of loss control. “This report looks at today’s state of play and growing opportunities for insurers of manufacturers,” Light comments.

“Besides making things, modern manufacturing plants make data—a lot of data: data from machinery, data from power sources, data from mobile equipment, data from wearables,” Light comments. “But data by itself is worthless. It only creates value when it is analyzed, and the resulting insights are cycled back into operations—or into risk assessment, mitigation, and avoidance.”

Image source: Celent. (Click to enlarge.)

To get value from that data, insurers need to follow a four-step process, according to Light. First, they must gather, process and store data from remote sensors; second, they must apply AI and advanced analytics to the data; then they must produce implementable findings; and finally, they must take steps to mitigate losses and/or improve manufacturer’s operations.

Sensors can produce a large amount of raw data, but it must be treated, for example by being cleaned, normalized, anonymized, etc., according to the Celent study. It will typically need to be stored in the cloud. The quality of the data is the indispensable prerequisite for gleaning value from it, Light stresses. “Otherwise, the old adage applies: ‘Garbage in, garbage out,” he says.

Among the analytical tools an insurer might apply to the cleansed data include AI, Machine Vision and Gen AI. ‘Some of these could be fairly intuitive, e.g., running a machine for X hours past its scheduled downtime for maintenance will lead to more hours of equipment breakdown insurance losses,” Light writes. “Other hypotheses—maybe the more interesting ones—could be less intuitive.”

Donald Light, Director, North America Property/Casualty Practice, Celent.

Once the analytical step produces validated hypotheses, these can be considered for suggesting their introduction into the manufacturer’s production environment. Light stresses that these will be subject to the manufacturer’s own cost/benefit and other criteria for implementation. “For example, can the costs of implementing a new procedure be spread over 10 machines or 500 machines?,” Light elaborates. “Or will workers—or their union—accept a new procedure? Might there be regulatory or social constraints, for example on some types of video surveillance?”

Assuming the previous three steps are successfully undertaken, the fourth step is where the payoff happens, according to Light. In addition to significant mitigation of insured losses, the insurer may enjoy other benefits, such as greater retention of profitable clients or an improved public image. Light suggests that benefits to the manufacturer client result in a kind of partnership in reducing losses both parties have an interest in avoiding. “In many cases, there will also be benefits to the manufacturer, e.g., higher productivity, improved quality, safer and more engaged workers,” Light writes.

Light cautions that manufacturers may be reluctant to share their data, obviating the execution of the four steps. However, in many cases this is because manufacturers employ many of their own risk-mitigation measures. This shouldn’t stop insurers from offering whatever services that might support or complement the manufacturer’s efforts. The study provides an example of an insurer offering additional services to a client with existing robust mitigation measures, resulting in the creation of a risk-mitigation ecosystem.

The study reviews various ways sensors can be introduced to monitor the activities of industrial workers in the interest of greater safety and fewer losses. It also explores working with a clients safety officers and procurement authorities. Finally, Light discusses how operational areas of an insurance company—such as rating, risk engineering, underwriting and claims officers— can work with their counterparts at clients.


Light’s conclusion notes that three parties potentially can benefit from connected manufacturing: the client, the supplier of the equipment the client uses, and, of course, the insurer. All three parties share a common interest in improving the manufacturer’s operating and financial performance. However, Light cautions, “Given the relatively early developmental stage of connected manufacturing, the extent to which connected technologies can further these goals is speculative.”

New Technologies Let Insurers Economically Bridge Functionality Gaps: Celent Report

Anthony R. O’Donnell // Anthony O'Donnell is Executive Editor of Insurance Innovation Reporter. For nearly two decades, he has been an observer and commentator on the use of information technology in the insurance industry, following industry trends and writing about the use of IT across all sectors of the insurance industry. He can be reached at AnthODonnell@IIReporter.com or (503) 936-2803.

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