As food and beverage companies leverage data to improve insurance renewal outcomes, they can use the same information to become more resilient.
The world today looks much different than it was merely a year ago. The challenges of 2020 have seen many food and beverage companies scramble to adapt, face supply chain difficulties, introduce new health and safety measures to keep their employees safe, recover from natural catastrophes, and respond to calls for increased social justice.
As food and beverage companies continue to recover from the difficulties of the past year, they are also challenged by escalating insurance pricing and tightening terms and conditions. US property insurance pricing continued on a steep upward trajectory through 2020, registering 13 consecutive quarters of increasing pricing through the fourth quarter.
Further, the renewal process is becoming more taxing as underwriters closely scrutinize submissions and ask more questions than in the past. And more than ever before, underwriters expect data-focused responses that provide details about the risk profiles of insureds’ locations and the time and cost to rebuild any damaged properties.
Food and beverage companies possess copious amounts of data. Yet, some risk professionals may find it difficult to leverage existing information to support their insurance renewal process.
One main difficulty revolves around accessing data. Even when such information exists in-house, it is frequently dispersed across different departments. For companies with more than one location, and with information spread across each of these properties, it may be time-consuming to analyze location-specific data, such as type of roof construction or sprinkler coverage and type.
Even when risk managers are able to gather all available information, they are often faced with data that has been captured using diverse methodologies or by different individuals. Having “unclean” data in different formats and systems can make company-wide analyses geared towards identifying potential problems more difficult.
"Fragmented data, different collection methodologies complicate the analysis process."
Let’s take the example of an agricultural manufacturer with hundreds of sites across the globe. The company wants to identify potential weaknesses across its properties. Each location possesses detailed information, such as data about roof age, fire protection technology, and building construction materials. But it has been collected in different ways, presented in different formats, and stored in different locations, making it hard to bring all the data together and conduct a thorough analysis of risk quality.
Aside from the risk engineering attributes of different properties, carriers are also expressing keen interest in companies’ efforts to become more resilient. Property underwriters want to understand organizations’ efforts to improve their business continuity plans and remain operational — or reduce lost time — following different disruptions. This includes demonstrating the ability to get crucial supplies following a crisis, or the true capacity to ramp up production at an alternative location to make up for downtime at a plant that cannot operate for some time.
Underwriters have traditionally focused on locations generating the most revenue, since these tend to suffer bigger losses. However, they are now asking for risk management information about smaller locations. While losses in the latter tend to be less severe, they are also typically more frequent, leading to a higher aggregation of costs.
In today’s data-centric renewal process, food and beverage companies should focus on gathering the necessary information and making sure that data is comprehensive, verifiable, and accessible. To ease the burden of data collection, it may be helpful to think in terms of the exposures present at each location. For example, if one location is in a flood-prone zone, insurers are likely to request data on floor elevations and the value of equipment below grade.
"Information presented to underwriters needs to be comprehensive and verifiable, based on sound methodology."
Understanding the potential exposures of each location allows companies to purchase the coverage that best meets their needs, both for property damage and business interruption policies. Let’s take the example of a poultry processing plant that makes up around half of a company’s revenue. Senior leaders should understand how that particular plant could be affected by different risks, from a short power outage to a natural catastrophe that destroys part of the building, and seek to answer questions, including:
As carriers scrutinize each application and are increasingly judicious about the risk they are willing to take, in-depth analysis that demonstrates the effectiveness of mitigation plans can help food and beverage companies secure needed coverage at the best available price.
The ability to answer underwriters’ questions is not the only benefit of a data-focused risk management policy. As companies continue to recover from the various challenges of 2020, they should remain focused on becoming more resilient. And in order to succeed, they should identify the wide spectrum of possible risks they could face and measure their potential effects.
Food and beverage companies should not limit such exercises to their own locations but also understand the potential vulnerabilities of their critical suppliers.
When properly collected and analyzed, data can help organizations identify vulnerabilities, which in turn allows them to address challenges before they become costly problems. For example, comparing the locations of its known suppliers to drought conditions worldwide can help a grain-processing firm understand where shortages might occur during next quarter’s harvest and supply, allowing the organization to dynamically adjust its product strategy to account for such shortages.
Best-in-class companies have strong data quality and risk management information that can be converted into dynamic dashboards that allow risk managers to clearly visualize the state of play. This is the path taken by the global agricultural manufacturer in our earlier example; the company created a centralized property database that allows risk managers to analyze concentration of risk across geographies and product lines, among other categories.
This holistic view allowed the company to triage response based on the severity of challenges. Further, a single view of all data allowed risk managers to visualize vulnerabilities across different product lines, allowing them to focus on specific products that present the highest risk based on value and vulnerability.
A similar approach can be applied to supply chain analysis. A packaged food company, for example, may source a crucial ingredient from different sources. But analysis of geographic data uncovers that the majority of suppliers are in a specific, earthquake-prone region. A single catastrophe could disrupt the company’s supply chain, leaving it with a critical shortage of an important ingredient.
"Companies can use data to identify single points of failure and address challenges before they become costly problems."
When risk managers are able to analyze information about physical assets, such as buildings, inventory, and equipment, together with data from key business partners, they can better determine the effect of different perils on each location.
The events of 2020 underscore the need for businesses to be resilient and able to withstand a variety of shocks. Food and beverage companies should implement data-driven risk management plans that allow them to continuously collect, analyze, and leverage data. These insights can help companies identify risks and develop robust mitigation strategies, which, in turn, can help them negotiate more competitive insurance pricing and the terms suited to their needs.