The Limits of Spreadsheets in Quality Risk Management
Torino, 16th April 2026
Introduction
Quality issues rarely appear without warning. In most cases, defects, supplier problems, and process deviations develop gradually before they escalate into larger operational failures or product recalls.
Yet in many manufacturing organizations, the information needed to detect these early signals is spread across disconnected spreadsheets, managed by different teams, and updated manually. While spreadsheets remain a common tool for tracking quality activities, they often provide only a fragmented view of what is happening across processes, products, and suppliers.
This fragmentation makes it difficult to answer a critical question:
Do you have a clear and reliable view of your operational risk?
As manufacturing systems become more complex and product recall risk increases, the ability to structure, connect, and interpret quality data becomes essential, not only for internal decision-making, but also for how risk is evaluated externally, including by insurers.
Why Quality Teams Still Rely on Spreadsheets
Spreadsheets remain one of the most widely used tools in manufacturing quality management. They are easy to set up, flexible, and familiar to most teams. For tracking isolated activities such as defect logs, supplier issues, or corrective actions, they can be practical, especially in smaller or less complex environments.
However, as operations scale, quality management becomes more complex. Multiple suppliers, production lines, product variants, and regulatory requirements introduce a level of interdependency that spreadsheets are not designed to handle.
In practice, this often leads to quality data being spread across multiple files, owned by different teams, and updated manually. Over time, this creates inefficiencies and makes it harder to maintain a consistent view of performance. As ISACA notes in its discussion of manual risk workflows, repetitive spreadsheet-based processes tend to reduce efficiency and make workflows harder to manage consistently
Limits of Spreadsheets in Quality Risk Management
Operational Limitations
While spreadsheets can support basic tracking, they are not designed for controlled and traceable quality processes.
Common limitations include:
- lack of version control across multiple files
- manual data entry, increasing the risk of errors
- no reliable audit trail of changes
- difficulty in assigning and tracking responsibility
- limited ability to connect defects, root causes, and corrective actions
In addition, while Excel is widely used and familiar across organizations, its effective use requires a level of technical proficiency that is often underestimated. Many users rely on basic functionalities, while advanced features such as formulas and pivot tables remain underutilized, limiting its effectiveness in managing complex quality processes.
Impact on Risk Visibility
The limitations of spreadsheets not only affect data quality but also directly impact how risk is understood and managed.
When quality data is fragmented:
- recurring defects are harder to identify
- trends are not visible across products or suppliers
- early warning signals may be missed
- issue escalation is often delayed
- overall exposure to defects is difficult to assess
Product recalls rarely occur without warning. In many cases, they result from issues that were not detected, connected, or addressed early enough.
A well-known example is the Takata airbag recall. As reported in the NHTSA Takata recall spotlight, defective inflators supplied to multiple automakers led to one of the largest recalls in automotive history, affecting millions of vehicles worldwide.
In addition to operational challenges, unstructured quality data makes it difficult to define and evaluate risk accurately. When information is fragmented across spreadsheets and lacks consistency, visibility into defect patterns, root causes, and potential exposure is reduced. As a result, risk cannot be assessed in a structured and reliable way.
Why This Matters for Underwriting
The impact of limited visibility extends beyond operations into insurance and risk evaluation.
Product recall insurance underwriting typically relies on:
- questionnaires
- historical claims data
- general company and product information
While these inputs provide useful context, they often do not reflect how quality risk is managed in real time.
According to McKinsey’s work on data and analytics in P&C underwriting, insurers are increasingly looking for better data and stronger analytical capabilities to improve risk assessment and pricing decisions. At the same time, McKinsey’s research on the future of commercial P&C underwriting shows that underwriting teams still face fragmented data and manual workflows, which limit their ability to incorporate operational information effectively.
When manufacturers rely on spreadsheet-based quality management, the information available to insurers is often incomplete or difficult to interpret. As a result, underwriting decisions may not fully reflect actual operational performance.
From Spreadsheets to Structured Quality Data
To improve risk visibility, manufacturers need more than isolated data points. They need structured, connected, and traceable quality information.
This includes:
- centralized quality data across processes and suppliers
- clear links between defects, root causes, and corrective actions
- consistent and standardized data formats
- real-time visibility into quality performance
When quality data is structured in this way, it becomes easier not only to manage internal risk, but also to communicate that risk externally.
Rcalls addresses this gap by structuring and organizing operational quality data, translating manufacturing information into formats that insurers can use in underwriting and risk evaluation. For manufacturers, this also means better visibility into quality performance, earlier detection of issues, and stronger control over operational risk. By connecting quality management with insurance workflows, it enables greater transparency on how risk is managed in practice.
