CUSTOMER STORY
Faurecia Workforce Management Across 50+ Plants
85%
Improved Compliance
Fully automated risk control integrated across 30+ major cities.
Unplanned Overtime
Reduced & Controlled
Reduced unplanned labor costs through intelligent digital logic.
21%
Improved Employee Satisfaction
Provided mobile self-service tools for 50+ factories.
Faurecia needed one workforce-management foundation across a large manufacturing network.
Not one plant. Not one city. More than 50 plants, 17,000+ employees, different production rhythms, different employee groups, and local rules that still had to be handled correctly every day.
That is the real operating problem behind time and attendance at scale. The system has to record working time, but it also has to support overtime control, labor-cost visibility, workforce-data governance, and the practical decisions plant managers make when production demand changes.
By late 2024, Faurecia (FORVIA Group) was upgrading its time-management environment with GaiaWorks in Mainland China. The operational need was clear: give headquarters a consistent view of workforce data while allowing each plant to run its own production rhythm inside a controlled operating model.
Not one plant. Not one city. More than 50 plants, 17,000+ employees, different production rhythms, different employee groups, and local rules that still had to be handled correctly every day.
That is the real operating problem behind time and attendance at scale. The system has to record working time, but it also has to support overtime control, labor-cost visibility, workforce-data governance, and the practical decisions plant managers make when production demand changes.
By late 2024, Faurecia (FORVIA Group) was upgrading its time-management environment with GaiaWorks in Mainland China. The operational need was clear: give headquarters a consistent view of workforce data while allowing each plant to run its own production rhythm inside a controlled operating model.
The operating problem behind the upgrade
Faurecia’s plants do not all share the same schedules, staffing patterns, or local processes. That variation is normal in manufacturing. It becomes expensive when every site also uses different definitions, approval paths, and reporting methods.
The existing environment created friction in several areas:
Production demand was rising. Cooperation with automotive manufacturers in China was deepening, order volumes were growing, and production schedules were becoming more demanding. Manual work and disconnected systems made detailed labor planning harder to sustain.
Workforce data was difficult to compare. During broader enterprise data-governance work, Faurecia found that plants were using inconsistent data definitions. Reports could be produced, but cross-plant comparisons were difficult to trust.
Headquarters lacked a single operational view. Labor cost, attendance, and production-efficiency data were not consistently available in one structure, limiting group-level analysis.
Time processes required too much manual handling. Attendance reconciliation, overtime review, and local rule administration created avoidable work for HR and plant teams.
The project therefore had two connected goals: standardize the data needed for group oversight and preserve the flexibility needed on the factory floor.
The existing environment created friction in several areas:
Production demand was rising. Cooperation with automotive manufacturers in China was deepening, order volumes were growing, and production schedules were becoming more demanding. Manual work and disconnected systems made detailed labor planning harder to sustain.
Workforce data was difficult to compare. During broader enterprise data-governance work, Faurecia found that plants were using inconsistent data definitions. Reports could be produced, but cross-plant comparisons were difficult to trust.
Headquarters lacked a single operational view. Labor cost, attendance, and production-efficiency data were not consistently available in one structure, limiting group-level analysis.
Time processes required too much manual handling. Attendance reconciliation, overtime review, and local rule administration created avoidable work for HR and plant teams.
The project therefore had two connected goals: standardize the data needed for group oversight and preserve the flexibility needed on the factory floor.
From separate sites to a common operating model
The implementation centered on GaiaWorks Time and Attendance across more than 50 plants. The design had to support a shared group framework without treating every site as identical.
A common data foundation
Consistent workforce data is the starting point for meaningful operational analysis. The upgraded environment standardizes the core time-and-attendance records used across the rollout, giving headquarters and plants a common basis for reviewing attendance, overtime, and labor efficiency.
This also supported Faurecia’s data-standardization work. In the project discussions, the planned consolidation of 60 independent system tenants introduced a significant architecture requirement. The 60-tenant figure refers to the system landscape, not the number of plants. It required careful attention to governance, scalability, and data traceability.
GaiaWorks’ direct labor efficiency efficiency analysis and data-traceability capabilities were a strong fit for Faurecia’s continuous-improvement approach. Leaders can follow how operational data is formed and use comparable definitions when reviewing performance across sites.
This also supported Faurecia’s data-standardization work. In the project discussions, the planned consolidation of 60 independent system tenants introduced a significant architecture requirement. The 60-tenant figure refers to the system landscape, not the number of plants. It required careful attention to governance, scalability, and data traceability.
GaiaWorks’ direct labor efficiency efficiency analysis and data-traceability capabilities were a strong fit for Faurecia’s continuous-improvement approach. Leaders can follow how operational data is formed and use comparable definitions when reviewing performance across sites.
Local flexibility inside a group framework
Standardization does not mean forcing every plant into the same schedule. The system provides a shared management framework while allowing local configurations for different employee groups, shifts, attendance rules, and statutory requirements.
This is where local regulation belongs in the architecture: as a maintained rule layer inside a common operating model. It supports consistent controls without making compliance administration the center of the workforce strategy.
For global manufacturers, that distinction matters. A regulation-ready workforce platform should not be a local workaround. It should be an engineering capability: rules can change by country, state, province, city, plant, union agreement, employee type, or shift pattern, while the core data model remains consistent.
This is where local regulation belongs in the architecture: as a maintained rule layer inside a common operating model. It supports consistent controls without making compliance administration the center of the workforce strategy.
For global manufacturers, that distinction matters. A regulation-ready workforce platform should not be a local workaround. It should be an engineering capability: rules can change by country, state, province, city, plant, union agreement, employee type, or shift pattern, while the core data model remains consistent.
More disciplined overtime control
Overtime is both a cost issue and an operational signal. Faurecia’s upgraded process places overtime requests and approvals in a defined workflow. That makes additional hours visible before they become an unplanned labor cost and gives managers a clearer basis for reviewing staffing and schedule decisions.
Better schedule planning also helps plants reduce avoidable last-minute overtime. For a deeper view of the scheduling logic behind overtime control, read Why Frontline Overtime Is Often a Scheduling Problem. The purpose is not simply to approve or reject hours. It is to connect labor demand, production planning, and employee working time in the same management process.
Better schedule planning also helps plants reduce avoidable last-minute overtime. For a deeper view of the scheduling logic behind overtime control, read Why Frontline Overtime Is Often a Scheduling Problem. The purpose is not simply to approve or reject hours. It is to connect labor demand, production planning, and employee working time in the same management process.
Less reconciliation for HR teams
Automated attendance processing reduces the need for HR teams to repeatedly collect, check, and reconcile records from separate sources. With more reliable operational data available sooner, HR can spend more time on workforce planning, organization design, and decision support.
The value is practical. Managers get a clearer view of labor deployment and time cost. HR gets fewer manual exceptions to resolve. Employees get a more transparent way to review attendance records and submit leave requests through self-service tools.
The value is practical. Managers get a clearer view of labor deployment and time cost. HR gets fewer manual exceptions to resolve. Employees get a more transparent way to review attendance records and submit leave requests through self-service tools.
What the rollout changed operationally
The rollout enabled Faurecia to:
– manage time and attendance across a large, multi-site manufacturing scope through one structured environment;
– establish more consistent workforce-data definitions for group reporting and cross-plant analysis;
– route overtime through a visible approval process;
– use local rule configurations within a centrally governed framework;
– support faster analysis of production efficiency and labor cost;
– reduce manual attendance administration for HR and plant teams; and
– give employees clearer access to their own time and leave information.
These are foundational controls for a labor-intensive manufacturing network. They improve the quality of daily decisions before any advanced analytics or AI capability is added.en global organization.
– manage time and attendance across a large, multi-site manufacturing scope through one structured environment;
– establish more consistent workforce-data definitions for group reporting and cross-plant analysis;
– route overtime through a visible approval process;
– use local rule configurations within a centrally governed framework;
– support faster analysis of production efficiency and labor cost;
– reduce manual attendance administration for HR and plant teams; and
– give employees clearer access to their own time and leave information.
These are foundational controls for a labor-intensive manufacturing network. They improve the quality of daily decisions before any advanced analytics or AI capability is added.en global organization.
Frequently asked questions
What did GaiaWorks deploy for Faurecia?
GaiaWorks deployed its Time and Attendance solution across 50+ Faurecia China plants, covering 17,000+ employees within the rollout scope.
What was the main workforce-management challenge?
Faurecia needed consistent workforce data and group-level visibility while allowing plants to manage different production rhythms, employee populations, schedules, and local requirements.
Why is this case relevant for global?
The location is China, but the workforce-management problem is global: headquarters needs comparable data, local sites need operational flexibility, and the business needs reliable controls over working time, overtime, and labor cost.
How does the model handle local requirements?
The common operating framework keeps core data, workflows, and reporting consistent. Local configurations handle differences in schedules, employee groups, and statutory rules within that framework.
A global engineering lesson
Faurecia’s case is relevant to manufacturers operating across borders because the underlying problem is common: central teams need comparable data, while local sites need a system that reflects how work is actually organized.
The answer is a global framework with controlled local adaptation. The framework defines the data, workflows, ownership, and reporting logic that must remain consistent. Local configuration handles the differences that are operationally real, including schedules, employee populations, and statutory requirements.
That approach turns local regulation into an engineering input rather than a set of manual exceptions. It also gives a multinational organization a repeatable way to extend workforce management as plants, countries, and operating models change.
The answer is a global framework with controlled local adaptation. The framework defines the data, workflows, ownership, and reporting logic that must remain consistent. Local configuration handles the differences that are operationally real, including schedules, employee populations, and statutory requirements.
That approach turns local regulation into an engineering input rather than a set of manual exceptions. It also gives a multinational organization a repeatable way to extend workforce management as plants, countries, and operating models change.
