Enterprise Performance Management (software)

Enterprise performance management (EPM) is the process of monitoring and improving business processes, to inform decision-making and achieve business goals.

EPM usually refers to a category of business management software that consolidates and analyzes data from various sources across an organization. Traditionally, these sources are other business software or information technology (IT) systems used by the organization, such as front-office and back-office applications, data warehouses, and external data sources.

Historically, EPM software has been used for financial planning and analysis. Modern EPM solutions are typically delivered as a suite of software-as-a-service (SaaS) applications, providing enterprise-level visibility and business intelligence insights. They often are integrated with other cloud computing systems, such as e-commerce portals, enterprise resource planning (ERP) systems and customer relationship management software.

SaaS-based EPM software is often faster to implement at lower costs, enables more collaborative functions and simplifies connections to a wide variety of data sources.

Beginning approximately in 2019, EPM systems have expanded to include data from operational technology (OT) as well as IT systems. These EPM systems unify data from business processes, employees, equipment, machines and sensors to improve performance and automate decision-making.

Software history

EPM software for financial planning

The first software systems to be used for EPM were the earliest spreadsheet programs from the early 1980s, such as VisiCalc and Lotus 1-2-3. By the late 1990s, on-premises EPM software (then commonly known as corporate performance management) was widely available for strategic planning, budgeting, financial management, reporting and scenario modeling.

In the 2000s, cloud-based EPM software offered greater automation and data aggregation to replace the spreadsheets traditionally used for financial planning and business decision-making. By the 2020s, EPM systems linked data from business support functions with operational data, which until then had been managed by separate software systems.

Operational control and enterprise asset management systems

The first, most rudimentary computerized maintenance management systems (CMMS) emerged in the 1960s, written in Fortran or COBOL and read off punch cards by mainframe computers. Costly and limited to the largest manufacturing companies, they typically reminded maintenance technicians to perform routine tasks on industrial machines.

In the 1980s, the first enterprise asset management (EAM) software arose as an extension of CMMS for computerized maintenance. EAM software tracks maintenance cycles and spare parts inventory, as well as manages alarms, inspections, work orders and other business processes.

By the 2000s, CMMS and EAM systems were connected to on site or even cloud networks.  Although OT became increasingly connected to networks, businesses struggled with unifying, interpreting or acting on the data generated by many different assets.

With the implementation of IPv6 in 2017, the number of IP addresses available for physical devices was made practically unlimited. At the same time, low-cost sensors became widely available,  accelerating the deployment of Internet of Things (IoT) devices and significantly increasing the IoT data generated by companies.

In the 2010s, businesses steadily made progress piecing together industrial Internet of Things (IIoT) dashboards from different providers, running on different IT infrastructure with connection to a limited set of devices.

Industrial EPM software

As of 2020, providers such as Honeywell have designed EPM offerings that connect to data from physical assets, enabled by IIoT connectivity, as well as the financial and business function data traditionally processed by EPM software.

To make use of IIoT data, businesses often need to connect to a wide variety of devices (such as vehicles, sensors, buildings, wearable and handheld devices, machines, and other OT). This creates demands for specific skill sets and analytics capabilities to ensure accurate data. So-called “industrial EPM” systems  provide the centralized analysis and visualization of this IIoT data.

Pre-processing and analysis of IIoT data at the edge is often required to eliminate extraneous data. This could require EPM software to be paired with routers and edge computing devices.

Knowledge of the domain is required for users or software to structure the data for interpretation and insights that inform actions; lack of this knowledge could be costly, thwart automation or impair decision-making.

Use cases

As part of their broader digital transformation efforts, businesses use EPM software to centralize dataflows and automate decision-making and planning. EPM organizes that data, allowing business leaders to act on questions about performance, sustainability and safety.

Cloud-based services often provide data governance and data liquidity for the ecosystem of software assets connected to them. Users review and manipulate this data via dashboards, reports and alerts.

Modern EPM applications centralize and simplify a company’s ability to control a large and varied portfolio of assets. These assets may also be widely geographically distributed. These EPM systems provide analytics, control and automation at the enterprise, site and unit levels. Functions for these systems include workflow management, cybersecurity, APIs, and monitoring tools to manage business and operational processes.

Using these functions, companies may be able to unify and automate control of equipment, warehouses, buildings, fluid control systems, security systems and other operational infrastructure.

Additionally, EPM software accessible via mobile devices and applications may support safety protocols, manage physical access control  and improve productivity with connected worker capabilities.

Connected worker EPM applications are intended to address skills gaps within industrial workforces. Alongside smart assets that generate constant data streams, many businesses also operate legacy assets that generate no data. The labor-intensive maintenance of these various systems relies on technical experts with a high degree of training and hands-on knowledge. This integration of AI insights in the cloud, often available to workers on-site, is sometimes referred to as the “democratization of expertise.”

Using the AI analytics provided by EPM cloud solutions, businesses may be able to avoid expensive “rip and replace” capital projects, where legacy equipment is fully replaced with new systems.

See also

• Industrial internet of things
• Business intelligence
• Enterprise asset management
• Operational technology
• Predictive maintenance
• Fourth industrial revolution
• Digital transformation
• Enterprise resource planning
• Customer relationship management

References

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