Effective Equipment Asset Data: An Insider’s Perspective

Introduction

The timeliness and accuracy of any business decision depend on the availability of complete and accurate data. Whatever the aim, data also requires context and purpose. To that end, there’s a substantially less sexy and often mind-numbing lift to achieve the accuracy of decisions and transformation, including the operational and financial benefits that make C-suite folks happy. The less sexy tasks include organizational alignment to operational excellence (OE) or a similar “North Star” objective, including:

• Understanding interdependent and cross-functional workflows that support OE
• The data and metrics tied to each process step of each interdependent workflow
• The enterprise software tools and source of truth supporting each pillar, enabling OE
• The organizational requirements supporting the processes, data from the processes, data used in software, and governance

Done and maintained correctly, the enterprise is in a better position to manage the “white space” or better prepared to take advantage of current and future opportunities, including effective digital transformation and an effective artificial intelligence (AI) strategy [1].

Equipment Asset Data in the Design-to-Disposal Lifecycle

Data, specifically equipment asset data, is necessary for the sustainability and health of the overall design-to-disposal process, including the interdependent subprocesses affecting the equipment asset lifecycle and any analysis thereof. Additionally, the taxonomical and ontological organization of equipment asset data is the foundation for meaningful information from each step of the operational design-to-disposal lifecycle processes to ensure safe and reliable (efficient) operations. For this to happen, the enterprise must establish a library of common equipment-related data definitions that are meaningful to each operational pillar and step of the design-to-disposal process. The main pillars include:

• Engineering Design (begin equipment registry and status, RAM analysis)
• Purchasing and Receiving (initiate product and reliability strategy development)
• Building and Commissioning (construction)
• Operating, Monitoring, and Maintaining
• Renewing (strategy review or layup)
• Disposal (decommission or removal)

In addition to common definitions and the timely collection of complete and accurate equipment asset data in each pillar of the equipment asset lifecycle, it is important to ensure standardized processes and data quality metrics, which further enable business decisions, tracking performance, process health, and behavioral accountabilities associated with the lifecycle.

The hard work required to establish a foundation and ultimately achieve and sustain maximum efficiency is often underestimated, chaotic, and overlooked amidst the myriad of (enterprise) software tools. The most robust enterprise asset management (EAM) or asset performance management (APM) software, and the promise of advanced technologies like AI that promise to expedite predictive and prescriptive efficiency, will fail to meet full potential without a solid foundational process and data standard maintained by the enterprise. The detriment to operational efficiency manifests in many ways. Here are just a few: