October 10, 2019
In our previous post, I shared why MOC corresponds to the skeletal system for Intelligent Operations. If MOC is the skeleton, ELP is the connective tissue (tendons, muscles) in Intelligent Operations. ELP focuses on reducing risks or unwanted events. And, in a broad sense, Intelligent Operations is about reducing risk and waste. Intelligent Operations involves ensuring the right talent, using the right intelligence derived from the right data, can achieve the right outcomes for an entire enterprise. This intelligence should include plans and programs that apply the corporate risk protocol to all areas of an organization and not just traditional safety incidents. Loss of key personnel, the inability or unwillingness to innovate, and the failure to capture or use data appropriately often lead to unwanted incidents and in some cases threaten future financial viability.
It makes sense for ELP and MOC to be integrated because Incidents spawn MOCs and Corrective Actions.
At its most basic level, Incident Management is focused on EH&S regulatory compliance. But, as organizations are able to broaden their focus to assessing ALL of their unwanted events, they can identify all significant contributions to enterprise losses. In other words, the focus broadens from risk reduction to include waste elimination as clients mature.
To help organizations identify opportunities and to create a framework for ELP, we start by facilitating a “Levers of Loss” workshop. This not only helps clarify what Unwanted Events are occurring, but also helps prioritize which ones to formalize so that they can be quantified and managed within the risk tolerance of the organization.
The typical high-yield points are in areas like Production Loss Management. An incident such as a fire in the unit can cause equipment damage and have safety consequences. The equipment downtime due to such an event is often what actually costs the most money.
Areas like Capital Project Management can also yield significant gains. At a recent turnaround in a refinery in the western United States, a client experienced a $100M cost overrun on a capital project. Some of the root causes identified were poor planning, excessive wait time to get permits/certificates issued daily, and a less-than-robust defect elimination scheme.
The general business case for a large oil & gas enterprise with downstream assets might look like this given typical incidents by category for comparable entities:
Intermediate ELP enables a foundation for classifying, analyzing, and following through on failures including a Pareto analysis. This is critical to push organizations past “after-the-fact” reporting. After-the-fact reporting cannot identify patterns of failures to target them in advance for elimination. The classifications and analysis in Phase 2 sets us up for the Advanced ELP stage of maturity (Phase 3) where sensors and data historians can be leveraged for production information. Algorithms and artificial intelligence come into play to detect anomalies and predict failures before they occur. For this to work, data quality and consistency need to be adequate. Most of the organizations we have looked at have significant gaps in data quality, so a data quality assessment is essential to ensure AI is viable to address certain issues. In this stage, some significant value leakage can be identified and managed; areas like Asset Performance Management enter the value stream of ELP. As organizations reach Phase 4, Mature ELP, they are able to realize further value by proactively reducing losses in a variety of areas.
Scenario 1: If an entity is operating outside its safe operating envelopes on a piece of equipment or at a system level, it is enabling excess damage to equipment, overuse of valves, energy losses, and potential product quality issues (e.g., batch overheated). Intelligent Operations can identify the actual loss potential for staying in alarm state at an equipment or system level for a prolonged period of time and bring that visibility of a $1000/minute loss for certain scenarios. It also ensures the organization recognizes another long-term potential systemic consequence of prolonged operations in an alarm state is the weakening of competitive advantage due to product quality issues.
Scenario 2: When poor MOC procedures are utilized for alarm system setpoint changes (e.g., operators have full control to make alarm changes without MOC), an outcome can be alarm rationalization is required – which can cost $100,000 per unit to address in a typical chemical plant or refinery. The Intelligent Operations framework, with its foundation of ELP and MOC, ensures better decision support to prevent outcomes like necessitation alarm rationalization.
Scenario 3: A pump breaks, and maintenance goes to the warehouse to find a replacement. The pump hasn’t been maintained from a QA/QC perspective and the bearings have excess wear and damage. A new pump must be ordered, and there is a lead time. Unfortunately, the pump is critical to production and the downtime leads to a production loss. Intelligent Operations based on ELP and MOC means critical equipment pathways, maintenance, and spares are all integrated into the organization’s risk profile and loss prevention strategies.
Intelligent Operations is a framework to help organizations gain profitability while protecting their privilege to operate. Because Intelligent Operations is built on a core of ELP and MOC, it is naturally focused on minimizing value leakage while managing operational risks.
In our next post, we will discuss Engineering Information before wrapping up discussion on the foundational elements of Intelligent Operations with Risk Management.
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