| dc.description.abstract | The World Energy Council [World Energy Council, 2007/2013] defines numerous challenges in the global energy arena pressuring Owner/Operators to operate existing plant better and more efficiently. Internal Power Utility research by Jones confirms that the current business model is no longer sustainable [Jones, 2015]. Operating challenges, rising operating costs and deteriorating plant availability are creating a perfect storm that demands radical changes in the way the Utility is managing its business operations and how it addresses the challenges.
The current electricity–constrained South African environment and challenges are not unique, as is shown by the PWC Survey on Energy Sector business models, which covered 53 utilities in 35 countries. The study confirms similar business environment triggers that will initiate business model changes [Schwieters et al., 2015].
As such there is increasing focus on the use of business– and technical plant information and data to make better, more integrated and informed decisions on the plant. Biehn states in his research that data scientists believe as little as 5% of the “big data” gathered results in 95% of the value contribution of the data [Biehn, 2013]. And herein lies one of the biggest problems with data in business today – effectively identifying, modelling and analysing the 5% critical data to improve business operations.
Many companies gather vast amounts of data, but rarely take the effort to analyse the data or even asking the basic question of WHY they are gathering the data. Although data storage costs have significantly reduced, the impact of analysing critical business and plant data when it is buried in 95% of “low–value data” have a significant impact on productivity, situational analysis capability, incident response and decision times.
Most popular business improvement models centre their framework and approaches on business process improvement – thus on people, process and technology aspects of the business. They tend to drive business process elements and seldom evaluate the impact of not using high–quality critical plant design and control data effectively. As a result, these business models generally struggle to quantify their value proposition as they lack the plant and process data needed to demonstrate/prove value and return–on–investment.
The research study developed and refined an integrated plant information (IPI) framework and Business Improvement Model (IPI–BIM) (Figure 0–1) and used operational research between 2007 and 2016 to validate and prove how this BIM Framework and implementation approach can be used to improve business operations and provide decision–making insight. The IPI–BIM Framework differs from well–known business improvement framework models (e.g. the Baldrige Business Model Review Framework [NIST, 2015] and the European Foundation for Quality Management (EFQM) Excellence Model Review Framework™ [Von Rompuy, 2012]) in that it does not focus on process, core values and/or concepts, but rather integrated plant information and data analytics as the foundation for business improvement in a Process Plant/Utility.
Validation of the IPI–BIM Framework was done as a comparative study – evaluating and documenting the benefits of implementing it on the two power stations that formed part of the research study, and comparing the outcome against other power stations of similar size and complexity in the Power Utility where the IPI–BIM and implementation approaches were not used. The outcome confirmed that the proposed IPI–BIM is an operationally ready business improvement model. It is, however, a complex undertaking and the effort required to fully establish this framework should not be under–estimated.
Although operational research focus was Power Utility focused, the IPI–BIM and implementation approaches are generic enough in nature to be applicable to other process plant environments | en_US |
