Decision Support and the Aging Workforce Challenge
We’re hearing a lot about the aging workforce in the Oil and Gas business press. According to Jill Feblowitz at IDC Energy Insights, for example, as older workers retire and the industry experiences a reduction of highly experienced workers, investments in technology to make the remaining workers more effective are a must if the industry is going to meet the ever-increasing demand for energy.
This oft-repeated view of the human resource demographic has interesting implications for upstream operational decision support systems.
All companies rely on experienced people to make high-impact decisions in the field, in conditions that are often uncertain and that change rapidly. When these experienced people leave the workforce, how do we incorporate their “intelligence” in the software that is used to support operational decision making? And, how can less-experienced managers benefit from their predecessors’ experience using these decision support tools? Finally, how do we give end-users tools that incorporate less structured factors such as priorities, preferences, and intuition when evaluating decision alternatives?
Different organizations are approaching these challenges in different ways. Industry leaders realize that it’s difficult, if not impossible, to encapsulate the intelligence of more experienced managers using desktop software like Microsoft Excel and Project in complex scheduling and optimization environments. Large scale enterprise asset management (EAM) systems are also limited in this respect, because they do not provide the level of interactivity or decision support functionality required to evaluate multiple complex decision criteria. These systems are intended to track decisions, store transaction history, and provide organizational “memory” through a shared database.
Applications built using common desktop tools and large-scale transaction support systems are instances of “single expert” systems … where the end user is the expert! The user makes the decisions with the help of software that performs calculations, queries a database, and completes a transaction, or that draws a graphical representation of underlying transactional data.
A different and more powerful paradigm is available. It is called a “two expert approach”, and uses more powerful software that embodies expert decision support capability—something not available with MS-Excel. At the same time, the “two expert” system does not replace the human expert user. The user can make decisions based on options presented by the decision support system and then superimpose decision criteria, preferences, and priorities on the decision evaluation process, based on intuition and experience.
For example, the computerized decision support system may recommend that a given asset be moved to a certain site at a specific time to meet production targets. However, the expert user may realize that this alternative is perhaps not as good as deploying another asset that he/she knows is manned by a more effective/productive team, given the circumstances at hand. In this case, the system does the hard work of scheduling and optimization, but it does not eliminate the user from the decision making loop.
To respond to a skills shortage resulting from an ageing workforce, new IT solutions should include decision support applications that provide “two expert” capability. This reinforces the decision making ability of less-experienced users in complex operational environments without removing necessary human judgment and intuition from the decision making process.
