The New Knowledge Management Normal

    July 20, 2016 12:51 PM by Dr. Scott Shemwell

    Volume 5 Number 14—July 20, 2016

    A recent article lamented the prospect that the upstream oil and gas sector has reduced its workforce so dramatically, that future economic recovery may be difficult.  Time will tell but there is a contrarian perspective.

    As this and other pundits have commented, technology is changing the sector’s landscape and rapidly.  The often stated, “Do more with less” with enabling technologies has been the staple of industry transformations.  With a number of case studies across all industries, the statement is hard to rebuke.

    A Little History

    One suspects that as with the aftermath of multiple sector collapses of the 1980s, 1990s, and early periods in this century, somehow the sector will one more time muddle through.  One major similarity between the 1980s and 1990s and today is the explosive growth of technology and the subsequent enabled business process transformations.

    The 1980s saw the rise of CAD and decline of manual drafting.  Interestingly, now CAD is dramatically changing as well.  A similar transformation began with geophysical and petrophysical stand-alone graphics interactive workstations.

    Likewise, changes in how data were handle can be traced to this period as well.  There are many other new information technologies including the conversion of the back office beginning with the introduction of the IBM 360 (Mainframe).

    Drilling and production technology and processes were changing as well.  In the 1990s, the industry experimented with new business models integrating the supply chain more tightly into “risk sharing and profit sharing” relationships.

    These were major changes and some worked better than others.  This author was briefly the CIO for the Terra Nova project and it was one early example of global collaboration using the Internet, i.e., use of email to exchange information and engineering files as well as video conferencing.

    Today, the sector has more mature as well as significantly enhanced information tools at its disposal.  Yet in some ways this and other sectors have not matured as rapidly as the enabling technology has grown.  One example of this shortcoming is Knowledge Management.

    Value from Knowledge

    The current Knowledge Management (KM) construct is about 25 years old.  A definition of that period accredited to the Gartner Group is still in use today, “Knowledge management is a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise's information assets.  These assets may include databases, documents, policies, procedures, and previously un-captured expertise and experience in individual workers.”

    Capturing the vast understanding from the departing workforce; Baby Boomers and making it available (sharing) to those who will lead the business and engineering challenges over the next 25 years is one of the current charters for Knowledge Management champions.  But is a 25-year-old technology model adequate for the next 25 years?

    One of the challenges is that technology definitions are often ambiguous and in uncertain context.  This lack of a common vocabulary can dramatically weaken an organization/industry Culture.

    A strong Knowledge Management Culture is required if the value of KM is to be unleashed.  Having a conjoint morphology is critical.  Previously for a Culture of Safety, we defined, “This is functional Interdisciplinary Common Vocabulary (ICV) as opposed to traditional interpersonal communications models.  Not all members of an organization will speak the same human language, e.g. English, this is process communications from the past.  In this new linguistic framework, the syntax, phonology, morphology, and semantics of a common language of safety will bridge traditional dialectal barriers as a functional ICV will be foundational.”

    From this cultural perspective, KM as outlined and currently practiced by many is outdated.  Additionally, it does not capitalize on technological changes since its inception.

    Capturing, retrieving, sharing, etc. are not direct actionable decision-enabling tasks.  To meet the business imperatives a new KM model is needed.  One that is based on a functional ICV.

    From Motorola’s Six Sigma philosophy, the construct of DMAIC can be applied to KM.  These five components of the model are:

    •    Define—the Problem Statement process
    •    Measure—Key Performance Indicator (KPI) selection and data collection processes—leading indicators preferred
    •    Analyze—Identify gaps between actual performance and expected or desired behavior.  Causal analysis and rank order improvement portfolio development.
    •    Improve—Set of potential solutions identified and tested, i.e., process simulations.
    •    Control—Implementation of a monitoring and update (feedback) plan, including Verification of new process.

    In other words, this becomes a KM Adaptive Control System similar to those used with Digital Oilfield “Smart” solutions.  Incorporating this approach, KM solutions become proactive decision supports processes.

    Advanced models may include process simulation.  For example, a Drilling Risk Assessment Mental Model Simulation is available.  A model like this enables organizations to confirm various scenarios as part of a computer simulation decision-making process.

    Operational Excellence (OE) can be defined as a function of six criteria; top asset performance, a stellar reputation, comparative advantage of capabilities, culture of high performance, world class HSSE and best in class processes and systems.  Firms seeking to attain OE Leadership must effectively address all six criteria.

    Traditional Knowledge Management does little to effect OE.  Mostly likely this model will underperform best in class firms at most.

    However, the KM Adaptive Control System described herein enables knowledge stores to be converted into actionable workflows with feedback so that the workflow can “learn” going forward.  Moreover, instead of sharing knowledge, it is imparted on those using the workflow solving field operational and risk management problems.

    A Functional Interdisciplinary Common Vocabulary is the critical component of KM Adaptive Control Systems.  Without, this Lexicon, these multi-variant systems cannot work (communicate) properly.

    Organizations capitalizing on knowledge in this manner enable Operational Excellence and the stakeholder value associated with successful OE initiatives.  Knowledge Management no longer has the look and feel of an IT activity, but now the Core Competency of the firm!

    How does your organization make its Knowledge Actionable?

    About the Author
    Dr. Scott M. Shemwell has over 30 years technical and executive management experience primarily in the energy sector.  He is the author of six books and has written extensively about the field of Operations Excellence.  Shemwell is the Managing Director of The Rapid Response Institute, a firm that focuses on providing its customers with solutions enabling Operational Excellence and regulatory compliance management.  He has studied cultural interactions for more than 30 years—his dissertation; Cross Cultural Negotiations Between Japanese and American Businessmen: A Systems Analysis (Exploratory Study) is an early peer reviewed manuscript addressing the systemic structure of social relationships.

    See our Operations Management System solution to obtain Operational Excellence
    Free Economic Value Proposition Matrix version 2.0 (Realize the value of your investment)

    Known, Unknown, Unknowable

    July 6, 2016 2:35 PM by Dr. Scott Shemwell

    Volume 5 Number 13—July 6, 2016

    These three categories of risk are pretty straightforward; on the surface. Known risks are those sets that are identified to risk management. Slightly more complicated, Unknown risks are discoverable given inquisition and information. The third class, Unknowable are beyond the scope of the current risk identification and mitigation model.[i]

    This Risk Taxonomy is useful for complex adaptive systems found in critical sector infrastructure structures and integrated processes aka, Ecosystems. Systems of this nature are comprised of “multiple interacting scales.”[ii]

    The four statistical “scales of measurement,” nominal, ordinal, interval and ratio are the way variables are defined and categorized.[iii] This is an important consideration when using stochastic risk mitigation models as opposed to simple linear thinking.

    The mathematician Alan Turing, most famous for breaking the World War II German Enigma code also put forth the hypothesis that, “there cannot exist any universal algorithmic method of determining truth in mathematics, and that mathematics will always contain undecidable propositions.”[iv] This is important because it suggests that any quantitative decision support system by definition will always have Unknowables!

    He goes on to posit that catalytic agents in a multifaceted system acting locally can diffuse randomly throughout it and ultimately cause it to evolve into a new stable set of process patterns across the Ecosystem.[v] This statement is overly simplifying complex mathematics but the reader will get the point.

    Often significant incidents are the result of more than one (sometimes relatively) minor (catalytic) events that cascade into major disasters.[vi] The Swiss Cheese Barrier model recognizes this likelihood as well if multiple barriers are breached simultaneously due to human and/or system fallibilities.[vii]

    Very Interesting, However?

    Some may wonder that this is all very interesting, a bit academic and even theoretical but so what? What’s in this construct for me in my everyday job?

    Whether using a spreadsheet, ERP financials, engineering, statistics or other math driven analysis software solutions, we assume the arithmetic performed by these programs is correct. A myriad of crucial decisions are made under these assumptions.

    What if these critical software programs do not always correctly calculate the answer correctly?[viii] Usually, these anomalies are minor and perhaps not even noticed as they may occur outside of significant numerical digits.[ix] However, would you want the instrumentation your surgeon is using during the operations to be in error?

    The health of a risk management system depends on its proper construction! If the theoretical construct of your surgeon’s data systems or science and engineering is not sound, then all decisions flowing from these systems increase exposure, not reducing it.

    It is important that users understand the limitations of decision support systems and that includes the data component of the Ecosystem. Poor data quality is a very real problem that manifest may itself daily.[x]

    Circular Loops Do Compute

    A typical definition of taxonomy refers to either a relatively static technique for classifying members of a group, i.e., biological organisms, or the nomenclature of the members.[xi] The relationship among members of one class to other classes is often either inferred or not addressed.

    A Risk Taxonomy is dynamic. Risks assigned to one group such as Unknown can become Known. When Unknowables manifest themselves they likely become Known. This is unique.

    Known risks can become Unknown if the management systems used are “feed” poor or incorrect data. In this complex adaptive system, Unknown risks can morph into Unknowable if catalytic agents cause the effect of a new stable component to the Ecosystem.

    A Resilient organization will not have identified every possible combination of Ecosystem interaction and evolution, but it is prepared to rapidly address any risks migrating as the Unknowable/Unknown becomes Known. Risk managers must recognize these dynamics from a multifaceted system. Simple risk management models currently used are no longer applicable.

    Does your Organization’s Risk Taxonomy Reflect these Three Groups?

    About the Author

    Dr. Scott M. Shemwell has over 30 years technical and executive management experience primarily in the energy sector. He is the author of six books and has written extensively about the field of Operations Excellence. Shemwell is the Managing Director of The Rapid Response Institute, a firm that focuses on providing its customers with solutions enabling operations excellence and regulatory compliance management. He has studied cultural interactions for more than 30 years—his dissertation; Cross Cultural Negotiations Between Japanese and American Businessmen: A Systems Analysis (Exploratory Study) is an early peer reviewed manuscript addressing the systemic structure of social relationships.

    See our Operations Management System solution to obtain Operational Excellence

    Free Economic Value Proposition Matrix version 2.0 (Realize the value of your investment)

    End Notes

    [i] http://www.sei.cmu.edu/reports/93tr006.pdf

    [ii] http://www.ams.org/journals/bull/2003-40-01/S0273-0979-02-00965-5/S0273-0979-02-00965-5.pdf

    [iii] http://faculty.webster.edu/woolflm/statwhatis.html

    [iv] http://www.biography.com/people/alan-turing-9512017

    [v] http://www.ams.org/journals/bull/2003-40-01/S0273-0979-02-00965-5/S0273-0979-02-00965-5.pdf

    [vi] http://www.preventionweb.net/english/hyogo/gar/2015/en/bgdocs/inputs/Liu%20and%20Huang,%202014.%20Compound%20disasters%20and%20compounding%20processes%20Implications%20for%20Disaster%20Risk%20Management.pdf

    [vii] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1117770/

    [viii] http://www.journalofaccountancy.com/issues/2014/mar/excel-calculation-errors.html