Chemical Engineering January 2022 - 33

AIChE J., vol. 64, no. 11, pp. 3,868-3,881, May 2018.
methodology (SMART) to define the
improvement project [17]:
* Specific - What the project will
accomplish with a very specific goal
* Measurable - How the success
of the goal can be measured.
* Assignable - Who in the organization
(internally or externally) will
be responsible for this goal
* Realistic - Why this goal is
attainable
* Time-related (or time-bound) -
The timeframe for when the goal is
to be completed
The combination of the SMART
Goals, combined with the outcome
of the assessment, provides
management teams with the instruments
to measure the current
status, but also any future progression.
The defined goals provide a
method to execute specific projects
that will raise the level of maturity
on the topic of data quality.
■
Edited by Mary Page Bailey
References
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M2DDM - A Maturity Model for Data-Driven Manufacturing,
Procedia CIRP, vol. 63, pp. 173-178, 2017.
4. Noller, D., Myren, F., Haaland, O., et al, Improved Decision-making
and Operational Efficiencies through Integrated Production
Operations Solutions, Offshore Technology Conference, May
2012.
5. Kinsmen Group, Magic of AI & Machine Learning for Engineering,
Operations & Maintenance, Webinar, 2020.
6. Antonovsky, A, Pollock, C., and Straker, L., System reliability as
perceived by maintenance personnel on petroleum production
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7. Snitkin, B.S., Mick, B. and Novak, R., The Case for Developing an
AIM Strategy, ARC Strategies, July 2010.
8. Gallaher, M.P., O'Conor, A.C., Dettbarn, J.L. and Gilday, L.T., Cost
Analysis of Inadequate Interoperability in the U.S. Capital Facilities
Industry, NIST Advanced Technology Program, pp. 1-210,
August 2004.
9. Coetzer, E.O. and Volk, P.J., A standardized model to quantify the
financial impact of poor engineering information quality in the
oil and gas industry, South African J. Ind. Eng., vol. 30, no. 4,
pp. 131-142, December 2019.
10. Frank, A.G., Dalenogare, L.S. and Ayala, N.F., Industry 4.0 technologies:
Implementation patterns in manufacturing companies,
Int. J. Prod. Econ., vol. 210, September 2018, pp. 15-26,
2019.
11. Davenport, T.H. and Prusak, L., " Working knowledge: How organizations
manage what they know, " Harvard Business Press,
January 1998.
12. Reis, M.S. and Kenett, R., Assessing the value of information of
data-centric activities in the chemical processing industry 4.0,
13. Wand, Y. and Wang, R.Y., Anchoring Data Quality Dimensions in
Ontological Foundations, Commun. ACM, vol. 39, no. 11, pp.
86-95, November 1996.
14. Becker, J., Knackstedt, R., and Pöppelbuß, J., Developing Maturity
Models for IT Management, Bus. Inf. Syst. Eng., vol. 1, no.
3, pp. 213-222, June 2009.
15. De Bruin, T., Rosemann, M., Freeze, R., and Kulkarni, U., Understanding
the Main Phases of Developing a Maturity Assessment
Model, ACIS 2005 Proceedings, p. 11, January 2005.
16. Paulk, M.C., Curtis, B., Chrissis, M.B. and Weber, C.V., Capability
Maturity Model, Version 1.1, IEEE Softw., vol. 10, no. 4, pp.
18-27, February 1993.
17. Mind Tools, SMART Goals: How to Make Your Goals Achievable,
March 2016.
Author
Edwin Elmendorp is an information
architect at Kinsmen Group
(9595 Six Pines Dr, Ste 8210,
Houston, TX; Phone: 484-4019089;
Email: info@kinsmengroup.
com), a team of information management
specialists working with
numerous oil, gas, pharmaceutical
and utility companies. Elmendorp
has close to 20 years of consulting
experience related to engineering information management.
After initially graduating as an electrical instrumentation
engineer, he added a computer science degree
and recently graduated cum laude with a M.S. in
business process management and IT. Elmendorp has
worked with many owner-operators to digitally transform
how companies manage their information assets
spanning many different software solutions.
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