435979 How to Entepreneur - Measuring the Effort

Monday, November 9, 2015: 3:30 PM
Salon A/B/C (Salt Lake Marriott Downtown at City Creek)
Christina M. Borgese, PreProcess, San Ramon, CA

Measuring the Effort

An entrepreneurial effort requires different measurements for evaluating progress for many different audiences.  It is said to expect what you inspect.  Measurements are the easiest way to describe progress against goals.  No matter what the audience, measurements must be real, valid, and actionable.  The engineers designing the system have to be able to roll up the many measurements in the system to how the system affects the financial performance that the investors will be most interested in measuring.  Many tools are used, and the resulting trend and data analysis is only as good as the measurements being taken.  One of the most mis-understood aspects of a new system is the validity of a measurement.  Following good statistical methods will insure that decisions are made on good information. The critical element to the technical piece of the investment is the data that supports the claims.  Can the product be demonstrated to be reproducible on the systems that are proposed? Does the development of the data, both technical and commercial, follow sound and accepted practices?  The capital investment for chemical projects are large and models must be validated against actual data from bench and pilot systems.  Experimental Orders (EOs) and Key Learning Reports (KLRs) are a simple and organized way to define the runs that are required to produce the data that supports the claims being made for the investment.  Capital and Operating Costs must be justified to the financial investors to deliver the returns expected.  The information is only as good as the measurements used to distill the understanding of the effort.  Technical data must be correlated to the financial and commercial returns expected.

  1. MEB – Mass and Energy Balances
  2. DOE – Design of Experiments
  3. Measurement Methods and the Statistics of Variability
  4. Data Packages
    1. Experimental Orders
    2. Key Learning Reports
    3. FLRCs (Factors, Levels, Responses, Constants)
    4. Data to Support the Claims
    5. Technical Inputs to Financial Models
    6. Capital Cost Estimate
    7. Operating Cost Estimate
    8. Financial Data Packages

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