George, Michael (2007): Predicting the Profit Potential of a Microeconomic Process: An Information Theoretic/Thermodynamic Approach.
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Abstract It would be of great benefit if management could predict the huge profits that would result from modest investments in process improvement initiatives such as Lean, Six Sigma and Complexity reduction. While the application of these initiatives was initially restricted to manufacturing, they have been expanded to transactional processes such as product development, marketing, and indeed all microeconomic processes... This paper derives an equation that, subject to further testing, appears to make such a profit prediction possible allowing a rational investment in microeconomic process improvement. That the profit of a company is greatly increased by the reduction of internal waste was originally demonstrated by Henry Ford, but has been greatly extended by Toyota. All waste in a process results in longer lead times, measured from the injection of work into the process until its delivery to the customer or user. Thus the increase in profit is principally driven by the reduction of lead time through process improvement. The lead time of any process is governed by the Queuing Theory formula known as Little’s Law. The central result of this paper is that the reduction lead time as expressed by Little’s Law leads to an equation for the reduction of process Entropy. The expression is identical with the reduction of entropy and thermodynamic waste in a heat engine. Case studies are used to estimate the magnitude of Boltzmann’s Constant for Microeconomic processes. The resulting Equation of Profit allows the prediction of the amount of waste cost elimination based on explicit Lean, Six Sigma and Complexity reduction process improvement parameters. More data is needed to more accurately estimate the magnitude of Boltzmann’s constant for microeconomic processes.
|Item Type:||MPRA Paper|
|Institution:||Institute of Business Entropy|
|Original Title:||Predicting the Profit Potential of a Microeconomic Process: An Information Theoretic/Thermodynamic Approach|
|Keywords:||Profit Increase Prediction; Process Entropy; Information; Complexity; Waste; Equation of Profit; Little’s Law; Business Analogies with Thermodynamics; Boltzmann’s Constant of Business; Carnot; Shannon|
|Subjects:||D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D83 - Search; Learning; Information and Knowledge; Communication; Belief
D - Microeconomics > D2 - Production and Organizations > D23 - Organizational Behavior; Transaction Costs; Property Rights
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D82 - Asymmetric and Private Information; Mechanism Design
D - Microeconomics > D2 - Production and Organizations > D24 - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
|Depositing User:||Michael George|
|Date Deposited:||06. Oct 2007|
|Last Modified:||13. Feb 2013 19:59|
Director, Institute of Business Entropy, Dallas, TX; Founder, The George Group Fermi, Enrico 1956 “Thermodynamics”, Dover Work In Process are the units of work that have been released into production from Raw Material but have not yet become Finished Goods, and appears in a footnote in the 10K of public corporations. Lean Six Sigma for Service and Fast Innovation by George et al MIT LAI paper 2007:George, Works, Maaseidvaag, “The Application of Lean to Knowledge Processes” Goldstein, Herbert Classical Mechanics, Chapter 7, Addison-Wesley 1950 Bryant, John A Thermodynamic Theory of Economics, page 11, equation (3.26) the International Journal of Exergy, published by Interscience Publishers Int. J. Exergy, Vol 4, No. 3, pp.302-337.
Feynmann, R. P., Statistical Mechanics, p. 6, equation (1.3) US Patent 6,993,492 issued Jan 31, 2006
Op cit George 2002, Lean Six Sigma, McGraw Hill George and Wilson (2004),Conquering Complexity in Your Business,McGraw Hill US Patent 6,993,492 issued Jan 31, 2006. Walpole,Ronald et al 2002 “Probability and Statistics for Engineers and Scientists” p.37 Stirling’s formula is only in error by 1% when the number of products shipped per month is D=10, and of course is entirely negligible for most companies when D>>10. See Reif, F 1965, Fundamentals of Statistical and Thermal Physics, pp 613-614 for an investigation of the accuracy of Stirling’s formula. LAI paper 2007 op cit