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This chapter introduces the cost volume profit (CVP) method, which can assist management in evaluating current and future events regarding sales revenue inflow and cost outflows. A number of basic questions will be identified and discussed using examples to explain CVP analysis. A graphical explanation and presentation of CVP is then given, showing how the breakeven level of sales revenue can be determined and how the level of operating income (profit) for a particular volume of sales can be arrived at. Before discussing and illustrating the CVP equation (which eliminates the need for a graph), several specific key assumptions and limitations inherent in the CVP approach will be addressed. The equation is used to determine the breakeven level of sales revenue, the sales revenue needed to cover a new fixed cost, the additional sales revenue required to cover a changed variable cost, or multiple changes in costs. The CVP answers can be obtained in sales revenue dollars or sales end units, such as rooms sold or guests served. The CVP equation can also be used to determine the effect that a change in selling prices will have on operating results to determine additional sales volume required to cover a loss, or to analyze a new investment. This chapter illustrates how the CVP equation can be used to handle various situations concerning joint costs in multiple-department organizations and concludes with a discussion on incorporating income tax in the CVP calculation.

Young-McCaughan et al. (2003)

EC (2007) NMP-2008-32 2 Self-Learning Production Systems: Work Programme on Nanosciences, Nanotechnologies, Materials, and New Production Technologies, available at ftp:// ardhujiacil/pub/251/NMP_Fiche_small__2008_02pdf ERTC (2004) Conf Proc ERTC 2004, London, May 2004 Filev, D P, T Larsson, L Ma (2000) Intelligent Control for Automotive Manufacturing RuleBased Guided Adaptation IEEE Conf IECON-2000, Nagoya, Japan, October, pp 283 288 Filev, D, T Tseng (2006) Novelty Detection-Based Machine Health Prognostics Proc 2006 Int Symp on Evolving Fuzzy Systems, IEEE Press, pp 193 199 Fortuna, L, S Graziani, A Rizzo, M G Xibilia (2007) Soft Sensor for Monitoring and Control of Industrial Processes Advances in Industrial Control Series ( M J Grimble, M A Johnson, eds) Berlin: Springer-Verlag Goel, K, Prashant, Y (1999) Leveraging Knowledge Capital in Oil Re neries through Intelligent Systems Proc Int Conf PETROTECH 99 Kordon, A, G Smits (2001).

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The distance is obtained from the optical interrupter sensor on the wheels. The value comes from counting the number of beam interruptions caused from the toothed interrupter disc. In Roomba the disc has 32 slots (shown in Figure 5-2) and you know from 5 that the gearing provides a 25:1 reduction between wheel and disc. Thus the sensor s angular and distance resolution of the turning wheel is:

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x2 = 138.79, p = .21, df = 126, RMSEA = .018, 90% UL = .038, AIC = 113.21 German Sample Fantasy Aesthetics Feelings Actions Ideas Values .42 .56 .66 .00 .18 .11 .07 .08 .07 1.00 .27 .31 .24 .00 .26 .06 .01 .02 .07 1.00 .22 .04 .13 .00 .51 .37 .27 .00 .55 .59 .69 .00 .52 .91 .86 .98

Soft Sensor Development Using Genetic Programming Proc GECCO 2001, San Francisco, pp 1346 1351 Kordon, A, G Smits, A Kalos, E Jordaan (2003) Robust Soft Sensor Development Using Genetic Programming Nature-Inspired Methods in Chemometrics ( R Leardied) Amsterdam: Elsevier, pp 69 108 Li, W, H H Yue, S Valle-Cervantes, S J Qin (2000) Recursive PCA for Adaptive Process Monitoring J Process Control, Vol 10, No 5, pp 471 486 Mac J (1998) Application de la t cnica de calibracion PLS al control y modelado de as, e Processos PhD Thesis University of La Laguna, Tenerife, Spain Mac as-Hern ndez, J (2001) Design of a Production Expert System to Improve Re nery Process a Operations European Symposium on Intelligent Technologies, Hybrid Systems, and Their Implementation on Smart Adaptive Systems, Puerto de la Cruz, Tenerife, Spain, December 13 14, EUNITE 2001, pp 63 68 Macias, J, J.

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Feliu, A Bosch (2003) Simulation Workbench between HYSYS Rigorous Model and Its NN Extension: Case Study of a Complex Simulation with Re-circulation, Proc 4th European Congress on Chemical Engineering, ECCE 4, ISBN: 84-88233-31-0 Qin, S J, H Yue, R Dunia (1997) Self-Validating Inferential Sensors with Application to Air Emission Monitoring, Industrial Engineering Chemistry Research, Vol 36, pp 1675 1685 Rallo, R, J Ferre-Gine, A Arena, F Girault (2004) Neural Virtual Sensor for the Inferential Prediction of Product Quality from Process Variables, Computers and Chemical Engineering, Vol 26, pp 1735 1754 Rodriguez, M, J J Rubio, A Jimenez (1997) Naphta Cut Point Inference Using Neural Networks In Neural Networks in Engineering Systems (A B Bulsari, S Kallio,eds), Stockholm, Sweden, June, wwwdiquimaupmes/recursos/docs/hommepdf Smits, G, A Kordon, E Jordaan, C Vladislavleva, M Kotanchek (2006) Variable Selection in Industrial Data Sets Using Pareto Genetic Programming.

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