@ARTICLE{Hussain_Zahid_Implementing_2019,
 author={Hussain, Zahid},
 volume={vol. 10},
 number={No 4},
 journal={Management and Production Engineering Review},
 howpublished={online},
 year={2019},
 publisher={Production Engineering Committee of the Polish Academy of Sciences, Polish Association for Production Management},
 abstract={In the existent world of continuous production systems, strong attention has been waged to anonymous risk that probably generates significant apprehension. The forecast for net present value is extremely important for any production plant. The objective of this paper is to implement Monte Carlo simulation technique for perceiving the impact of risk and uncertainty in prediction and forecasting company’s profitability. The production unit under study is interested to make the initial investment by installing an additional spray dryer plant. The expressive values acquied from the Monte Carlo technique established a range of certain results. The expected net present value of the cash flow is \$14,605, hence the frequency chart outcomes confirmed that there is the highest level of certainty that the company will achieve its target. To forecast the net present value for the next period, the results confirmed that there are 50.73% chances of achieving the outcomes. Considering the minimum and maximum values at 80% certainty level, it was observed that 80% chances exist that expected outcomes will be between \$5,830 and \$22,587. The model’s sensitivity results validated that cash inflows had a greater sensitivity level of 21.1% and the cash inflows for the next year as 19.7%. Cumulative frequency distribution confirmed that the probability to achieve a maximum value of \$23,520 is 90 % and for the value of \$6,244 it is about 10 %. These validations suggested that controlling the expenditures, the company’s outflows can also be controlled definitely.},
 title={Implementing Monte Carlo simulation model for revenue forecasting under the impact of risk and uncertainty},
 URL={http://journals.pan.pl/Content/114821/PDF/9-269.pdf},
 doi={10.24425/mper.2019.131448},
 keywords={risk analysis, management, Monte Carlo simulation model, crystal ball package software, production uncertainty},
}