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Implementing Monte Carlo simulation model for revenue forecasting under the impact of risk and uncertainty

Hussain Zahid

Management and Production Engineering Review

2019

Vol. 10, No. 4

81--89

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.

The Classification Of Scheduling Problems Under Production Uncertainty

Wojakowski P., Warżołek D.

Research in Logistics & Production

2014

Vol. 4, No. 3

245--256

One of the most important issue regarding scheduling problem is production uncertainty. From the standpoint of real-world scheduling problem, it is necessary to find solution of building the schedule which can be insensitive to production disruptions such as machine breakdowns, incorrectly estimated time interval of machines maintenance, absence of workers, unavailability of materials or tooling, production defects, variable processing times, etc. This research study is conducted to answer the question: how the researchers cope with the problem of production uncertainty taking into account the scheduling problem? Our investigation focuses on approaches developed for scheduling with production uncertainty consideration. Among these approaches the most popular ones are: reactive scheduling, proactive scheduling, predictive scheduling and robust predictive-reactive approaches. A brief explanation of individual approaches are presented. Further, models of uncertainty used in scheduling approaches are highlighted.