Critical Decision-Making: Developing Simulation Model

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Table of Contents

Limitations of study
Conclusion
References

Making business decisions is never easy, especially when it involves some complicated scenarios, which require critical decision-making (Taylor, 2010). Simulations are useful tools in accomplishing such a task as they provide a value, which often forms the basis for decision-making (Buglear, 2005).

Various computer applications support this function, including Excel. In this paper, excel simulation used to come up with convincing decisions regarding whether or not the business should purchase a back-up copier.

The number of days required for copier repair whenever it is out of service is generated based on a discrete distribution model, with the help of Excel. The discrete distribution is assumed is as follows:

Repair time (Days taken)	Probability
1	0.2
2	0.45
3	0.25
4	0.1

The number of days required to repair the copier are generated from excel. The simulation model developed is based on the functions described below:

The Rand function is used to generate random probabilities, which are subjected to test in order to simulate the possible situation. To generate the number of repairs likely to be performed over the duration, the function is used. It simulates the number of times (days) that the copier will be subjected to repair.

An extract of the obtained results is in the table below:

	Repair times (days)
1	1
2	2
3	3
4	2
5	2
6	2
7	2
8	2
9	2
10	1
11	2
12	3
13	1
14	3
15	1

A continuous distribution model is used to simulate the interval between successive breakdowns (in weeks) is developed using Excel functions. The model assumes a straight-line function. Randomly selected probability values are selected using the RAND () function. The y function is therefore solved using the function

This helps in generation of the intervals amidst successive breaks. Part output is revealed in the table below:

Repair times/days (1^st15 simulations)	Random probability	Break intervals
1	0.4	4.72314135
2	0.7	2.002696731
3	0.7	5.255727961
2	0.5	4.292089408
2	0.1	2.457429285
2	0.6	5.081012821
2	0.2	3.356681152
2	0.8	3.235287261
2	0.5	4.470013043
1	0.5	2.380419757
2	0.1	2.00572022
3	0.1	1.722544679
1	0.5	4.66851108
3	0.3	4.97296724

Daily revenues lost when the copier is not in service is generated based on the difference between the copies produced every day. Additionally, the cost of each copy is factored in, that is $0.1 each. Using the information, downtime value will be calculated and compared against the set threshold revenue loss of $12,000 to decide whether to purchase the copier or not. The daily revenue loss generated is given by the expression below:

Whereby the RAND () function represents the generated random probability value. The simulation returns a cumulated downtime loss less than the 12,000 set as threshold for purchasing a new copier, implying that they will not need to buy a backup copier (see excel sheet attached). The simulation of the last 15 variables shown below illustrates this.

Repair times/days	Random probability	Break interval	Cost of revenue	Revenue lost when copier waiting for repair	Total time in days	Cumulative downtime loss (daily)
3	0.9	2.43548	722.470	2167.41230	20.048390	10722.59519
2	0.9	5.16072	729.982	1459.96573	38.125048	10760.72023
1	0.1	5.81797	289.455	289.455781	41.725825	10802.44606
2	0.3	4.48262	374.037	748.075830	33.378409	10835.82447
2	0.1	5.82270	240.754	481.509578	42.758951	10878.58342
2	0.7	5.21720	639.826	1279.65282	38.520413	10917.10383
2	0.4	4.47012	448.656	897.312391	33.290872	10950.39471
1	0.1	5.43246	275.254	275.254722	39.027245	10989.42195
2	0.1	5.42346	247.304	494.608908	39.964289	11029.38624
2	0.4	3.82001	414.358	828.717117	28.740070	11058.12631

The ability of the generated model to use the prevailing conditions to simulate the conditions likely to prevail and hence aid planning in terms of resources.

It is presumed that if the model is able to generate good values based on the functions Rand () in Excel, then equal amounts of random values should be generated in the long run. This helps in developing a test for the model efficiency, using excel. The obtained excel results are illustrated in the figure below:

Random probability value	cumulative frequency	Frequency	Expected percentage	Obtained percentage	Difference
0.1	35	35	10%	9.59	0.41
0.2	75	40	10%	10.96	-0.96
0.3	110	35	10%	9.59	0.41
0.4	155	45	10%	12.33	-2.33
0.5	192	37	10%	10.14	-0.14
0.6	223	31	10%	8.49	1.51
0.7	260	37	10%	10.14	-0.14
0.8	302	42	10%	11.51	-1.51
0.9	327	25	10%	6.85	3.15
1	365	38	10%	10.41	-0.41

The obtained deviations in relation to the expected value of 10 are small, and hence, this forms the basis for confirming that the model is well suited to predict the future scenarios and hence can be used to decide whether investment should be directed towards acquiring a new copier.

Limitations of study

Despite the fact that the simulation offers a good basis for making decisions, like any other tool, it is not without limitations. Computational efficiency may not well reflected the immediate situations to be witnessed during the initial stages of the business.

The study does not factor in other factors that might influence the businesses performance during the initial study. This is taking into consideration the fact that initial businesses often suffer various performance challenges.

Conclusion

In conclusion, it is important to mention that excel provides a good avenue for development of simulation models in decision-making. From the developed model, a minimum threshold revenue loss value is set and a simulation run to establish if a new back up should be purchased.

It generally attests to the fact that buying a back-up copier will not add to the groups revenues. The model provides minimal variations in terms of probability distribution indicating that it is an effective tool for making decisions.

References

Buglear, J. (2005). Quantitative methods for business: the A to Z. Oxford, U.K.: Elsevier Butterworth-Heinemann.

Taylor, B. M. (2010). Introduction to management science (10th ed.). Upper Saddle River, NJ: Pearson/Prentice Hall.

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Critical Decision-Making: Developing Simulation Model

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