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Jet Copiers Case Study


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.

The Rand function.

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

The model assumes a straight-line function.

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

Repair times/days (1st15 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:

The daily revenue loss generated.

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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IvyPanda. (2020, July 6). Jet Copiers Case Study. Retrieved from https://ivypanda.com/essays/jet-copiers-case-study-report/

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"Jet Copiers Case Study." IvyPanda, 6 July 2020, ivypanda.com/essays/jet-copiers-case-study-report/.

1. IvyPanda. "Jet Copiers Case Study." July 6, 2020. https://ivypanda.com/essays/jet-copiers-case-study-report/.


Bibliography


IvyPanda. "Jet Copiers Case Study." July 6, 2020. https://ivypanda.com/essays/jet-copiers-case-study-report/.

References

IvyPanda. 2020. "Jet Copiers Case Study." July 6, 2020. https://ivypanda.com/essays/jet-copiers-case-study-report/.

References

IvyPanda. (2020) 'Jet Copiers Case Study'. 6 July.

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