According to Cuylen, Kosch, and Breitner (2016), the digitalization of billing processes offers an appropriate opportunity for firms to cut down it expenses maximize administrative duties, and to optimize competitiveness and efficiency. However, Wong and Voysey (2015) assert that digitalization is constrained by, legal uncertainty, numerous software solutions, incompatibility of information system infrastructure, low-knowledge base, and complex demands.
Consulting companies as mentioned in the case study of Payment Time (Young Consulting, Ernst, and Accenture) apply statistical analysis when reviewing the appropriateness of the system the firm design for their clients. The Payment Time Case under this review concerns a designed electronic system for CA trucking Company by the name Stockton. The electronic system under the review allows direct electronic sending of invoices to the each company's customers through their computers. Therefore, customers can check and correct possible errors that may be present in the invoices. The primary aim of the billing system to cut down the time customers take to honor their payments ( Fleming et al., 2014; Mazibuko, 2014) . Currently, with the old mode of payment, customers are taking not less than 39 days which exceeds the set industrial standard of 30 days.
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The persistent need for this information compels for the desire the new system of calculating the time required to make payment on invoices. The consulting firm applying electronic systems conceive that they can lower the taken by customers to make their payment by 50%. However, as already seen in this case, customers take roughly 39 days. Therefore, the new billing system should take customers less than 19.5 days. In an attempt to establish the effectiveness of the new system, the consulting firm shall randomly select a sample of 65 invoices from at least 7,000 invoices that would be processed using the new electronic system. The company has installed these billing systems in the rest businesses make it easy to establish the standard deviation of population payment times as 4.2 days. The number of payment for the 65 invoices is recorded manually into an excel spreadsheet to be used in this case study.
Calculating 95% Confidence Level | |||||
Std Dev = | 4.2 | ||||
Mean = | 18.1077 | ||||
Sample Size = | 65 | ||||
Confidence Level = | 95% | ||||
Standard Error of Mean = | 0.49133016 | ||||
Z Value = | 1.96 | Z value for 95% confidence level | |||
Standard Error = | 0.52094589 | Based on 95% confidence interval 17.0866 & 19.1287 are both below 19.5 days | |||
Interval Half Width = | 1.02105394 | ||||
Interval Lower Limit = | 17.0866 | ||||
Interval Upper Limit = | 19.1287 | ||||
Calculating 99% Confidence Level | |||||
Std Dev = | 4.2 | ||||
Mean = | 18.1077 | ||||
Sample Size = | 65 | ||||
Confidence Level = | 99% | ||||
Standard Error of Mean = | 0.49133016 | ||||
Z Value = | 2.576 | Z value for 95% confidence level | |||
Standard Error = | 0.52094589 | Based on 99% confidence interval 16.7657 & 19.4496 are both below 19.5 days | |||
Interval Half Width = | 1.3419566 | ||||
Interval Lower Limit = | 16.7657 | ||||
Interval Upper Limit = | 19.4496 | ||||
Given the population mean payment time to be 19.5 days, the probability of observing a sample mean payment time for sixty-five invoices that be equal to or less than 18.1077 days can be computed as follows:
Z = (18.1077-19.5)/ 0.5209
= -2.67
P (Mean x < 18.1077)
P (Z < 18.1077)
= 0.0038
Conclusion
From the excel spreadsheet analysis, it is proved that the electronic billing system is indeed effective. From the sample, the mean payment time is 18.1077 days while that touted by the consulting firm was 19.5 days. Since 18.1077 days < 19.5 days, therefore, the billing system is effective. Besides, using both 95% and 99% CI, we notice that the data has all values below 19.5 days. This is also a clear proof of the effectiveness of the system given the sample size of 65 invoices. Increasing the sample size could also lower error I and error II of hypothesis and hence reduced sample size. Besides, were all 7,000 invoices incorporated rather than using the sample size, a more accurate value of analysis could have been established. Nevertheless, with the chosen sample size, the time taken to customers to make payments has been reduced drastically.
References
Cuylen, A., Kosch, L., & Breitner, M. H. (2016). Development of a maturity model for electronic invoice processes. Electronic Markets , 26 (2), 115-127.
Fleming, N. S., Becker, E. R., Culler, S. D., Cheng, D., McCorkle, R., Graca, B. D., & Ballard, D. J. (2014). The impact of electronic health records on workflow and financial measures in primary care practices. Health services research , 49 (1pt2), 405-420.
Mazibuko, G. P. (2014). The Impact of the municipal billing system on revenue collection in selected South African cities (Doctoral dissertation, University of Pretoria).
Wong, M., & Voysey, K. (2015). U.S. Patent Application No. 14/696,276 .
