Assignment Sample on Operations and Business Analytics
Introduction
This assignment is divided into two essential reports in which the calculation will be done on the basis of the Student’s Union Shop with the help of various kinds of modelling tools. The modelling tools are also helped to utilize the decision making characteristics of the shop. As a part of the management team, this shop needs to employ some staff on the basis of evaluating the work on the platform of “adequate service level”. This report helps to analyze the service models on the basis of current issues that are faced by the shop and provides recommendations of the efficient tools required to build the model. The calculations will be done in the second report that helps to determine the busy time, arrival time and service time in a day. For determining the busy times in a day, it is needed to initialize the opening and closing hours of the Union Shop, and for that reason, the modelling structure helps to clarify the service model.
Background
In this Student’s Union Shop, the management team tried to work out how the cashier and the management team needed to work out with the number of the cashiers, and for that reason, they wanted to employ the adequate service model. The main customers in the shop were queuing up and called forward for paying the purchase when they will become accessible for the helping characteristics of the customers, and the management team decides to record the arriving time of the number of customers in the busy time of the day in the interval time of five minutes gap in the opening hours also from 8 a.m. to 6 p.m. (Halibaset al., 2018). This recorded data is calculated in the five weeks period interval, and for that reason, all the data are collected in the term line.
This shop mainly performs with the help of various kinds of functions with the help of only three cashiers, but the shop has the capacity to operate or support upto six cashiers that can manage the customers in an easy way. But it can be recorded that the extra cashiers can be caused almost £20 in just an hour. This Student’s Union Ship is mainly open for approximately 50 weeks each year. That is why the management team has collected the primary data to provide an opportunity for the cashiers to take payment (Liu et al., 2018). The data is recorded in an Excel file, and more than 1000 data added to it is recorded from the Student’s Union Shop. The management team is mainly concerned that the customers who wish to buy food may choose other types of outlets in the university like Noddy Bar or “The Big Ear Sandwich Shop”.Apart from that, the management team is also concerned about the staff’s turnover system that can reduce their stress and can also stop the customers from getting bored. The customers are mainly interested in hearing the innovative operation strategies that help them to provide relaxation during busy hours.
Current Issues
The main issue of the Student’s Union shop is the current issue that the staff are facing. There is an increasing amount of problems to provide an adequate service level to the customers in the busy time of the day. The cashiers are relatively small in number, and for that reason, they faced various kinds of problems to provide service to the customers. These problems are generally caused by having a weak model of work (Mai, 2020). The Student’s Union Shop has the capacity of 6 workers or cashiers, but they have only two because of the money issue.
There is no such strong modelling structure in the shop, and for that reason, the problems are arising to the management team, and that is why the customers are looking at the items in the shops. The strong modelling structure helps to mitigate various kinds of problems. The arrival time and service time data help provide a solid reason to analyze the issues generated in the Student’s Union Shop (Ariyarathna and Peter, 2019). The main existing problems about the shop is there are no such efficient tools that can measure the current performance of the shop. The suggestion of improvements is provided in the following sections that help the management team to mitigate the issues and helps the customers to rely on the Union Shop.
Improvements on Current Strategies
The current strategies should be improved by the multiQ modelling structure that helps to form the passive estimation of the “path properties” that can be ranged for the “overlay network path optimization” process (Chehbiet al., 2018). This model mainly helps to build the current use of the cashiers in the shop and provides the management team with why it is essential to employ a more significant number of cashiers in the shops.
In Excel calculations, there can be a problem of mixing data. The model helps to mitigate the problem and provides the management team with appropriate and suitable data for the estimation process. Multi is mainly considered as a tool that calculates the passive capacity measurement, which is suitable for the Student’s Union Shop. This model helps to discover the central capacity of the “multiple congested links” that can make a path for the single flow race of the structured measurement techniques (Barber et al., 2018). This model is used to perform the equally “spaced model graphs” that can perform the resulting mean, median and mode of the data. Other improvements of the current strategies can be measured with the help of the Outlier model. This model helps to mitigate the problem of the cashier also. This model helps to find the path rate in terms of the standard deviation model.
Recommendation of efficient tool
This Student’s Union Shop is related to customer-centric outcomes. That. That is why the recommendation system should come up with the idea of the business analytics and operation model. The shop needs to initiate the business models that help the shop to provide the analytical tools, which shows them the importance of an extra cashier system in the busy hours of the shop. The business should be recommended as:
- There should be analysis of the data in a more frequent platform that helps to increase the shop’s efficiency and helps to identify the essential and new “business opportunities” which are overlooked in the form of customer segments.
- The model should be implemented in the platform of calculation of the data and the basis of intelligence. The potential characteristics of profitability and growth help to initiate the endless intelligence to the project or the shop.
- The data analytics system should be implemented with the help of advanced engineering models that can start with customer-centric outcomes in the aspects of big data. The shop should develop essential strategies in big data that can help identify the business priorities and build the strategies on the problems.
- For that reason, SAS business analytics tools and the Model tool is appropriate to provide the solution to the issues of the shop. These tools are moreover the same as each other.
Conclusion
In the last, it can be said that the shop really needs to analyze the data and provide the strategies that come up with the solution to management problems. That is why this report mainly included the recommendation of the tools that help the shop to build a structural model. The issues are briefly described, which are associated with the process of developing the cashiers in the shop. There are two cashiers in the shop, and that is why the fundamental problem in the shop is related to the management team. The best and appropriate model is also described in terms of the business management system. The Business Analytic model is also discussed in terms of business development strategies and provides an essential solution to the business analytics tools.
Reference List
Journals
Ariyarathna, K. and Peter, S., 2019. Business analytics maturity models: a systematic review of literature. Focus, 3(10), p.4.
Chehbi-Gamoura, S., Derrouiche, R., Malhotra, M. and Koruca, H.I., 2018, June. Adaptive management approach for more availability of big data business analytics. In Proceedings of the Fourth International Conference on Engineering & MIS 2018 (pp. 1-8).
Halibas, A.S., Shaffi, A.S. and Mohamed, M.A.K.V., 2018, March. Application of text classification and clustering of Twitter data for business analytics. In 2018 Majan International Conference (MIC) (pp. 1-7). IEEE.
Liu, Y., Han, H. and DeBello, J., 2018. The challenges of business analytics: Successes and failures.
Mai, F., 2020. Essays in Business Analytics (Doctoral dissertation, University of Cincinnati).
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