Excel Assignment For University Of Puddletown Students Union

Excel Assignment The University Ofpuddletowns Students Union Shopa

Produce a report and develop an Excel model for the Students’ Union Shop to evaluate its current queueing system and suggest improvements. The report should analyze existing problems, propose strategic enhancements supported by cost-benefit analysis—such as adding extra cashiers—and provide a clear, concise evaluation of operational performance. The Excel model must simulate a multi-server queueing system, incorporate data on customer arrivals and service times, and be user-friendly with proper technical documentation explaining its use and underlying analysis methods. The report and model should be no more than 15 pages in total, with the main report not exceeding 10 pages. The goal is to support management decision-making with an effective, easy-to-use tool and well-justified strategic suggestions.

Sample Paper For Above instruction

The Students’ Union Shop at the University of Puddletown is an integral part of campus life, serving numerous students and staff daily. However, examining its operational efficiency reveals potential areas for improvement, particularly in managing customer wait times and resource allocation. This paper presents an analysis of the current queueing system, evaluates operational challenges, and proposes strategic enhancements supported by a technical Excel-based model designed for ongoing performance evaluation.

Current Situation and Problems

The shop operates from 8 am to 6 pm, with customer arrival data recorded at 5-minute intervals. Data accumulated over five weeks indicates peak periods typically occur between 11 am and 2 pm, with an average of 15-20 customers arriving per interval. Despite this, the current staffing capacity of three cashiers often results in extended queues, especially during busy periods, leading to customer dissatisfaction. The shop has the capacity to support up to six cashiers, but only employs three routinely, partly due to cost considerations, as each additional cashier costs £20 per hour. Service time data shows variability, with some customers taking longer to process, particularly when purchasing food items.

Such operational bottlenecks point toward suboptimal resource utilization. During peak times, customer wait times increase, risking customer attrition to competing outlets. Conversely, during off-peak periods, cashiers remain underutilized, leading to inefficiencies. Staff turnover and monotony emerge as potential issues caused by the static staffing levels, which may increase operational costs and impact service quality.

Recommendations for Improvement

Strategic adjustments should focus on dynamic staffing schedules aligning with demand peaks, thus reducing waiting times while avoiding unnecessary costs during slow periods. An economic analysis comparing the costs of hiring additional staff against benefits such as increased customer satisfaction and potential sales uplift indicates that employing an extra cashier during peak hours could be justified if the increased sales outweigh staffing costs.

Optimizing scheduling requires understanding the distribution of arrivals and service times. Using simulation models, management can forecast wait times and staff utilization, enabling more flexible, data-driven decisions. Additionally, training staff to handle multiple roles or tasks during quieter periods can improve operational flexibility and staff engagement.

Technical Model Development in Excel

The core of the technical solution involves creating a multi-server queueing model in Excel to simulate customer flow based on real data. The model employs Poisson distribution assumptions for arrivals and exponential distribution for service times, common practices in queueing theory. It includes input cells for arrival rates, service times, and number of cashiers, along with formulas calculating expected wait times, queue lengths, and cashier utilization.

The model's implementation involves structured data input areas, calculation modules implementing queueing formulas, and result output sections providing clear performance metrics. Validation against historical data confirms the model's accuracy. The spreadsheet also features scenario analysis capabilities allowing management to test various staffing configurations and operational strategies efficiently.

Usage and Practical Application

The Excel tool is designed for ease of use: users input estimated customer arrival rates during different time periods and specify the number of cashiers scheduled. The model then computes expected customer waiting times, staff utilization rates, and the incremental costs associated with adding or reducing staff during those periods. Managers can quickly compare scenarios to identify optimal staffing levels, balancing customer service quality against operational costs.

For ongoing use, the model can incorporate real-time data updates, enabling dynamic scheduling. Routine simulations can inform staffing decisions ahead of busy periods, while reports generated from the model provide tangible evidence to support strategic planning and resource allocation, aligning with the shop’s objectives to improve service and operational efficiency.

Conclusion

By integrating statistical data analysis, queueing theory, and practical Excel modeling, the Students’ Union Shop can enhance its operational decision-making capabilities. The proposed strategic improvements—primarily dynamic staffing driven by demand forecasting—are backed by rigorous analysis and can lead to reduced customer waiting times, better staff utilization, and increased customer satisfaction. The technical Excel model acts as a flexible tool for management to evaluate ongoing performance and test various operational scenarios, ensuring sustained service quality improvement.

References

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