Mini Case – Answer The Questions At The End Of The Case Belo

Mini Case – answer the questions at the end of the case below Tri-Metro Investments

The task involves developing a short-term forecasting method for call volume at the Tri-Metro Help Desk, which supports fiduciary operations globally. The Help Desk employs 20 staff members, including customer service representatives (CSRs), support employees, and managers, handling about 2,000 calls weekly from internal and external clients. Accurate call volume forecasting is crucial to improve staffing efficiency, reduce costs, and enhance service levels.

The provided data includes daily call volumes over 16 days. Hamlet, a new team member, is tasked with analyzing this data using three forecasting methods: (a) the simple average, (b) a two-period moving average with a graph, and (c) exponential smoothing with a smoothing constant of 0.3. The goal is to generate reliable short-term demand forecasts to aid staffing decisions.

Along with quantitative forecasts, initial qualitative analysis should consider factors such as recent trends, staff feedback, special events, or organizational changes that might influence call volume. Developing a combination of these methods will enable the team to recommend a robust forecasting procedure that balances accuracy with practicality, supporting the Help Desk's goal of efficient staffing and improved service delivery.

Paper For Above instruction

Introduction

Accurate forecasting of call volume is vital for the efficient operation of call centers like the Tri-Metro Help Desk. With a staff of 20 personnel supporting a high volume of client queries worldwide, it is paramount to develop reliable short-term demand forecasts. This paper explores three different forecasting methods—simple average, two-period moving average, and exponential smoothing—to predict daily call volumes based on the provided data. Additionally, the paper considers qualitative factors that could influence call trends, ultimately aiming to recommend a comprehensive forecasting approach to optimize staffing levels and enhance service quality.

Analysis of Forecasting Methods

1. The Simple Average Method

The simplest approach to short-term demand forecasting involves calculating the average call volume over the observed days. For the 16 days of data, the mean provides a baseline forecast for subsequent days. Computing the average call volume across the 16 days gives insight into typical daily demand and serves as a benchmark. For example, if the total calls over 16 days sum to 32,000, the average would be 2,000 calls per day. This method is straightforward but assumes demand consistency without accounting for trends or seasonal variations, which may limit its accuracy in dynamic environments.

2. The Two-Period Moving Average

The moving average method smooths short-term fluctuations and reveals underlying trends. Using a two-day window, the forecast for day three, for example, would be the average of call volumes on days one and two. Extending this to all 16 days produces a series of moving averages that can be plotted to visualize trends. The graph of the two-period moving average typically shows smoothed demand fluctuations, helping identify whether call volume is increasing, decreasing, or stable. This method is more responsive than the simple average but may lag behind actual demand shifts.

Graph illustration: A line graph plots daily call volumes alongside the corresponding two-period moving averages, allowing visual assessment of demand patterns.

3. Exponential Smoothing

Exponential smoothing is a sophisticated forecasting method assigning exponentially decreasing weights to older observations. Using a smoothing constant (α) of 0.3, recent data points influence the forecast more heavily. The formula is:

Forecast for next day = α × actual call volume today + (1 – α) × forecast for today

Starting with an initial forecast (often the first actual call volume), the process iterates through the data series. This method adapts quickly to recent changes in demand, making it suitable for environments where call volume can fluctuate unpredictably. The choice of α = 0.3 balances responsiveness and stability, but sensitivity analysis may be necessary to optimize forecast accuracy.

Qualitative Factors Influencing Call Volume

While quantitative methods provide a numerical forecast, qualitative factors play a crucial role in understanding demand fluctuations. Recent organizational changes, policy shifts, or external factors like industry events or macroeconomic shifts can cause deviations from historical patterns. For the Tri-Metro Help Desk, elements such as new product launches, seasonal periods, or external crises could influence call volume beyond what historical data suggests.

Therefore, a comprehensive forecasting approach would integrate both quantitative methods and qualitative insights. For instance, if a new product is launching, anticipated increases in inquiries should be accounted for during the forecast period. Staff feedback and organizational awareness can help refine model assumptions, ensuring forecasts remain relevant amid changing circumstances.

Recommendation and Conclusion

Combining the three quantitative methods yields a robust forecasting strategy. The simple average offers a quick baseline, advantageous for its simplicity. The two-period moving average captures short-term trends, providing more dynamic insights. Exponential smoothing, especially with a well-tuned α, offers a balance between responsiveness and stability, making it ideal for daily operational decision-making.

For practical application, the team should generate forecasts using all three methods and compare their accuracy using historical data. Validation techniques like Mean Absolute Error (MAE) or Mean Squared Error (MSE) can evaluate which method best predicts recent call volumes. Additionally, integrating qualitative insights—such as upcoming organizational changes—will enhance forecast reliability.

In conclusion, a hybrid forecasting approach leveraging all three methods, supported by qualitative analysis, will enable the Tri-Metro Help Desk to optimize staffing, reduce costs, and maintain high service quality. Implementing a dynamic and validated forecast system will position the Help Desk to respond effectively to demand fluctuations and plan resources proactively.

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