Business & Finance assignment

Belinda Ross has been pursuing a vision for more than two years. This pursuit began when she became frustrated in her role as director of Human Resources at Clean Sweep, a large commercial custodial company. At that time the Human Resources Department under her direction provided records and benefits administration for approximately 80,000 monthly cleans throughout the United States, and 35 separate records and benefits administration centers existed across the country. Employees contact these records and benefits centers to obtain information about dental plans and stock options, change tax forms and personal information, and process leaves of absence and retirements. The decentralization of these administration centers caused numerous headaches for Belinda. She had to deal with employee complaints often since each center interpreted company policies differently – communicating inconsistent and sometimes inaccurate answers to employees. Her department also suffered high operating costs since operating 35 separate centers created inefficiency.

Her vision? To centralize records and benefits administration by establishing one administration center. This centralized records and benefits administration center would perform two distinct functions: data management and customer service. The data management function would include updating employee records after performance reviews and maintaining the human resource management system. The customer service function would include establishing a call center to answer employee questions concerning records and benefits and to process records and benefits changes over the phone.

One year after proposing her vision to management, Belinda received the go-ahead from Clean Sweep corporate headquarters. She prepared her “to do” list – specifying computer and phone systems requirements, installing hardware and software, integrating data from the 35 separate administration centers, standardizing record-keeping and response procedures, and staffing the administration center. Belinda delegated the systems requirements, installation, and integration jobs to a competent group of technology specialists. She took on the responsibility of standardizing procedures and staffing the administration center.

Belinda had spent many years in human resources and therefore had little problem with standardizing record-keeping and response procedures. She encountered trouble in determining the number of representatives needed to staff the center, however. She was particularly worried about staffing the call center since the representatives answering phones interact directly with employees. The customer service representatives would receive extensive training so that they would know the records and benefits policies backwards and forwards – enabling them to answer questions accurately and process changes efficiently. Overstaffing would cause Belinda to suffer the high costs of training unneeded representatives and paying the surplus representatives the high salaries that go along with such an intense job. Understaffing would cause Belinda to continue to suffer the headaches from customer complaints – something she definitely wants to avoid.

The number of customer service representatives Belinda needed to hire depended on the number of calls that the records and benefits call center would receive. Belinda therefore needed to forecast the number of calls that the new centralized center would receive. She approached the forecasting problem by using judgmental forecasting. She studied data from one of the 35 decentralized administration centers and learned that the decentralized center had serviced 20,000 monthly cleans and had received 2,500 calls per month. She concluded that since the new centralized center would service four times the number of customers, it would receive four times the number of calls, 10,000 calls per month.

Belinda slowly checked off the items on her “to do” list, and the centralized records and benefits center opened one year after Belinda had received the go-ahead from corporate headquarters.

Now, after operating the new center for 13 weeks, Belinda’s call center forecasts are proving to be terribly inaccurate. The number of calls the center receives is roughly three times as large as the 10,000 calls per month that Belinda had forecasted. Because of demand overload, the call center is slowly going to hell in a handbasket. Customers calling the center must wait an average of five minutes before speaking to a representative, and Belinda is receiving numerous complaints. At the same time, the customer service representatives are unhappy and on the verge of quitting because of the stress created by the demand overload. Even corporate headquarters has become aware of the staff and service inadequacies, and executives have been breathing down Belinda’s neck demanding improvements.

Belinda needed help, and she approached Haley, a corporate analyst, to forecast demand for the call center more accurately.

Luckily, when Belinda first established the call center, she realized the importance of keeping operational data, and she provided Haley with the number of calls received on each day of the week over the last 13 weeks. The data (refer to Clean Sweep Student File No. 1) begins in week 44 of the last year (2022) and continues to week 5 of the current year (2023).

Belinda indicates that the days where no calls were received were holidays.

As a start, Haley used the data from the past 13 weeks and applied five different time-series forecasting methods in preparing a trial forecast of the call volume for each day of the upcoming week (Week 6). She provided a different forecast for each day of the week by treating the forecast for a single day as being the actual call volume on that day.

From plotting the data, Haley could see that demand follows “seasonal” patterns within the week. For example, more employees call at the beginning of the week when they are fresh and productive than at the end of the week when they are planning for the weekend. Therefore, Belinda prepared and used seasonally adjusted call volumes for the past 13 weeks. After Week 6 ended, Haley compared the five forecasts with the actual volumes and calculated the Mean Absolute Deviation (MAD) values for each method. The result of Haley’s work is summarized below:

Clean Sweep

Week 6 Forecast vs. Actual Daily Call Volume

After many months of work and with Haley’s help, Belinda has been able to stabilize the call center operation. Belinda now has a better handle on how to forecast the daily call demand, and she is able to prepare effective weekly staffing schedules for handling the daily variation in volume.

However, Belinda is still experiencing difficulty in forecasting the volume from month to month. Clean Sweep has been very active in acquiring new companies while, at the same time, selling off portions of their existing business. Belinda believes that this activity is causing fluctuations in call volume because it is affecting the employee head count of Clean Sweep.

Belinda has assembled monthly data for call volume and head count for the past 18 months (refer to Clean Sweep Student File No. 2). Belinda also suspects that there are other factors which may be affecting the call volume, and she has noted these factors on the attached spreadsheet. Based on the upcoming acquisition of Messy’s Cleaners on 7/1/2023, the forecast of monthly cleans for July 2023 is 95,000.

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