Reference no: EM132130263

Summary:

Mark Lawrence has been pursuing a vision for more than two years. This pursuit began when he became frustrated in his role as director of Human Resources at Cutting Edge, a large company manufacturing computers and computer peripherals. At that time the Human Resources Department under his direction provided records and benefits administration to the 60,000 Cutting Edge employees 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 Mark. He had to deal with employee complaints often since each center interpreted company policies differently â€" communicating inconsistent and sometimes inaccurate answers to employees. His department also suffered high operating costs since operating 35 separate centers created inefficiency. His 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 his vision to management, Mark received the go-ahead from Cutting Edge corporate headquarters. He prepared his “to do” list â€" 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. Mark delegated the systems requirements, installation, and integration jobs to a competent group of technology specialists. He took on the responsibility of standardizing procedures and staffing the administration center. Mark had spent many years in human resources and therefore had little problem with standardizing record-keeping and response procedures. He encountered trouble in determining the number of representatives needed to staff the center, however. He was particularly worried about staffing the call This case was adapted from Hiller, Frederick S. & Mark S. Hillier (2014). Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, 5th ed., McGraw-Hill/Irwin, pp 429-432. 2 center since the representatives answering phones interact directly with customers â€" the 60,000 Cutting Edge employees. The customer service representatives would receive extensive training so that they would know the records and benefits policies backwards and forwards â€" enabling them to answer questions accurately and process changes efficiently. Overstaffing would cause Mark 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 Mark to continue to suffer the headaches from customer complaints â€" something he definitely wanted to avoid. The number of customer service representatives Mark needed to hire depended on the number of calls that the records and benefits call center would receive. Mark therefore needed to forecast the number of calls that the new centralized center would receive. He approached the forecasting problem by using judgmental forecasting. He studied data from one of the 35 decentralized administration centers and learned that the decentralized center had serviced 15,000 customers and had received 2,000 calls per month. He concluded that since the new centralized center would service four times the number of customers â€" 60,000 customers â€" it would receive four times the number of calls â€" 8,000 calls per month. Mark slowly checked off the items on his “to do” list, and the centralized records and benefits center opened one year after Mark had received the go-ahead from corporate headquarters. Now, after operating the new center for 13 weeks, Markâ€^TMs call center forecasts are proving to be terribly inaccurate. The number of calls the center receives is roughly three times as large as the 8,000 calls per month that Mark 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 Mark 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 Markâ€^TMs neck demanding improvements.

QUESTIONS:

Question 1a: Define a problem statement which reflects the challenge facing Mark as he planned for the opening of the new center.

Question 1b: Why was Markâ€^TMs initial forecast of call volume so far off? What could have been the reasons for this?

Question 1c: What could Mark have done differently to improve his initial forecast?

SUMMARY:

Mark needed help, and he approached Harry, a corporate analyst, to forecast demand for the call center more accurately. Luckily, when Mark first established the call center, he realized the importance of keeping operational data, and he provided Harry with the number of calls received on each day of the week over the last 13 weeks. The data (refer to Cutting Edge Student File No. 1) begins in week 44 of the last year (2012) and continues to week 5 of the current year (2013). Mark indicates that the days where no calls were received were holidays. As a start, Harry 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). He 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, Harry 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, Mark prepared and used seasonally adjusted call volumes for the past 13 weeks. After Week 6 ended, Harry compared the five forecasts with the actual volumes and calculated the Mean Absolute Deviation (MAD) values for each method. The result of Harryâ€^TMs work is summarized below:

Cutting Edge

Week 6 Forecast vs. Actual Daily Call Volume

Chart:

Day                 Last value        Averaging       Moving Average         Exp Sm 0.1     Exp Sm 0.5                

Monday           795                  982                  735                              802                  701

Tuesday           774                  946                  668                              768                  689

Wednesday      809                  1037                737                              837                  763

Thursday         947                  1227                833                              962                  773

Friday              759                  1032                676                              782                  572

MAD              171                  399                  104                              184                  116     

Actual Call Volume

Monday: 723

Tuesday: 698

Wednesday: 534

Thursday: 578

Friday: 697

*MAD=Mean Absolute Deviation

*Exp Sm= Exponential Smoothing

Answer Questions 2a through 2e below:

Question 2: Describe the details of each forecasting method used by Harry and explain its accuracy (MAD value) in comparison with the accuracy of the other methods. (Hint: In answering this question, it is helpful to review a time-series plot of the 13 weeks of data.)

2a) Last Value

2b) Averaging

2c) Moving Average (5 days)

2d) Exponential Smoothing (alpha= 0.1)

2e) Exponential Smoothing (alpha= 0.5)

SUMMARY

After many months of work and with Harryâ€^TMs help, Mark has been able to stabilize the call center operation. Mark now has a better handle on how to forecast the daily call demand and he is able to prepare effective weekly staffing schedules for handling the daily variation in volume. However, Mark is still experiencing difficulty in forecasting the volume from month to month. Cutting Edge has been very active in acquiring new companies while, at the same time, selling off portions of their 6 existing business. Mark believes that this activity is causing fluctuations in call volume because it is affecting the employee head count of Cutting Edge. Mark has assembled monthly data for call volume and head count for the past 18 months. Mark also suspects that there are other factors which may be affecting the call volume, and he has noted these factors on the attached spreadsheet. Based on the upcoming acquisition of Cutter Corp on 7/1/2015, the forecast of head count for July 2015 is 77,000.

Answer Questions 3a through 3d below:

Question 3a: Prepare a forecast of call volume for July 2015 by applying Exponential Smoothing (with alpha = 0.5) to the prior 18 months of data. Use the appropriate Excel template from the Hillier text to prepare your forecast and assume that initial call volume is 24,000. Show your forecast below and attach the completed template.

Call Volume Forecast for July 2015 (Exponential Smoothing, alpha=0.5): _________________

Question 3b: Apply Linear Regression to predict call volume from head count using the appropriate Excel template. Show your forecast below and attach the completed Excel template.

Call Volume Forecast for July 2015 (Causal Forecasting based on head count): _________________

Question 3c: Calculate the Mean absolute deviation value of the Exponential Smoothing model (Question 3a) and the Average Estimation Error of the Linear Regression model (Question 3b). Explain the difference between these two values.

Mean absolute deviation of Exponential Smoothing model, alpha=0.5:

Average Estimation Error for Causal Forecasting model based on headcount:

Explanation of the difference in values:

Question 3d: Considering your answers to Questions 3a, 3b and 3c and all the factors that have been described above, prepare your best forecast for July 2015. Show your forecast value below and explain and justify how you came up with this forecast.

Call Volume Forecast for July 2015 (My forecast): _________________

Explanation and Justification of Your Method: