Call Centers. November 1999

Transcription

1 Call Centers Version 2.1a Thomas A. Grossman, Jr., University of Calgary, Canada* Douglas A. Samuelson, InfoLogix, Inc., Annandale, Virginia Sherry L. Oh & Thomas R. Rohleder, University of Calgary, Canada November 1999 To appear in Encyclopedia of Operations Research and Management Science, 2nd Edition, S. I. Gass and C. M. Harris, editors +1 (403) Faculty of Management University of Calgary Calgary, Alberta T2N 1X1 Canada

2 A call center can be defined as any group whose principal business activity is talking on the telephone to customers or prospects (Mehrotra, 1997). In 1994, American call centers employed 6.5 million people in 350,000 businesses, a dramatic increase from over 500,000 people in 1,650 businesses in 1980 (Brigandi et al., 1994). More recent estimates, from non-archival sources, are that some 69,500 call centers in the U.S. generated $23 billion in services revenues in 1998, compared with $15.4 billion two years earlier. These sources caution, however, that definitions of what constitutes a call center vary, and consequently the numbers vary as well. Outbound telemarketing generated $482.2 billion in sales in Growth in the call services market averaged about 20% per year for the past five years and is expected to continue at a similar rate (CCNS, 1999). The application of OR/MS is increasing in parallel with their growth, and opportunities for research and practice are emerging rapidly as the industry grows and professionalizes. Call centers are central to operations for a broad range of businesses, including travel reservations; product support; MIS help desk services; order taking; emergency services dispatch ( 911 ); and financial transactions. Call centers are a strategic asset because they provide firms with a direct line to customers, drive customer perception of quality, and generate significant numbers of transactions (Mehrotra, 1997). Call centers have significant general management challenges in human resources (recruitment, absenteeism, emotional support, burnout, call monitoring policies), MIS (multi-user multi-site databases, customer tracking, systems integration), training, and quality (MacPherson, 1988; Cleveland and Mayben, 1997; Australian National Audit Office, 1996; Perkins and Anton, 1997). As these challenges are better managed and call centers grow larger and more costly, opportunities to use operations research techniques are of increasing interest to the industry. Call center consultants and telecommunications firms (e.g., AT&T) engage in significant non-published research and application,

3 and there is a need for more public-domain work on the challenges faced by this important industry. Call center terminology comes from traffic engineering and is essentially the same as that used for telecommunications queue modeling. Call center managers use Erlang B and Erlang C to refer to the classical results of the M/M/c/c (= M/G/c/c) and M/M/c queues, respectively, and use the unit of erlangs to refer to the dimensionless quantity of total offered traffic = 8/µ. Call center operations research has its roots in the work of A. K. Erlang, whose B formula is still in common use for estimating the number of trunk lines required for a new call center. Call centers can be inbound call centers (which answer calls from customers), outbound call centers (which originate calls to customers, generally for telemarketing purposes), or blended centers that do both. For inbound call centers, call volume and length are random inputs, whereas for outbound call centers call volume and length are decision variables. Inbound Systems: Inbound call centers are driven by random customer call arrivals, usually to a tollfree number (area codes 800 and 888 in North America). Quick connection to an available telephone service representative (TSR) is essential to maintain acceptable customer service, and to minimize telephone charges from customers waiting on hold. Inbound call center managers need to balance customer response performance with cost performance. Customer performance is typically measured by service level (the percentage of calls answered within a target time), percentage abandonments (percentage of callers who hang up before speaking to a TSR), and the percentage of calls blocked (calls which receive a busy signal due to insufficient trunk lines). Cost performance is typically measured by TSR utilization. Forecasting, capacity, staffing, routing, scheduling and performance estimation are important and interrelated problems. A good general business overview of inbound call center management is Cleveland and Mayben (1997).

4 The advent of interactive voice response ( press 1 for flight arrival information, press 2 for domestic reservations ) and computer-telephony integration combined with increasing complexity of call transactions is leading to the use of complex skills-based routing systems. These systems send customers to different TSR s depending on their needs and support the creation of a hierarchy of TSR s with highly skilled (and highly paid) personnel handling only the most challenging calls. There is opportunity for research regarding design and appropriate performance measures in such systems. Inbound call centers are managed using short-interval (15 60 minute) time blocks for forecasting call arrivals and for assigning TSR s. Managers must staff each time block to assure customer response without incurring low TSR utilization, while honoring TSR work rules. This is a significant managerial and technical challenge. This problem seems to first appear in the literature (in the context of toll booths) in Edie (1954), later extended by Dantzig (1954). Although details vary at different call centers, the staffing process can be decomposed into five distinct activities: forecasting (call arrivals by time block), performance estimation (predict service level and utilization for various TSR levels in each time block), staff requirements (select desired number of TSR s in each time block), shift scheduling (convert staff requirements into shifts, including breaks), and rostering (assign individual people to shifts). Each of these activities is a research area unto itself. The first activity, forecasting, is inherently difficult for call centers due to the small size of the time blocks. Established forecasting techniques (Winters exponential smoothing method, ARIMA, and regression) are useful for call centers, and improved forecasting is proving valuable (Andrews and Cunningham, 1995; Klungle and Maluchnik, ; Mabert, 1985). Generally, using an appropriate forecast modeling approach will cut forecast errors at least in half. However, many call centers have difficulty with forecasting due to the expertise required to harness these techniques to complex call patterns and messy data. In particular, call centers must often deal with demand spikes

5 and lag effects that may occur at random or without the same effect every time they occur. Research to convert the craft skills of forecasting onto a systematic footing would be valuable. In addition, research into understanding the lost demand (abandoned and blocked calls) that occurs at peak times would be of great value in managing call centers. The second activity, estimating system performance for a given staffing level in a time block, is an essential capability. Some call centers use average utilization calculations (Mehrotra, 1997; Cleveland and Mayben, 1997) despite poor performance from ignoring queueing effects. The Erlang C queueing theory model is widely used. More sophisticated queueing theory models may be relevant, but do not seem to be widely used. Queueing theory applications to short time blocks with blockdependent arrivals and staffing levels are problematic due to the steady-state assumption; furthermore, queueing theory is inadequate for complex, traffic-dependent routings. Simulation is becoming an increasingly important tool (Brigandi et al., 1994; Mehrotra, 1997). However, call center staff tend to lack OR expertise, and deployment of OR technology is a significant challenge. The Call$im package by Onward Inc. (a template on top of Systems Modeling Corporation s Arena simulation package) appears to be successful in making the power of simulation available to call centers. Complex call routings are multi-class queuing networks which have interesting simulation properties that are under research (Henderson and Mason, 1998). The third activity, staff requirements, is typically done for each time block by minimizing the required number of staff subject to a service level goal. This decision is driven by the output of the performance estimation activity. The last two activities, shift-scheduling and rostering, are particularly challenging for call centers, due to the large number of time blocks (there are minute time blocks per day in a 24- hour center). Staff requirements may dictate drastically different numbers of TSR s for each time

6 block. Creating shift schedules which meet target requirements, satisfy employee and organizational requirements, and do not lead to the under-utilization of TSR s requires careful planning and can benefit from the application of OR/MS tools (Buffa, Cosgrove, and Luce, 1976; Andrews and Parsons, 1993; Thompson, 1997). Others have chosen to use constructed heuristics to build effective schedules that have broad industrial applications (Buffa et al., 1976; Thompson, 1997). Most recently, there has been increased interest in applying metaheuristics such as simulated annealing (Brusco and Jacobs, 1993, Thompson, 1996), tabu search (Glover and McMillan, 1986; Dowsland, 1997) and genetic algorithms (Abboud et al., 1998). The classic, early work on this subject is Segal (1974), which introduced the idea of applying network flow theory to shift scheduling. Two useful survey papers summarizing the older results in this area are Bechtold et al. (1991), and Tien and Kamiyama (1982). Shift-scheduling, and in particular, tour-scheduling problems involving the assignment of specific shifts and days off to create a week-long tour often cannot be solved optimally and further research into appropriate heuristics is required. It is common practice to perform each of the five activities in the staffing process sequentially and independently from the others. Separating these activities can cause difficulties since, for example, highly variable staff requirements can make shift scheduling more difficult. There are opportunities to improve performance by integrating these activities rather than performing them in strict sequence, and integration is an emerging theme in this growing area of research (Aksin and Harker, 1997; Brusco et al., 1995; Mason, Ryan, and Panton, 1997). Among the more prominent contributions of OR/MS analysts to this field is AT&T s call processing simulator (CAPS), which uses queueing theory and simulation to evaluate design features of inbound call centers. This work generated an estimated $1 billion in benefits to AT&T and won the 1993 Edelman Prize (Brigandi et al., 1994).

7 Outbound Systems: The development of new, less expensive microcomputers and digital switching equipment in the 1980s led to major growth and turnover in the market for outbound systems. In these systems, the computer initiates dialing to additional called patrons, usually while most of the system s telephone representatives are busy talking to patrons. It recognizes busy signals and no-answers and processes them automatically, switching live answering patrons to operators. If a patron answers when no representative is available, the system may play a hold message or more typically simply hang up. The latter event is a nuisance for the called patron and a wasted expense for the system s owner. Maximizing representatives utilization and more or less equivalently the system s productivity, while limiting or eliminating nuisance calls, became the focus of a number of OR/MS analysts during the late 1980s and early 1990s. Systems in the early 1980s used primitive dial-ahead schemes in which the system supervisor could vary a set interval, typically three to eight seconds, between dialing starts. In early 1987, International Telesystems Corporation (ITC) introduced Smart- Pace, a method based on statistically estimating durations of service and of patron acquisitions, and synchronizing dialing attempts to expected service completions. This method resulted in the first U.S. patent based on queueing theory (Samuelson, 1989) and in a number of similar methods developed by ITC and others (e.g., see David, 1997.) Emerging business challenges where OR/MS may be of value for outbound call centers include improved scheduling and routing for systems running multiple campaigns simultaneously, where some representatives may be shifted among campaigns in real time; handling of combined inbound and outbound systems; and scheduling for systems in which called patrons may be switched to a recorded message, then return to talk to another representative. The general class of queueing problems in

8 which customers arrive in response to a system-initiated acquisition attempt, rather than at random, provides an interesting set of theoretical questions, as well. See Communication networks; Forecasting; Networks of queues; Queueing theory. References [1] Abboud, N., M. Inuiguchi, M. Sakawa, and Y. Uemura (1998). Manpower allocation using genetic annealing, European Jl. Operational Research, 111, [2] Aksin, O. Z., and P. T. Harker (1997). Staffing an Inbound Call Center. INSEAD working paper 97/15/TM, Fontainebleau, France. [3] Andrews, B. H. and S. M. Cunningham (1995). L.L. Bean Improves Call-Center Forecasting. Interfaces 25(6), [4] Andrews, B. and H. Parsons (1993). Establishing Telephone-Agent Staffing Levels through Economic Optimization. Interfaces 23(2), [5] Australian National Audit Office (1996). Telephone Call Centres Better Practice Guide and Accompanying Handbook. Web page version, accessed 30 July 1998, Canberra [available at [6] Bechtold, S. E., M.J. Brusco, and M.J. Showalter (1991). A Comparative Evaluation of Labor Tour Scheduling methods, Decision Sciences 22, [7] Brigandi, A. J., D. R. Dargon, M. J. Sheehan, and T. Spencer, III (1994). AT&T s Call Processing Simulator (CAPS) Operational Design for Inbound Call Centers. Interfaces 24(1), [8] Brusco, M. J. and L. W. Jacobs (1993 ). A Simulated Annealing Approach to the Solution of

9 Flexible Labour Scheduling Problems, Jl. Operational Research Society 44, [9] Brusco, M. J., L. W. Jacobs, R. J. Bongiorno, D. V. Lyons, and B. Tang (1995 ). Improving Personnel Scheduling at Airline Stations. Operations Research 43, [10] Buffa, E. S., M. J. Cosgrove, and B. J. Luce (1976). An Integrated Work Shift Scheduling System. Decision Sciences 7, [11] Call Center News Service (CCNS) (1999). The CCNS Newsletter, 3(2), Dawson Publishing, Brooklyn, New York [available from Web site, [12] Cleveland, B. and J. Mayben (1997). Call Center Management on Fast Forward: Succeeding In Today s Dynamic Inbound Environment. Call Center Press, Annapolis, Maryland. [13] Dantzig, G. B. (1954). A Comment on Edie s Traffic Delays at Toll Booths. Jl. Operations Research Society America 2, [14] David, J.E. (1997). Outbound Call Pacing Method Which Statistically Matches the Number of Calls Dialed to the Number of Available Operators, U. S. Patent No. 5,640,445, June 17, Washington, DC. [15] Dowsland, K.A. (1998). Nurse Scheduling: Tabu Search and Strategic Oscillation, European Jl. Operational Research, 106, [16] Edie, L.C. (1954 ). Traffic Delays at Toll Booths, Jl. Operations Research Society America 2, [17] Glover, F. and C. McMillan (1986). The general employee scheduling problem: An Integration of MS and AI, Computers and Operations Research 13, [18] Henderson, S. G. and A. J. Mason (1998). Rostering by Iterating Integer Programming and Simulation. Proceedings of the 1998 Winter Simulation Conference, D. Watson and J. Medeiros, eds., IEEE Press, Piscataway, New Jersey.

10 [19] Klungle, R. and J. Maluchnik ( ). Call Center Forecasting at AAA Michigan. Jl. Business Forecasting 16(4), [20] Mabert, V. A. (1985). Short Interval Forecasting of Emergency Phone Call (11) Work Loads. Jl. Operations Management 5, [21] MacPherson, Jr., G. F. (1988). What Senior Managers Need to Know About Incoming Call Centers. SLN, Inc., Annapolis, Maryland. [22] Mason, A. J., D. M. Ryan, and D. M. Panton (1997). Integrated Simulation, Heuristic and Optimisation Approaches to Staff Scheduling, Operations Research 46, [23] Mehrotra, V. (1997). Ringing Up Big Business. OR/MS Today 24(4), [24] Perkins, D. S. and J. Anton (1997). Best Practices for Customer Service Call Centers - An Exclusive Statistical Study, Telemarketing & Call Center Solutions 15(10), [25] Samuelson, D.A. (1989). System for Regulating Arrivals of Customers to Servers, U.S. Patent No. 4,858,120, August 15, Washington, DC. [26] Samuelson, D.A. (1999 ). Call Attempt Pacing for Outbound Telephone Dialing Systems, Interfaces 29(5). [27] Segal, M. (1974). The Operator-Scheduling Problem: A Network-Flow Approach, Operations Research, 22, [28] Thompson, G. M. (1996). A simulated Annealing Heuristic for Shift-Scheduling Using Non- Continuously Available Employees, Computers and Operations Research 23, [29] Thompson, G. M. (1997). Assigning Telephone Operators to Shifts at New Brunswick Telephone Company. Interfaces 27(4), [30] Tien, J.M. and A. Kamiyama (1982). On Manpower Scheduling Algorithms, SIAM Review 24,