The Application of Waiting Lines System in Improving Customer Service Management: The Examination of Malaysia Fast Food Restaurants Industry

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1 IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS The Application of Waiting Lines System in Improving Customer Service Management: The Examination of Malaysia Fast Food Restaurants Industry To cite this article: Zurina Ismail and Shahrul Suhaimi AB Shokor 2016 IOP Conf. Ser.: Earth Environ. Sci View the article online for updates and enhancements. This content was downloaded from IP address on 13/05/2018 at 06:14

2 The Application of Waiting Lines System in Improving Customer Service Management: The Examination of Malaysia Fast Food Restaurants Industry Zurina Ismail 1, Shahrul Suhaimi AB Shokor 2 College of Business Management & Accounting, Universiti Tenaga Nasional, Muadzam Shah, Pahang, zurina@uniten.edu.my Abstract. Rapid life time change of the Malaysian lifestyle had served the overwhelming growth in the service operation industry. On that occasion, this paper will provide the idea to improve the waiting line system (WLS) practices in Malaysia fast food chains. The study will compare the results in between the single server single phase (SSSP) and the single server multi-phase (SSMP) which providing Markovian Queuing (MQ) to be used for analysis. The new system will improve the current WLS, plus intensifying the organization performance. This new WLS were designed and tested in a real case scenario and in order to develop and implemented the new styles, it need to be focusing on the average number of customers (ANC), average number of customer spending time waiting in line (ACS), and the average time customers spend in waiting and being served (ABS). We introduced new WLS design and there will be prompt discussion upon theories of benefits and potential issues that will benefit other researchers. 1. Introduction Queuing theory or WLS is the most important part of operations and can be described as valuable tools for operation managers (OP). Service industry such as retailing, banking and fast food providers continuously looking for an option to reduce the customer s frustration waiting in a slow line. Generally, the retail industry seems to be retaining the multiple line / multiple checkout system, banks and fast food providers had switched over the recent years to the queuing system where customers wait for the next available cashier. The fast food chain is categorized under the quick service food provider based on the business characteristic [1, 2]. For the industry, speed is the factor which provides efficiency of the service operations of each fast food chain [3]. To choose an accurate queue of a server, most of the customer will refer to certain criteria which include waiting time and how long the waiting line is [4]. The reason is that the management eventually avoiding the customer s negative perception while waiting to be served with a good experience [5, 6]. They might lose their customers if they failed to meet the expectation in providing a speedy quality service. The customer will frequently decide to change the queuing system based on the length and the amount of time they have to take to get the service. The terms balking is best to describe the customer s rejection, hence the possibilities to withdraw from the queue based on the service speed (queue length and time they must 1 Marketing & Entrepreneur Department. 2 Human Resource Department. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under licence by IOP Publishing Ltd 1

3 wat to be served) [7]. Either balking or reneging, both actions will affect negatively to the fast food chain. These research papers have the intention to reduce balking and reneging scenarios by proposing the new WLS. By focusing on queuing method, researchers are continually urged to ensure the current model, including single-queue (SQ), and SSSP are upgraded. It is a challenge to suggest and prove SQ, SSSP and SSMP design since it had a significant relationship in improving the model thus the growing concern with the queuing method in the fast food industry. The research paper provides the interpretation of alternative WLS (multiphase) and at the same time differentiate the effectiveness of the existing WLS (SSQ, SSSP). Its focus mainly on similarity and dissimilarity of data based on the average sum of time customer will spend in lining up until being served for current and proposed WLS. 2. The Queuing System Design A real case scenario was carried out in this study, which was based on the previous literature of WLS. By using MQ analysis (M/M/1), data gathered were used to design and analyse accurately for the convenience of the research. For sampling, Six (6) fast food restaurants in Klang Valley were identified. The actual WLS processes were physically recorded, then extracted and analysed by using Monte Carlo Simulation and Markovian waiting line analysis technique. This research prepared a conclusion which either the Markovian SQ Single Server of SSMP waiting line design provides a better effect on the efficiency of the WLS for the fast food restaurants in Malaysia. Generally in Malaysia, SQ single server (parallel) WLS were frequently used by most of the fast food restaurant. The effectiveness of the system can only be determined when one server serving one customer at a time. This was overwhelmingly problematic for the fast food restaurant in recent years when the customer had to wait for the next available cashier without any other option in this particular system. Illustration of the SQ single server (parallel) WLS based on this assumption; arrivals are described by a Poisson probability distribution and come from an infinite or very large population, arrivals are independent of preceding arrivals, but the average number of arrivals (arrival rate) does not change over time, arrivals are served on a first-in, first-out (FIFO) basis, and every arrival waits to be served, regardless of the length of the line of the queue, and service times vary from one customer to the next and are independent of one another, but their average rate is known. Measuring the queue s performance by comparing between proposed and current WLS based on the number of phase for customer to get their services completed. The difference will be explained in figure 1 and figure 2 as below: Figure 1: Current Design, Single Queue, Single Server (Parallel) Waiting Lines Figure 2: Proposed Design Single Queue, Multi Server Waiting Lines 3. Data Analysis Overall data gathered was based on 1664 customers, who came to the respective fast food restaurants to buy their meals (Table 1), and the fastest interval time between customers arrival was exactly 1 minute and the longest time was 6 minutes. On the other hand, the fastest customer being served was 2 minutes and the longest time was 11 minutes. Table 1. Interval Time In Frequency Time Serve Frequency 2

4 Total number of customer Data collected shown the value of which is a value for the arrival rate per hour and is a calculated value for service rate per hour. Therefore, based on the actual event the value of = and the value of is = In order to get the value of and more accurate, Monte Carlo Simulation was used and the average value of and both respectively are and (Table 2). Number of simulation Table 2. Average Service Time Total service Time Per simulation Software called QM for Windows was used to determine the efficiency for both waiting line designs, since the value of and was calculated. The result of analysis for each waiting line design can be referred in table 3. Table 3. The Comparison Analysis between Waiting Lines Design Parameter Value Minutes Value Minutes Average server utilization.9.9 Average number in the queue (Lq) Average number in the systems (L) Average time in the queue (Wq) Average time in the systems (W)

5 By looking at the table, the info stated the average time for a customer have to wait in a queue using the Single Server (Parallel) design was minutes and the average time for a customer has to wait in the system was minutes. For Multiple Server design, the average time a customer has to wait in the queue is improving which was minutes; saving about 70% of the waiting time in queue and for the system was minutes which saved 62.7% of waiting in the system. As referred to the data, the research has the evidence supporting the multiple server design will improve the quality of the services in Malaysia fast food restaurants. A good waiting line design will provide better and more conclusive results on how to attract more customers or even make them faithful to the organization itself. A very good waiting line design will assure an advantage to gain more benefits and compete much better in the industry. 4. Conclusion There are many research opportunities and upgrades that can be developed with the idea of waiting line system proposed in this paper. To determine the success of organizations or individuals in achieving their objectives and strategies, Performance Management can be used. Reference [9] suggested that we can extend the study to discuss the organization performance base on 4 dimensions, employees measures, customer measures, financial measures and suppliers measures. This research proposed the three WLS design which includes Markovian Single Queue, Single Server and Multiphase Queuing will give significant effect on the organization performance positively. The main focus of this research is to justify and to share the idea of effectiveness for improving the queuing design for fast food restaurants. The data analysed includes the average number of customers in system, average number of customers in waiting line, average time a customer spends waiting and being served and average time customer spends waiting in the queue. The study also provides several potential issues related to organizational performance that can be carried out for future study. 5. References [1] Jackson, J. (2011). Kinds of Food Service in a Restaurant. html (accessed May 4, 2013) [2] Walker, J. R. (2011). The Restaurant: from Concept to Operation, 6th ed. New Jersey. John Wiley and Sons, Inc. [3] Drysdale, J. A. and Galipeau, J. A. (2009). Profitable Menu Planning, 4th ed. New Jersey: Pearson International Edition [4] Adan, I.J.B.F., Boxma1, O.J., Resing, J.A.C. (2000), Queuing models with multiple waiting lines, Department of Mathematics and Computer Science, Eindhoven University of Technology [5] Dharmawirya, M & Adi H.O.E (2012), Industrial Engineering Letters, ISSN , Vol 2, No.5, 2012 [6] Zhao, X., Lau, R.S.M, and Lam, K. (2002). Optimizing the service configuration with the least total cost approach. International Journal of Service Industry Management, Vol. 13 No. 4, pp [7] Broyles JR, Cochran JK. (2007). Estimating business loss to a hospital emergency department from patient reneging by queuing-based regression. IIE Industrial Engineering Research Conference. Nashville, TN. [8] Kumar, R & Sharma S.K, An M/M/1/N Queueing Model with Retention of Reneged Customers and Balking, American Journal of Operational Research 2012; 2(1): 1-5 doi: /j.ajor [9] Dimovski, V., Škerlavaj, M. (2005), Performance Effects of Organizational Learning in atransitional Economy, Problems and Perspectives in Management, No. 4.References to preprints. 4