Reducing Customer Waiting Time with New Layout Design

Transcription

1 Reducing Customer Waiting Time with New Layout Design 1 Vinod Bandu Burkul, 2 Joon-Yeoul Oh, 3 Larry Peel, 4 HeeJoong Yang 1 Texas A&M University-Kingsville, vinodreddy.velmula@gmail.com 2*Corresponding author Texas A&M University-Kingsville, joon-yeoul.oh@tamuk.edu 3 Texas A&M University-Kingsville, larry.peel@tamuk.edu 4 Cheong Ju University, hjyang@cju.ac.kr Abstract We always see queues in any service industry but analyzing these queues is often neglected. Guaranteeing the control and support quality in service industries will always generate a high customer expectations. This paper is to study the queue and its system for managing the circulation of consumer within a decreased waiting time in a queue. In this paper the queuing concepts are applied to a local grocery store in Texas. The present implemented server system has some difficulties and long waiting time for customers. This paper recommends new design for better customer services. The simulation results indicate that the proposed new designs have better performance than the current design in use. Keywords: Queuing theory, Layout design, Waiting time, Customer service, Customer Satisfaction 1. Introduction Supermarkets have made life easy for people. It used to take lot of time to get all the grocery and other household items because these items were not available at one place. Customers used to visit different shops to buy items they need. But now supermarkets have all items on offering from grocery, vegetables to electronic and hardware stuff under one roof. The main concern for waiting customers are the time to be served, limited queue and waiting area. The number of customers varies depending upon the market type, location and seasonal offers. Arrival pattern is arrival of customers within in successive intervals of time. Balked, reneged and jockey are the usual observed arrival patterns [1]. These usual patterns effect the customer satisfaction and expectations. Customer satisfaction is the main factor to be competitive in market. Customer service plays vital role for better business. To provide excellent service to customers, their waiting time is the most important factor for satisfaction. There are various factors which lead to increase in waiting time for customers such as over flow of customers, less number of available servers, time to serve the customer, space limitation etc. Delays may also be due to some technical difficulties faced by the employees while serving customer. The mean waiting time and the mean waiting time in queue gets affected even due to these small delays [1] [2]. To gain more and more profit along with customer satisfaction, long waiting time has to be avoided. This paper is to consider the waiting time of customers during peak hours of operations at the checkout counters in a local supermarket in Texas. Data reading were recorded for the incoming time for customer, wait time in the queue, time to get served etc. Using the methods of queuing theory, the research is conducted to analyze service standards. This enables in improving the sales with better customer satisfaction. 2. Literature review & background Every service industry faces some kind of delay [3]. This can be elaborated by an example of customers waiting for getting their items checked out during big discount periods. During this period the number of customers is unexpected and there is sudden rise or fall of customers. Due to this unexpected flow at the checkout, the service time changes and effects average wait time in queue [4]. Queuing Theory can be defined as the branch of mathematics dealing with analysis and models the waiting time in any queuing system [2]. Analysing the models using this theory helps in research about queuing system by determining the mean wait time and the length of queues [3]. This theory was first proposed by Agner Krarup Erlang, when he was working at Copenhagen telephone exchange in International Journal of Engineering and Industries(IJEI) Volume6, Number2, June

2 2.1. Basic model of queuing Figure 1 shows the basic schematic representation of queuing concept. Using this model the queue length and wait time is predicted which helps in making managerial decisions about business and resources if required Little's theorem & Kendall's notation Figure 1. Basic Queuing Model The relationship between arrival rate and service rate (throughput rate), cycle time and number of customers in system (work in progress) can be defined using Little's theorem [5]. As per this theorem number of customers in steady state system can be defined by equation: L =, where L = Number of customers, λ = Average customer arrival rate, T = Average service time. There is direct relationship between number of customer and average service time. The average customer arrival rate will be increased if number of customers increases and average service time decreases [5][6]. Little's theorem is vital to judge the performance of system when it is modified and achieving the maximum operational improvement [5]. To represent the six characteristics of queuing theory, Kendall in 1953 put forth notation system as: A/D/S/L/R, where A: Inter-arrival time distribution, D: Service time distribution which does not depend on arrival time, S: Servers operational in system, commonly we have single queue single server system, L: Length of queue, either finite or infinite (Optional parameter), R: Size of population in system [5][6]. 3. Practical observations of implemented model The operating manager in the supermarket has claimed to have considerable amount of customers visiting the store. The number goes to high of 3,000 each single day and during peak hours of operation the flow is more than the average. First-In-First-Out (FIFO) policy is used to efficiently serve the customers at check out registers with the layout as shown in Figure 2. Total numbers of operational servers are 16. Figure 2. Single-Queue-Single-Serve Model with parallel layout of multiple servers & queues 9

3 The data was collected from 3 servers during peak hours of operation for more considerable results. Flow pattern at each server was analysed for an hour each day. Data reading were for the customer arrival time in a queue, customer wait time to get served, serve time and finally the departure time. Service industry has unpredictable flow of customer which is exponential in nature, M/M/1 and M/M/c models are best suited for these types of industries [6]. These models serve only customer at any given time at each server in operation. Data analysis of the recorded reading for this project is done using QTS-Plus program. Fundamental aspects of queuing theory are analysed in this software to solve different types of queuing models. Initially, we considered the total time from customer arrival to exit and the time frame for three different servers. This helped in analysing the customers mean wait time (W) and the mean wait time in the queue for customer (Wq) [6][7]. Table 1 shows the current system s results obtained by QTS-Plus. Table 1. Results of as-is model Queue 1 Queue 2 Queue 3 Arrival rate (l) Mean service time (1/m) Mean inter arrival time (1/l) Service rate (m) Server utilization (r) Mean number of customers in the system (L) Mean number of customers in the queue (Lq) Expected non-empty queue size (Lq') Mean waiting time (W) Mean waiting time in the queue (Wq) Mean length of busy period (B) The units for the arrival rate, service rate is the number of customers, and the units of the mean service time, mean inter arrival time, mean waiting time, mean waiting time in the queue and mean length of busy period is hours. Table 2. Average of three queues Arrival rate (l) Mean service time (1/m) Mean inter arrival time (1/l) Service rate (m) Server utilization (r) Mean number of customers in the system (L) Mean number of customers in the queue (Lq) Expected non-empty queue size (Lq') Mean waiting time (W) Mean waiting time in the queue (Wq) Mean length of busy period (B)

4 As seen from the table 2 the average arrival rate of customers for queue 1, 2 & 3 was 18.3 during peak hours of operations and the variation is not drastic. The flow is slightly higher on weekends. The average server utilization was about 86%, which is good as per industry standards; the average service time for customer was hours, which is 2.83 minutes. The mean wait time for the system was 0.33 hours (= 20 min). The average expected wait time in the queue was hours (17.2 min). There is direct relation between the arrival rate of the customers and the utilization, which decrease as the total service rate decreases [6]. The utilization with the flow has to be less than one to get better operational results, and the system is not overflowed [10]. 4. New improved models by merging of lanes First proposed models, merging two queues, Queue 1 & Queue 2; Queue 2 & Queue 3; Queue 1 & Queue 3 were developed as seen in the figure 3. Figure 3. Single Queue & Two servers in parallel This model has a waiting line but two servers will provide the services. Second proposed model: Queue 1, Queue 2 and Queue 3 are merged together into one single queue with three servers A, B and C as seen in the figure 4. Figure 4. Single Queue & Three servers in parallel Table 3. Results of New design systems with combining the queues Q1 & Q2 Q1 & Q3 Q2 & Q3 Q1, Q2 & Q3 Arrival rate (l) Mean service time (1/m) Number of servers in the system (c) Mean inter arrival time (1/l) Service rate (m)

5 Average # arrivals in mean service time (r) Server utilization (r) Fraction of time all servers are idle (p0) Mean number of customers in the system (L) Mean number of customers in the queue (Lq) Mean wait time (W) Mean wait time in the queue (Wq) Probability arriving customer is delayed in queue (1-Wq(0)) By combining two queues the customer arrival rate has increased to about 36 on average compared with 21.3 in present model as seen on table 3. The result in one queue and two server model reduced the wait time from 20 minutes to 9.1 minutes on average. The waiting time reduced by more than half of time, which is about 55.5% decrement. In the case of combining all the three queues 1, 2 and 3 as one single queue with three servers 1, 2 and 3, the waiting time in the system reduced to 6.24 minutes (0.104 hours). The decrement for the waiting time in the system was about 68.8% on average compared to the model with single queue and single server. The study consisted of observing the M/M/1 and M/M/c models. Present system at a local supermarket in Texas is using single queue been served by single server. All these servers are placed in parallel. The utilisation rate for queue 1, 2 and 3 varies from 85% to 87.6% as seen in table 1. By merging the queues with combined servers of two lanes and three lanes also show high utilisation rate in the range of 80.9% to 85.1% as seen in table 3. Merging of two queues into single queue with two server model yields in reducing the customer mean wait time in the system (W) and mean wait time in the queue (Wq) with the little reduced utilization rate (ρ) ranges up to 83% from 85%. As we combine the more and more queues the lesser is mean wait time (W) and (Wq) seen from table 3. There is not much difference in the service rate and mean service time. There is considerable reduction in waiting time if the queues are combined. As seen from the table by combining three, the waiting time is drastically reduced compared to present system implemented. Parameters Table 4. Comparing models Waiting time Reduction percentage Mean number of customers in queue Present Model 20.0 min Queues Combined 3 Queues Combined 9.1 min 55.5% min 68.8% 2.95 Another important thing was the decrement in the mean number of customers in system and queue. As the queues are combined, the mean number of customers and waiting time were decreased as seen in Table Queue layout 5.1. Stacks at the waiting lanes 12

6 The queues in most of supermarkets are separated by stacks. The stacks are used to display items which are impulsive sales items. Use of stacks at the register is only to improve the sales of small items which might attract customers at the end while they are waiting in the queue. These are the items which are not in the shopping list of the customer but finally add up to impulsive sales. When customers complete shopping all the items they move towards the cashier register to get them billed. Before entering one cashier counter for checkout, customer jockeys around the queuing lanes to check for the number of people waiting in the queue, sometimes also the number of items in the shopping cart of previous customer. In this process of searching counter with least customers waiting, they move from one counter to the other thus checking all the possible counters open during that period of sales. The customer has to carry cart to all available counters which are bifurcated using stacks to display impulsive sales items. This increases the movement of customer within the checkout counter area even though all shopping items are in cart and also consumes time which is not accounted in any sort of wait time in the queue. As the number of cashier counter increase the distance also increases due to space for the stacks. This does not increase waiting time in the queue but makes delay before entering the queue. Figure 5. Traditional Layout: Single Server single queue with two side stack layout Each server serves one particular queue is the most traditional type of layout as seen in the figure 5. The type of layout as has stacks on each side of the queue. A waiting line priority rule determines which customer is served next. A frequently used priority rule is first-come first-served. The priority rule used affects the performance of the waiting line system. In single server single-queue system, customer is served once completed. Taking example as, first-come first served is generally considered fair but sometimes it is biased against customers requiring short service times. When checking out using first-come first-served as a priority rule, a customer waiting behind another customer with a large number of items waits longer than a customer waiting behind a second customer with only a few items. Although processing is sequential, the wait times vary because of the preceding customer. Also priority rules besides first-come first-served may imply that some customers wait extremely long periods of time depending upon the work efficiency of the sever as well. In single server queuing system wait time or performance of system depends on efficiency of serving person or service machine. 5.2 New suggested layout The results from the data analyzed shows that combining queues will reduce the wait time for customer based on this the traditional layout can be modified with minimum changes as shown in the figure 6. This layout helps in reducing the floor space used and reduces the waiting time for customer. To facilitate smooth flow of customer at the checkout counters a divider block is placed in between 13

7 servers. Depending upon the availability, customers choose among the two servers. Divider block will also help in bifurcation of space at checkout counters. Figure 6. Suggested layout for one queue with two servers In reality, combining three or more queues is possible which further reduces the customer wait time. As seen in the combined lanes models, the value of the mean number of customers in system (L) and mean number of customers in queue (Lq) are not significantly different from the present single lane single server model. Actually the numbers for L and Lq are smaller because of the faster service, so this combining two lanes model is realistic. However, if lanes are combined more than two, there may cause instant increase in the number of customers in the queue and also there would be space limitation issue. 6. Different types of check out system that can be incorporated 6.1. Self check out system Even today most independent and chain retailers are using the typical staffed checkout counters and express checkout lanes, self check out can be the next innovative technology adopted in retail industry. Late 1990s saw the introduction of this system using numbers and barcodes. Considering the young generation of shoppers age 18-45, 43% are using the automated self checkout systems [11]. With innovation of technology each day people are becoming more tech savvy. Customers are gaining more knowledge about the user friendly interfaces found at the ATMs, gas pumps, and kiosks. The social advancements, within and outside the retail sector, has led to customers becoming more in favor to decide on the economical self-checkout station over the long lines related to a staffed checkout station [12]. Most of the self check out system also has multiple lanes. These also can be improved by combining the lanes Handheld Devices provided by the store Before dropping any item into the cart, customers basically stroll up to the scanning system docks, run their commitment card, take the portable scanning system and check out items bar code scanners using a portable scanner reading device available [12]. After this is done the customer simply walks to the express checkout counters, transfers the data collected by portable scanning device into the computer. All the items scanned will be loaded by the counter and the customer pays bags and leaves. This will help reduce the long waiting time in the queue and lining up for getting checked out for small items. The point can be noted as smart-phone use has been growing and currently around 60% of people from developed countries use them, an application that supports scanning will be great substitute to the 14

8 scanning devices instead [12]. With the use of this technology, trust issue of the customer plays important role Mobile queue busting Serving customer without making them wait in queue would be an ideal way to reduce the long queues and save customers time. System consisting of tablet & printer can be handled by a store sales assistant or customer service representative, using these devices some of the functions performed at the checkout counter or point of sales can be performed intermittently to bust the queues. Profitable and loyal customers are built by excellent customer service and one of the power tool is saving valuable time of customers [4][11]. From the service industry to the retail store outlets, long queues at the checkouts and delay times in getting served are the biggest customer issue. Advanced mobility technology is efficient, quick and sure way of reaping far-reaching benefits. The bottom line can be improved by implementing mobility technology in service industry to keep the queues flowing smoothly while making customer smile. 7. Conclusion Service sector experiences queues commonly, analysis of present model implemented at Wal-mart, Kingsville Texas could help understand various changes that can be incorporated to reduce the wait time. Considering the space constraint, further analysis of the layout design can be carried out to reduce the customer wait time. Different types of checkout systems can be installed for quick customer service. Setting up some small impulsive items will increase the sale and profit but this consumes space at the checkout servers. Redesigning of the display system can help in optimization of space with better customer service. The entrance and exit of the store can be defined in specific pattern depending upon the customer. Long queues are the main factor for affecting customer service. If customers have choice, they prefers to pocket out some more money to get served better without having to wait for long queues. This can be serious loss of business to any organisation and effect the supply-demand network with the group. These factors are also very essential from management point of view; concern of all these factors for the long run research can help in offering a better outlook to provide the customers from better to best service possible. References [1] Donald Gross, John F. Shortle, James M. Thompson, Carl M. Harris, Fundamentals of queuing theory, 4th Edition, John Wiley and Sons, New York, [2] Kardi Teknomo, Queuing Rule of Thumb based on M/M/s Queuing Theory with Applications in Construction Management, Civil Engineering Dimension, Vol. 14, No. 3, pp , [3] Richard C. Larson, The queue inference engine: deducing queue statistics from transactional data, Management science, Vol. 36, No. 5, pp , [4] Frederick S Hillier, Gerald J Lieberman, Introduction to Operations Research, 9 th edition, McGraw Hill, New York, [5] John D. C. Little, A Proof for the queuing formula, Operations Research, vol. 9, No. 3, pp , [6] Fred Choobineh, Ardavan Asef-Vaziri, Huang, X., Fleet sizing of automated guided vehicles: a linear programming approach based on closed queuing networks, International Journal of Production Research, Vol. 50, No. 12, pp , [7] Sanjay Kumar Bose, An Introduction to Queuing Systems, Kluwer Academic Plenum Publications, New York, [8] Nkeiruka Ameh, Bauth Sabo, M. O. Oyefabi, Application of queuing theory to patient satisfaction at a tertiary hospital in Nigeria, Nigerian Medical Journal, Vol. 54, No. 1, pp 64-67, [9] Saeed Yaghoubi, Due-date assignment for multi-server multi-stage assembly systems, International Journal of Systems Science, Vol. 46, No. 7, pp ,

9 [10] Jianzhe Luo, Vidyadhar G. Kulkarni, Serhan Ziya, A Tandem Queueing model for an appointment-based service system, Queueing Systems, Vol. 79, No. 1, pp [11] Alan Ulman, NCR SelfServ Checkout, NCR Corporation Research, [12] Vladimir Muliukha, Alexander Ilyashenko, Oleg Zayats, Vladimir Zaborovsky, Preemptive queueing system with randomized push-out mechanism, Communications in Nonlinear Science & Numerical Simulation, Vol. 21, No. 1/3, pp ,