EXECUTIVE SUMMARY
Queues are a common sight wherever we go, and it is not uncommon to hear
Singaporeans complaining about the long queues as they feel that it is a complete
wasteof time. The team hencefeels that itis important for NTUC FairPrice, being the
largest retail supermarket locally, to understand the queueing situation at their retail
outlets especially their human-manned cashiers during the weekend. Hence, in this
report we be discussing the queueing situation at FairP rice outlets on weekends.
The report will briefly introduce NTUC FairP rice, followed by a flow process ofa typical
shopper at NTUC FairPrice. This will be followed by data collected by the team at 5
different NTUC outlets islandwide.
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The team has identified 2 variabilities, namely demand variability and supply variability
which contribute to the length and waiting time of queues during both the nonpeak and
peak hours. And has concluded that some of the consequences of poor queue
management include loss of potential customers, loss of potential profit and
congestion of shopping aisle.
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To carry out detailed data analysis, we worked with the following assumptions due to
limited time and resources in the collection of our data (i.e. a small sample size and
lack of more information due to FairPrice’s strict internal Standard Operating
Procedures). Firstly, we assume that our chosen outlets are representative of all other
outlets in their respective region. Secondly, the midpoint between peak and offpeak
hours are a relatively accurate estimate of the average demand and supply of the
period. Thirdly, the average length of queue per cashier is the same due to consumer
psychology and lastly, a full basket of items is equivalent to 5 plastic bags of items.
From the team’s observations and data analysis, we have determined that cashier
Speed and modes of payment are bottlenecks in the process. The team hence
suggests the following recommendations to improve queue management and
customer experience:
1. Increase the number of single-basket cashiers
2. Venture into RFID Checkout system
3. Interactive games and calming music
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1. INTRODUCTION TO NTUC FAIRPRICE
NTUC FairPrice Co-Operative Ltd, Singapore’s largest retailer, was foundedin 1973
with the aim of moderating the cost of living in Singapore. Under its belt are 200 outlets
consisting of FairP rice supermarkets, FairP rice Shop, FairP rice Finest, FairP rice Xtra
and Unity Pharmacies, serving more than 600,000 shoppers daily. In 2018, they
introduced FairP rice On to leverage on the emerging trend of online shopping, offering
400,000 subscribers an alternative avenue to make their purchases (FairPrice, 2018).
The presence of the online store has helped to reduce queues at physical stores as
the customer pool is now split between online and physical stores. At its outlets,
FairPrice has various checkout methods which include the typical human-manned
cashiers, self-checkout machines, green checkouts (bring your own bag) and cardonly
cashiers. For the purpose of our report, we will be focusing on the physical
Supermarkets and its human-manned cashiers.
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The objective of this report is to help improve FairPrice’s current queuing system so
that itcan achieve higher operational efficiency and cater to more customers
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2. OPERATION MANAGEMENT & ON-SITE OBSERVATIONS
The process flow ata typical FairPrice is as illustrated in Appendix 1. A typical
customer which is the main input (flow unit) goes through three steps: (1) shopping
around for items they want to buy; (2) waiting in the queue to checkout; (3) checking
out. The output is the departure of customers who checked out from the cashier
counter(s).
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FairPrice adopts a separate queue system where demand is initially divided among
different servers, but customers are ultimately served only by one cashier (Appendix
2). Hence, using the queuing model with a single queue and server (Appendix 3), we
will be identifying R as our flow rate (i.e. rate at which customers leave the queue), I,
as the number of customers in queue, I, as the number of customers being served, I
as the total number of customers at the cashier counter (i.e. l=1, + Ip), Ta as the
waiting time from when a customer enters the queue to when he/she is served, p as
the processing time from when a customer is served to when his/her transaction is
completed, and T as the flow time (T, +p). Moreover, we will be using the three key
process metrics of Little’s Law (I = R*T) to help us in our review of the efficiency of
FairPrice’s current queuing system.
To derive an accurate representation of the queuing situation during the peak and
nonpeak periods, wevisited 5 FairPrice’s outlets island wide (Appendix 4) on a Sunday
to record and observe the queuing on a typical weekend. We observed 5 cashier
counters from each outlet for approximately an hour during both peak and non-peak,
averaging out the results for each time period per outlet. Refer to Appendix 5 for the
raw data gathered. Moreover, qualitative observations were made both at the physical
outlets (Appendix 6) and on FairPrice’s Facebook page to help us identify some
possible causes and consequences of long queues. Additionally, we have decided to
not focus on self-service counters as they are comparatively moreefficient in clearing
customers with no significant queue formed as observed.
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Variability is an important inhibitor of queue systems and they correspond to the
changes in demand and supply over time. We have observed that there is a huge
uncertainty in the entire process of checking out. For demand variability, we have
identified it in terms of customer arrivals and customer behaviours. Firstly, customers
visit F airP rice atvery differenttimes of the day and week but that variability is generally
predictable. We have observed thatthe 5 FairPrice outlets generally experience higher
demand with longer queues at time periods of 12pm to 2pm and 5pm to 8pm on
weekends. Wethus defined those time periods as FairPrice’s peak hours, and the time
periods from 9am to 12pm and 2pm to 5pm as non-peak. Refer to Appendix 7 for a
detailed breakdown of FairPrice’s opening hours. Secondly, customers vary in terms
of the number of items they purchase — from hand-carry to basket or trolley purchases.
For supply variability, we have spotted variability in terms of the speed of cashiers and
the different modes of payment which contribute to variations in processing times and
the build-up of queues.
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Taking the aboveinto consideration, it was observed that non-peak periods have their
Capacity exceeding demand, suggesting that on average, there should be no queue
formed for the non-peak hours. However, due to the presence of variability as
discussed earlier, there can be times in which a queue will form. Conversely, for peak
periods, it was observed the demand is constantly greater than the capacity of the
cashiers. Hence, there is a sustained queue throughout the period, and the queue will
only be cleared when demand decreases as it approaches the non-peak periods to a
point where demand is lesser than capacity.
The build-up of queues would result in consequences such as firstly, the loss of
customer’s faith in FairPrice. As FairPrice is not the only grocery retailer, customers
may choose to shop at Sheng Siong or Giant instead if FairP rice has been poor in its
queue management consistently. This would in turn translate into potential loss for its
future profits as well. Refer to Appendix 8 for an example of a customer's complaint
about FairPrice’s long queues as posted on its Facebook page. Secondly, there could
be a loss in potential profit when customers choose to leave without purchasing
anything (especially if they are only buying a handful of items) after seeing the long
queues. Lastly, there may be a congestion of shopping aisles when queues extend to
the aisles. This might cause irritation and frustration from other shoppers who are
seeking out items which are blocked by those who are queuing.
3. ASSUMPTIONS
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Certain assumptions were made when conducting this research. Firstly, itis assumed
that observations made atthe 5 outlets were representatives of the other outlets in the
Same region (i.e. outlets in the North, South, East, West and Central). Secondly, we
assumed that the midpoints between peak and off-peak hours would be a relatively
accurate estimate of the average demand and supply of the period. Thirdly, we
assumed that an average customer buys about a full basket of items which is
equivalent to 5 plastic bags worth of items (Appendix 9). Lastly, as it is common and
natural for customers to switch from a longer queue to a shorter one while queuing to
checkout, we assumed that the average length of each cashier counter is
approximately the same.
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4. ANALYSIS OF DATA
The presence of bottlenecks, which are the payment modes (cash, NETS, cards and
vouchers) and the cashier’s processing speed, has resulted in an increase in the
system’s flow time, primarily caused by the increase in waiting time in queue. Hence,
to determine the effects of each bottleneck on the length and waiting time of queue,
we will vary one of the bottlenecks in each of our observations. For the first set of
observations, we have used the average customer of 1 full basket purchases who
pays by cash/NETS with the variation of 5 different cashiers. For the second set of
observations, we identified each outlet’s fastest cashier to observetheir transactions
with variation of “cash/cards” and “NETS/vouchers” payment, totaling to 10
observations — 5 with
“cash/cards” and 5 with “NETS/vouchers’”.
Bottleneck 1: Cashier’s speed (Appendix 10)
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From detailed calculations made from the 5 different outlets and 5 different cashiers
(Appendix 11), we have noted that the non-peak periods of FairP rice queues pose no
significant issue as they only have an average of 6.40 customersin queue, with their
transactions from entering to completion taking about 7.20 minutes.
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Conversely, for the peak period, we have calculated the queue growth rate (Demand
— Capacity) to be 0.25 customers/minute which translate to a maximum queue of 45
customers in the span of a 3-hour peak period from 5pm to 8pm (Appendix 12).
Furthermore, based on our calculations, it takes 3 hours to clear the queue during the
peak periods. While this value might seem ridiculously long, it reflects the reality of the
customers during those hours as many customers have been seen dropping their
small hand carry and basket purchases for another time in view of the long queue.
This is because customers are generally impatient when faced with a queue longer
than expected (i.e. about 10-15 customers). Hence, FairP rice is incurring huge losses
from losing that many customers as the maximum queues typically observed are
approximately half of that amount (i.e. 20 customers). We thus conclude that it is
imperative for us to analyse the queue situation during peak hours at FairPrice to see
how queues can be shorten or how the perception of a shorter queue be created.
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Furthermore, based on our calculations of coefficient of variation (C.V.) of the
interarrival and processing times for non-peak periods, it Supports our earlier
observations that there is significant uncertainty in the process of checking out as
denoted by both the C.V. for processing times and arrival times being greater than 1,
1.91 and 1.66 respectively. While we have concluded that non-peak period queues
pose no significant impact, we can nonetheless make feasible improvements on it.
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More importantly, we decided to calculate the C.V. for peak periods to ascertain the
level of uncertainty as peak periods have the longest queues. Our suspicions were
confirmed with a 3.34 C.V. for processing times and a 4.78 C.V.for interarrival
times. This meant that cashiers handled a broad scope of work which is inherently
uncertain and thus there is a lot of room for improvement in reducing waiting times
which will be discussed in the later section.
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Bottleneck 2: Modes of payment
From our observations, cashiers sometimes run into disruptions when processing
transactions through payment modes like NETS and Vouchers. For NETS, they often
run into problems like the NETS terminal's inability to process the payment resulting
in delays when customers are asked to rekey their pin(s). For vouchers, cashiers may
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experience disruptions in the form of the checking process/tasks that they have to
perform (e.g. checking for the vouchers’ expiry and stamping them).
Figure 1
illustrates the findings gathered from the observations we made on 5 cashiers who
dealt with the different payment modes.
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COMPARISON OF CASHIERS’ PROCESSING TIME BY
PAYMENT MODES
== CASH&CARDS
= NETS & VOUCHERS
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Mei Hua
(Jurong Point)
Lilian (White
Sands)
Deepa
(Serangoon
Hazminah
(Woodlands)
Rasidul
(Chinatown)
NEX)
Cashier Name & Outlet
Figure 1: Comparison of cashiers’ processing time by payment modes
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For a detailed breakdown of the above calculations, refer to Appendix 13.
5. LIMITATIONS OF DATA
We recognise that limitations are imminent in this research due to the limited time and
resources. Firstly, this study was based on a sample size of 10 observations (5
nonpeak, 5 peak) at 5 different outlets on a single Sunday which did not include
Saturdays, and hence might not reflect the true situation for both weekends.
Furthermore, we did an analysis of normal day conditions as itis the more accurate
overview throughout the year but did not include festive seasons which commonly
have even higher levels of demand and longer queues. Nevertheless, our analysis is
considered reflective of the weekend situation, and our improvements will be able to
bring about smaller queues for FairPrice during non-festive and festive occasions.
Lastly, due to FairPrice’s strict internal Standard Operating Procedures (SOP), we
were unable to get a more detailed review or information from its staff to add greater
depth to our current research.
6. IMPROVEMENTS
To resolve the queue problems at FairPrice, our team will be proposing 3 solutions.
Firstly, in the short run, FairPrice can increase the number of‘single basket only’
queues. Throughout our observations at FairPrice, we noticed that more customers
use baskets than trolleys. Thus, by having more ‘single basket only’ queue, FairPrice
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would be able to better cater to their customers and potentially shorten customers’
waiting time as cashiers would not haveto transact trolley purchases. Moreover, they
can assign cashiers of older age or with lesser experience to those counters as a form
of training and better work integration while also reducing the uncertain nature of their
work.
Secondly, in the long run, FairPrice can venture into an RFID check out system,
similar to that of AmazonGo, where customers only need to download an application
which will allow them to check out without going to any cashiers or self-checkout
counters. Refer to Appendix 14 for details of launching process. This would be able to
eliminate both bottlenecks identified, greatly improving the overall shopping
experience for customers.
Lastly, checkout aisles could be made more useful with better designs such as
Interactive games (e.g. lucky draws/scratch-and-win/promotional activities) in
replacement of the current confectionery aisles. The completion. of the
games/activities would entitle customers to redeem a gift (e.g. merchandise/voucher)
at the redemption/customer service counter(s) after checking out at the cashier(s). By
keeping queuing customers entertained, it would give the perception of a shorter
waiting time while levitating their shopping experience at F airP rice.
7. CONCLUSION
FairPrice has been implementing various solutions which have been relatively
effective in reducing length and waiting time of queues for example directing traffic to
their FairPrice Go online store. However, more can be done especially during peak
periods as we haverealised that FairPrice might have been losing a large number of
physical customers when length of queue and waiting times are not ideal. Hence, with
the recommendations, we are confident that FairPrice will be able to address these
issues and better fulfill its mission of providing customers with the best value, quality
products and excellent service.
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REFERENCES
FairP rice, (n.d.). About NTUC FairPrice. Retrieved from
https ://www.fairprice.com.sg/wps/portal/corporate/corpHome
J eremy Lim. (n.d.). Retrieved from https://www.facebook.com/thatsmyfairprice/posts/imboycotting-ntuc-fairprice-forgood-amk-hub-forever-long-queues-and-i-had-tow/10155283435356409/
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Pocket-lint. (2019, February 18). What is Amazon Go, whereis it, and how does it work?
Retrieved from https://www.pocket-lint.com/phones/news/amazon/139650what-isamazon-go-where-is-it-and-how-does-it-work
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TSernov, K. (2017, August 02). 4 Ways to Reduce Wait Time. Retrieved from
https ://www.qminder.com/reduce-wait-time-with-enjoyable-queues/
TSernov, K. (2017, December 22). Long Waiting Times Cost You Sales. Retrieved from
https ://www.qminder.com/long-waiting-times-sales/
Vles, J. (2018, November 25). Commentary: Why is this queue so long? Retrieved from
Channel News Asia: https://www.channelnewsasia.com/news/commentary/whyis-thisqueue-line-so-long-10949254)
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APPENDIX 1
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PROCESS FLOW FOR A FLOW UNIT ATA TYPICAL FAIRPRICE SUPERMARKET
Shopping
enkanisr
>
around for
items
Customer
Checking Out
>|
Waiting to
checkout
Resources:
Resources:
items, store
manual cashiers,
assistants
self-checkouts
APPENDIX 2
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FAIRPRICE’S SEPARATE QUEUE SYSTEM
Separate Queue System
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Queue
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eo
©
—>_
ee
beni
me
=
&
&
beri tag
e
——_>
®@
beri
———_>
@ Customer
pp
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APPENDIX 3
FAIRPRICE’S QUEUING MODEL — SINGLE
QUEUE & SINGLE SERVER
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Single-Server Queue System
Queue
—_->+
Origa
©@
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Inventory in
queue
Inventory in
progress
@ Customer
APPENDIX 4
SELECTED FAIRPRICE OUTLETS ISLANDWIDE FOR OBSERVATIONS
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.Q WHITE SANDS OUTLET
=
°
SERANGOON NEX OUTLET +.
F
.
=
0 newfie “sada e commie
“re
*
1
er
-
*
25
Z
ok ;
*s
.
°
e
®
®
-¢
se
;
° e
e
-
es
e
ee
gt tay Ee
_CHINATOWN OUTLET
‘
*
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APPENDIX 5
pH
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DATA GATHERED FROM ON-SITE OBSERVATIONS DATA GATHERED FROM ON-SITE
OBSERVATIONS
All data sets were collected on 31 March 2019 (Sunday)
Data Set 1:
Time: 3pm — 4pm
Venue: FairP rice Xtra, J urong Point (Level 3)
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
Average of 3-4 people in the queue (including the one being served)
Observing for non-peak period, processing times and interarrival times, holding the modes of
payment constant.
*
Counter 1: Middle-aged man is processed for 228s, interarrival time of customers: 311s,
payment mode by Cash
Counter 2: Older lady is processed for 231s, interarrival time of customers: 326s, payment
mode by Card
Counter 3: Older man is processed for 240s, interarrival time of customers: 320s, payment
mode by Card
Counter 4: Middle-aged lady is processed
for 232s,
interarrival time of
customers: 244s, payment mode by Cash
Counter 5: Middle-aged man is
processed
for 234s,
interarrival time of
customers: 279s, payment mode by Cash
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Data Set 2:
Time: 6pm — 7pm
Venue: FairPrice Xtra, J urong Point (Level 3)
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
Average of 5-8 people in the queue (including the one being served)
Observing for peak period, processing times and interarrival times, holding the modes of
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payment constant.
*
Counter 1: Secondary school girl is processed for 244s, interarrival time of customers:
117s, payment mode by Cash
Counter 2: Middle-aged lady is processed for 230s, interarrival time of customers: 114s,
payment mode by Cash
Counter 3: Older lady is processed for 220s, interarrival time of customers: 121s, payment
mode by Card
Counter 4: Middle-aged lady is processed for 221s, interarrival time of customers: 120s,
payment mode by Cash
Counter 5: Older man is processed for 205s, interarrival time of customers: 118s, payment
mode by Card
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APPENDIX 5 (CONT’D)
Observing a single counter, cashier: Mei Hua with varying modes of payment
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Customer 1:
Customer 2:
Customer 3:
Customer 4:
Customer 5:
Processed
Processed
Processed
Processed
Processed
for
for
for
for
for
200s,
198s,
181s,
205s,
202s,
payment mode
payment mode
payment mode
payment mode
payment mode
by
by
by
by
by
Cash
Card
Cash
Card
Cash
Customer 1:
Customer 2:
Customer 3:
Customer 4:
Customer 5:
Processed
Processed
Processed
Processed
Processed
for
for
for
for
for
229s,
241s,
256s,
245s,
229s,
payment
payment
payment
payment
payment
by
by
by
by
by
NETS
Voucher
NETS
Voucher
NETS
mode
mode
mode
mode
mode
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Data Set 3:
Time: 3pm — 4pm
Venue: FairP rice, White Sands
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
Average of 4 people in the queue (including the one being served)
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Observing for non-peak period, processing times and interarrival times, holding the modes of
payment constant.
- Counter 1: Middle-aged man is processed for 221s, interarrival time of customers: 279s,
payment mode by Cash
Counter 2: NSF is processed for 258s, interarrival time of customers: 298s, payment mode
by Card
Counter 3: NSF is processed for 240s, interarrival time of customers: 298s, payment mode
by Cash
Counter 4: Middle-aged lady is processed for 215s, interarrival time of customers: 268s,
payment mode by Cash
Counter 5: Middle-aged man is processed for 236s, interarrival time of customers: 302s,
payment mode by Card
Data Set 4:
Time: 6pm — 7pm
Venue: FairP rice, White Sands
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
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Average of 10 people in the queue (including the one being served)
APPENDIX 5 (CONT’D)
Observing for peak period, processing times and interarrival times, holding the modes of
payment constant.
Counter 1: Girl in 20s is processed for 269s, interarrival time of customers: 133s, payment
mode by Cash
Counter 2: Older man is processed for 260s, interarrival time of customers: 118s, payment
mode by Cash
Counter 3: Boy in 20s is processed for 248s, interarrival time of customers: 124s, payment
mode by Card
Counter 4: Middle-aged lady is processed for 259s, interarrival time of customers: 124s,
payment mode by Cash
Counter 5: Middle-aged man is processed for 254s, interarrival time of customers: 136s,
payment mode by Cash
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Observing a single counter, Cashier: Lilian with varying modes of payment
Customer 1:
Customer 2:
Customer 3:
Customer 4:
Customer 5:
Processed
Processed
Processed
Processed
Processed
for
for
for
for
for
225s,
218s,
234s,
222s,
238s,
payment
payment
payment
payment
payment
mode
mode
mode
mode
mode
by
by
by
by
by
Cash
Card
Cash
Card
Cash
Customer 1:
Customer 2:
Customer 3:
Customer 4:
Customer 5:
Processed
Processed
Processed
Processed
Processed
for
for
for
for
for
237s,
237s,
241s,
245s,
241s,
payment mode
payment mode
payment mode
payment mode
payment mode
by
by
by
by
by
NETS
Voucher
NETS
Voucher
NETS
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Data Set 5:
Time: 3pm — 4pm
Venue: FairPrice, Serangoon NEX (Level 3)
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
Average of 4 people in the queue (including the one being served)
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Observing for non-peak period, processing times and interarrival times, holding the modes of
payment constant.
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Counter 1: Woman in 40s is processed for 234s, interarrival time of customers: 317s,
payment mode by Card
Counter 2: Older lady is processed for 257s, interarrival time of customers: 312s, payment
mode by Card
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Counter 3: Older man is processed for 241s, interarrival time of customers: 299s, payment
mode by Card
Counter 4: Man in 30s is processed for 231s, interarrival time of customers: 294s, payment
mode by Card
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APPENDIX 5 (CONT’D)
Counter 5: NSF is processed for 232s, interarrival time of customers: 298s, payment mode
by Cash
Data Set 6:
Time: 6pm — 7pm
Venue: FairPrice, Serangoon NEX (Level 3)
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
Average of 12 people in the queue (including the one being served)
33yAt42JnkxG5z
Observing for peak period, processing times and interarrival times, holding the modes of
payment constant.
Counter 1: Middle-aged man is processed for 238s, interarrival time of customers: 114s,
payment mode by Card
Counter 2: Older lady is processed for 260s, interarrival time of customers: 118s, payment
mode by Card
Counter 3: Older man is processed for 236s, interarrival time of customers: 99s, payment
mode by Cash
Counter 4: Middle-aged lady is processed for 256s, interarrival time of customers: 108s,
payment mode by Cash
Counter 5: Middle-aged man is processed for 242s, interarrival time of customers: 110s,
payment mode by Cash
Observing a single counter, Cashier: Deepa with varying modes of payment
Customer 1:
Customer 2:
Customer 3:
Customer 4:
Customer 5:
Processed
Processed
Processed
Processed
Processed
for
for
for
for
for
232s,
240s,
237s,
238s,
231s,
payment mode
payment mode
payment mode
payment mode
payment mode
by
by
by
by
by
Cash
Card
Cash
Cash
Card
Customer 1:
Customer 2:
Customer 3:
Customer 4:
Customer 5:
Processed
Processed
Processed
Processed
Processed
for
for
for
for
for
244s,
253s,
241s,
251s,
255s,
payment
payment
payment
payment
payment
by
by
by
by
by
NETS
NETS
NETS
NETS
NETS
mode
mode
mode
mode
mode
33yAt42JnkxG5z
33yAt42JnkxG5z
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Data Set 7:
Time: 3pm — 4pm
Venue: FairP rice, Woodlands
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
Average of 5 people in the queue (including the one being served)
33yAt42JnkxG5z
33yAt42JnkxG5z
APPENDIX 5 (CONT’D)
Observing for non-peak period, processing times and interarrival times, holding the modes of
payment constant.
33yAt42JnkxG5z
-
Counter 1: Middle-aged man is processed for 254s, interarrival time of customers: 296s,
payment mode by Cash
Counter 2: Older lady is processed for 257s, interarrival time of customers: 308s, payment
mode by Card
Counter 3: Older man is processed for 253s, interarrival time of customers: 298s, payment
mode by Card
Counter 4: Middle-aged lady is processed for 249s, interarrival time of customers: 301s,
payment mode by Cash
Counter 5: Middle-aged man is processed for 247s, interarrival time of customers: 297s,
payment mode by Cash
33yAt42JnkxG5z
Data Set 8:
Time: 6pm - 7pm
Venue: FairP rice, Woodlands
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
Average of 8 people in the queue (including the one being served)
Observing for peak period, processing times and interarrival times, holding the modes of
payment constant.
+ Counter 1: NSF is processed for 240s, interarrival time of customers: 116s, payment mode
by Cash
Counter 2: NSF is processed for 238s, interarrival time of customers: 112s, payment mode
by Card
Counter 3: Older man is processed for 216s, interarrival time of customers: 110s, payment
mode by Card
Counter 4: Middle-aged lady is processed for 244s, interarrival time of customers: 103s,
payment mode by Cash
Counter 5: Middle-aged lady is processed for 212s, interarrival time of customers: 134s,
payment mode by Cash
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
Observing a single counter, Cashier: Hazminah with varying modes of payment
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Customer 1:
Customer 2:
Customer 3:
Customer 4:
Customer 5:
Processed
Processed
Processed
Processed
Processed
for
for
for
for
for
236s,
233s,
249s,
233s,
239s,
payment
payment
payment
payment
payment
mode
mode
mode
mode
mode
by
by
by
by
by
Cash
Card
Card
Card
Cash
33yAt42JnkxG5z
Customer 1: Processed for 251s, payment mode by NETS
Customer 2: Processed for 243s, payment mode by NETS
Customer 3: Processed for 238s, payment mode by NETS
APPENDIX 5 (CONT’D)
Customer 4: Processed for 236s, payment mode by NETS
Customer 5: Processed for 245s, payment mode by NETS
Data Set 9:
Time: 3pm — 4pm
Venue: FairPrice, Chinatown
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
Average of 5 people in the queue (including the one being served)
33yAt42JnkxG5z
33yAt42JnkxG5z
Observing for non-peak period, processing times and interarrival times, holding the modes of
payment constant.
- Counter 1: Middle-aged man is processed for 225s, interarrival time of customers: 320s,
payment mode by Cash
Counter 2: Older lady is processed for 260s, interarrival time of customers: 306s, payment
mode by Card
Counter 3: Older man is processed for 246s, interarrival time of customers: 300s, payment
mode by Card
Counter 4: Middle-aged lady is processed for 235s, interarrival time of customers: 308s,
payment mode by Cash
Counter 5: Middle-aged man is processed for 244s, interarrival time of customers: 321s,
payment mode by Cash
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
Data Set 10:
Time: 6pm — 7pm
Venue: FairPrice, Chinatown
Scope: Queues and checkouts, comparison of human-operated cashiers, self-checkouts, full
basket purchases only.
Resources: Selected 5 cashier counters, self-checkout line
Average of 10 people in the queue (including the one being served)
pH
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33yAt42JnkxG5z
Observing for peak period, processing times and interarrival times, holding the modes of
payment constant.
*
33yAt42JnkxG5z
Counter 1: Middle-aged man is processed for 261s, interarrival time of customers:
payment mode by Cash
Counter 2: Middle-aged man is processed for 246s, interarrival time of customers:
payment mode by Cash
Counter 3: Middle-aged lady is processed for 240s, interarrival time of customers:
payment mode by Card
Counter 4: Middle-aged lady is processed for 253s, interarrival time of customers:
payment mode by Card
Counter 5: Middle-aged man is processed for 215s, interarrival time of customers:
128s, payment mode by Cash
138s,
135s,
119s,
140s,
APPENDIX 5 (CONT’D)
Observing a single counter, Cashier: Rasidul with varying modes of payment
Customer 1:
Customer 2:
Customer 3:
Customer 4:
Customer 5:
Processed
Processed
Processed
Processed
Processed
for
for
for
for
for
187s,
193s,
199s,
203s,
208s,
payment
payment
payment
payment
payment
mode
mode
mode
mode
mode
Customer 1:
Customer 2:
Customer 3:
Customer 4:
Customer 5:
Processed
Processed
Processed
Processed
Processed
for
for
for
for
for
225s,
245s,
253s,
251s,
229s,
payment mode
payment mode
payment mode
payment mode
payment mode
by
by
by
by
by
Cash
Card
Cash
Card
Cash
by NETS
by NETS
by NETS
by Voucher
by NETS
33yAt42JnkxG5z
33yAt42JnkxG5z
pH
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33yAt42JnkxG5z
APPENDIX 6
QUALITATIVE OBSERVATIONS — QUEUING SITUATIONS AT THE 5 SELECTED OUTLETS
1) OUTLET (NORTH): WOODLANDS
es
a
33yAt42JnkxG5z
on =a
Non-peak (3pm — 4pm)
2) OUTLET (SOUTH): CHINATOWN
33yAt42JnkxG5z
Non-peak (3pm — 4pm)
33yAt42JnkxG5z
$9 thinkswap
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Peak (6pm — 7pm)
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3) OUTLET (EAST): WHITE SANDS
33yAt42JnkxG5z
Non-peak (3pm — 4pm)
Peak ‘Spm —"TIpm)
APPENDIX 6 (CON’TD)
UALITATIVE OBSERVATIONS —
QUEUING SITUATIONS AT THE 5
SELECTED OUTLETS
4) OUTLET (WEST): J URONG POINT
33yAt42JnkxG5z
Non-peak (3pm — 4pm)
Peak (6pm — 7pm)
33yAt42JnkxG5z
5) OUTLET (CENTRAL):SERANGOON NEX
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
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33yAt42JnkxG5z
Non-peak (3pm — 4pm)
Peak (6pm — 7pm)
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
APPENDIX 7
DETAILED BREAKDOWN OF PEAK AND NON-PEAK PERIODS DURING OPENING
HOURS
Period
ail)
ate
|steseo[re
Ce
[sw
=
[nw
33yAt42JnkxG5z
33yAt42JnkxG5z
pH
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APPENDIX 8
CUSTOMER’S COMPLAINT OF LONG QUEUES ON FAIRPRICE’S FACEBOOK
Jeremy Lim } NTUC FairPrice ©
5 February 2018-
Singapore a
G
Im boycotting NTUC Fairprice for good. AMK Hub forever long queues and i
had to wait 20 mins at express queue. At least 1/3 of the counters not
opened.
Nownewshasit that NTUC is operating 24 hours on CNY eve. Has the
Union considered the fact that the staff deserve to spend quality time with
their family? | see so manyof the cashiers looking worn out and nowyouare
depriving them oftheir rest.
33yAt42JnkxG5z
Utterly disgusted by this money grabbing act. What has happened to work
life balance? Is overworking your staff the right way to go?
@O3
1 comment
@ Share
33yAt42JnkxG5z
APPENDIX 9
MEASUREMENTS AND STANDARDISATION
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Hand Carry/Basket(H/F)/Trolley(H/F)
No. of items (Range)
No. of plastic bags
Hand Carry
1to5
1
Basket(Half)
6 to 10
3
11 to 15
5
Trolley (Half)
16 to 30
8
Trolley (Full)
31 to 50
10
Basket(Full)
33yAt42JnkxG5z
33yAt42JnkxG5z
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APPENDIX 10
33yAt42JnkxG5z
BOTTLENECK — CASHIERS’ SPEED
J) URONG POINT (WEST)
Mean (Processing Time)
s,
Mean (Interarrival Time)
33yAt42JnkxG5z
Non Peak
Non Peak
Observation Processing Time Observation Interarrival Time
33yAt42JnkxG5z
Mean
228
231
240
232
234
233
311
326
320
244
279
33yAt42JnkxG5z
Mean
296
Peak
Peak
Observation Processing Time Observation Interarrival Time
33yAt42JnkxG5z
244
117
221
120
230
220
Mean
205
224
114
121
118
118
Mean
33yAt42JnkxG5z
WHITE SANDS (EAST)
Mean (Processing Time)
Mean (Interarrival Time)
33yAt42JnkxG5z
Non Peak
Non Peak
Observation Processing Time Observation Interarrival Time
221
33yAt42JnkxG5z
258
240
215
236
pH
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APPENDIX 10 (CON’TD)
BOTTLENECK — CASHIERS’ SPEED
SERANGOON NEX (CENTRAL)
33yAt42JnkxG5z
33yAt42JnkxG5z
pH
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33yAt42JnkxG5z
APPENDIX 10 (CON’TD)
33yAt42JnkxG5z
BOTTLENECK — CASHIERS’ SPEED
CHINATOWN (SOUTH)
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Mean (Interarrival Time)
Non Peak
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
311
pH
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33yAt42JnkxG5z
APPENDIX 11 DETAILED CALCULATIONS MADE FROM 5 OBSERVED OUTLETS
& CASHIERS
Interarrival time
Processing tim
a cn
Peak
|
Queue Growth Rate
PPT
Peak
Demand
- Capacity
Demand
(customer/
33yAt42JnkxG5z
No. of customers
in 1 hour
Capacity (customer/min)
te
33yAt42JnkxG5z
Serve 15 customers
in 1 hour
Time in queue (Tq)
eX=¥)4
| Service Time * (Utilisation/(1-Utilis ation)*((C Va2+C Vp)/2)
3.198125
Inventory in queue(Iq)
33yAt42JnkxG5z
|
33yAt42JnkxG5z
pH
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tLe
R*Tq
Peak
NA
6.39625
Inventory in process (Ip)
te
R*p
Peak
NA
8
33yAt42JnkxG5z
33yAt42JnkxG5z
APPENDIX 11 (CONT’D)
DETAILED CALCULATIONS MADE FROM 5 OBSERVED OUTLETS & CASHIERS
S.D. (Processing
Time)
33yAt42JnkxG5z
Non Peak
33yAt42JnkxG5z
7.64852927
33yAt42JnkxG5z
5
13.36038922
|
C.V (Processing Time)
Non Peak
33yAt42JnkxG5z
yet}
Se
240
7.64852927
i CereT a)
Se
240
13.36038922
1.912132318
3.340097304
33yAt42JnkxG5z
S.D. (Interarrival
Time)
Non Peak
|
pH
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33yAt42JnkxG5z
5
9.565563235
|
C.V (Interarrival Time)
Non Peak
| Nea
33yAt42JnkxG5z
Cae
300
8.276472679
1.655294536
120
9.565563235
4.782781617
APPENDIX 12
Length of the queue
FAIRPRICE’S QUEUE FORMATION AND CLEARING FOR PEAK PERIODS
33yAt42JnkxG5z
33yAt42JnkxG5z
Queue cleared @ t=360, 6 mins later
= (45/0.25) = 180
Time (minutes)
33yAt42JnkxG5z
APPENDIX 13
BOTTLENECK — MODES OF PAYMENT
MODES OF PAYMENT(CASH & CARD)
pH
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33yAt42JnkxG5z
Outlet
(Cashier
Name)
A
Cashier
Name
iia
Jurong Point
MeiHua
200
198
181
205
202
To7 2
White Sands
Lilian
225
218
234
222
238
227.4
<
=240
33yAt42JnkxG5z
Serangoon Nex|
Deepa
232
240
237
238
231
235.6
Woodlands
Hazminah
236
233
249
233
239
238
Chinatown
Rasidul
187
103
199
203
208
198
33yAt42JnkxG5z
ad
MODES OF PAYMENT(NETS & VOUCHERS)
reyes(=\9
Jurong Point
Cashier Name
MeiHua
A
2
22 9
241
256
245
220)
240
>240
240.2
Serangoon Nex|
248.8
Deepa
33yAt42JnkxG5z
Woodlands
242.6
Hazminah
240.6
*timings recorded in seconds
APPENDIX 14 RFID CHECKOUT LAUNCHING PROCESS
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
pH
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RFID CHECKOUT SYSTEM
4
33yAt42JnkxG5z
Installation
Upgrade inventory
mand install
security
equipment
33yAt42JnkxG5z
33yAt42JnkxG5z
33yAt42JnkxG5z
Step lr
Download the
Application
p 2: Tap your
phoneat the gantries
before entering
Step
2: T
.
pe 3: Sa
Pping::
WE ARE
DONE!!!
Step 5: Check your ereceipt onthe
application
Step 4: Tap your
phone at the gantries
to exit
|
Trial run at popular outlets
for 2 weeks
Official launch at popular
Launchatotheroutlets
outlets
33yAt42JnkxG5z
>
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