Cashier downtime in retail vsf 07 12 11 final web

CASHIER DOWNTIME IN RETAIL
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CONTENTS
Background Cashier Downtime
Cashier Downtime Analysis
Queuing Theory
Queuing Scenarios
Solution to Reduce Cashier Downtime
Metrics to Capture Cashier Downtime & Productivity
Examples to Illustrate Cashier Downtime & Productivity Metrics
Conclusion
About the Author
Mass merchandisers, discount retailers, grocery retailers and department stores have a unique challenge at the point of sale related to their cashier workforce.
While these varied retail formats sell many products, the value and volume per sale is lower and the typical customer return frequently to the store. To meet
the expected service level standards and reduce customer wait times, these environments have to keep multiple lanes open at all times. This leads to not only
higher cost for the front lanes but also increased downtime per lane. Without accurate measurements, the operational expenses related to the cashiers
cannot be reduced. This article gives an overview of cashier downtime and metrics to calculate and control it on a real time basis.
By Amit Dhall, Wipro Technologies
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Background Cashier Downtime
In a retail environment, with the enormous push to improve customer shopping experience, there is a need to keep
as many checkout lanes open as can be and thus bring the customer wait time to the minimum or in other words
reach the idle state of one customer in a lane with no wait time. But to reach this stage, the store has to put an
enormous cost on the payroll.
Due to intense competition and customer service becoming a very critical
parameter, store managers generally ignore the cost involved in keeping
excessive lanes open and hence the payroll cost gets skewed towards the
cashiers. This is further compounded by a tendency of the management to
ignore operational expenses of the front lanes in the name of
customer service.
This can lead to the neglect of other parts of the store particularly with a
limited store budget.
A scenario wherein the focus is to reduce checkout time by keeping the
cost skewed towards the front lanes can lead to the store managers
keeping the workforce on the front lanes and thus overlooking other
important areas in the store which though may not be critical but act as
hygiene factors and aid in store sales.
• An example could be the spillover of a liquid in an aisle making the
entire aisle dangerous for walking which could either lead to no sales
of products in that aisle or even injuries. It could also put off
customers from future visits to the store due to a feeling of untidiness.
• Another scenario could be a lack of focus on the backroom operations
which could lead to empty aisles, inadequate replenishment or wrong
inventory stocking; all three contributing to lost sales.
Thus, even though providing excellent service through minimal wait time
is an important store performance criteria it should not lead to reduced
attention towards other parts of the store.
Cashier Downtime Analysis
Cashier downtime is defined as the amount of time when a cashier is
logged-on to a register but is not ringing a transaction or in other words
the cashier is waiting for a guest to arrive in his lane.
Each minute of this time costs the payroll and leads to no productive
output as the cashier keeps manning the lane. If the cashier was not
manning the register waiting for customers he/she could be used to
perform other store activities. This would increase store productivity and
also would help in reducing store payroll costs as additional workforce
would not be required to perform those tasks.
But not all cashier downtime is useful time. An example to illustrate this is
as follows:
Consider a scenario wherein a cashier ends a transaction and there is no
new guest in his lane. The cashier waits for a minute before the new guest
arrives and the cashier starts ringing the transaction. This intervening time
period when the cashier was free is not useful downtime as the cashier
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couldn’t log off from the register and perform some other useful activity.
This downtime also depends on the number of cashiers manning the lanes
in a store at a time which in turn depends on the service level to be given
by the store to its guests. Some common service levels are a 1+ 0, 1+1 or
1+ 2. This means that at any given point of time their would be 0, 1 or 2
customers waiting in line while a transaction for a customer is being rung
on any lane of the store. If this threshold is exceeded the store needs to
open another lane.
Queuing Theory
When a queue in a retail store - be it a checkout queue or a returns queue
or even an inquiry queue - gets congested, there is a delay in service.
A good understanding of the cause of congestion and the resulting delay is
essential for optimizing the number of lanes which should be kept open at
a given point of time in a store to achieve the defined service levels.
The different components of delay in a lane can be classified into the
following categories:
Processing Delay: This is the delay between the guest arriving time at the
counter to the transaction completion time. This delay depends on the
cashier’s speed and on the speed of the application. Better the training
imparted to the cashier and more fine tuned the Point of Sale application,
lesser will be this delay.
Queuing Delay: This is the delay between a guest entering the queue to
the time his transaction is completed. This delay depends on the number
of guests in the queue which in turn depends on the number of lanes which
are open for guest service. More the lanes, lesser will be this delay.
Little's Theorem
Little's theorem states that:
The average number of customers (N) in a queue can be determined
from the following equation:
N = λ T
Where Lambda (λ) is the average customer arrival rate and T is the
average service time for a customer.
For a retail store, consider a scenario where the customer arrival rate
(Lambda) doubles but the customers still spend the same amount of time
in the store for shopping. This will effectively double the number of
customers in the store (N). By the same logic if the customer arrival rate
remains the same but the customer service time doubles, it will double the
total number of customers in the store.
The calculation can be illustrated by the following three examples which
represent three different scenarios:
A) In a checkout lane let's assume that the cashier always takes two
minutes to check out a customer. Let's also assume that there is
no line and that a new customer walks up to the cashier at the
exact moment that another customer was done checking out.
This will mean that their will be absolutely no delay between
customer checkouts.
B) If we assume that 10 people used the same checkout lane, but
each person arrived in line one minute after the last person was
done checking out. The cashier is just waiting for a customer for
one minute. The cashier is still capable of checking out a
customer in two minutes, so their will be absolutely no delay
between customer checkouts but for each new customer the
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cashier will have a downtime of one minute which will be
useless downtime.
C) If we assume that 10 people used the checkout line at nearly the
same time; in other words, there was a line of nine people behind
a customer who is being checked out. For a customer the
checkout time is the amount of time from when they get in line
until they are done checking out. The first person to get to the
checkout lane wouldn't wait at all. The first person in line (not
the one being checked out currently) would wait two minutes to
start checking out, the second person would wait four minutes,
and so on until the last person who would wait 18 minutes to
start being checked out.
From a customer's perspective, the average customer checkout time is
greater, even though the cashier is still working at the same speed and is
able to push 10 people through the line in 20 minutes.
The checkout lane is saturated at the point when the rate at which new
customers getting into the lane exceeds the rate at which old customers
are being checked out.
In this last example, service levels start degrading when the customer
arrival rate to a lane becomes less than two minutes.
So the rate at which customers are getting in line and the number of lanes
which are open to counter the increase/decrease in rate makes all
the difference.
Queuing Scenarios
Scenario A: Regular Flow of Customers Leading to Zero Cashier Downtime
and a Need to Open Register 6 to Meet Service Levels
POS Register 1
(Closed)
POS Register 2
(Open)
POS Register 3
(Open)
POS Register 4
(Open)
POS Register 5
(Open)
POS Register 6
(Closed)
The actors represent the customers. The color of the actors represents
the queue they are in. Those customers who don’t have any color can
move to any of the queues depending on the queue length.
Scenario B: Irregular flow of Customers Leading to Useless Cashier Downtime
Solution to Reduce Cashier
Downtime
Queuing requirements of a retail store will depend upon the
following factors:
• Customer arrival rate and the time of the day of arrival
(morning, afternoon, evening or weekends)
• Average shopping time per customer
• Variation in time duration for customer service time and
factors on which this variation occurs (type of goods bought
by the customer, type of transaction)
POS Register 1
(Closed)
POS Register 2
(Open)
POS Register 3
(Open)
POS Register 4
(Open)
POS Register 5
(Open)
POS Register 6
(Closed)
The actors represent the customers. The color of the actors represents
the queue they are in. Those customers who don’t have any color can
move to any of the queues depending on the queue length.
Scenario C: Sparse Flow of Customers Leading to Useful Cashier Downtime
at Register 5
POS Register 1
(Closed)
POS Register 2
(Open)
POS Register 3
(Open)
POS Register 4
(Open)
POS Register 5
(Open)
POS Register 6
(Closed)
The actors represent the customers. The color of the actors represents
the queue they are in. Those customers who don’t have any color can
move to any of the queues depending on the queue length.
• Average number of lanes open in the store at any given time
and the rate change based on time of the day
In order to improve the performance of the store, queuing can be made
multi-threaded by adding more lanes based on the time of the day and the
day (weekend, holiday etc.) This concurrency will help in two ways:
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1. It will minimize the response time that each customer
experiences by reducing wait times in line
2. It will increase the number of transactions completed in a given
time period
Let's say there are 10 lanes and 10 customers that get to the checkout area
at the same time, each customer goes to a different lane, and each
checkout takes two minutes. Thus their will be absolutely no delay
between customer checkouts. But in this case, the number of cashiers has
increased to improve service level leading to higher payroll costs.
The solution to this issue is to analyze cashier downtime for a particular
duration of time for all times of the day with an assumption that any new
customer will try to get into a lane which will have the least waiting time i.e.
minimum length of the queue.
The next step would be to compare this downtime with the sales forecasts
for that particular store for the same time period. The breakup of the sales
Store X (A)
8:00 AM to 9:00 AM
9:00 AM to 10:00 AM
10:00 AM to 11:00 AM
11:00 AM to 12:00 PM
12:00 PM to 1:00 PM
1:00 PM to 2:00 PM
2:00 PM to 3:00 PM
3:00 PM to 4:00 PM
4:00 PM to 5:00 PM
5:00 PM to 6:00 PM
6:00 PM to 7:00 PM
7:00 PM to 8:00 PM
8:00 PM to 9:00 PM
9:00 PM to 10:00 PM
Sales
($) (B)
5000
7000
6000
10000
8000
2000
3000
3000
14000
9000
6000
4000
1000
1000
Average
Cashier Rate
($) / hour
(C)
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No. of
Cashiers
(D)
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20
20
16
16
16
20
12
12
12
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Cashier
Cost ($) per
hour
(E) = (C)
* (D)
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128
160
160
128
128
128
160
96
96
96
64
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forecast would have to be done for all hours of the day for all days of
the week.
The resulting comparison will illustrate that cashier downtime is inversely
proportional to the sales of the store and directly proportional to the
number of open lanes in the store.
Once we get the comparison we can deduce the number of cashiers
required in a store at a given point of time to provide a defined level of
service (1+0, 1+1, 1+2 etc.) and the acceptable level for
cashier downtime.
Once the retail chain sets a standard for acceptable cashier downtime it
can be monitored across all stores and brought to the defined standard.
For e.g.: If cashier downtime is more for a store from 12:00 PM to 3:00 PM
as compared to the sales; we need to reduce the number of cashiers in the
store at that time.
The table given below illustrates this concept:
Cashier Downtime Reduction
Cumulative
Cashier Downtime
per hour
(Hours) (F)
* At this cost the service level (1+0, 1+1, 1+2) as desired by the store is achieved.
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2
3
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3
3
Cashier
Downtime
Cost ($) (G) =
(C) * (F)
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24
32
40
40
40
40
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16
24
32
24
24
Acceptable
Downtime
Cost ($) * (per
1000$ of
sales) (H)
1
Current Cost
($) per 1000$
of sales
(I) = (G) *
1000 / (B)
3.20
1.14
4.00
3.20
5.00
20.00
13.33
13.33
0.57
1.78
4.00
8.00
24.00
24.00
Cashier
Downtime
Cost reduction
opportunity ($)
(J)= (I) - (H)
2.20
0.14
3.00
2.20
4.00
19.00
12.33
12.3
-0.43
0.78
3.00
7.00
23.00
23.00

Metrics to Capture Cashier
Downtime & Productivity
Metrics Formula
Total Hours worked
by the Cashier for a
day
Cashier Downtime
for a day*
Cashier Productivity
percentage for a day
∑ (Cashier Signoff - Cashier Login) [Cashier Login
on a register, Cashier Sign off on that register] for all
intervals of a day for all registers logged in by the cashier
∑
(Cashier Auto Logoff - Cashier Login)
for all intervals of a day for all registers logged in by the cashier
+ ∑ { [Next Transaction Start Time – Previous
Transaction End Time] if it is > X} [Cashier Login on a
register, Cashier Logoff on that register] for all intervals of a
day for all registers logged in by the cashier
Where X is a threshold defined by the business
users
{ 100 – [(Cashier Downtime for a day*) + (Cashier
Login on a Register Time – Cashier Clock Punch-in
time) + (Cashier Clock Punch-out Time – Cashier
Logoff on a Register Time) / ∑ [ (Cashier Scheduled
End Time - Cashier Scheduled Start Time) -
(Cashier Scheduled Meal Time + Cashier
Scheduled Break Time) ] [Cashier Clock Punch In,
Cashier Clock Punch Out] *100] }
Examples to Illustrate
Cashier Downtime &
Productivity Metrics
The metrics can be illustrated by the following examples:
Total Hours worked by the Cashier for a day
= ∑ (10:30 AM – 8:30 AM) + (1:00 PM – 10:45 AM) +
(3:30 PM – 1:30 PM) + (4:30 PM – 3:45 PM)
= (120 + 135 + 120 + 45) / 60
= 7 hours
Cashier Downtime for a day
= (2:30 PM – 2:15 PM) + ∑ {[8:50 AM – 8:30 AM) + (9:20 AM – 9:00
AM) + (10:45 AM – 10:10 AM) + (12:27 PM – 11:51 AM) + (1:49 PM
– 1:21 PM) + (3:30 PM – 3:06 PM) + (3:59 PM – 3:33 PM)] if it is
> 20 minutes}
= {(15) + ∑ 20 + 20 + 25 + 36 + 28 + 24 + 26} / 60
= 3.23 hours
Cashier Productivity percentage for a day
= {100 – [(194) + (8:45 AM – 8:15 AM) + (4:45 PM – 4:30 PM) /
∑ [(4:45 PM – 8:15 AM) – (30 + 15 + 15)] *100]}
= {100 – [194 + 30 + 15 / ∑ [510 – (30 + 15 + 15)] *100]}
= {100 – [239 / 450] *100]}
= {100 – [239 / 450] *100]}
= 46.88 %
Conclusion
There are no definite studies which give the cost of cashier downtime as a
percentage of total store operational cost, however keeping the service
level high by loading the cost on to the cashiers is a common phenomenon
among discount, mass merchandizing, departmental and grocery retailers.
Defining metrics for computing cashier downtime will enable awareness of
productivity and guest wait opportunities i.e. service level of 1+0, 1+1,
1+2 etc.
This solution will enable immediate resolution to service level
performance and guest wait issues and the scores will motivate store team
members to improve their performance as well get training to enhance
their productivity.
Reduction of cashier downtime will also lead to improvement in cashier
productivity and could be used for generating progress reports,
recognition awards and mentoring store team members.
However zero cashier downtime is also not good as it implies that the
cashiers are busy all the time and there are guests waiting in line to be
serviced. So a balance has to be achieved to bring the downtime to a level
where it doesn’t affect the service levels.
Improving performance and guest shopping experience is a core function
of delivering exceptional guest service and reducing operational expenses
and cashier downtime reduction can lead to achievement of both.
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About the Author
Amit Dhall is a Consulting Manager in Retail Industry Advisory Group at Wipro Technologies. In this role, he is responsible for bringing thought leadership
and innovative solutions for Wipro's global retail clients. He has worked on various consulting assignments with US and European retailers in the areas of
Workforce Management, Store Solutions, Point of Sale, Supply Chain and Marketing Analytics. He can be reached at amit.dhall@wipro.com
About Wipro Technologies
Wipro Technologies, the global IT business of Wipro Limited (NYSE:WIT) is a leading Information Technology, Consulting and Outsourcing company, that
delivers solutions to enable its clients do business better. Wipro Technologies delivers winning business outcomes through its deep industry experience and a
360 degree view of “Business through Technology”– helping clients create successful and adaptive businesses. A company recognized globally for its
comprehensive portfolio of services, a practitioner’s approach to delivering innovation and an organization wide commitment to sustainability, Wipro
Technologies has 130,000 employees and clients across 54 countries. For more information, please visit www.wipro.com
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