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A Dunkin’ Dilemma: Addressing Areas to Improve Customer Order Times

By Brandon Daley, Michelle Prester, and Mike Buonanno

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Table of Contents

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1. Responsibilities RACI 2

2. Project Charter 2

3. SIPOC 4

4. Process Flowchart 5

5. VSM 5

6. Measurements and Data Collection 6

7. Fishbone Diagram 8

8. Solution Prioritization Matrix 11

a. Recommendation to Management

9. Implementation and Expected Outcome 12

10. How the Process Will Remain “In-Control” 12

11. Additional Outside Sources 13

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Responsibilities RACI

The RACI depicts the allocation of all project deliverables for our group during the Fall 2018 semester.

Project Charter

The project charter encompasses scope, objective, and business case for our chosen process improvement.

Scope

•Livingston Student Center Dunkin' Donuts and staff

Objective

•To improve the drink service process by reducing customer order times

Business Case

•Operational efficiency to exceed a defined standard of excellence

• Increased customer satisfaction resulting from greater product churn

•Shorter order delivery times from customer decision to counter placement

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The company of focus for this project is Dunkin’ Donuts. The scope of this project includes the Livingston

Student Center Dunkin’ Donuts, its staff, and its customers. The objective of this case is mainly to reduce

the drink order times and potential motion waste. By observing the day-to-day operations of this

establishment, our team noticed significant wait times especially during peak hours of service throughout

the day. Some observations included the absence of a “floater” employee to take orders when lines are

extremely long. Another was the lack of cup organization, which increased wait time as the baristas

searched for the correct cup size.

From this study, we hope to accomplish greater operational efficiency that exceeds a defined standard of

excellence for Dunkin’ employees. This would be accomplished by reducing motion waste to reduce order

time and would be accompanied by a possible additional employee during peak hours. The customer impact

we hope to achieve is improved customer satisfaction via greater product churn and reduced lead time.

Some solutions the team has identified are to hire employees to fill a “floater” position, audit processes,

and re-train employees. These solutions will be further discussed in the remainder of this report. Ideally,

the “floater” would be used during peak operating hours to take orders from customers before they reach

the pay station. This would allow the staff to fulfill drink orders before the customer pays.

Organizational equipment would allow the varying cup sizes to be sorted in a manner that would promote

quick and easy filling and reduce order time. Retraining employees would allow current employees to learn

a new organizational system. For recent new hires, the on-boarding process would be adjusted to include

basic training for how to perform job duties, particularly those ascribed to the “floater” position.

To achieve these goals, the expected financial impact is around $400 which would be used to train new

employees, on-board potential “floater” employees and purchase new equipment. In order to reduce order

time and improve efficiency, employees would have to be re-trained, new employees would have to be

hired and on-boarded, and organizational equipment would need to be purchased as discussed before.

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SIPOC

A SIPOC structure was used to clarify how the overall process should look before we explored additional

six sigma frameworks, tools, and concepts.

We removed information pertaining to the source of supply (South American farms), and instead chose the

Livingston Student Center Dunkin’ Donuts as our supplier. As a group, this would allow us to focus our

process and better find improvement.

We found blends, flavors, enhancers, storage, and equipment to contribute to each customer’s order.

Order collection, payment, preparation, and serving are steps in the process we seek to control.

Coffees, Americanos, Lattes, Macchiatos, Chai, Hot Chocolates, Espressos, and Cappuccinos are order

options that result from the process.

Rutgers students, faculty and staff, parents, and construction workers are customers with access to this

establishment.

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Process Flowchart

The process flow chart for drink orders has two decision variables. The process begins when the employees

receive an order for a drink. Once the order is placed, the staff must communicate to the baristas whether

the order is for here or to go. If the order is to go, it has a label applied and is sent straight to the pouring

phase. Else, the order requirements are handwritten and then sent along to pouring. Next, the employee

must then see if there are enhancers that need to be added. If so, they are added. If not, the cup is placed on

the counter to be served. This process flow chart is depicted above.

The most time consuming portions of the order fulfillment process are when baristas fill out the order and

serve the cup. These are the two phases in which we plan to implement changes.

When customer orders are exchanged from cashier to barista, they occasionally struggle to find cup sizes

which increases order time. In addition, when baristas call order numbers, they have to wait for customers

to retrieve their orders from the counter. Customers may be on their phones or have headphones in during

busy periods. This impairs their ability to recognize when their order is ready.

The time it takes for baristas to fulfill customer orders is guaranteed to shorten with greater efficiency and

training.

VSM

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The value stream map provides a more in-depth view of the SIPOC. The process begins with a demand of

about 800 drink orders per day and a lead time of 5 to 6 minutes per order. This number could vary

depending on the time of day.

Daily orders and monthly forecasts are derived electronically and used to produce an MRP. This is used to

produce the store’s weekly forecast. Once completed, it is then sent electronically to suppliers who make

deliveries once a week to the store location.

Receiving a drink order begins with the customer ordering in line or via the Tapingo app. The Tapingo app

allows customers to buy online and takes 8 seconds. After receiving orders, the blend is poured which takes

another 3 minutes and 35 seconds depending on the type of drink ordered. If enhancers are added, it takes

15 seconds to fulfill the customer’s requirements. The cup is labeled and sealed which takes 3 seconds.

Lastly, the cup is served which takes 10 seconds. The total cycle time is 4 minutes and 11 seconds. The

total value-added time is 3 minutes and 56 seconds.

The discrepancy between the total cycle time and the value-added time are mainly due to two factors;

pouring and serving. After receiving a drink order, a cup is picked up and customer requirements are written

on it. However, baristas may pick the wrong cup or fill the wrong requirements because the order is being

relayed to them by the cashier. Orders are not given directly from the customer, which can cause defective

orders from incorrect cup sizes.

A customer may also not hear their order number once the drink is served, which prolongs order time.

Measurements and Data Collection

Prior to collecting data, we wanted to select one quantitative variable and one qualitative variable to see if

there would be an interaction or correlation between the two. Collectively, we chose to measure overall

service time and customer drink type to explain why customer order times increased.

Our intent for collecting data was to observe how the store would process and prepare orders when there

were high volumes of customers joining the queue. This was where we believed inefficiencies would be

most glaring and we could identify and narrow our critical X’s.

We collected data during the lunch period from 12:00-1:00 pm. Predictably, students had just released from

their 10:20 am classes and were most in need of a pick-me-up.

During this time, thirty-four orders were sampled using a simple random approach. Each customer order

was given an equal chance to be selected.

Additional information regarding the experiment can be seen in the visual below.

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Fishbone Diagram

Upon collecting data, there were apparent factors that made the time to process customer drink orders

longer.

1. Number of Members on Duty

An overutilization of staff members increased customer order times.

Five members were staffed when most store chains traditionally opted for two or three. The roles and count

in the Livingston outlet were:

Role Responsibility # Staffed

Cook ● Prepared and packaged breakfast sandwiches

● Collected donuts and munchkins

1 1

Cashier ● Fielded orders once customers reached the front of

the queue

● Processed payment

● Communicated order, cup size, and additional specs

to baristas

1 1

Barista ● Prepared drink orders

● Called order number once orders were complete

● Placed orders at counter for pickup

2 2

“floater” ● Fielded orders for customers before they reached the

front of the queue

● Communicated order, cup size, and additional specs

to cashier

1 1

We found the staff to exhibit motion waste because there was an excess of people behind the counter.

Baristas tended to bump into other baristas or the “floater”. This was prevalent because their role required

them to move from the back, where the machines were placed, to the front of the kiosk, where orders were

delivered for customers to pick up.

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In addition, baristas moved more carelessly when transporting cold drinks because they did not want to

burn their arms or fingers transporting hot ones. While we did not collect data by counting the number of

defects, this was a considerable observation given the critical X.

2. Absence of a Second “floater”

The absence of a second “floater” increased customer order times.

”Floaters” were responsible for capturing order requirements before customers reached the front of the line.

When the “floater” took a mandatory lunch break after sample 14, there was a gross increase in customer

order times.

Orders took 31 seconds to fulfill before the “floater’s” absence (samples 1-14) and 114 seconds to fulfill

during the “floater’s” absence (samples 15-21).

This was found to be our most critical X, which we will discuss later in the ‘Solutions’ and ‘Implementation’

sections of our report.

3. Transfer of Cold Brews

The time taken by staff members to transfer cold brews increased customer order times.

The staff would refrigerate iced coffee overnight because it has an eight-hour shelf life. Only when needed,

the staff takes the brew out of the refrigerator and places it on the shelf.

After sample 19, there was a series of consecutive cold drink orders where the store operated on limited

supply and needed to retrieve more brew from the back. The customer order time during this interval fell

above the cold drink average time.

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Customer Order Time

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Customer Order Times Before and During “Floater” Absence

Before During

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Orders took 150 seconds to fulfill during the transfer compared to 42 seconds to fulfill on average.

Though not directly a critical X, this factor is a key component of our recommendation to create or rework

documents for how to perform processes. Cup placement and determining what is a small vs. a big line

magnify this recommendation.

4. Customer Attentiveness

Once orders were placed to the cashier, a diminished customer attentiveness increased customer order times

because they did not promptly collect their drinks.

This was evident from samples 15 to 21, when the absence of a second “floater” and a noted inefficiency

to transfer cold brews made customers text, look in the other direction, or listen to music.

The volume of noise in the auditorium contributed to customers not hearing orders being called out by the

baristas. Sbarro’s and the Rock Café are adjacent to the Dunkin’ Donuts outlet.

While this does falls out of the staff’s control, it is important to consider, as a simple process improvement

could remedy this.

5. Cup Placement and Order Customization

A barista’s ability to identify cup sizes and customize orders served to increase customer order times.

The cups and lids were scattered in a way where the baristas did not know where to find them.

If a customer requested to customize an order by adding caramel, whipped cream, or another drink

enhancer, the baristas took longer to assemble the order. While customization is proven to be beneficial in

satisfying the customer, it is not ideal for establishments that mass produce.

While we feel we should have collected data for the order size, the variable was difficult to capture due to

the amount of noise circulating the auditorium.

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Customer Order Time

# o

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sCustomer Order Times During Transfer of Supply

(Compared to Cold Drink Average)

Average During

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6. Determining What is a Small vs. a Big Line

Similar to variable 2, the store manager’s decision to remove the “floater” during critical working hours

increased customer order times.

There needs to be a standard for what to do when the line reaches a certain capacity. This factors into our

recommendation to create or rework process documents.

Solution Prioritization Matrix

Initially, to brainstorm solutions, we found reversing the problem to be beneficial by asking how we can

theoretically dent the business.

We identified a lack of “floaters” and process performance standards as main inhibitors. A second “floater,”

thorough process audit, or pilot employee training program would serve to mitigate these risks.

Once brainstorming was complete, we identified criteria that would help us narrow suggestions to the one

best solution.

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Criteria Description Cumulative or Multivoting Weight

Impact ● Effect the solution has on the business 1.15

Cost ● Capital required to implement solution 0.75

Ease to Follow ● Repeatability of a process 0.40

Agreeability ● Alignment to a process as it relates to a to

the company’s vision

0.35

Ease to

Implement ● Success of process adoption in lowering

resistance to change management

0.35

Each member evaluated the solutions and criteria on a 1 to 5 scale (1 being very low and 5 being high).

After summing our results, the addition of a second “floater” proved most viable because the decision is a

talent and not a theoretical investment. This would be easy to implement because there is no significant

overhaul in what the company needs to do. A focused training on the new hire would be enough to suffice.

While the addition of a second “floater” contradicts the critical X of having excess staff on-duty, we believe

it is more important to test if the action will improve the process first before pursuing recourse.

An extensive training program and process examination should still be done in conjunction with the fix if

our recommendation does not meet requirements.

Implementation and Expected Outcome

It is in our best interest to speak with the store manager, Stephanie Roguso, to propose the addition of a

second “floater”.

With the information we have compiled regarding root causes and solutions, she could use this knowledge

to hold a staff meeting to clarify her expectations (i.e. quicker service) and monitor results to see if the order

times improve.

Our group would work with her and the team in a consulting capacity until the process remains in-control.

How the Process Will Remain “In-Control”

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Performance Frequency of Occurrence (%)

Good 64.71%

Above Average 14.71%

Average 5.88%

Below Average 2.94%

Poor 11.76%

Very Poor 2.94%

An accurate assessment of the current state is critical to forecast the future state.

To better understand the current state, we assigned our own standard of weighting to categorize customer

order times.

● Orders fulfilled between 0 and 30 seconds were denoted good

● Orders fulfilled between 30 and 60 seconds were denoted above average

● Orders fulfilled between 60 and 90 seconds were denoted average

● Orders fulfilled between 90 and 120 seconds were denoted below average

● Orders fulfilled between 120 and 150 seconds were denoted poor

● Orders fulfilled between 150 and 180 seconds were denoted very poor

Nearly 79.5% of orders processed were good or above average, while 14.7% of orders processed were poor

or very poor. In addition, our I-MR chart indicates considerable variation in customer order times between

consecutive samples. There are four instances where the time to fulfill customer orders exceeds the upper

control limit.

For the future state of this process, there needs to be agreement upon performance targets within each

segment. These can satisfy one or both extremities.

● Greater than 80% of orders must be ‘Good.’

● Less than 5% of orders must be ‘Poor’ and ‘Very Poor.’

If the firm does not achieve its targets, they should consider auditing their processes. This would entail

examining any process flows, job aids, standard work documents, business policies, and SOPs deemed

relevant to the business. If none exist, they would need to be created.

A thorough process audit could be the foundation for a training program that would show and affirm how

each process is done. Order times would likely stabilize as the processes become more routine.

If these options fail, a change in management is still not advisable because it adversely affects the work

environment and culture. The firm should find a way to satisfy its mission to insure the highest level of

quality product for its customers and the best possible value for their money.

Additional Outside Sources

http://templatelab.com/raci-chart/

http://www.dunkindonuts.sa/English/AboutDunkinDonuts/Pages/Mission.aspx