Systematizing In-Store Traffic and Minimization of Service Quality Gaps of a Retail Store through SERVQUAL (Yutuc, 2018)

Running Head: SERVICE QUALITY AND IN-STORE TRAFFIC IN RETAIL
SYSTEMATIZING IN-STORE TRAFFIC AND MINIMIZATION OF SERVICE QUALITY
GAPS OF A RETAIL STORE THROUGH SERVQUAL
A Research Proposal
Presented to the Faculty of the
Department of Industrial Engineering
School of Engineering and Architecture
Holy Angel University
In Partial Fulfillment of
The Requirements for the Degree of
Bachelor of Science in Industrial Engineering
Angelo T. Yutuc

SERVICE QUALITY AND IN-STORE TRAFFIC IN RETAIL
Acknowledgment
I painstakingly finished this research paper as an offering to the Almighty, who never failed a
single second to bless me with the wisdom and will that are essential in my studies.
To my Family in Pandacaqui; friends, classmates and supportive teachers at Holy Angel
University; considerate colleagues at United Parcel Services – Clark; staff of Victa Mart; and all
that supported and nourished me intellectually and emotionally in doing this research.
Thank you.
To my adviser, Hazel Jane Canilao, for the patience, trust and knowledge shared.
To the oral defense panel, Maria Elena Timbang and Ruselle Andrew Manalang.
To the entire faculty of the Industrial Engineering Program of Holy Angel University.
Thank you.
To all the forces, internal and external; human and abstract; constructive and pernicious,
that brought me to where I am today.
Thank you.
Angelo T. Yutuc

Table of Contents
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
List of Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Problem Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
General and Specific Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Purpose/Significance of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Sample Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Survey Dispersion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Participants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
ServQual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
ServQual Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Product Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Shopping Cart Redesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Baseline Service Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Root-Cause Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Root-Cause Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
In-Store Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Redesigning the Shopping Cart . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Comparative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Decision Matrix for Cart Selection . . . . . . . . . . . . . . . . . . . . . . . . . 51
Implementation Outcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Post-implementation RATER Survey . . . . . . . . . . . . . . . . . . . . . . . . 51

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Letter to the Thesis Company
Certificate of Proofread
Certificate of Plagiarism Scan
Researcher's Resume

List of Figures
Figure 1 : Victa Mart Store Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Figure 2 : Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Figure 3 : Model of Service Quality Gaps (Parasuraman, 1988) . . . . 18
Figure 4 : Gap score per dimensions . . . . . . . . . . . . . . . . . . . . . . . . . 28
Figure 5 : Gap Score Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Figure 6 : Victa Mart Traffic Flow in Store Area . . . . . . . . . . . . . . . 33
Figure 7 : Pareto Chart of In-store Traffic Disruptors . . . . . . . . . . . . 35
Figure 8 : Current In-store Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Figure 9 : Simulated Desired In-store Traffic . . . . . . . . . . . . . . . . . . 41
Figure 10 : Shopping Cart used at Victa Mart . . . . . . . . . . . . . . . . . . . 42
Figure 11 : Shopping Cart Specification by Omcan Machinery . . . . . 43
Figure 12 : 3D CAD Drawing of the Redesigned Cart . . . . . . . . . . . . 44
Figure 13 :
The Front, Side, Top views in 3D drawing of the
Prototype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Figure 14 : Actual Prototype (Side, Front, Rear Views) . . . . . . . . . . . 46
Figure 15 : Figure 15: 2-Tier Basket Trolley. . . . . . . . . . . . . . . . . . . . 47

List of Tables
Figure 1 : Victa Mart Store Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Figure 2 : Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Figure 3 : Model of Service Quality Gaps (Parasuraman et al., 1988) . . . 18
Figure 4 : Gap score per dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Figure 5 : Gap Score Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Figure 6 : Victa Mart Traffic Flow in Store Area . . . . . . . . . . . . . . . . . . . 33
Figure 7 : Pareto Chart of In-store Traffic Disruptors . . . . . . . . . . . . . . . . 35
Figure 8 : Current In-store Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Figure 9 : Simulated Desired In-store Traffic. . . . . . . . . . . . . . . . . . . . . . 41
Figure 10 : Shopping Cart used at Victa Mart. . . . . . . . . . . . . . . . . . . . . . . 42
Figure 11 : Shopping Cart Specification by Omcan Machinery . . . . . . . . . 43
Figure 12 : 3D CAD Drawing of the Redesigned Cart . . . . . . . . . . . . . . . . 44
Figure 13 : The Front, Side, Top views in 3D drawing of the Prototype . . 45
Figure 14 : Actual Folded Prototype (Side, Front, Rear Views) . . . . . . . . . 46
Figure 15 : Actual Prototype (Side, Front, Rear Views) . . . . . . . . . . . . . . . 46
Figure 16 : 2-Tier Basket Trolley. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

List of Appendices
Appendix A : Planogram Instruction Manual . . . . . . . . . . . . . . . . . . . . . . 61
Appendix B : DTI Business Registration Online Validation . . . . . . . . . . 83
Appendix C : Victa Mart Store Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Appendix D : Victa Mart Traffic Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Appendix E : Model of Service Quality Gaps . . . . . . . . . . . . . . . . . . . . . 86
Appendix F : Rater Survey Questionnaire (English Version) . . . . . . . . . . 87
Appendix G : Rater Survey Questionnaire (Filipino Version) . . . . . . . . . . 88
Appendix H : Preliminary Consumer Survey Result . . . . . . . . . . . . . . . . 89
Appendix I : Pre-Study RATER Survey Results: Expectation Score . . . . 90
Appendix J : Pre-Study RATER Survey Results: Perception Score . . . . . 91
Appendix K :
Post-Implementation RATER Survey Results: Expectation
Score. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
Appendix L : Post-Implementation RATER Survey: Perception Score. . . 93
Appendix M : Summary of Pre-Study RATER Survey. . . . . . . . . . . . . . . 94
Appendix N : Summary of Post-Implementation RATER Survey . . . . . . 95
Appendix O : Likert-Scale Result Comparison . . . . . . . . . . . . . . . . . . . . . 96
Appendix P : SERVQUAL Mathematical Model . . . . . . . . . . . . . . . . . . 97
Appendix Q : Service Quality Gap Computation . . . . . . . . . . . . . . . . . . 99
Appendix R : Product Category Closeness Rating . . . . . . . . . . . . . . . . . . 100

1
Abstract
Service Quality is broadly defined as how well a service
is delivered. Specifically, it is considered a critical determinant
of competitiveness. In the retail industry, the quality of service is
often left to the marketing and sales perspective, often leaving the
science behind other factors that are known to drive the level of
competitiveness. Thus, an integration of the technical approach
of engineering with a traditional quality management framework
built in this study offered a new perspective in optimizing the
level of service quality. This study utilizes SERVQUAL, a
quality management framework that measures the level of service
quality of the subject community retail store in five quality
dimensions: reliability, assurance, tangibility, empathy, and
responsiveness. SERVQUAL is a multi-dimensional research
instrument, designed to capture consumer expectations and
perceptions of a service along the five dimensions that are
believed to represent service quality (Parasuraman et al., 1988).
The result is a highly technical yet traditionally-modelled
improvement proposal targeting both the systematization of the
retail entity’s in-store traffic and reduction of service quality
insufficiencies.
Keywords: retail store, service quality, ServQual, in-store
traffic

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Systematizing In-Store Traffic and Minimization of Service Quality Gaps of a Retail Store
through ServQual
Despite the internet communication boom resulting to easy and convenient remote shopping
through online sellers and markets, brick-and-mortar in-store grocery shopping has consistently
attracted sufficient foot traffic to remain afloat and thriving, business- and operational-wise. In
the Philippines, businesses related to shopping include convenience stores, grocery stores, and
supermarkets.
The Philippine Statistics Authority [PSA] publishes the Annual Survey of Philippine
Business in Industry. In 2006, it classifies grocery stores in the wholesale and retail trade
business sector which, in 2003, ranked as second in most number of business establishments in
the Philippines at 4,317 establishments comprising 21% of all businesses currently operating in
the Philippines as (PSA, 2006. With this figure and by observation, brick-and-mortar shopping is
a fundamental economic force that involves the participation of the buying public and store
operators, as well as merchandise suppliers, product manufacturers, and other related business
functions. In a local community setup, grocery stores are businesses that sell products to a
specific and limitedly-define geographical market with product offerings ranging from canned
products, fresh produce, and processed food items, household miscellaneous, and other
commodities needed in a day-to-day living of a person or household. As such, there is a strong
need to make every shopping experience an easy, convenient, and efficient one.
Pyle (1926) outlined the three principles that must be obeyed to draw a positive and
productive experience to buyers, while also improving the operational efficiency of a retail
store. The following principles are: (1) Principle of Convenience, (2) Principle of Circulation,
and (3) Principle of Coordination.

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The study of Pyle (1926) determined the following:
In the Principle of Convenience, the space layout, product placement, queue
management, and other service factors must make it certain that the buyer can navigate
through the grocery establishments with as easy as possible, free from hassles and
inconveniences such as prolonged wait time, excessive length of in-store travel,
misidentification of prices and quantities for displayed products. These problems arise
due to several factors.
Queensland (2016) states that “an effective store layout directs customers to where they should
go, generates interest and can potentially create additional sales. Successful store designs use
layouts and floor plans that encourage customers to walk past a high volume of products, keep
browsing and buy the products”. However, this may not always stand true generally because
local community buyers maintain a specific list of products to buy or constrained budgets that
limits the unplanned spending on items that are not originally part of the intended items to buy.
There has to be a balance between the two to generate convenience. On the part of the business,
it is important to ensure that the limited space, especially that the local grocery in Barangay
Pandacaqui is relatively small compared to commercialized supermarkets, but adequate to
accommodate a local community, will not be detrimental in keeping sales high and the
operation efficient.
Pyle (1926) further elaborated that in the Principle of Circulation, “buyers are encouraged to
circulate within the area in order to generate more sales due to unplanned spending or the act of
buying items that are not originally part of the shopping list (physical or mental)”. The study by
Hui, Inman, Yuang, and Suher (2012) discovers that “the elasticity of unplanned spending on
travel distance is 57% higher than the uncorrected ordinary least squares estimate. Simulations

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based on the authors’ estimates suggest that strategically promoting three product categories
through mobile promotion could increase unplanned spending by 16.1%, compared with the
estimated effect of a benchmark strategy based on relocating three destination categories
(7.2%)”. However, the effect of this principle in a small-scale grocery store could result to more
people crowding within the limited space (about 250 square meters).
The last is Principle of Coordination which essentially encourages the synergy between
products in making more value is by putting products that are complimentary next to each other,
or at least close enough that the buyer can easily spot them and associate them together. Layout
algorithms of shelves and product containers can systematize this by identifying correlations
between products in terms of purchase frequency.
1.1. The Effects of In-store Designs on Consumers
1.1.1. Number of Facings/ Product Elasticity
According to Eisend (2014), the effectiveness of shelf design is often determined in
terms of shelf space elasticity. This elasticity is a parameter that indicates to what
extend additional shelf space has influence on product sales. Desmet and Renaudin
(1998) elaborates that in their research on the influence of shelf space allocation on
products’ sales, some important conclusions on shelf space elasticity were discovered.
Desmet and Renaudin (1998) found the following:
The type of product purchase influences the effect of shelf space allocated to a
particular product. Shelf space allocation is most effective on impulse purchases,
which means that shelf space has a causal effect on sales. In addition to this
conclusion, the amount of space given to a particular product in relation to the

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product category within the whole shelf gives a positive effect to the products’
sales.
1.1.2. Product placement on shelf
Elbers (2016) states that another way in which retailers can increase their sales on
products is to provide attractive shelf displays. He proposes that “in this section
(product placement on shelf), there will be a closer look to which factors are
considered when retailers have to determine the ideal position within shelves for their
products. The four different characteristics of product placement on shelves: (1)
horizontal positioning; (2) vertical positioning; (3) product adjacencies and (4)
category arrangement”.
To further understand the four different characters of product placement on
shelves, several literatures have been studied and the explanations are as follows:
1.1.2.1. Horizontal positioning
The research of Valenzuela, et al. (2013) details that “consumers consider
products that are placed in the center of a shelf as the most popular ones”. While a
study of Sorensen (2005) concludes that “products placed at the end of shelves are
given more so-called face time than products placed more centrally”. This means
that products at the horizontal extremes of shelves attract far more attention of
consumers than products placed more in the middle of the shelves. On top of that,
Sorensen (2005) argues that “when familiar products are placed at the end of a
shelf, this results in far more traffic in those specific paths”. Another advantage of
products placed at the horizontal extremes of a shelf, according to Van Nierop, et
al. (2008), is the “ease with which products in those places are more easily

6
reached when consumers come from the main aisles”. Considering these facts,
Chandon et al. (2009) revealed that “products that are placed at the center of a
shelf are more likely to be noticed, and that this position helps the products’
sales”.
1.1.2.2. Vertical positioning
According to Raghubir and Valenzuela (2008), “the effects of vertical product
positioning on shelves are much stronger than the effects of horizontal product
placement”. This statement is strengthened by the research of Hansen, et al.
(2010); in their research on retail shelf allocation, they conclude that “vertical
location effects have twice more impact on sales than horizontal shelf lengths”.
Furthermore, based on the study of Van Nierop, et al. (2008), when determining
the best vertical location for the product, the eye-level is the most effective
location for product placement. The conclusion was further justified by
Sigurdsson, et al. (2009) who states that this might be the case “due to the fact
that products placed at eye-level are seen with less far less effort than products
placed on the vertical extremes of a shelf”.
There are several ways in which retailers can influence the consumers’
perception of products using vertical product placement. Raghubir and Valenzuela
(2008) lead a research on the optimal arrangement of products in a particular shelf
that concludes that “when retailers want their products to be considered cheap, the
best place for their products is at the bottom of the shelf, and luxury products are
perceived to be on top of the shelves”.

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1.1.2.3. Product adjacencies
Influencing consumers to buy particular products is can be achieved efficiently by
structuring product adjacencies within shelves. To illustrate; a research of Chen et
al. (2006), came up with the fact that:
Retailers can improve purchases by up to 70% by using visual product
adjacency. They state that retailers currently are not fully aware of the fact
that product adjacency can improve combined purchases by carefully
putting products side-by-side on shelves. One way in which product
adjacencies can influence product sales is by the way in which consumers
perceive products presented next to each other.
To illustrate futher, in their research on brand equity dilution, Buchanan, et al.
(1999) come up with some interesting findings on product perception based on
adjacency:
Their research was emphasized on the effect of display conditions on the
consumers’ perception of products, divided in two products: high-equity
brands and unfamiliar brands. Out of their research results, they conclude
that there are some ways in which the consumers’ expectations can have
implications for both the high-equity brands as the unfamiliar brand. They
state that the way in which consumer perceive a certain product, is
influenced by the way the products are presented on the shelves, in
relation to other products. When, for example a high-equity product and
an unfamiliar product are placed within the same shelf, there are several
factors that can determine how the consumers’ pre-existing product

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evaluation will be affected; such as price and package design differences
between the two products. From the high-equity product perspective, it is
undesirable to be compared with unfamiliar brands. In order to be
dissimilar to the unfamiliar option, Buchanan, et al. (1999) state that the
high-equity brand should both:
a) “…be the preceded choice option above the unfamiliar
product.”
b) “…not be placed in a way that the unfamiliar product is easily
compared with the high equity brand product.”
1.1.2.4. Category arrangement
Mogilner, et al.(2008) found out in their research on the ‘mere categorization
effect’ that the “number of categories provided by retailers within shelves have a
positive influence on the overall consumer satisfaction”. The declaration
presupposes that greater amount of categories on shelves influences both the
“consumers’ perception of variety, as well the evaluation on the choice they have
made”. Morales, et al. (2005) elaborates that “aside from expanding the number
of product categories, another way to assess consumer satisfaction is to provide a
store layout that is congruent to a consumers’ internal product structuring”. These
internal product structuring schemas help consumers to avoid losing track on all
different product categories provided in supermarkets (Alba and Hutchinson,
1987). According to Stayman et al. (1992), retailers can use these consumer
schemas. They state that when retailers conform their product arrangement to the
internal schema of consumers, it becomes more convenient for consumers to

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internally process the shelves, which leads to both a greater consumer satisfaction
and positive affection with the assortment.
The related literatures on shelf space optimization, product placement categorization, and factors
that heighten the impulse buying tendency of consumers have been adopted to form the service
quality of the retail store subject: Victa Mart.
Victa Mart, a community grocery store that employs 10 people and serves as one of the
major retailer in Barangay Pandacaqui, has been operating for six (6) years. With a total floor
area of 300 sq. m. allocated for storage, administrative office, and general store area, it is too
compact resulting to unmanaged in-store traffic and poor product placement and replenishment.
Figure 1: Victa Mart Store Area
Figure 1 illustrates the store layout. The selling area is limited in terms of floor area and faces
challenges in accommodating the community buyers of Pandacaqui. There is no area to expand
so the next best solution is to organize the store from within.

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With limited space to accommodate inventories, products on sale, and customer traffic, the
following challenges were observed:
1. Narrow walkways. The 1-meter walkways in between facing shelves can accommodate
two persons walking side-by-side just fine, but when one or the other brings a shopping
cart it becomes difficult to navigate with one person needing to adjust in order to give
way.
2. Shopping carts are bulky and are difficult to maneuver in narrow space. The shopping
carts available for use when purchasing heavy or large products are similar to those in
big supermarkets. These carts are good for use when there is sufficient space but in the
case of the community grocery such as Victa Mart, they are difficult to maneuver.
3. In-store traffic is not systematic. The flow of traffic within the store has consistently
become a cause of dissatisfaction for consumers. While the buyers have gotten used to
this routine over time, it can be heard from their comments that they want it better.
4. Product Placement. The inter-shelves and intra-shelf product placement can be
improved by positioning the right product type and quantity to the right position in the
shelf.

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This research’s main objective is to measure the level of service quality and review the
systematical challenges in the operation of a community grocery store in Barangay
Pandacaqui, Mexico, Pampanga that affect the buyer’s perception of quality through
their in-store experiences. The study also discovers the underlying factors that affect the
store’s operational efficiencies. The identified causes are evaluated and analyzed to
create a recommendation that will improve the store’s reliability, assurance, tangibility,
empathy, and responsiveness factors of service quality.
The specific objectives are:
1. To evaluate the store’s service quality and to determine the gap score between the
customer’s perception and expectations in the dimensions of reliability, assurance,
tangibility, empathy, and responsiveness using SERVQUAL
2. To redesign the shopping cart that is efficient and convenient in maneuvering through
narrow walkways, reduces bending over to pick up items at the bottom of the cart/basket,
segregates items in an organized manner, and prevents stacking of individual products
over another
3. To create a system that optimizes product placement and shelf space optimization in
order to systematize in-store traffic, drive impulse buying among customer, and
implicate the principles of convenience, coordination, and circulation.

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Figure 2: Conceptual Framework

13
The conceptual framework in Figure 2 links the cause-and-effect of the dependent and
independent variables that are likely to affect the systematization of consumer traffic, moreover
the benefits of said outcome:
a) The five (5) dimensions of Service Quality namely: Reliability, Assurance, Tangibility,
Empathy, and Responsiveness will be assessed to determine the areas of improvement
with the highest gap between the customer’s perceived and expected level of service
quality
b) Redesigning the shopping cart to fit the narrow walkways is essential in making the flow
of in-store traffic smooth and uninterrupted. The design also intends to put less pressure
on the user by reducing or eliminating the bending of the body when reaching out to
products at the bottom of the basket/case. This is especially helpful when dealing with
heavy or bulky items. The cart also improves stacking of commodities by allowing
partitions.
c) Planogram is a system and a visual plan which designates the placement of products on
the shelves. It is a store plan to optimize allocation of products within the shelf and
distribute the product categories in a strategic position within the store that will
systematize the in-store traffic.

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Ultimately, the study aims at systematizing the in-store consumer traffic and achieve most or
all of the following outcomes:
a) Higher convenience and satisfaction from consumers means that there is a strong
likelihood that they will return for their grocery needs. There is a tight competition
among retail stores in a local community due to the nearness in which they are situated
and the similarity of services and product offerings where a customer might not need
to weigh too many considerations and just choose to buy at one nearest them, not to
mention the threats of chain stores that are able to sell products at much lower cost due
to their focus on volume while still being accessible just outside the community.
Therefore, a convenient way or buying products will be an advantage in the
competition.
b) A situation where both the store benefits through profit while the customer also
benefits through convenience and positive store experience.
c) Improve the store sales by positioning the right product to the appropriate shelf
level (intra-shelf), and the right product category to the appropriate shelf location
(inter-shelf). According to Chavosh (2011) and Soeseno (2010), “a person who
has high characteristic of shopping enjoyment tends to perform in-store
browsing longer and is then expected to feel stronger urge to make impulsive
buying”. Thus, offering an enjoyable in-store experience may offer added sales
benefit to the business through impulse buying.

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With several retail stores varying in size and portfolio available in a local community, the
researcher limits the study to an independent, non-chain, brick-and-mortar retail grocery
store that has a minimum of 10 employees. Mid-range grocery stores and minimarts
(small supermarket) can benefit from the study, but not counter stores (aka sari-sari
stores) or stores that do not let consumer choose and pick their own products. This study
covers a three-month period from December 2017 to March of 2018 but skips the “peak
season” from the third week of December 2017 to the second week of January 2018
where the data on customer volume, sales, and in-store traffic tend to skew upwards due
to the seasonal demands, which could disfigure the normal data on non-peak months.

16
2. Methods
2.1. Data Collection
To gain empirical knowledge of the customer retail store setup that is essential for data analysis,
collection of first-hand information was carried out through a systematic questionnaire designed
after the Gap Model of Service Quality or SERVQUAL, developed by Parasuraman, et al.
(1991).
2.1.1. Survey
The sample size was calculated using Slovin’s formula:
n =
N
1+Ne2
; where n is the sample size; N is the population; and e is the margin of error
This random sampling technique identifies the sample population when the population does
not follow a normal distribution pattern (Howard, 2015), such as measuring perceptive
quality of the store experience at Victa Mart.
In order to satisfy Slovin’s formula in calculating the sample size, the base population
was identified via counting the number of store visits on a daily and hourly basis. This was
done through counting the number of incoming persons via the closed circuit camera
positioned at the door way within a week’s time. The videos were played with adjusted speed
to maximize time
2.1.2. Sample Size
Formula:
n =
N
1+Ne2
; where n is the sample size; N is the population; and e is the margin of error
Note: the confidence level is set to 95%, which, according to Taylor (2012) can be used
as standard confidence level for base population less than or equal to one hundred
(N≤100)

17
Computation:
n =
67
1+(67)(0.05)2 = 57.39 ~58 respondents
2.1.3. Survey Dispersion
After determining the sample size of 58 respondents, a reduction ratio was used to match
the survey respondents with the hourly interval average entry. A reduction ratio of 0.47
was calculated by taking the ratio of the sample size against the average hourly entry
(58/124).
Table 1
Survey Dispersion
Hourly Interval Hourly Total Reduction Ratio Distribution
From To 47%
7:00 AM 8:00 AM 5 3
20
8:00 AM 9:00 AM 7 3
9:00 AM 10:00 AM 11 5
10:00 AM 11:00 AM 12 6
11:00 AM 12:00 PM 7 3
12:00 PM 1:00 PM 4 2
16
1:00 PM 2:00 PM 4 2
2:00 PM 3:00 PM 8 4
3:00 PM 4:00 PM 18 8
4:00 PM 5:00 PM 16 8
30
5:00 PM 6:00 PM 16 7
6:00 PM 7:00 PM 9 4
7:00 PM 8:00 PM 7 3
Total 124 58 58
Table 1 identifies the ideal number of survey respondents assigned to every hourly
interval but since distributing a certain number of survey forms every hour is impractical,
it was re-grouped to morning, afternoon, and evening batches with the corresponding
survey quantity.

18
2.2. Participants
The store manager was involved in the study due to her clerical and administrative
knowledge of the entire operations and day-to-day business activities. The counter staff and
the store crew helped in the observation and data collection for the inventory and consumer
traffic, as well as participated in the interview. Customers who are both frequent and new to
the store were involved in the survey process to measure the level of perceived quality of the
store vis-à-vis the quality expectation.
2.3. Procedure
2.3.1. SERVQUAL
Figure 3: Model of service quality gaps. Adopted from “Refinement and Reassessment of the
SERVQUAL scale”, by Parasuraman, Berry, and Zeithaml (1988). Retrieved from
http://www.dbmarketing.com/articles/Art183.htm

19
Parasuraman (1988) explains that:
The psychometric properties of the SERVQUAL scale have been the subject of
considerable research in recent times. The scale was developed from an initial
pool of 97 items generated through a series of focus group sessions conducted
with consumers. Figure 3 illustrates the Conceptual Model of SERVQUAL,
identifying five (5) gaps between the customer’s perceived service level and
expected service level.
The questionnaire is composed of five question categories known as the RATER Scale –
Reliability, Assurance, Tangibility, Empathy, and Responsiveness. Each category is
further composed of 3-5 Likert-scale type of questions rated from 1 to 7. The questions
add up to a total of 22 statements. The first set of 22 questions intend to measure the
customer’s perception of excellent service. The second set of 22 questions measure the
customer’s perceived quality or rating of the store.
According to Brown and Bond (1995), "the gap model is one of the best received
and most heuristically valuable contributions to the services literature".
The model identifies five key discrepancies or gaps relating to managerial
perceptions of service quality, and tasks associated with service delivery to customers.
The five gaps are identified as functions of the way in which service is delivered. In the
following, the SERVQUAL approach is demonstrated:
Gap 1: Consumer expectation-management perception gap, which is the gap
between consumer expectations of service quality and management perceptions of
these expectations

20
Gap 2: Management perception-service quality perception gap, that is, the gap
between management perceptions of consumer expectations and the firm's service
quality specifications
Gap 3: Service quality specifications-service delivery gap, the gap between
service quality specifications and actual service quality.
Gap 4: Service delivery-external communications gap, or the gap between actual
service delivery and external communications about the service
Gap 5: Expected service-perceived service gap, which is the gap between
expected service and perceived service
A total of 44 statements were created to measure the level of customer’s perception of
quality in relation to their expected level of quality. Table 2 categorizes the questions into
five main categories known as the RATER Model, a mnemonic for Reliability,
Assurance, Tangibility, Empathy, and Responsiveness.

21
Table 2
SERVQUAL Statements
RELIABILITY [RE
]
Code Expectation Statements Perception Statements
Q1RE 1. A reliable store has all the products that I
need
1. Victa Mart has all the products that I need
Q2RE 2. A reliable store has prices displayed accurately and
up-to-date
2. Victa Mart displays prices accurately and up-to-
date
Q3RE 3. A reliable store should have shelves where
products can be easily located
3. Victa Mart makes it easy to locate products in
shelves
Q4RE 4. A reliable store should not make me check
every shelves to find the product that I need
4. Victa Mart does not make me check every
shelves just to find the products I need
Q5RE 5. A reliable store should not made me wait too
long or inconveniently at checkout counters
5. Victa Mart does not make me wait long or
inconveniently at checkout counters
ASSURANCE [AS
]
Q6AS 1. I am assured when the store experience is not
too time-consuming
1. I can save time when shopping at Victa Mart
compared to other stores
Q7 AS 2. I am assured when the products being sold
are of good quality
2. The products at Victa Mart are of good quality
Q8 AS 3. I am assured if the price of the products are
not inflated
3. The prices of the products at Victa Mart are
affordable
Q9 AS 4. I should feel safe and comfortable within the
store
4. I feel safe and comfortable at Victa Mart
Q10AS 5. I am assured if the shelves are adequately
replenished to avoid out-of-stocks
5. Shelves at Victa Mart are adequately
replenished so there is no stockout
TANGIBILITY [TA
]
Q11TA 1. An excellent store should have adequate
lighting and ventilation
1. Victa Mart has adequate lighting and ventilation
Q12TA 2. An excellent store should have enough space
to move around
2. Victa Mart has enough space to move around
Q13TA 3. An excellent store should have shopping cart
that is easy to maneuver
3. Victa Mart has shopping carts that are easy to
maneuver
Q14TA 4. An excellent store should have enough check-
out counters
4. Victa Mart has enough check-out counters
Q15TA 5. An excellent store should be organized (e.g.
free from roadblocks, tidy, etc.)
5. Victa Mart is organized (e.g. free from
roadblocks, tidy, etc.)

22
EMPATHY [EM
]
Q16EM 1. An excellent store should operate within hours
most convenient to customers
1. Victa Mart operates within hours most
convenient to customers
Q17EM 2. An excellent store staff should act courteously
and professionally
2. Victa Mart has store staff that are courteous
and professional
Q18EM 3. An excellent store experience should be
hassle-free
3. Victa Mart offers a store experience that is
hassle-free
RESPONSIVENESS [RS
]
Q19RS 1. A responsive store should have an avenue
for and should be open to customer feedback
1. Victa Mart is open to customer feedback
Q20 RS 2. A responsive store should make it easy to
return a product
2. Victa Mart makes it easy to return a product
Q21 RS 3. A responsive store staff should know where
the products are located when asked
3. Victa Mart hires staff who know where the
products are located when asked
Q22 RS 4. A responsive store should make it easy to
make inquiry on staff or with the store manager
4. Victa Mart makes it easy to inquire to their staff
or with the store manager
Adapting the statements into a question that will be easily understood and appreciated by the
survey respondents was considered. A Filipino translation is available to prevent intimidating
or overwhelming the survey participants with the quantity and form of the questions.

23
2.3.2. SERVQUAL Data Processing
Measuring the service quality of the store is computed using the SERVQUAL model
introduced by Parasuraman, et al (1991), with the following formula:
2.3.2.1. SERVQUAL Mathematical Model
SQ =
∑ (𝐸𝑖−𝑃𝑖)𝑖=𝑛
𝑖=1
𝑛 ∗ ∑ (𝐸𝑖)𝑖=𝑛
𝑖=1
∗ 100% (Total Service Quality Gap Score)
SQj =
∑ (𝐸𝑖𝑗−𝑃𝑖𝑗)
𝑖𝑗=𝑛
𝑖𝑗=1
𝑛𝑗 ∗ ∑ (𝐸𝑖𝑗)
𝑖𝑗=𝑛
𝑖𝑗=1
∗ 100% (Service Quality Gap Score per Dimension)
Where SQ is the Total Service Quality score
SQj is the Service Quality score of dimension j
Eij is the Expectation score i for dimension j (expected level of quality)
Pij is the Perception score i for dimension j (perceived level of quality)
i is the statement (individual question)
j is the dimension
n is the maximum count of statements
nj is the maximum count of statements for dimension j
To further represent the data into recognizable information, the following mathematical
model were used to compute analyze the survey data:
2.3.2.2. Total Expectation Score
Computes for the total expectation score by taking the summation of all E where i
is statement; j is dimension; Eij is the Quality Expectation i for dimension j.
∑(𝐸𝑖𝑗)
𝑖=𝑛
𝑖=1

24
2.3.2.3. Total Perception Score
Computes for the total perception score by taking the summation of all P where i
is statement; j is dimension; Pij is the Quality Perception i for dimension j.
∑(𝑃𝑖𝑗)
𝑖=𝑛
𝑖=1
2.3.2.4. Total Gap Score
Computes the gap score or the difference between the desired (expected) versus
the actual (perception) scores.
∑(𝐸𝑖𝑗 − 𝑃𝑖𝑗)
𝑖=𝑛
𝑖=1
2.3.2.5. % Gap
Computes for the ratio of the gap score to the total expectation score where i is
statement; j is dimension; Pij is the Quality Perception i for dimension j; Eij is the
Quality Expectation i for dimension j
∑ (𝐸𝑖𝑗 − 𝑃𝑖𝑗)𝑖=𝑛
𝑖=1
∑ (𝐸𝑖𝑗)𝑖=𝑛
𝑖=1

25
2.3.3. Product Configuration
According to the study of Dreze, et al (1994):
The success of any retailer depends on its ability to match its changing environment
by continually deciding between how much of which products to shelve where and
when. Indeed, the shelf location of products can significantly affect the products,
and thus merchandise category, performance. Thus, retailers benefit by expanding
their focus from product-level performance to the total shelf-space configuration.
Shelf-space configuration deals with the arrangement of products in shelves such
as in what level of the shelf should product A be positioned, in which shelf from
the n number of shelves in the selling area, and so on.
Engilbertsson (2015) expounded that instore buying behavior involves unplanned
purchases which brands should be able to capitalize on with the right placement strategy.
Table 3
The Effects of In-store Designs on Consumers (Elbers, 2016)
Shelf Characteristics Product Sales Product Perception
Product Facing
The more space is given to a
particular product, the higher the
product's sales.
The amount of facings
determines the importance a
retailer assigns to a product.
Horizontal Positioning
Products placed at the extremes
of shelves are perceived to be
discounted. Central position of
product is related to perceived
popularity.
Vertical positioning
eye-level is the most profitable
location
Products placed on lower shelf
parts are expected to be cheap,
products placed on high shelves
are perceived to be expensive.
Product Adjacency
Product adjacencies can improve
product's sales
Product adjacencies influence
the perception of both products
Category
Arrangement
Goal-based product categorization
increases product's sales

26
2.3.4. Shopping Cart Redesign
The shopping carts available for use when purchasing heavy or large products are similar
to those in supermarkets. These carts are good for use when there is sufficient space but
in the case of the community grocery such as Victa Mart, they are difficult to maneuver.
Therefore, a customized cart that attends to the needs of the customers in relation to the
store layout should be adapted.
2.3.4.1. Cart Design Objectives:
1. To take up more vertical space but not impede the user’s vision of the pathway in
front of him/her
2. Maneuverable in an easy way; without swiveling when pushed
3. Allow separation of products e.g. food and cleaning
4. Can be tucked when not in use
2.3.4.2. Cart Design Specifications
1. Taller than wider
2. Wheels are synchronized rather than independent to prevent swiveling
3. Partitions to accommodate the segregation of different products especially food
from non-food

27
3. Results
3.1. Baseline Service Quality
The baseline service quality acts as the comparison point between the pre-study
assessment and post-study results. The information was collected through the RATER
Survey on five quality dimensions (reliability, assurance, tangibility, empathy,
responsiveness) as guided by Parasuraman’s Model of Service Quality (ServQual). Table
4 summarizes the survey results.
Table 4
Survey Results
Code SERVQUAL Statements Expectation Perception Gap
Q1RE
Completeness of products 287 218 69
Q2RE
Accurate display of prices 288 255 33
Q3RE
Ease of locating products inter-shelves 271 235 36
Q4RE
Ease of locating products intra-shelves 272 205 67
Q5RE
Waiting lines at counter 256 214 42
Q6AS
Time consuming 290 228 62
Q7 AS
Product quality 273 236 37
Q8AS
Product price 270 171 99
Q9AS
Safe and comfortable experience 281 199 82
Q10 AS
Product replenishment/stock-out 282 172 110
Q11TA
Adequate lighting and ventilation 265 145 120
Q12 TA
Enough space to move around 277 140 137
Q13 TA
Ease of using the shopping cart 277 198 79
Q14 TA
Sufficiency of checkout counters 284 253 31
Q15 TA
Organized layout (blockades, tidiness) 280 265 15
Q16EM
Hours of operation 279 262 17
Q17 EM
Staff courtesy and professionalism 286 253 33
Q18 EM
Hassle-free store experience 274 214 60
Q19RS
Ease of customer feedback 277 181 96
Q20 RS
Ease of returning a product 269 147 122
Q21 RS
Store staff knowledge and helpfulness 271 233 38
Q22 RS Ease of inquiry (staff and store
manager)
272 174 98

28
In Table 4, as the gap score increases, the level of quality is interpreted as decreasing. Therefore,
the goal is to close the gap (zero gap) or keep it at the lowest possible figure. In an initial survey
of 58 respondents, the Reliability and Tangibility dimensions recorded the widest gap at 39%
and 24% respectively. Table 5 shows the expectation and perception scores of each dimensions.
Table 5
Gap Table
Dimensions Expectation Perception Gap Percentage
Reliability Score 1382 841 541 39%
Assurance Score 1374 1127 247 18%
Tangibility Score 1351 950 401 30%
Empathy Score 833 635 198 24%
Responsiveness Score 1129 1033 96 9%
The survey result suggests that four in every ten customers are dissatisfied with the store’s
Reliability dimension, one in every four in Tangibility dimensions, while one in every five
customers are dissatisfied with the store’s Assurance and Empathy dimensions.
Figure 4: Gap score per dimensions
528
414
247
198
96
0
100
200
300
400
500
600
Reliability Tangibility Assurance Empathy Responsiveness
Gap Score Per Dimension

29
The survey further identifies the difference in gap among the five dimensions of service quality.
Reliability has 541 gap points, Tangibility with 401 gap points, Assurance with 247 gap points,
Empathy with 198 gap points and the lowest is Responsiveness with only 96 gap points.
Table 6
Score Summary
Score Summary
Total Expectation Score (E) 6069
Total Perception Score (P) 4586
Total Gap Score 1483
Gap Rate 24%
Min Gap 15 (Q20 RS
Ease of returning a product)
Max Gap 137 (Q4RE
Ease of locating products inter-shelves)
By plotting the gap score into a Pareto Chart, it is discovered that 48% of dissatisfaction or
quality gaps are resulting from seven (7) determinants out of 22.

30
Figure 5: Gap Score Analysis
The Reliability score is the highest among all five determinants, registering the highest gap of
541 points. The standout dissatisfaction element is the Ease of locating products inter-shelves at
92 points. This can be interpreted as having to go through different shelves to find the product.
Further, it can be associated with the design of the shelf positioning or how the products are
designated in shelves. The Tangibility score is the second highest among all five determinants,
registering gap score of 319 points. This means that customers are second mostly dissatisfied
with the physical aspects of the store, among other criteria. The highest in this criteria is
organized layout (blockages, tidiness) with a gap score of 82 points, also Ease of using the
shopping cart which scored 71 points in the gap scale.
137
122 120
110
99 98 96
82
79
69 67
62 60
42
38 37 36
33 33 31
17
9%
17%
26%
33%
40%
46%
53%
58%
64%
68%
73%
77%
81%
84%
86%
89%
91%
94%
96%
98% 99% 100%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
20
40
60
80
100
120
140
160
R4 T3 R3 R2 E3 T5 T2 R1 R5 A1 A4 E1 T1 A5 T4 E2 A3 A2 RS4 RS1 RS3
Gap Score Analysis
SERVQUAL Score Cumm. Wt

31
3.2. Root-Cause Identification
Further data collection reveals that there are several factors leading to the high service
quality gap on the top 7 statements comprising 50% of the total dissatisfaction score. By
using the survey results in detecting the areas where the service quality is lagging, the
researcher was able to conduct more in-depth investigation to account for the
dissatisfaction scores.
Table 7
Top Dissatisfaction Reasons
Dimension ServQual Statements Gap Score Weight Cum. wt.
Reliability4
Ease of locating products inter-shelves 137 9% 9%
Tangibility3
Ease of using the shopping cart 122 8% 17%
Reliability3
Ease of locating products intra-shelves 120 8% 26%
Reliability2
Accurate display of prices 110 7% 33%
Empathy3
Hassle-free store experience 99 7% 40%
Tangibility5
Organized layout 98 7% 46%
Tangibility2
Enough space to move around 96 6% 53%
This creates the statement that more than fifty per cent of the dissatisfaction comes from thirty
per cent of the causes, identified as:
1. Ease of locating products inter-shelves
2. Ease of using the shopping cart
3. Ease of locating products intra-shelves
4. Accurate display of prices
5. Hassle-free store experience
6. Organized layout (blockades, tidiness, etc.)
7. Enough space to move around

32
Identifying the general factors that are driving the service quality allows for the
determination of the specific factors that contribute to the dissatisfaction of the customers.
Moreover, relating these quality gaps to the performance of the store is a crucial in order to
know if there is relevance to proposing a solution for them in the first place. The results lead
to the determination of three general solution categories that are designed to close the gap
between the customer’s service quality perception and expectations.
Table 8
Gap Criteria Identified Groupings
Ungrouped In-Store Traffic Inter- & Intra-
shelf Configuration
Shopping Cart
Accurate display of
prices
Time consuming Completeness of
products
Ease of using the
shopping cart
Waiting lines at counter Safe and
comfortable
experience
Ease of locating
products inter-
shelves
Hassle-free store
experience
Product quality Enough space to
move around
Ease of locating
products inter-
shelves
Enough space to
move around
Product price Organized layout
(blockades, tidiness,
etc.)
Time consuming
Adequate lighting and
ventilation
Hassle-free store
experience
Organized layout
(blockades, tidiness,
etc.)
Sufficiency of checkout
counters
Hours of operation
Staff courtesy and
professionalism
Ease of customer
feedback
Ease of returning a
product
Store staff knowledge
and helpfulness
Ease of inquiry (staff
and store manager)

33
3.3. Root-Cause Analysis
3.3.1. In-Store Traffic
Elbers (2016) defines Traffic flow as the “movement of customers through the
store. It is a critical aspect of service quality due to the impact that it can have on
customers both practically and psychologically”. A well-designed layout not only
influences the movement of customers through the store, it can also encourage certain
shopping behaviors. For example, according to a research by JSW.org (2010), “a
supermarket may deliberately make the aisles small and crowded to create a feeling of
economy and order. This encourages the customers to move consistently through the
store in an ordered pattern. It may also imply that the store sells many more lines of
product than they actually do”. However, in a community retail store setup,
intentionally designing narrow walkways and crowded spaces could mean negative
experience to buyers and sometimes causes them to turn away and look for a different
store, or if not, lessen the amount of visit to that store.
Figure 6: Victa Mart Traffic Flow in Store Area

34
Figure 6 identifies that the sale area adapts a block layout where shelves are positioned with
equal distances. This reveals the following:
1. There is a single shared entry and exit point coming in and out of the store and in
and out of the sale area
2. Customers have to line up at the counters very closely from the shelves (1.5m)
which can cause traffic when:
a. more than five people without shopping cart are standing in line;
b. more than three people with shopping cart are standing in line.
3. The walkway between shelves is only 1-meter, making it difficult to pass through
when two customers with shopping carts tend to pass simultaneously.
The in-store traffic is often disrupted by factors that are mostly human-driven. Table 9
quantifies these factors in a frequency table while Figure 7 puts them into perspective via a
Pareto Chart which help identify the vital few from the trivial many.
Table 9:
In-store Traffic Disruptors
Code Description Frequency Weight Cumulative Wt.
A Shelf replenishment 15 13.4% 13.4%
B Staff assistance 6 5.4% 18.8%
C Intra-shelf product search 11 9.8% 28.6%
D Inter-shelves product search 32 28.6% 57.1%
E Cart blockade 24 21.4% 78.6%
F Product blockade 1 0.9% 79.5%
G Checkout queue 15 13.4% 92.9%
H Customer pile-up 7 6.3% 99.1%
I Janitorial/repair 1 0.9% 100.0%
Total 112 100%

35
Similar to the identified causes in the RATER survey, there are items falling to any of
the dimensions of responsiveness, assurance, tangibility, empathy, and reliability that
also affect the in-store traffic flow. This result means that there are correlation between
the observed scenarios and the customer’s perceived level of quality
Code Description
A Shelf replenishment
B Staff assistance
C Intra-shelf product search
D Inter-shelves product search
E Cart blockade
F Product blockade
G Checkout queue
H Customer pile-up
I Janitorial/repair
Figure 7: Pareto Chart of In-store Traffic Disruptors
D E A G C H B F I
Weight 28.6% 21.4% 13.4% 13.4% 9.8% 6.3% 5.4% 0.9% 0.9%
Cumulative Wt. 28.6% 50.0% 63.4% 76.8% 86.6% 92.9% 98.2% 99.1% 100.0%
28.6%
21.4%
13.4% 13.4%
9.8%
6.3%
5.4%
0.9% 0.9%
29%
50%
63%
77%
87%
93%
98% 99% 100%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%

36
It is determined that 77% of the traffic disruptors are from four sources, while only 23% is
accounted by the remaining five sources (77/44). The four primary drivers are:
1. Inter-shelves product search. Accounts for 28.6% of the in-store traffic disruptions.
This means that customers who browse through various shelves, moving from one
area of the store to another and stops at the shelf that stocks the right product. When
a customer is shopping with a wide product variety, the tendency to browse inter-
shelves is highly likely.
2. Cart Blockage. Accounts for 21.4% of the observed in-store traffic disruptors and is
mainly cause by the collaboration of bulky cart design and narrow walkways.
3. Shelf Replenishment. Accounts for 13.4% of in-store traffic congestion. This is an
essential activity in order to serve the customers but could lead to blockade if
unplanned or not done systematically.
4. Checkout Queue. Accounts for 13.4% of the in-store traffic. Based on observation,
it is the location of the checkout counters that is causing the blockade.
3.3.1.1. Stock-Keeping Unit (SKU) Configuration
Using Victa Mart’s 12-month average sales data from 2017, the inter-shelfd and
intra-shelf relationship was plotted through sale quantity and sales volume.
3.3.1.2. SKU Categories
The SKU were categorized into the following major groups:
 Bakery/Pastry are bread and other baked products
 Beverages ranges from bottled water, bottled flavor drinks, sodas, tetra-packed
juices, and other ready-to-drink liquids

37
 Candle and Lighting are wax candles, light bulbs, and other peripherals for
lighting
 Candy include sweets such as hard candy, soft candy, marshmallows, chocolate
and chocolate-based candy items, and sweet syrups
 Canned Goods are variety of products in can such as processed meat, canned
drinks, fruits in can, cooking ingredients in can
 Cigarette different brands of tobacco products
 Cleaner ranges from powdered, bar and liquid detergents, fabric conditioners,
dishwashing liquid and pastes, bleaching products and others
 Food Commodity are items that are considered for daily consumption such as rice
and sugar
 Instant are a wide range of ready-to-eat food items, requires few preparation such
as instant coffee, instant noodles, and other packed foods.
 Milk and Dairy are items in can or boxes including powdered milk, fresh/liquid
milk, cheese, and processed milk such as condensed and evaporated milk
 Miscellaneous are products not falling in any specific categories such as batteries
and plastic cups
 Personal Care ranges from face and body cream, soap, and toners; shampoo,
conditioner, lotion, and other hair products
 Seasoning and Cooking are items used for cooking or food preparation such as
cooking oil, salt and seasoning granules
 Snacks and Junk Food Items such as chips, biscuits, cookies

38
Table 10
Average Sales Volume by Product Category
General And Specific Categories 2017 Average Monthly Sales Volume
Beverages 2891
Beverages 1695
Bottled Drinks 1196
Candle And Lighting 861
Candle And Lighting 861
Candy 1653
Canned Goods 54350
Canned Juice 907
Canned Meat 29605
Canned Sardines 21377
Canned Tuna 1001
Family Milk 865
Spread 595
Cigarette 11913
Cigarette 11913
Cleaner 19108
Detergent Bar 8780
Fabric Conditioner 2067
Powder Detergent 8261
Food Commodity 9830
Egg 2541
Food Commodity 950
Rice 2857
Sugar 3482
Instant 55471
Instant Coffee 31089
Instant Noodles 17845
Powder Juice 6537
Miscellaneous 1174
Miscellaneous 1174
Personal Care 43193
Alcohol 1518
Personal Care 950
Powder Detergent 946
Shampoo 27361
Soap 11678
Toothpaste 740
Milk & Dairy 13928
Evaporated /Condensed 5593
Family Milk 7218
Instant Coffee 1117
Baked & Pastry 5753
Bakery/Pastry 5753
Seasoning & Cooking 20158
Oil 1316
Salt 466
Seasoning And Cooking 3119
Soy Sauce 7702
Vinegar 7555
Snack & Junk Food 9471
Cookies/Biscuits 1922
Snacks And Junk Food 7549
Total 249754

39
Table 11
SKU Sales per Category
Categories Food Non Food Total
Bakery/Pastry 2.4% 2.4%
Beverages 3.0% 3.0%
Candle and Lighting 0.3% 0.3%
Candy 0.6% 0.6%
Canned Goods 21.8% 21.8%
Cigarette 4.6% 4.6%
Cleaner 7.4% 7.4%
Food Commodity 3.8% 3.8%
Instant 23.8% 23.8%
Milk and Dairy 5.4% 5.4%
Miscellaneous 0.5% 0.5%
Personal Care 16.8% 16.8%
Seasoning and Cooking 7.6% 7.6%
Snacks and Junk Food 1.8% 1.8%
Total 70.4% 29.6% 100.0%
3.3.1.3. Intra-shelf Product Configuration
Intra-shelf product configuration determines what product categories go to which
shelf within the store. Generally, the store is divided into non-food and food
sections, and within those two sections are further divisions down to general
product categories and specific product categories as itemized in Table 10.
Further, Elbers (2016) provides highlight to the importance of intra-shelf product
configuration by acknowledging that “…a well-structured shelf design can be
advantageous for both consumer and retailer. This statement is explained by the
fact that consumers’ overall shopping satisfaction increases when the in-store
shelf design is structured well”.

40
3.3.1.4. In-store Traffic Results
Using thickness of lines represented by the average monthly sales volume for 2017, the
traffic created by such volume is represented in a shelf planogram. In response to the high
Reliability gap under Ease of locating products intra-shelves, creating a better layout for
products in order to improve coordination is proposed.
Table 12
Sales Volume per SKU
Category
Sales
Volume
(SKU)
Weight
No of
Slots
Thickness
of Line
(1:1500)
Candy 1,653.00 1% 3 1.10
Snacks and Junk Food 4,723.00 3% 12 3.15
Bakery/Pastry 6,098.00 3% 6 4.07
Beverages 7,716.00 4% 8 5.14
Food Commodity 9,830.00 5% 7 6.55
Milk and Dairy 13,928.00 8% 4 9.29
Seasoning and Cooking 19,588.00 11% 7 13.06
Canned Goods 56,154.00 31% 10 37.44
Instant 61,305.00 34% 13 40.87
Grand Total 180,995.00 100% 70 120.663
The thickness of line was computed by dividing the sales volume by 1500 (1:1500). The
thickness was used to represent the line thickness in Figures 8 and 9

41
Figures 8 and 9 show comparisons of the current in-store traffic and the desired traffic. With the
current shelf configuration, it can be noticed that there is overlapping traffic expressed in
thickness of lines between shelves E and G, where two highly-sought product categories intersect
Figure 8: Current In-store Traffic
Figure 9: Simulated Desired In-store Traffic

42
3.3.2. Redesigning the Shopping Cart
The shopping carts available for use when purchasing heavy or large products are
similar to those in supermarkets. These carts are good for use when there is
sufficient space but in the case of the community grocery such as Victa Mart,
they are difficult to maneuver and can result to blockages of walkway areas.
3.3.2.1.1. Current Shopping Cart
Victa Mart uses a commercially-available generic shopping cart
manufactured by Omcan Machinery for the store as seen in Figure 10
Figure 10: Shopping Cart used at Victa Mart
Since the specifications are generic, there is no customized feature that can best benefit
the store and the users. The shopping cart experience earned the second highest gap in the
RATER Survey, registering a 122-point gap score equivalent to 8% of the total gap score.

43
Table 13
Gap Score for Shopping Cart
Code
SERVQUA
LStatement
Expectation
Perception
GapScore
Gap%
Cum.Wt.
T3 Ease of using the shopping cart 269 147 122 45% 17%
The current cart enlists the following specifications:
Figure 11: Shopping Cart Specification by Omcan Machinery
.

44
3.3.2.2. Proposed Shopping Carts
3.3.2.2.1. Redesigned Push Cart
The proposed cart bears the quality of maneuverability and ease of use, with
technical and cost specifications outlined in the succeeding tables. Figure 12
shows the 3D computer-aided drawing of the redesigned prototype cart.
Figure 12: 3D CAD Drawing of the Redesigned Cart

45
Figure 13: The Front, Side, Top views in 3D drawing of the Prototype
The prototype cart was tested for use on Victa Mart to test the desired versus
actual outcome to the service quality and customer satisfaction. The result showed
good results in the survey. The redesigned cart was also compared against the new
cart in terms of costing, technical specification, and desirability in the succeeding
pages.
Table 14
Redesigned Shopping Cart Mark Sheet
Design objectives: Result
To take up more vertical space but not impede the user’s vision of
the pathway in front of him/her
Met
Maneuverable in an easy way; without swiveling when pushed Met
Allow separation of products e.g. food and cleaning Met
Can be tucked when not in use Met
Design specifications Result
Taller than wider Met
Partitions to accommodate the segregation of different products
especially food from non-food
Met

46
The prototype was created and the actual figures were documented as follows:
Figure 14: Actual Folded Prototype (Side, Front, Rear Views)
Figure 15: Actual Prototype (Side, Front, Rear Views)

47
3.3.2.2.2. 2-Tier Basket Trolley
Another way to reduce the bulkiness of the current push cart in the store which
adds to the walkway traffic is by replacing them with a trolley that has no built in
basket, rather just a frame holder for carry-on grocery baskets. A trolley is a metal
cart on wheels used to hold groceries while shopping. There are commercially-
available basket trolley but the most useful are the 2-tier that can hold two baskets
at a time.
Figure 16: 2-Tier Basket Trolley. Adopted from Alibaba.com. Retrieved from
https://www.alibaba.com/product-detail/french-cheap-shopping-basket-trolley-for_1610350115.html

48
3.3.2.2.3. Costing
The total material cost was computed based on the actual values in the prototype
manufacturing. It is notable that the cost of PhP. 1,119 could significantly be
brought down if mass produced due to the purchase of raw materials in
bulk/wholesale.
Table 15
Material Cost of the Redesigned Cart
Item Price per unit Quantity Total Cost
9 mm round bar 145 2 290
Bolts and Nuts 5 4 20
25.4 mm square metal tubing 265 1 265
Washer 1.5 10 15
Cutting Disk 35 1 35
Automotive Paint (1L) 184 1 184
Wheels (swivel, with locks) 55 4 220
Welding rod 9 10 90
Total 699.5 33 1,119
Due to the commercial availability of basket trolley in the market, what’s
considered is commercial selling price rather than the manufacturing cost. Table
14 contains the price information from Alibaba.com.
Table 16
Commercial Price of 2-Tier Basket Trolley
Item Price per unit Quantity Total
Push Cart 787.75 1 785.75
Basket 250 2 500
Total 1,285.75

49
3.3.3. Comparative Analysis
To better understand the improvement in the redesigned cart, the specification of the
current and the proposed cart were contrasted in various specifications. Table 15 itemizes
the specification comparison while Table 16 details the functionality comparison.
3.3.3.1. Measurements
The most obvious difference between the two designs is the structure, that is, the
current cart extends horizontally while the proposed cart extends vertically. Table
15 puts together the basic differences of the two carts.
Table 17
Specification Comparison: Current vs Redesigned Cart
Current Proposed Unit
Width 21 Width 14 in
Height 38 Height 39 in
Depth 33 Depth 20 in
Load Capacity 110
Upper Cart 22
lbsLower Cart 55
Total 77
3.3.3.2. Functionality
Some of the functionalities of the current cart had to be traded off in order to achieve
more useful functionalities. The design heavily relied on whether or not it will be
functional. Table 16 itemizes and compares the functional distinction of the current and
the proposed cart.

50
Table 18
Functionality Comparison: Current vs Redesigned Cart
Criteria Current Redesigned
2-Tier Basket
Trolley
Decision
Load up to 110 lbs.
up to 77 lbs.
(22/55)
up to 60 lbs. (30
lbs./basket)
Current
Product
separation
Single cart
two cart for product
segregation
two cart for
product
segregation
Change
Navigability Wider and bulkier Narrower Narrower
Change
Ease of use
Heavier, requires
more push/pull effort
Lighter, requires
less push/pull effort
Lighter, requires
less push/pull
effort
Change
Comfort
Single product
entry/exit
Open rear and open
top baskets for the
user's easy product
access
Single product
entry/exit
Change
Safety
Requires bending to
unload items from
cart
Requires less
bending especially
for the upper cart
Requires less
bending
especially for the
upper cart
Change
Design
Child seat with
restraining strap
None None Current
Swivel wheel Swivel wheel Swivel wheel Retain
Convenience
Can be tucked to
other carts
Can be folded for
storage
Can be folded for
storage
Retain
Cost 2,500 - 4,000 Pesos 1,119 1,285.75 Change

51
3.3.4. Decision Matrix for Cart Selection
To help determine which cart to use, a decision matrix was used to plot the considerations
that the store manager prefers as shown in Table 19.
Table 19
Decision Matrix for Cart Selection
Consideration Current
Push Cart
Redesigned
Cart
2-Tier Basket
Trolley
Loading of products over 77 lbs. (35 kg.) x
To take up more vertical space but not impede the
user’s vision of the pathway in front of him/her
x x
Maneuverable in an easy way; without swiveling
when pushed
x x
Allow separation of products e.g. food and
cleaning
x x
Can be tucked when not in use x x x
Easy restocking* x
Cost x
Total 2 5 5
Both the 2-Tier Basket Trolley and the Redesigned Cart earned 5 points. Thus:
 2-Tier Basket Trolley – advantageous for customers that have more products to
buy that won’t normally fit in two baskets, which is the capacity of one cart.
Therefore, the buyer can *restock the cart, leave them at the counter or a waiting
area, and go back to purchase more items.
 Redesigned Cart – Helpful in cost reduction as it is cheaper than all three
selection, which still offering the ease of use that the study intends to achieve.

52
3.4. Implementation Outcome
3.4.1. Post-implementation RATER Survey
To understand how the intra-shelf and inter-shelf reconfiguration have made an impact to the
store, a similar survey study used during the data collection was employed in aid of the
implementation outcome. The results showed a gap reduction from the pre-study score of
24% to just 19% in only two weeks. The reduction in prime quality dimensions is also
remarkable as shows in the Gap Analysis.
3.4.1.1. Survey Result
The Survey result yields a reduction in all Service Quality Categories, aka RATER. The
table below contains the post-implementation survey results.
Table 20
Post-Implementation Survey Summary
SERVQUAL Category Expectation Perception Gap
Reliability Score 1382 1002 380
Assurance Score 1374 1130 244
Tangibility Score 1363 1093 270
Empathy Score 833 646 187
Responsiveness Score 1129 1033 96
Total Expectation Score 6081
Total Perception Score 4904
Total Gap Score 1177
% Gap 19%
Min Gap 21 (Q20)
Max Gap 113 (Q4)

53
3.4.1.2. Gap Analysis
It can be observed that there are some dimensions that earned higher gap scores
compared to previous. While those dimensions are not specifically targeted by this study,
it is worth checking what drove them to increase in gap score which can be recommended
for future researches. However, the gap score for most targeted dimensions in reliability,
assurance, tangibility, empathy, and responsiveness have all shown improvement as
shown in the table below.
Table 21
Gap Difference
Dimensions Pre-
Study
Post-
Study
Difference
Completeness of products 82 84 2
Accurate display of prices 110 96 -14
Ease of locating products inter-shelves 120 75 -45
Ease of locating products inter-shelves 137 73 -64
Waiting lines at counter 79 52 -27
Time consuming 69 48 -21
Product quality 33 48 15
Product price 36 48 12
Safe and comfortable experience 67 56 -11
Product replenishment/stock-out 42 44 2
Adequate lighting and ventilation 60 52 -8
Enough space to move around 96 57 -39
Ease of using the shopping cart 122 50 -72
Sufficiency of checkout counters 38 38 0
Organized layout (blockades, tidiness, etc.) 98 73 -25
Hours of operation 62 50 -12
Staff courtesy and professionalism 37 24 -13
Hassle-free store experience 99 113 14
Ease of customer feedback 31 43 12
Ease of returning a product 15 25 10
Store staff knowledge and helpfulness 17 21 4
Ease of inquiry (staff and store manager) 33 37 4
Total 1483 1177 -306

54
Discussion
The retail store business is a booming industry despite the growing portfolio of online
shopping and marketplace where purchases can be made in just a click. Based on the statistics by
the Philippine Statistics Authority (PSA) in the Annual Survey of Philippine Business in Industry
in 2006, grocery stores in the wholesale and retail trade business sector ranked as second in most
number of business establishments in the Philippines at 4,317 establishments comprising 21% of
all businesses currently operating in the Philippines as of 2003 (PSA, 2006).
Harnessing the strength of the retail industry means that a retail store must deliberate ways
to enhance its competitive advantage through service quality. In this study, the level of service
quality of a community retail store was measured via ServQual, a “multidimensional research
instrument designed to measure service quality by capturing respondents’ expectations and
perceptions along the five dimensions of service quality” (Parusaraman, 1988). The five
dimensions of service quality employed in the methodology are Reliability, Assurance,
Tangibility, Empathy and Responsiveness. A sample size of 58 buyers were surveyed to complete
a Likert-scale type of survey comprising 44-statements, distributed across the five dimensions of
service quality.
It was discovered that although there are several factors leading to gaps between the
customer’s expectation (ideal level of service) and the perception (actual level of service), 53%
of the gap score can be traced to seven (7) causes out of 22 predominantly in the Reliability
(24%), Tangibility (21%), and Assurance (8%). Further evaluation identified that the
dissatisfaction level among customers is intensified by two major root-causes: in-store traffic and
shelf space allocation.

55
Further investigation was conducted to collect empirical information on the issues
contributing to the in-store traffic and issues relating to shelf space allocation. Interviews with
the store manager and store crew produced an affinity diagram to map the issues occurring in the
the configuration of products to shelves. Additional observations such as as via the closed circuit
television identified frequencies of stock replenishment in shelf, blockages in traffic areas of the
store, and traffic caused by the physical environment of the store.
Ultimately, the largest contributor to the in-store traffic and 53% dissatisfaction rating
(gap score) were understood and a proposal to address the challenges was created. A Microsoft
Office Excel-based program to optimize product allocation, entitled Planogram, was designed to
systematize the shelf space configuration by allocating the appropriate product brands intra-shelf
(in different slots within the same shelf) and proper product categories inter-shelf (how product
categories are distributed across the store). Additionally, to address the 30% gap score of the
tangibility dimension of the store’s service quality and aid in the systematization of the in-store
traffic, a redesigned grocery cart was contrived.
The planogram offered a simulation of the traffic build up in the fast-moving products
that registered the highest sales volume from a 12-month data of the store’s sales volume. The
same areas observed during the initial phase of the study to render the most in-store traffic have
now been justified. The result is to relocate or reconfigure the product categories so that the fast-
moving or high sales won’t be in the same bay as in the current setup. The result yielded a
reduction in the overall in-store traffic and promoted better customer circulation which, in
theory, could promote impulse buying (unplanned purchases) among customers. According to
Desmet and Renaudin (1998), "Shelf space allocation is most effective on impulse purchases,
which means that shelf space has a causal effect on sales.”

56
On the other hand, the redesigned cart varied the customary design from consuming
horizontal space into more vertical space. The design was guided by the goal of reducing
blockages in walkways and in bays between shelves, to reduce body bending to reach out
products at the bottom of the cart, and to segregate products particularly food from non-food via
multiple baskets. These goals were achieved and a the consumers who piloted the cart registered
a reduction in the "Ease of using the shopping cart” statement from to 122 points to 50 points, a
51% reduction in dissatisfaction score.

57
Recommendations
While majority of the 22 service quality aspects were resolved during the study, several
issues were not given equal attention due to limitations raised by the store manager herself, but
could be a point of further study, specifically, checkout counter queues. The ServQual Survey
revealed that waiting lines at the checkout counters registered a 79-point service quality gap.
While the queue at checkout counters is only 5% of the total gap score (dissatisfaction rating)
and contributes to 13% of the in-store traffic, it is imperative to consider that with the store’s
improvement of service quality and reduction of in-store traffic upon successful implementation
of the proposal, a related increase in buyers will not only boost sales but will also increase the
foot traffic of the store to a point where the checkout counters might no longer be able
conveniently accommodate. Despite the store manager’s decision to maintain the current number
of counters at three (3) citing space constraints, studying the checkout queue will offer a basis for
the management to reconsider their initial decision in improving the service at the checkout
counters. The study may include the capacity of each counters in terms of number of customers
served per a given time period versus the forecasted increase in buyers.

58
Conclusion
The retail industry may not survive in its own without the engineering approach in
problem identification and solution conceptualization. Heavily relying on pure marketing or
management practices in improving a retail entity may leave some important points unchecked,
often the technical aspects of the operations. Thus, generally, an engineering approach will
supplement the strategies by offering technical know-hows and tools to the marketing and
management master-plan. Specifically, disciplines that have the expertise in dealing with the
design, improvement, and installation of integrated system of man, method, milieu, materials,
and money could perform important functions in maximizing the resources of the retail entity to
achieve optimal returns.
This study aims to preserve the important role of engineering in the service industry.
Capitalizing on the technical factors and methodologies while employing business strategy, in
this case is SERVQUAL, synergized the retail’s business goals and offered visibility to areas
never considered as problematic or opportunity-blocking by the retail entity’s management and
administrators.
While the focus of the study is in achieving optimal service quality through in-store
traffic systematization and product configuration, the results are not only expected to target
service quality improvements but will also pave the way for increased sales through impulse
buying or unplanned spending, better flow of foot traffic which allows more time for actually
browsing the store to purchase items rather than having to wait in checkout or in hampered
walkways, greater visibility for the administrator with the use of simple yet effective tools in
manipulating the configuration of products within the store as necessary, and ultimately attract
greater number of customers or increase the spending allowance of existing customers.

59
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share-retail-strategies-213012835.html

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Appendix A
Planogram Instruction Manual
Planogram Instruction Manual
Designed for use of:
Victa Mart
District 2, Pandacaqui, Mexico, Pampanga
Created on:
18-February-2018
Revision:
22-March-2018
Author Note:
For technical assistance with this manual or with the MS Excel-based Planogram, as well as for
improvement feedback, contact Angelo T. Yutuc at ayutuc1@gmail.com

64
Table of Contents
1. ABOUT THE PROGRAM ………………………………………………………. 3
1.1 Sheet 1: Intrashelf Product Configuration ………………………...… 4
1.2 Sheet 2: Intershelf Product Allocation ………………………….…… 6
1.3 Sheet 3: Pivot …………………………………………………..……… 7
1.4 Sheet 4: Sales Data ……………………………………………...…… 8
1.5. Sheet 5: Intershelf Data ……………………………………….…….. 9
1.6. Sheets 6 & 7: References & Algorithm …………………….………. 10
2. HOW TO USE THE PLANOGRAM …………………………………………… 11
2.1 System Requirements …………………………………………..……. 11
2.1.1 Hardware …………………………………………………….. 11
2.1.2 Software Program …………………………………….……. 11
2.2 Step-By-Step: Updating The Product And Sales Information ….... 12
2.3 Step-By-Step: Updating The Pivot Table …………….……………. 14
2.4 Step-By-Step: Viewing and Printing the Intrashelf Product Allocation 15
3. IMPLEMENTATION PLAN ………………………………………………………. 16
3.1 Major Implementation (One-Time Change)…………………… ……… 16
3.2 Mid-range Implementation (Gradual Change) ………………….…….. 17
3.3 Minor Implementation (Daily Change) ………………….……………… 18
4. SUMMARY OF DETAILS …………………………………………………………. 19
General …………………………………………………….…………………... 19
Summary …………………………………………………………………….… 20
Statistics ……………………………………………………………………….. 21
Content ………………………………………………………………………… 22
Victa Mart Planogram Manual |Revised 03/22/18 | Page 2 of 22
Appendix A (cont’d)

65
1. ABOUT THE PROGRAM
This Planogram is a Microsoft Excel-based program for the systematization of product-to-shelf allocation. It
is composed of seven (7) interconnected worksheets that facilitate the entry of data and storage of product
categorization algorithm to optimize the intra-shelf and inter-shelf placement of commodities.
The 7 Planogram worksheets are:
IntraShelf Product Configuration
InterShelf Product Allocation
Pivot
Sales_Data
Intershelf Data
References
Algorithm

66
1.1 SHEET 1: INTRASHELF PRODUCT CONFIGURATION
The first sheet in the the MS Excel workbook that is readily-printable. It resembles the front-facing product
shelf in a matrix of 7x15 squares representing the 7 layers of shelf from top to bottom (vertical) and 15
columns from side to center (horizontal).
Legend:
A – Type: Indicates whether the commodity is food (can be eaten or consumed) or non-food (all
other products that are not to be consumed as food items).
B – Specific Category: Indicates the specific kind of item within the product type and is
categorized as:
 Bakery/Pastry are bread and other baked products
 Beverages ranges from bottled water, bottled flavor drinks, sodas, tetra-packed juices, and
other ready-to-drink liquids
 Candle and Lighting are wax candles, light bulbs, and other peripherals for lighting
 Candy include sweets such as hard candy, soft candy, marshmallows, chocolate and
chocolate-based candy items, and sweet syrups
 Canned Goods are variety of products in can such as processed meat, canned drinks,
fruits in can, cooking ingredients in can
 Cigarette different brands of tobacco products
 Cleaner ranges from powdered, bar and liquid detergents, fabric conditioners, dishwashing
liquid and pastes, bleaching products and others

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 Food Commodity are items that are considered for daily consumption such as rice and
sugar
 Instant are a wide range of ready-to-eat food items, requires few preparations such as
instant coffee, instant noodles, and other packed foods.
 Milk and Dairy are items in can or boxes including powdered milk, fresh/liquid milk,
cheese, and processed milk such as condensed and evaporated milk
 Miscellaneous are products not falling in any specific categories such as batteries and
plastic cups
 Personal Care ranges from face and body cream, soap, and toners; shampoo, conditioner,
lotion, and other hair products
 Seasoning and Cooking are items used for cooking or food preparation such as cooking
oil, salt and seasoning granules
 Snacks and Junk Food Items such as chips, biscuits, cookies
C – Shelf Grid: the space where the brands of specific product categories are positioned. This is
the actual shelf space for guide in replenishing stocks where the number of slots equal the number
of shelf space in the store. It has 7 vertical layers and 15 horizontal columns (105 slots)
D – Worksheet Name: The title of the worksheet for easy access and reference.
.

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1.2 SHEET 2: INTERSHELF PRODUCT ALLOCATION
The InterShelf Product Allocation is the “top view” of the sale area with all the eight (8) shelves are shown.
The 14 specific product categories are assigned to a specific number of layers or slots depending on their
quantity.
Legend:
A – Shelf Code: The shelf code are to identify the shelf, an alphabetized coding from A to H
indicating the number of shelf facings available for replenishments.
B – Worksheet Name: The title of the worksheet for easy access and reference
C – Traffic Lines: These are the computed traffic volume at a given specific product category. The
red lines are computed from the volume of sales of that category and are assigned a specific
thickness. The more red lines are in the walkway, the greater traffic there is.
D – Product Volume: The quantity of sales are indicated right next to the product category and is
used to present the thickness of lines in the walkway area.
E – Product Category Name: Where the specific product category is assigned. The sales quantity
is automatically assigned based on the indicated specific product category.

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1.3. SHEET 3: PIVOT
The pivot sheet is where the data is manipulated. The sorting of items to be assigned on the particular layer
in the vertical spacing and horizontal spacing are determined here. This sheet utilizes MS Excel’s Pivot
function, thus, the sheet title.
Legend:
A – Pivot Filter for Specific Product Category: Selecting the type and product category
B – Grid Features: Computes the number of rows and columns the grid has for shelf space
allocation.
C – Pivot Sort (By Brands): Sorts per product brand to ensure that the same brands are situated
together or next to each other in the shelf.
D – Pivot Sort (By Profit Margin): Sorts per profit margin to optimize sales by assigning the top
selling products to the centermost sections of the horizontal and vertical shelf space.
E – Sales Volume: Computes quantity of sales for that selected product category
F – Positioning: Assigns numbers where 1 is the most optimized slot and n is the least (n=total
product quantity)
G – Product Assignment: Based on the sorting, assigns products ranked from 1 to n in “F” and
“C / D”
H – Product Sales Volume: Retrieves the corresponding sales quantity of the product in the
same grid position.
I – Worksheet Name: The title of the worksheet for easy access and reference.

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1.4.SHEET 4: SALES DATA
The sheet is where the data from the actual sales are inputted. It has 12 section headers with information
used in the various worksheets to create both the intershelf and intrashelf product allocations.
Legend:
A – Section Header: The types or information found in each column. There are 12 sections
namely:
 Code – A numeric figure to identify the specific product brand and type starting from 1
 Brand – The actual brand name of the product
 Type – Indicates whether the item is food or non-food.
 Gen Category – assigns the general product category based on the brand
 Specific Category – assigns the specific type of product based on the brand
 Unit Price – the selling price per piece of the brand
 Sales Volume – The quantity of sales per brand in a given period (monthly, yearly,
etc.)
 % Sales Volume – the fraction of that brand’s sales volume over the total store
volume for a given period (brand sales quantity divided by total store sales quantity)
 % Sales Amount – The fraction of that brand’s sales amount in peso over the total
store sales for a given period (brand sales amount divided by the total store sales
amount)
 Profit Margin – The store’s profit out of the specific brand
 PM*Sales – The computed actual profit from the brand by multiplying the profit margin
per unit to the sales volume of that brand.
B – Worksheet Name: The title of the worksheet for easy access and reference

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1.5. SHEET 5: INTERSHELF DATA
The Intershelf Data sheet utilizes MS Excel Pivot to show the accompanying sales volume per specific
product categories. The information in this sheet are important in properly assigning the correct number of
slots per product brands and product categories in the Intershelf Product Allocation and Intrashelf Product
Assignment.
Legend:
A – Row Labels: Itemizes all the general product category
B – Sum of Sales Volume: The equivalent sales volume or the number of sold products under
the the general product category
C – Occupied Slots: Counts the dedicated shelf space for the product category and uses the
formula:
=COUNTIF('InterShelf Product
Allocation'!$B$3:$P$90,'InterShelf Data'!A3)
C – Total: The maximum quantity among the Sum of Sales Volume and uses the formula:
=MAX(B3:B16)

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1.6. SHEETS 6 & 7: REFERENCES & ALGORITHM
The References Sheet assigns the number of slots by rounding the product quantity to the nearest 7th
number range. This information is used to compute the number of horizontal space to be allocated on the
product category.
The Algorithm sheet has all the grid combinations ranging from 1 x 7 (columns x layers) to 15 x 7. In each
of the slots, there are numbers assigned ranging from 1 to ‘n’, where n is the maximum number of product
for a specific product category (e.g. if there are 17 brands in product A, then n = 17). The information here
are useful in properly allocating the brands into the shelf space for IntraShelf Product Allocation.

Systematizing In-Store Traffic and Minimization of Service Quality Gaps of a Retail Store through SERVQUAL (Yutuc, 2018)

Systematizing In-Store Traffic and Minimization of Service Quality Gaps of a Retail Store through SERVQUAL (Yutuc, 2018)

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