Implementing lean service principles in restaurants: A data-driven approach with fuzzy logic. First Author\[1\]\*, Second Author2 (Double Blind Review: Please do not type or edit anything here until final-camera ready submissions) *1First affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)* *2Second affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)*
ARTICLE INFO ABSTRACT

Article history:

Received

Revised

Accepted

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Published 1 March 2024

 Customers' perceptions of a restaurant and their overall satisfaction level can be significantly improved by investing in and improving its physical environment. Applying Lean Service Principles in restaurants enables the identification of areas for enhancement and proposes solutions to achieve exceptional outcomes efficiently, using minimal time and resources. Restaurant management needs more information about customers' preferences to overcome existing weaknesses. The research aims to strengthen the service quality of a restaurant that implements fuzzy logic by studying the data attributes provided inside a service quality leanness assessment. Ten physical environment data attributes were collected from data attributes within a leanness assessment of quality service for use in this study. Recognizing the weaker attributes would assist restaurant management in improving the physical environment of their restaurant so that they could capture more customers in the future and positively impact loyal customers. The result shows that, there are three Three attributes with ranking scores of 0.49 fell below the management criterion of 1.0, which are according to the findings: visually appealing dining area (RI2), restaurant's décor typical of its image and price range (RI4), and easily readable menu (RI5). The study also revealed that parking lots with visually appealing features (RE1) and well-functioning parking management systems (RE2) obtained a maximum score of 3.35. In response, the restaurant must take the appropriate actions to improve them. Besides the physical environment, future research may also consider implementing various lean service principles to assess the performance of restaurants. These include Value Stream Mapping, Just-in-Time (JIT) Inventory, and Kanban System. (No need to include in the abstract)

Keywords:

Fuzzy logic

Restaurant service quality

Lean assessment

Physical environment

DOI:

10.24191/jcrinn.v9i1
# Introduction Customer service, as well as constant quality development are top priorities in the restaurant business. Customers are exploring new tastes, enjoying a relaxing environment, and making exciting moments because of changes lifestyle (Akhil & Suresh, 2021). Customers choose to enjoy a meal at a restaurant not only because of nutrient requirements, but instead to structure special moments, to be with someone and get away from the stress and the habit of living (Tuzunkan & Albayrak, 2016). To stand out in a competitive marketplace, restaurant owners should gain customers’ interest and support. Food type and quality, as well as the restaurant’s public identity, environment, and vibe, are important factors in customers’ restaurant decisions (Lee et al., 2022). However, the physical surroundings of a restaurant will be the first thing customers notice when they walk in, and it is an important factor for them (Tuzunkan & Albayrak, 2016). A restaurant’s physical environment could enhance a restaurant’s reputation, restructure customers’ perspectives, and directly impact customer satisfaction (Jang & Lee, 2020). According to Hanaysha (2016), all tangible and intangible components, inside and outside the restaurant, have been included in the physical environment, including temperature, lighting, scent, noise, environment, and music. The writer also mentioned that a well-kept physical environment could help a restaurant retain current customers while also attracting new ones. Furthermore, ambient factors such as noise, smell, taste, and touch, as well as design features such as restaurant decoration and setup, can significantly impact customer behaviour (Jang & Lee, 2020). The restaurants’ management teams must put money into interior designs, including interior decoration, floor cleanness and other equipment, since these expenditures are the most crucial investments to win over customers. Customers will more likely return to a restaurant with a well-maintained physical environment (Hanaysha, 2016). The physical environment not only retains the restaurant’s current customers, but also serves as an important aspect of attracting new customers. Many consumers value a pleasant and unique restaurant surrounding above the meal and service. Consumers demand something more than food; they would like a unique dining experience dissimilar from what they already have at home (Canny, 2013). The lean assessment can identify areas where a restaurant can improve and provides a sequence and method for doing so to achieve the best results in the shortest amount of time with the fewest resources. In addition, Leanness can be used as a decision-making tool to determine the current state of lean adoption, its effects on the business's performance, and the extent of upcoming changes. As an outcome, the assessment emphasizes progress, increases management focus, and fosters a desire for high marks throughout the business (Mathew Alexander & Saleeshya, 2022). This study aims to examine the characteristics of the physical setting at a restaurant and its impact on customer satisfaction, with the goal of attracting a more extensive client base. This study aims to ascertain how physical factors impact customers' decision-making while choosing eateries. In addition, this study examines the variables that influence customers' decision-making process in selecting restaurants for dining. With this survey, restaurant management could not bear the current deficiencies in their establishments due to a lack of knowledge about their consumers' preferences. Tuzunkan and Albayrak (2016) emphasized the importance of identifying the specific characteristics of a restaurant's physical environment that are more desirable than others. This is because the physical environment can enhance or suppress consumers' emotions, influencing their satisfaction and subsequent behaviours. Neglecting client feedback is a grave error that business management has committed. The potential outcomes of this could have irrevocable consequences in some instances, such as financial loss and harm to one's reputation (Tuzunkan & Albayrak, 2016). **The primary goal of this research is to enhance the quality of service by examining the data attributes included in a leanness evaluation of service quality. The study also aims to determine which physical environment data elements are relevant to leanness assessment and hence impact diners' restaurant choice. This helps restaurant management improve their physical atmosphere to attract more guests and impress regulars. Good restaurant management listens to consumers and improves to be better. Knowing which company variables affect the business helps save wasteful spending and free up funds for other restaurant needs like manufacturing. There are some confusing here, because these two paragraphs are about the objective of this study. Suggestion, remove the last paragraph or restructure both paragraphs. Need to mentioned the method used in this study.** # Literature Review Leanness assessment, also known as lean quantification, can be interpreted as estimating the level of poor achievement qualitatively, quantitatively, or both. Leanness assessment metrics have some essential qualities that are measurable and consistent with the organization's strategic goals and customer values. It also additionally allows control and assessment of performance, provides valuable resources within the expertise of the modern-day situation, and assists in figuring out possibilities for improvement. Finally, it is updated and realistic. Repetitive assessment of leanness will become essential, as it might help assess the contribution of lean practices carried out by using the organization to enhance its operational and financial performance. Initially, leanness assessment was characterized by using ambiguity and multi-possibility, expressed in linguistic terms (Yap & Ng, 2018). Leanness efficiently reduces waste and various non-value-added operations to raise quality, boost output, and cut costs. It primarily focuses on fewer inputs and their expenses to generate higher production and the associated increase in customer satisfaction. Leanness is a term used to describe how becoming lean affects business goals (Tekez & Taşdeviren, 2016). Leanness assessments were required since they help determine the present status, monitor the overall effectiveness of the business, and identify weak spots (Kumar et al., 2019). In addition, customers' psychology is influenced by the layout accessibility, facility aesthetics, seating comfort, electronic equipment, hygiene, and social characteristics of a facility (Horng et al., 2013). Kilic et al. (2021) published research that provided a technique for systematically monitoring leanness from managerial and application viewpoints and highlighting improvement opportunities across businesses on their path. It evaluates leanness using a neutrosophic DEMATEL (The Decision-Making Trial and Evaluation Laboratory) based scoring structure and a large variety of lean criteria with a wholly prepared questionnaire. The approach can be used to evaluate the leanness of production systems, and then it can be used as a starting phase and a guideline for businesses that want to change into lean businesses. Mathew Alexander and Saleeshya (2022) suggest significantly comparing performance characteristics while selecting numerous solutions in lean production. The research focused on Multi-Criteria Decision Making (MCDM) tools. The five MCDM methods, AHP, ANP, TOPSIS, VIKOR, and Fuzzy Logic and their combinations (ensemble methods) are thoroughly analysed. The approach utilized demonstrates that MCDM is well suited to lean manufacturing. Most lean manufacturing operations rely on traditional approaches, with a clear preference for uncertainty principles, like fuzzy logic. Lupo and Bellomo (2019) suggested measuring service quality in the restaurant surroundings concerning the University of Palermo's three significant restaurants. The research used a new multi-criteria decision-analysis (MCDA) approach that merged the DINESERV model with the hierarchical TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution). The DINESERV model is a tool for measuring service quality in restaurants. In more context, hierarchical TOPSIS is used to make comparisons of the quality of restaurant services based on the DINESERV theoretical framework of restaurant service quality. The results suggest that the technique is applicable and that using it enables the identification of both best practices and flaws in given services that need to be fixed. According to Akhil and Suresh's study (2021), they proposed determining the restaurant's present level of service quality and concentrating on poorer characteristics to increase service quality. The study developed a restaurant service quality assessment system using the Multi-Grade Fuzzy (MGF) technique. The research results discovered that the restaurant's weaker components are service workers who should indeed review the customer needs, comfy seats in the restaurant, clients' needs to be appreciated by the service workers, accurate pricing, receiving feedback from customers on the expertise, and maintaining speed and service quality throughout busy periods. Improving a restaurant's service with fuzzy logic is the goal of this research, which suggests doing the same thing by studying the data attributes of the physical enablers in a restaurant within a leanness assessment of service quality. (this statement can be moved to the introduction because you not mentioned anything about the method in the introduction). The inventor of fuzzy logic is Lotfi Zadeh. He produced significant contributions to the field of fuzzy logic. Fuzzy logic is not a stand-alone knowledge system. However, there are a variety of approaches ~~to using~~ (change to - based on) fuzzy logic to cope with uncertain and ambiguous situations (Alekhya et al., 2022). Intuitionistic fuzzy logic can identify such uncertainty when doubts cannot be addressed due to specific information termed as hesitation that has not been wrapped up in favourable or unfavourable categories (Sharma et al., 2021). Also, by applying lean service concepts in restaurants using a fuzzy logic method, this research will determine if physical enablers can affect customers' choice of restaurants. # Research Methodology Within the context of a leanness assessment of service quality to customer happiness, this study focuses on the attributes of the physical environment of a restaurant. This study uses the criteria for the restaurant's exterior and interior presented in the research article by Akhil and Suresh (year). The restaurant's exterior features include effective parking management systems and visually appealing parking lots. A restaurant's interior includes visually appealing building exteriors, a visually appealing dining area, clean, orderly, and appropriately attired staff, décor appropriate for the restaurant's image and price range, an easy-to-read menu, and a visually appealing menu, a pleasant dining area, and clean restrooms. Fuzzy logic was used to do this evaluation. Table 1 shows the attributes used in this study. Table 1. Conceptual model for service quality assessment in restaurants
Enabler Criteria Attributes
Tangible Restaurant Exterior (RE)

Visually attractive parking area (RE1)

Efficient parking management systems (RE2)
Restaurant Interior (RI)

Visually attractive building exteriors (RI1)

Visually attractive dining area (RI2)

Clean, neat, and appropriately dressed staff (RI3)

Restaurant’s décor typical to its image and price

range (RI4)

Easily readable menu (RI5)

Visually attractive menu (RI6)

Comfortable dining area (RI7)

Clean rest rooms (RI8)
## Source: Akhil & Suresh, 2021 (in bracket) Table 2. Linguistic variables and fuzzy numbers for rating and weights. | Performance rating | | Importance weighting | | | ------------------- | -------------- | ---------------------- | ----------------- | | Linguistic variable | Fuzzy number | Linguistic variable | Fuzzy number | | Worst (*W*) | (0, 0.5, 1.5) | Extremely Low (*EL*) | (0, 0.05, 0.15) | | Very poor (*VP*) | (1, 2, 3) | Low (*L*) | (0.1, 0.2, 0.3) | | Poor (*P*) | (2, 3.5, 5) | Medium (*M*) | (0.2, 0.35, 0.5) | | Fair (*F*) | (3, 5, 7) | Moderately High (*MH*) | (0.3, 0.5, 0.7) | | Good (*G*) | (5, 6.5, 8) | High (*H*) | (0.5, 0.65, 0.8) | | Very good (*VG*) | (7, 8, 9) | Very High (*VH*) | (0.7, 0.8, 0.9) | | Excellent (*E*) | (8.5, 9.5, 10) | Extremely High (*EH*) | (0.85, 0.95, 1.0) | Linguistic terms are allocated to each attribute to quantify the leanness attribute's performance ratings and importance weights. The subsequent methodology was utilised to ascertain the principal obstacles to leanness attributes. The fuzzy numbers and linguistic variables used in the importance weights and performance rating are presented in Table 2 (Suggestion: state the justification, how the fuzzy numbers and linguistic variables in the Table 2 are formed, or if they are based on previous study, you need to state or cite). The following method (but where is the method?) was used to determine the main obstacles to leanness attributes. The notations used to incorporate the fuzzy logic model are displayed in Table 3. Table 3. Fuzzy logic assessment model notations. | Indices | Abbreviations | | ------- | ---------------------------------- | | W | Importance weightage | | R | Performance rating | | FPII | Fuzzy Performance Importance Index | | W’ | Complement of importance weight | | RE | Restaurant Exterior attributes | | RI | Restaurant Interior attributes | # Finding and Discussion The performance ratings and significant weighting for each service quality criterion of the restaurant's leanness assessment are presented in Table 4. The respondents for the data collection were chosen based on the number of them coming to the selected restaurant. Customers (R1,..., R5) who had visited the restaurant for the first time were asked to score the performance rating of the restaurant's physical environment within the context of a leanness assessment of service quality. To give weightage to the physical environment features, another client (W1,..., W5) who has visited the restaurant multiple times will assist. The table was constructed using the questionnaire responses to make decisions. The questionnaire uses a strategy to answer selections based on linguistic variables. Table 4. Importance weightages and performance ratings of attributes rated by decision-makers. | Attributes | Attributes weightage | Attributes rating | | | | | | | | | | ---------- | -------------------- | ----------------- | -- | -- | -- | -- | -- | -- | -- | -- | | | W1 | W2 | W3 | W4 | W5 | R1 | R2 | R3 | R4 | R5 | | RE1 | M | M | VH | H | H | F | G | G | VG | F | | RE2 | MH | M | VH | MH | H | G | G | G | VG | F | | RI1 | VH | EH | EH | H | EH | VG | VG | E | VG | G | | RI2 | VH | EH | EH | EH | EH | VG | VG | E | VG | G | | RI3 | VH | H | EH | VH | EH | VG | VG | G | G | G | | RI4 | VH | EH | EH | VH | EH | VG | VG | G | VG | G | | RI5 | EH | EH | EH | EH | EH | VG | VG | VG | VG | G | | RI6 | EH | MH | EH | EH | EH | G | VG | G | G | G | | RI7 | VH | EH | EH | H | EH | VG | G | E | F | G | | RI8 | H | EH | H | VH | VH | VG | G | E | G | G | A median operation was performed to integrate the viewpoints of the experts, and the results of this procedure are provided in Table 5. A spectrum of fuzzy indices for each leanness trait is provided by the data that is shown in Table 2. Using Table 2, all the linguistic factors that are shown in Table 5 are converted into fuzzy numbers. In Table 6, you can find the fuzzy number representations of the factors that pertain to the linguistic variables. Table 5. Importance weightages and performance ratings of physical attributes. | | | | | ------------------- | -- | -- | | Leanness attributes | W | R | | RE1 | MH | G | | RE2 | MH | G | | RI1 | VH | VG | | RI2 | EH | VG | | RI3 | VH | G | | RI4 | EH | VG | | RI5 | EH | VG | | RI6 | VH | G | | RI7 | VH | G | | RI8 | VH | VG | | RE1 | MH | G | | RE2 | MH | G | | RI1 | VH | VG | Table 6. Linguistic terms approximated by fuzzy numbers. | | | | | ------------------- | ----------------- | ----------- | | Leanness attributes | W | R | | RE1 | (0.3, 0.5, 0.7) | (5, 6.5, 8) | | RE2 | (0.3, 0.5, 0.7) | (5, 6.5, 8) | | RI1 | (0.7, 0.8, 0.9) | (7, 8, 9) | | RI2 | (0.85, 0.95, 1.0) | (7, 8, 9) | | RI3 | (0.7, 0.8, 0.9) | (5, 6.5, 8) | | RI4 | (0.85, 0.95, 1.0) | (7, 8, 9) | | RI5 | (0.85, 0.95, 1.0) | (7, 8, 9) | | RI6 | (0.7, 0.8, 0.9) | (5, 6.5, 8) | | RI7 | (0.7, 0.8, 0.9) | (5, 6.5, 8) | | RI8 | (0.7, 0.8, 0.9) | (7, 8, 9) | | RE1 | (0.3, 0.5, 0.7) | (5, 6.5, 8) | | RE2 | (0.3, 0.5, 0.7) | (5, 6.5, 8) | | RI1 | (0.7, 0.8, 0.9) | (7, 8, 9) | The Fuzzy Performance Importance Index (FPII) is used in this work to help identify and analyse obstacles. FPIIs are indicators that assess the relative importance of different factors or criteria in a fuzzy decision-making or evaluation process. The following demonstrates how to calculate the ~~Fuzzy Performance Important Index~~ (FPII) (just used FPII without bracket, since you already introduced the abbreviation before) by integrating the performance ratings and necessary weights of data categories in a lean service quality assessment. FPII can be expressed using Eq. (1). | | | | | | ------------- | --- | | | FPII = W′ × R | (1) | The value of W' shows the important weight complement of the attribute, while the value of R represents the performance rating of the attribute. The expression that is provided is given below: | | | | | | ---------------------- | --- | | | W’ = \[(1, 1, 1) ‒ W\] | (2) | where W is the importance weight of the attribute for the given expression. An FPII contributes to the leanness of a restaurant to a greater extent than it would otherwise. A display of the FPII results and computations may be found in Table 7. Table 7. Excerpt of fuzzy performance importance index (FPII). | | | | | | ---------- | ----------- | --------------- | ---------------- | | Attributes | R | W’ | FPII | | RE1 | (5, 6.5, 8) | (0.3, 0.5, 0.7) | (1.5, 3.25, 5.6) | | RE2 | (5, 6.5, 8) | (0.3, 0.5, 0.7) | (1.5, 3.25, 5.6) | | RI1 | (7, 8, 9) | (0.1, 0.2, 0.3) | (0.7, 1.6, 2.7) | | RI2 | (7, 8, 9) | (0, 0.05, 0.15) | (0, 0.4, 1.35) | | RI3 | (5, 6.5, 8) | (0.1, 0.2, 0.3) | (0.5, 1.3, 2.4) | | RI4 | (7, 8, 9) | (0, 0.05, 0.15) | (0, 0.4, 1.35) | | RI5 | (7, 8, 9) | (0, 0.05, 0.15) | (0, 0.4, 1.35) | | RI6 | (5, 6.5, 8) | (0.1, 0.2, 0.3) | (0.5, 1.3, 2.4) | | RI7 | (5, 6.5, 8) | (0.1, 0.2, 0.3) | (0.5, 1.3, 2.4) | | RI8 | (7, 8, 9) | (0.1, 0.2, 0.3) | (0.7, 1.6, 2.7) | | RE1 | (5, 6.5, 8) | (0.3, 0.5, 0.7) | (1.5, 3.25, 5.6) | | RE2 | (5, 6.5, 8) | (0.3, 0.5, 0.7) | (1.5, 3.25, 5.6) | | RI1 | (7, 8, 9) | (0.1, 0.2, 0.3) | (0.7, 1.6, 2.7) | The ranking of fuzzy numbers is determined using the centroid technique, which considers the membership function (a, b, c) where a, b, and c represent the lowest, middle, and upper values of triangle fuzzy numbers. To determine the rank, use Eq. (2). | | | | | | --------------- | --- | | | Ranking score = | (2) | Table 8. Ranking score for leanness attributes.
Attributes Ranking score
RE1 3.35
RE2 3.35
RI1 1.63
RI2 0.49
RI3 1.35
RI4 0.49
RI5 0.49
RI6 1.35
RI7 1.35
RI8 1.63
RE1 3.35
RE2 3.35
RI1

1.63

Repeating?
The values of a, b, and c are derived from the information presented in Table 6. As previously stated, the rank is determined by using Equation (2) (be standardized, because previously you used Eq.). For illustrative purposes, the ranking score for the initial attribute is computed. Similarly, the ranking score for other attributes are calculated and presented in Table 8. | | | | | | ---------------------- | | | | Ranking score = = 3.35 | | The results shown in Table 8 indicated that three traits with ranking scores of 0.49 fell short of the management requirement of 1.0 (suggestion: please state the reason/justification why the ranking score should be \> 1.0, or maybe according to any citation), necessitating the restaurant to make the necessary efforts to strengthen them. The three characteristics are as follows: 1) A visually attractive eating space (RI2) 2) A restaurant’s décor representative of its image and price range (RI4) 3) An easily accessible menu (RI5) According to the ~~poll~~ (not suitable to use poll, just use According to the ranking), the two features with the highest-ranking score, 3.35, were effective parking management systems (RE2) and visually attractive parking lots (RE1). Due to the industry's intense competition, a visually appealing dining area (RI2) is one of the weak attributes. The restaurant may add unique and eye-catching dining places to attract customers and provide the best dining experience. The restaurant may also distinguish itself from its rivals by decorating consistently with its brand and pricing range (RI4). The restaurant may then develop a style that reflects its image and pricing range. This restaurant needs a simple-to-read menu (RI5) with excellent, gorgeous, and well-taken cuisine photos. # Conclusion This research demonstrates that evaluating a restaurant's physical qualities might help increase customer satisfaction. Without the findings of this study, restaurant managers would be unable to address their company's current challenges since they would not understand what their customers want. The primary purpose of this research is to improve the service quality given by a restaurant that employs fuzzy logic by analysing the data characteristics discovered in a leanness evaluation of service quality. Another research goal is to apply fuzzy logic to find data elements of the physical environment inside the leanness evaluation that may impact consumers' restaurant decisions. To summarise, the restaurant sector prioritises offering outstanding customer service and continuous quality improvement. To be successful in the restaurant sector, owners must stand out to customers. Diners would gaze around the restaurant before ordering meals to understand the ambience. The décor of a restaurant may dramatically impact customers' perceptions, experiences, and overall happiness. The lean evaluation may help a restaurant discover areas for development while providing a strategy and sequence for attaining the most significant outcomes in the shortest period with the fewest resources. Based on the study's findings, it is strongly advised that future research explores fuzzy logic to enhance restaurant services by analysing data attributes related to service quality. Other methods, such as Fuzzy Multi-Criteria Decision-Making (MCDM), Fuzzy Conjoint Analysis, and Fuzzy Analytic Hierarchy Process (AHP), should be considered for further investigation. In addition, future studies can utilise the fuzzy logic methodology to address a distinct problem, although one that is still related to customer services. In addition to the physical environment, various other lean service principles can be employed to assess a restaurant's performance, including Value Stream Mapping, Just-in-Time (JIT) Inventory, and the Kanban System. # Acknowledgements The authors express their gratitude to the reviewers for their valuable contributions in enhancing the quality of the research. They also thank the Journal of Computing Research and Innovation (JCRINN) for allowing this work to be published. # Conflict of Interest Statement The authors acknowledge that the data for this study was collected from a single restaurant, and at the time of publication, all authors declared no competing interests. # References Akhil, A., & Suresh, M. (2021). Assessment of service quality in restaurant using multi-grade fuzzy and importance performance analysis. 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