Fuzzy logic is a procedure that allows the processing of multiple truth variables using the same variable. In this case, the researcher uses an open and imprecise spectrum of data and statistical approaches to draw accurate conclusions [18]. He encourages the generalization of standard logic by illustrating that a concept has a certain degree of truth that can vary between 0.0 and 1.0. In formal logic, ideas are often believed to be totally true (truth value 1.0) or completely false (truth value 0.0) [19]. However, fuzzy logic suggests that some concepts humans use to define problems or analyze situations may be vague and characterized by subjective or relative definitions. Therefore, they cannot be classified as absolute truth or absolute false [20]. For example, describing someone as “beautiful” is subjective and cannot be classified as an indisputable fact. Fuzzy logic provides a flexible reasoning engine that can deal with partial truths or determine conclusions for real-life situations that are difficult to decide whether true or false [21]. Therefore, fuzzy logic is a problem-solving technique used to evaluate all available information and thus make the best decisions.

Although Lotfi Zadeh introduced the concept of fuzzy logic in 1995, its history and development are rooted in earlier theories of logic that examined the structure and principles of correct reasoning. For example, Aristotle and his predecessors developed ideas in logic and mathematics, including the Law of Excluded Middle, which implies that every proposition can be true or false [22]. However, the Greek philosopher Plato disagreed with this notion, arguing that there is a third realm beyond true and false where concepts ‘fell’ [23]. These different perspectives influenced the mathematical study of logic and later led to the development of fuzzy logic [24]. For example, in the 19th century, George Boole used the Aristotelian perspective to develop an algebra. He set a theory that mathematically dealt with Aristotelian two-valued logic by mapping true and false to 1 and 0, respectively. Jan Lukasiewicz developed a three-valued logic (true, possible, faulty) in the early 20th century. However, he did not win the approval of other mathematicians and philosophers.

Other philosophers studied the principles of uncertainty and imprecision before the fuzzy set theory was adopted in mainstream research and practice. For example, Bertrand Russell proposed the paradox of “all sets that do not contain themselves” [25] (p. 72). Likewise, Max Black, a German philosopher and scientist, introduced the theory of “vague sets”, which analyzed the problem of imprecision. Max Black argued that Russell’s theory confused vagueness with generality, thus suggesting that vagueness should be represented with appropriate details, such as terms and symbols that describe borderline cases [26]. The German philosopher proposed that a consistency curve or profile should be used to analyze the ambiguity of a symbol or word. These curves resemble the fuzzy set membership functions (type 1) in Lofti Zadeh’s fuzzy set theory [27]. Therefore, the concepts and ideologies of fuzzy logic developed over time from various thinkers in different fields who recognized the shortcomings of the traditional two-valued truth logic.

The concept was officially recognized in 1965 when Professor Lotfi A. Zadeh of the University of California at Berkeley published “Fuzzy Sets”. The professor based the notion of a fuzzy set on the concept of partially or gradually belonging to a set [28]. Zadeh explained that most objects do not have precise criteria for association in the real physical world, although they play a critical role in human thinking. For example, these imprecise categories influence various aspects of pattern recognition [29] as well as the communication and abstraction of information [30].

An expert system is a computer program that uses knowledge and inference steps to deal with complex problems and provide decision-making capabilities comparable to humans [32]. The first expert system was developed in 1970 as part of artificial intelligence (AI) to solve complex questions like an expert [33]. It uses heuristics and facts stored in the knowledge base to facilitate decision-making and improve performance [34]. It has three main components, user interface, inference engine, and knowledge base [35]. The expert system uses the user interface to interact with the user, taking the user’s query as input and displaying the query results as output. Thus, it helps the user to communicate with the expert system to find a solution [36]. The inference engine is the brain of the expert system that takes the query and processes it by applying the inference rules or engine rules to the knowledge base to deduce new information or derive a conclusion [37]. An inference engine based on facts and rules is called a deterministic inference engine and provides conclusions that are assumed to be true. On the contrary, a probabilistic inference engine is based on probability and contains uncertainties in the solutions [38]. The third component of the expert system, the knowledge base, is used to store knowledge from various experts in a given field and solve problems [39]. Knowledge can be factual, that is, based on facts, or heuristic, that is, based on practice, evaluations, and experiences [40]. The expert system increases efficiency and expertise in a specific domain, allowing automated access to vast knowledge from various experts and rapid processing to acquire desired, high-quality solutions.

A fuzzy expert system (FES) is used to interpret vague or incomplete information to overcome data challenges using fuzzy sets and logic. This system solves decision-making problems without an exact algorithm, using human approximate reasoning mechanisms expressed in fuzzy if–then rules [41]. Integrating fuzzy set theory into FES increases its ability to linguistically describe a given process or phenomenon and represent them with few flexible rules [42]. A primary advantage of FES is that using specified steps, mathematical formulas, and interconnected subsystems makes it possible to explain how the results are obtained [43]. The FES subsystems or components comprise fuzzification, inference rules, knowledge database, and defuzzification [44]. Therefore, FES is well suited to solving challenges resulting from imprecision, inaccuracy, and subjectivity.

The Rulebase contains all the rules and if–then conditions experts provide to regulate decision-making [45]. However, recent updates in fuzzy theory require a significant decrease in rules and conditions, providing various methods for designing and tuning fuzzy controllers. Fuzzification converts inputs by transforming crisp numbers into fuzzy sets [41]. Under this subsystem, the sensors measure the sharp numbers before passing them to the control system for further processing [46]. The inference engine is the subsystem used to determine the degree of correspondence between rules and fuzzy input [47]. After deciding the % match, the inference engine identifies the specific rules to develop the control actions [48]. The final component of the fuzzy expert system is the defuzzification that performs the crisp yield operations that involve converting the fuzzy sets into crisp values that indicate the degree of truth.

FES can be designed for implementation in various fields, including marketing, medicine, and automotive [49]. Although the factors and variables tested are different depending on the area, the successful design of a good FES follows the following steps [50]:

(i)

Identify the problem and select the appropriate type of fuzzy system: it is recommended to adopt a modular system, as it can be configured in several ways to serve several purposes and satisfy multiple needs [51]. Furthermore, system modules can be improved over time to accommodate changes and new challenges, thus improving performance;

(ii)

Definition of input and output variables: it is essential to identify the input parameters and classify the crisp values based on ambiguity indices in different fuzzy sets [52];

(iii)

Defining the fuzzy heuristic rules: the if–then rules provide a convenient way of expressing knowledge, providing interpretations that can process information in a specific way at the inference level [53]. For example, some rules express certainty or obligation, while others describe possibility or feasibility [54];

(iv)

Selection of fuzzy inference method: Fuzzy inference refers to the process of formulating the mapping of a specific input to output using fuzzy logic [55]. Thus, it involves the determination of aggregation operators for preconditions and conclusions [56]. This procedure creates the basis from which decisions are made and standards identified;

(v)

Defuzzification methods: this step involves converting the fuzzy output into a crisp value. Examples of shapes that can be used for this procedure include the center of maximum (CoM), the center of the area (CoA), less than maximum (SoM), weighted average (WA), and greater than maximum (LoM);

(vi)

Test the prototype of the fuzzy system to ensure that it works properly and make the appropriate adjustments in the membership functions, fuzzy rules, and objective function between input and output fuzzy variables.

For any successful company seeking long-term participation in a market, it is essential to have a clear marketing strategy that organizes marketing activities and resources to enhance an organization’s ability to gain competitive advantage [57]. Morgan and others [58] (2019) define a marketing strategy as an integrated pattern of decisions of a company that specifically identifies the products and services it offers, target markets, marketing activities, and resources. Keropyan and Gil-Lafuente [56] (2012) also indicate that it involves exchange, communication, and relationships to achieve specific goals. The marketing literature widely suggests that a firm’s economic performance in a given market is determined by its marketing strategy, which guides critical marketing activities such as resource deployment [59]. Furthermore, the marketing strategy identifies specific objectives that help streamline organizational business processes, marketing efforts, customer needs, and other stakeholder expectations to improve performance in the competitive business environment.

The formulation–implementation dichotomy of marketing strategy encourages the establishment of long-term decision-making structures that look to the company’s future. In this perspective, the formulation of marketing strategy requires that managers and their teams make precise decisions about “what” about the objectives they intend to achieve. Similarly, Khatwani and Srivastava [60] (2016) state that it also identifies the techniques they intend to apply to achieve them, including target market selection, scheduling, positioning, and determination of value offerings [61]. Therefore, this marketing strategy involves executing detailed marketing techniques and accompanying them with resources and actions necessary to implement the marketing decisions made earlier. The success of the marketing strategy formulation and implementation processes is determined by properly using the strategy content and the strategic process [62]. The content of the marketing strategy should specifically identify several aspects, including specifying target segments, the company’s value proposition, selecting marketing media, and planning sales force incentives [63]. In this case, the marketing strategy content addresses the specific strategic decisions and identifies the appropriate tactical marketing program decisions to ensure the success of the marketing efforts. Instead, the marketing strategy process identifies the organizational mechanisms that lead to these decisions [64]. These processes include situation assessment, marketing mix planning, performance measurement and monitoring, top-down versus bottom-up strategic planning process, budgeting, and goal setting [65]. Therefore, a competitive marketing strategy encompasses the decisions, activities, and procedures necessary to achieve a company’s desired objectives over time, including the means to develop, deliver, and communicate its offerings to target markets.

Organizations adopt different marketing strategies depending on multiple factors, including objectives, marketing relationships, products or services offered, target markets, and resources. Igor Ansoff, in 1957, identified four broad categories of marketing strategies: marketing penetration strategy, market development strategy, product development strategy, and market diversification strategy [66]. These ratings are based on the nature of the company’s products and existing and new customers.

The market penetration strategy begins with assessing the size of the market and the percentage of consumers who buy the company’s products and services. From this aspect, marketing creates and implements a strategy to overcome competitors to acquire a larger market share [67]. Therefore, market penetration strategy refers to a company’s initiatives to make its existing products and services in an already booming market to increase sales and organizational performance [68]. According to Hussain et al. [66], the market penetration strategy improves business performance by increasing sales among existing customers or looking for new customers for existing products or services. Various tactics can be adopted to achieve these goals, including lowering prices to compete with alternative products, acquiring competitors, and revamping the digital marketing roadmap to increase brand awareness among target markets [68]. Therefore, the main objective of this marketing approach is to generate more revenue by promoting or repositioning existing products to new or existing customers that fit the target market.

A market development strategy is an approach that companies use to introduce existing products or services to new markets. Once a company reaches maturity in its current market, exploring new markets for an ongoing product is essential to increase sales and ensure organizational performance and stability [69]. The product remains the same but is promoted to new target customers to facilitate corporate growth [70,71]. A market development strategy is an essential marketing tool as it helps companies reach a wider audience of potential customers, especially in the modern-day globalized business environment [72]. In addition to acquiring new customers, it can improve the quality of products or services, reduce the cost of production per unit, increase brand awareness, build organizational resilience, generate more leads and sales, and bolster long-term corporate growth and financial performance [73]. However, companies must conduct market research to identify development opportunities, develop a marketing plan and allocate adequate resources to ensure the success of the market development strategy.

Product development strategy refers to an organization’s tactics of launching new products into a market or modifying existing ones to meet customer demands and expectations. Hussain et al. [66] explain that this strategy involves the development of organized methods to guide all processes related to introducing a new item in the target market. The product development strategy can be applied in multiple situations [74]. For example, companies can develop new products when they see a decline in demand for an existing product in current market segments to secure growth and improve financial performance [75]. In addition, companies can develop new products that offer solutions to specific customer problems, basing all their production processes and activities on a comprehensive analysis of their needs, wants, and demands [76]. Demand for items or services creates business opportunities that can be exploited to achieve greater organizational performance. A great strength of product development strategy is that it uses market research to create a plan for successfully launching products or services in specific markets. In this case, planning can help companies overcome multiple challenges, adopting appropriate methods and techniques throughout product development [77]. Various tactics can be used under this marketing strategy to remain competitive, including changing product ideas, modifying existing ones and creating new products, specialization, customization, discovering new markets, and increasing product value [72]. A solid and clear product development strategy can help an organization turn an idea into a profitable product and enable modifications to remain competitive in the marketplace.

Companies use market diversification to expand their market share or increase their market presence by acquiring or launching new products or entering new markets. Some techniques used in this strategy include licensing, acquisitions, and mergers [73]. This strategy’s main objective is to increase an organization’s profitability, expanding into markets and sectors that have not yet been explored [78]. In addition to greater profitability, companies can diversify to reduce the risks of an industry downturn, improve brand recognition and image, and defend against increased competition in local markets [73]. Various forms of diversification strategies are adopted based on the company’s business objectives and offerings. These include horizontal, concentric, conglomerate, and vertical diversification.

Horizontal diversification is when companies expand their market presence by introducing products and services unrelated to their original offerings [79]. Vertical diversification is a strategy used when a company assumes some or all the functions associated with the production and distribution of its main products [78,80]. Concentric diversification is a strategy used to enter a new market with a new product technologically similar to the company’s current one. This approach allows the company to obtain multiple advantages, leveraging its industry experience, already implemented manufacturing processes, and technical know-how [66]. A cluster diversification strategy occurs when a company diversifies into entirely new markets, offering new products unrelated to its current sales to reach new consumer bases [81]. Regardless of a company’s choice of diversification strategy, these approaches, if employed correctly, can help organizations offer a broader range of products and services, build a stronger brand image, and increase company profitability.