Product Quality Research Articles

Big data analytics for quality variation over work shifts in manufacturing systems

ABSTRACT In the manufacturing industry, mass production enables manufacturers to produce parts with high precision and lower costs. Multiple shifts operate production processes to maximize efficiency. Several researches have been conducted on the impact of shift work on labor’s health and habits. However, there have been few studies on the influence of shift work on the consistency of product quality. This paper provides a methodology to analyze the impact of different work shifts in a real electronic board manufacturing industry. The study uses big data and analytics to assess product quality from data. Non-parametric kernel density estimation is used to approximate the distribution of good products in each work slot. Then several metrics are used to measure the dissimilarity between the estimated densities. The approach can be leveraged in various problems related to process performance and quality. The obtained results show that there is no significant difference in terms of product quality between work shifts. These prove the consistency of the manufacturing processes and the homogeneity of performance across work shifts in the studied factory. Compared to the literature, the paper presents the first quantitative analysis to compare production process performance and product quality over shift works.

Important drivers for customer satisfaction – a Swedish crisis reflection

Purpose The purpose of this study is to examine crisis effects on customer satisfaction and underlying drivers by adding a new set of data to previous research. The core questions are: are the findings from Hallencreutz and Parmler (2019, 2021) sustained or can new customer demands, needs, expectations and behaviours be traced in the wake of the ongoing crisis? Design/methodology/approach A first study covering 2005–2017 was completed in 2018, published online in 2019 and in print in 2021 (Hallencreutz and Parmler, 2021). This new study adds the years 2018–2023 to the data set and reuses the partial least squares (PLS) approach to structural equation models, also known as PLS path modelling. Findings This additional study sustains the results from the initial study (Hallencreutz and Parmler, 2019, 2021). The variable product quality has been substituted by service quality as one of the most crucial drivers for customer satisfaction together with brand image, and the current state of permacrisis has not changed that. Research limitations/implications The study is built on Swedish data from the EPSI Rating Initiative (Eklöf and Westlund 2002) covering customer perceptions in banking, insurance (life and non-life), telco (mobile operators, broadband and Pay-tv) and energy (trade, distribution and heating) over the years 2005–2023. Practical implications The study emphasizes the importance of understanding how customer satisfaction drivers evolve over time in different industries and societal sectors, especially during crises. This additional study sustains the paradigm shift in the studied industries – product quality has been substituted by service quality as one of the most crucial drivers for customer satisfaction, and the current state of economic downturn has not changed that. Social implications Society will have to learn to live with political and economic instability and unpredictability for the foreseeable future. To recognize the increasing value deriving from firms’ intangible assets while providing flawless deliveries seems to be a way forward in troublesome times. This is also a catalyst for existing societal trends: the necessary reforms to master sustainable transformations will require an ongoing adaptation process, with both winners and losers across continents. Originality/value The world has coped with a global pandemic, and Europe is currently experiencing a humanitarian, political and economic crises caused by a war in Ukraine. This extended period of global instability and insecurity could be called a permacrisis (Collins dictionary, 2022). This study offers a unique quantitative analysis built on Swedish data from EPSI Rating initiative.

The Effects of Selling Formats and Upstream Competition on Product Pricing and Quality Design

The Effects of Selling Formats and Upstream Competition on Product Pricing and Quality Design

Investigating consumers' experiences with community supported agriculture: Convergent parallel design methods.

Community-supported agriculture (CSA) represents a collaborative model where local farms and community members form partnerships to facilitate the direct delivery of fresh produce from farms to consumers. This study primarily investigates the experiences of current CSA members, focusing on the key factors influencing their retention intentions. Employing a convergent parallel mixed methods approach, this study gathers and analyzes both quantitative data (such as factors affecting members' retention intentions) and qualitative data (derived from interviews reflecting members' perceptions of their CSA experiences). The integration of these datasets provides a comprehensive understanding of the factors that shape CSA membership dynamics. The research findings underscore that Convenience, Product Quality, and Positive Interactions are pivotal factors that contribute to members' Intentions to continue their CSA memberships. These insights are crucial for enhancing the services provided to CSA members and hold significant implications for the broader scope of CSA membership research. This study not only fills a critical gap in understanding the Chinese CSA context but also contributes to the global discourse on sustainable agriculture practices and community engagement.

Bacillus velezensis B63 and chitosan control root rot, improve growth and alter the rhizosphere microbiome of geranium

The root rot complex of geranium plants caused by Rhizoctonia solani and Macrophomina phaseolina is a major threat, and control of these pathogens predominantly relies on chemicals. This study explored multifaceted applications of Bacillus velezensis (strain B63) and chitosan, assessing their biocontrol efficacy against root rot, and their subsequent effects on rhizosphere communities. Strain B63 was antagonistic to R. solani and M. phaseolina. Under field conditions, greatest efficacy was obtained with strain B63 (36% and 33% disease reductions in, respectively, two growing seasons), chitosan soaking + foliar spray 0.2% (CSF 0.2%) (33 and 27% reductions), and 0.1% chitosan soaking + foliar spray (CSF 0.1%) (33 and 26% reductions). These treatments also changed rhizosphere microbiota, as shown by numbers of colony-forming units (CFU) and 16S rRNA gene microbiome analyses. Concomitant with rhizosphere shifts, essential oil yields and composition were positively affected, as shown by gas chromatography analyses. Chitosan soaking + foliar spray 0.2% increased concentrations of citronellol (1.36-fold), geraniol (1.37-fold), citronellyl formate (1.54-fold), and geranyl formate (1.94-fold) in geranium essential oil, compared with the experimental controls. Strain B63 also increased these essential oils by 1.04- to 1.27-fold. B63 also enhanced eugenol levels by 1.35-fold. Treatments with B63 were more effective than chitosan in improving the geranium plant morphological parameters (plant height, numbers of branches, biomass). These results show that B. velezensis strain B63 treatments have potential for enhancing yields and product quality from geranium plant under root rot infection.

Dual-branch information extraction and local attention anchor-free network for defect detection

In the production process, the presence of surface defects seriously affects the quality of industrial products. Existing defect detectors are not suitable for surface with scattered distribution and complex texture of defects. In this study, a dual-branch information extraction and local attention anchor-free network for defect detection (DLA-FCOS), which is based on the fully convolutional one-stage network, is proposed to accurately locate and detect surface defects of industrial products. Firstly, a dual-branch feature extraction network (DFENeT) is proposed and used to improve the extraction ability of complex defects. Then, a local feature enhancement module is proposed, and a residual connection is established to enrich local semantic information. Meanwhile, the self-attention mechanism is introduced to form local attentional residual feature pyramid networks (LA-RFPN) to eliminate the influences of feature misalignments. The mean average accuracy (mAP) and frames per second (FPS) of the proposed DLA-FCOS on the cut layer of the tobacco packet defect dataset (CLTP-DD) are 96.8% and 20.7, respectively, which meets the requirements for accurate and real-time defect detection. Meanwhile, the average accuracy of the proposed DLA-FCOS on the NEU-DET and GC10-DET datasets is 78.4% and 67.7%, respectively. The results demonstrate that the DLA-FCOS has good feasibility and high generalization capability to perform defect detection tasks of industrial products.

Transcriptomic and metabolomic correlation analysis: effect of initial SO2 addition on higher alcohol synthesis in Saccharomyces cerevisiae and identification of key regulatory genes

IntroductionHigher alcohols are volatile compounds produced during alcoholic fermentation that affect the quality and safety of the final product. This study used a correlation analysis of transcriptomics and metabolomics to study the impact of the initial addition of SO2 (30, 60, and 90 mg/L) on the synthesis of higher alcohols in Saccharomyces cerevisiae EC1118a and to identify key genes and metabolic pathways involved in their metabolism.MethodsTranscriptomics and metabolomics correlation analyses were performed and differentially expressed genes (DEGs) and differential metabolites were identified. Single-gene knockouts for targeting genes of important pathways were generated to study the roles of key genes involved in the regulation of higher alcohol production.ResultsWe found that, as the SO2 concentration increased, the production of total higher alcohols showed an overall trend of first increasing and then decreasing. Multi-omics correlation analysis revealed that the addition of SO2 affected carbon metabolism (ko01200), pyruvate metabolism (ko00620), glycolysis/gluconeogenesis (ko00010), the pentose phosphate pathway (ko00030), and other metabolic pathways, thereby changing the precursor substances. The availability of SO2 indirectly affects the formation of higher alcohols. In addition, excessive SO2 affected the growth of the strain, leading to the emergence of a lag phase. We screened the ten most likely genes and constructed recombinant strains to evaluate the impact of each gene on the formation of higher alcohols. The results showed that ADH4, SER33, and GDH2 are important genes of alcohol metabolism in S. cerevisiae. The isoamyl alcohol content of the EC1118a-ADH4 strain decreased by 21.003%; The isobutanol content of the EC1118a-SER33 strain was reduced by 71.346%; and the 2-phenylethanol content of EC1118a-GDH2 strain was reduced by 25.198%.ConclusionThis study lays a theoretical foundation for investigating the mechanism of initial addition of SO2 in the synthesis of higher alcohols in S. cerevisiae, uncovering DEGs and key metabolic pathways related to the synthesis of higher alcohols, and provides guidance for regulating these mechanisms.

A Review of the Impact of Starch on the Quality of Wheat-Based Noodles and Pasta: From the View of Starch Structural and Functional Properties and Interaction with Gluten

Starch, as a primary component of wheat, plays a crucial role in determining the quality of noodles and pasta. A deep understanding of the impact of starch on the quality of noodles and pasta is fundamentally important for the industrial progression of these products. The starch structure exerts an influence on the quality of noodles and pasta by affecting its functional attributes and the interaction of starch–gluten proteins. The effects of starch structure (amylopectin structure, amylose content, granules size, damaged starch content) on the quality of noodles and pasta is discussed. The relationship between the functional properties of starch, particularly its swelling power and pasting properties, and the texture of noodles and pasta is discussed. It is important to note that the functional properties of starch can be modified during the processing of noodles and pasta, potentially impacting the quality of the end product, However, this aspect is often overlooked. Additionally, the interaction between starch and gluten is addressed in relation to its impact on the quality of noodles and pasta. Finally, the application of exogenous starch in improving the quality of noodles and pasta is highlighted.

Application of Lean Six Sigma in Dermatology Practice

The Lean Six Sigma (LSS) methodology integrates two quality improvement techniques to enhance efficiency, reduce waste, and improve the quality of processes and products within an organization. While it has found application in various healthcare settings, to the best of our knowledge, there is currently no literature addressing its utilization in a community dermatology clinic. This study aimed to identify efficiency issues within a local dermatology clinic and implement targeted strategies to bolster collaboration, streamline processes, reduce wait times, augment patient volume, and enhance clinical outcomes and the quality of care. Five areas for improvement were identified: the building complex, front office, EMA system, medical assistants, and general improvements. The executed changes effectively standardized multiple processes, mitigated the potential for errors, and minimized task duration. These findings underscore the efficacy of LSS as a potent tool for enhancing efficiency and reducing waste. Efforts should be directed toward the incorporation of LSS techniques for quality improvement within healthcare systems, both internally and across interconnected entities.

Stress-mediated polysorbate 20 degradation and its potential impact on therapeutic proteins.

Polysorbates are the most commonly used surfactants in formulations to stabilize therapeutic proteins against interfacial stresses. Polysorbates can undergo oxidative or enzyme-mediated hydrolytic degradation to produce free fatty acids (FFAs) and subvisible particles in formulations. To determine which product related variables contribute to PS20 degradation, we investigated the effects of storage temperature, formulation, pH, presence of hydrolytic enzymes, and specific fatty acid composition on different grades of PS20 in relation to their PS20 degradation profile and consequently the quality of protein drug products. Bevacizumab and T-DM1 were reformulated in the freshly prepared therapeutic protein formulations containing either compendial PS20 or non-compendial PS20 with high % lauric acid and spiked with exogenous esterase or lipase. The release of FFAs and formation of particles were monitored at 4°C and 37°C. Protein quality was assessed for secondary structures, purity, and biological activity. Hydrolytic release of FFAs and formation of subvisible particles were found to be dependent on grades of PS20, types of enzymes used, incubation temperature, and pH. Esterase- or lipase-mediated degradation of PS20 and formation of subvisible particles in drug formulation showed no significant impact on the biological activity and stability of therapeutic proteins against degradation or aggregation. Our study suggests that degradation of PS20 and formation of FFA particles depend on the fatty acid composition of PS20, types of hydrolytic enzymes, pH, and temperature. The presence of FFA subvisible particles showed no significant impact on the purity and biological activity of the therapeutic proteins under the tested conditions.

Microbial community structure of plant-based meat alternatives

A reduction in animal-based diets has driven market demand for alternative meat products, currently raising a new generation of plant-based meat alternatives (PBMAs). It remains unclear whether these substitutes are a short-lived trend or become established in the long term. Over the last few years, the trend of increasing sales and diversifying product range has continued, but publication activities in this field are currently limited mainly to market research and food technology topics. As their popularity increases, questions emerge about the safety and nutritional risks of these novel products. Even though all the examined products must be heated before consumption, consumers lack experience with this type of product and thus further research into product safety, is desirable. To consider these issues, we examined 32 PBMAs from Austrian supermarkets. Based on 16S rRNA gene amplicon sequencing, the majority of the products were dominated by lactic acid bacteria (either Leuconostoc or Latilactobacillus), and generally had low alpha diversity. Pseudomonadota (like Pseudomonas and Shewanella) dominated the other part of the products. In addition to LABs, a high diversity of different Bacillus, but also some Enterobacteriaceae and potentially pathogenic species were isolated with the culturing approach. We assume that especially the dominance of heterofermentative LABs has high relevance for the product stability and quality with the potential to increase shelf life of the products. The number of isolated Enterobacteriaceae and potential pathogens were low, but they still demonstrated that these products are suitable for their presence.

Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review.

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.

RADIATION AND ECOLOGICAL STUDIES OF VEGETATION OF LUTSK DISTRICT OF VOLYN REGION

Abstract. The content of radionuclides of 137Сs, 90Sr and heavy metals in vegetation and agricultural products was found out in the settlements of Lutsk district of Volyn region. The results of our own field expeditionary research, as well as the results of generalization of stock materials of the Laboratory of Ecological Land Safety and Product Quality of the Volyn branch of the state institution “Institute for Soil Protection of Ukraine” (Lutsk) are the basis of the research. The plant products contain a small content of radioactive elements in the settlements of the Lutsk district of the Volyn region. The greatest pollution of plant products was found in the settlements of Sytnytsia and Omelіano. The content of the 137Сs, 90Sr does not exceed the maximum permissible concentrations. Exceeding the maximum permissible concentrations for the content of heavy metal salts was found in potatoes in the villages of Pozharky, Vesele. The results of the radiation-ecological study can be taken into account for the optimization of nature management and are necessary for state emergency management bodies of natural-technogenic content, for the possibility of reasonable planning and effective implementation of measures and projects for strengthening ecoenvironmental safety. The data obtained are important for the development of the National Concept to overcome the consequences of the Chernobyl disaster on the contamination of agricultural products with radionuclides and heavy metals.

The Linder hypothesis for foreign direct investment revisited

AbstractThis article investigates the Linder hypothesis for foreign direct investment (FDI) within the three‐way gravity framework, utilizing a newly compiled and extensive dataset encompassing greenfield and brownfield investment activities across diverse sectors from 2003 to 2018. The Linder hypothesis posits that multinational firms invest in countries with comparable income levels to their home country. Our primary findings affirm the relevance of the Linder hypothesis in the context of horizontal FDI. The influence of the Linder effect varies among sectors, with the service sector exhibiting the most pronounced effect, while no detectable effect is observable for the manufacturing sector. We also find that the Linder effect depends on the sector's position within the value chain and the degree of quality differentiation. Sectors closer to final consumer demand and those characterized by higher product differentiation exhibit greater exposure to the Linder effect. Additionally, our analysis reveals that the Linder effect is subject to variations based on the income levels of the host country and highlights the significance of consumer preferences in shaping FDI patterns. Our article underscores the pivotal role of industry dynamics, product quality considerations, and value chain positioning in influencing the Linder effect on FDI.

ВЫБОР ПАРАМЕТРОВ ТЕХНИЧЕСКОГО СОСТОЯНИЯ ЭЛЕМЕНТОВ ПОГЛОЩАЮЩЕГО АППАРАТА, ОПРЕДЕЛЯЮЩИХ ЕГО БЕЗОТКАЗНОСТЬ

The problems to plan and ensure a given level of reliability of mechanical systems at all stages of their design and operation occupy an important place in the problem of ensuring the quality of machine-building products. With a significant number of scientific methods and approaches to solving theoretical and applied issues of reliability theory, one of the key directions is to determine the reliability indicators of technical systems and their elements, in particular a wide range of products including sliding friction pairs with periodic impacts. The study of failure origins, from the point of view of physical-mechanical processes occurring in surface layers of elements, under the influence of external factors, allows selecting parameters characterizing the functional state of the pair, and, as a result, its reliability. For example, for sliding friction pairs with periodic impacts, such a parameter is the geometric dimensions of the conjugate elements. The decrease in the geometric dimensions of conjugate elements, due to wear, characterizes the rate of change of the initial parameter values to the maximum permissible ones determining the failure, and can be approximated by linear, power, exponential and other dependencies obtained from actual measurements. According to the obtained functional dependencies of parameter changes over operating time, it makes possible to determine the norm for the parameter regulating their pre-failure state, beyond which failure prediction is possible with varying probability, and, as a result, the reliability of the system as a whole. The use of this method to reliability forecasting through the determination of the pre-failure state of the system can be projected onto technical objects, which performance indicators are geometric parameters that ensure the performance, first of all, of the functions of the elements themselves and the entire technical system, and worn out during operation.

BASES OF THE METHOD OF PHYSICAL MODELING OF THE PROCESS OF CRUSHING OF MUNICIPAL SOLID WASTE

A large number of studies are aimed at increasing the energy efficiency of grinding processes of various solid materials, while maintaining the values ​​of other important indicators, such as material consumption, productivity, etc. Based on this trend, a new method for physically modeling the process of grinding municipal solid waste (MSW) was proposed for the first time. Existing physical modeling techniques are designed for homogeneous and isotropic materials (for example, soil, crushed stone, snow, coal, etc.). The strength properties of solid waste vary widely due to the significant heterogeneity of their components. Consequently, when crushing solid waste, traditional crusher designs have low efficiency in terms of energy intensity, material intensity and product quality. The purpose of this work is to develop a new technique for physical modeling of the grinding process, based on the main principles of similarity theory and modeling, considering the properties of waste heterogeneity. As a result of the research, a block diagram of the physical modeling methodology for the interaction of the working bodies of impact crushing machines with solid waste was developed. A list of tasks for the modeling process and similarity criteria have been determined based on the development of rheological models of the “working body - municipal solid waste” system and the laws of mechanics that characterize the waste grinding process. Based on the developed similarity criteria, scale equations for the grinding process are substantiated and formulas are derived for determining the expected parameters of the original based on the parameters measured on the model. The developed methodology makes it possible to create a crusher design with improved energy efficiency indicators with the least material and labor costs.

Novel intrusion detection system based on a downsized kernel method for cybersecurity in smart agriculture

Smart farming aims to ameliorate the quantity and quality of farms products using modern information and communication technologies. Agriculture has always used cutting-edge technology to improve profitability, efficiency, and safety as well as creating the opportunity for more environmentally friendly operations. In smart agriculture network communication between smart devices is necessary to managing the agriculture process. Consequently, the possibility of information being exposed to hacking is very likely, which makes the information security system extremely important and cannot be neglected. Even though many solutions have been developed to overcome cyberattacks in smart networks, some limitations are observed, such as solving multi-class problems, high feature dimensions, and overhead computation. To address the aforementioned issues, an intelligent Intrusion Detection System (IDS) for identifying cyberattacks in the Internet of Agriculture Things (IoAT) is proposed. The developed model uses a proposed reduced kernel method, the Downsized Kernel Partial Least Square (DKPLS) to extract and reduce data feature dimension to improve detection performance. The DRKPLS approach is used to reduce the dimension of a kernel matrix generated using the Kernel Partial Least Square (KPLS) technique by selecting the most important features. In classification phase, the Kernel Extreme Learning Machine (KELM) is used to classify data for binary and multi-class classification. The proposed IDS is evaluated on a new Industrial IoT Dataset, X-IIoTID. The developed approach achieved higher performances for binary classification and for multi–class classification compared to others machine learning and deep learning approaches. The proposed approach achieved an accuracy rate of 99.92% for binary classification and 99.99% for multi–class classification. Furthermore, the higher percentage in multi-class classification is obtained for the two types of attacks Ransom Denial of Service (RDoS) and Command_Control respectively.

Fabric phase sorptive extraction increases the accuracy of liquid chromatography-fluorescence detection of zearalenone in food samples

ABSTRACT The accuracy of mycotoxins detection in the food samples has always been controversial and more sensitive methods help better regulatory activities and, therefore, the quality of final products. Zearalenone is a toxic substance from mycotoxins mainly produced by Fusarium fungi that has proven endocrine effects and is potentially carcinogenic. This research aimed to apply the fabric phase sorptive extraction (FPSE) technique to the liquid chromatography- fluorescence detection method to increase the sensitivity and accuracy of zearalenone extraction and determination in food samples. Applying graphene nanoparticles in the sol-gel process modifies the method depending on the amount of nanoparticles, volume and type of the back-extraction solvent, sample pH, ionic strength, and back-extraction time, which are all considered and investigated. At analytical conditions, the limit of quantification (LOQ) 16.57 and detection (LOD) of 4.97 ng/mL were observed. The method’s accuracy was determined by three spiking levels of 100, 150, and 200 ng/mL in the real samples. The mean recovery percentage in wheat were 61.69, 70.56, and 97.95%, and in the rice, 58.13, 66.00, and 102.59, respectively. The average percentages of the relative standard deviation of the instrument for intra-and inter-laboratory repeatability were less than 1.7%. This work provided a superior analytical condition to the conventional detection procedures in food safety laboratories.

How do e-commerce platforms and retailers implement discount pricing policies under consumers are strategic?

In the era of the rapid development of e-commerce, many retailers choose to launch promotional activities to become consumers' first choice for shopping. Since price discounts can greatly attract consumers, the e-commerce platforms have also begun to implement discount pricing. It is urgent for e-commerce platforms and retailers to formulate reasonable discount strategies to achieve sustainable business. In this paper, we construct a dynamic game model for implementing discount pricing on an e-commerce platform and two retailers, we study the market equilibrium between the two retailers and the e-commerce platform under various scenarios that considering consumers' strategic waiting behavior and competition between the two retailers, we further discuss the effectiveness of retailer discount pricing and the double discount pricing of the platform and retailers. We show that the optimal pricing decreases as the difference in product quality narrows under both pricing strategies. Low-quality retailers implementing a double discount pricing strategy are in relatively higher demand only when the difference in product quality is small. High-quality retailers implementing the retailer discount pricing strategy are in relatively higher demand only when the product quality difference is large. Double discount pricing is desirable for both e-commerce platforms and retailers and can be used to effectively achieve Pareto improvement in the market by increasing their expected profit. Our results emphasize the role of product quality and the value of the double discount pricing strategy. The double discount pricing strategy weakens the profit advantage that retailers and platforms gain from it as the rebate intensity and rebate redemption rates increase.

Analysis of Marketing Mix Implementation on Purchasing Decisions for Skintific Skincare Products (Survey of the Community in Jabodetabek)

This research investigates the impact of marketing mix implementation on purchasing decisions for Skintific Skincare Products among consumers in the Jabodetabek region. A quantitative research design was employed, surveying 200 respondents to gather insights into consumer preferences, brand perceptions, pricing influences, and marketing mix synergies. Results indicate that product quality and competitive pricing emerged as significant determinants of consumer satisfaction, brand loyalty, and purchase intention. Furthermore, correlations between marketing mix elements such as product quality, pricing, distribution channels, and promotional activities underscored their collective influence on consumer behavior and brand positioning. The findings offer strategic recommendations for Skintific to enhance market performance, sustain competitive advantage, and drive long-term success in the skincare industry.