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Small retail stores in the luxury market face significant challenges due to fluctuations in market demand. This task turns challenging as it requires effectively coordinating and translating customer needs into specific requirements that align with retail goals and available resources. However, limited empirical research exists investigating how managers can address service value and quality attributes in small retail stores. This article aims to bridge this gap by investigating the role of quality function deployment (QFD) in improving market and quality requirements management in small retail stores.

Based on the case study, a customer survey was initially conducted to gather information on critical characteristics valued in the luxury retail segment. QFD was used to assist the company in identifying and prioritizing key quality attributes to meet customer requirements effectively.

The findings demonstrate that implementing QFD in small luxury retail stores empowers managers to identify previously neglected product and service quality aspects. The article shows that QFD informs organizational adaptations that align with the demands of the retail market, leading to an improved ability to meet customer expectations and enhance customer value through the development of enhanced products and services. The study showcases the efficacy of the tested methodology in effectively capturing and prioritizing both tangible and intangible customer needs in retail.

Findings offer valuable insights to retail managers of small luxury stores, providing actionable market-oriented strategies. By implementing the recommended practices, managers can improve the store’s competitiveness and better cater to the customer base.

This study contributes to bridging persistent knowledge gaps by addressing the unique context of small luxury retail stores and introducing the application of QFD in this setting. The insights gained from this research are relevant to both retailing and quality management literature. Considering the growing prevalence of transformations in the retail industry, the study provides practical implications for retail managers in effectively navigating these changes.

This paper aims to identify the interrelationships among critical factors for digital servitization (DS) implementation.

A multi-method research was used. Critical factors for a successful DS implementation were identified using a systematic literature review and expert interviews. The interpretive structural modeling (ISM) method was used to develop a hierarchical model of the identified factors, followed by the fuzzy Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis to assess their dependence and driving powers.

A total of 23 factors for DS implementation were identified, and the ISM model was developed. Based on MICMAC analysis, the factors were also grouped under four categories (dependent, driving, autonomous and linkage). A conceptual framework is proposed, highlighting that DS implementation relies on three main layers of critical factors: crafting alignment, scaling the change and achieving results.

The ISM and fuzzy MICMAC methods used in this study provided valuable insights into the interrelationship among the identified DS factors through a conceptual framework. To the best of the authors’ knowledge, the study is one of the first to identify critical factors influencing DS implementation and develop hierarchical relationships among them.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.