Development of mass customization implementation guidelines for small and medium enterprises (SMEs)

Mass customization (MC), an organization’s ability to provide customized products and services that fulfil each customer’s idiosyncratic needs without considerable trade-offs in cost, delivery and quality, is gaining importance among companies. To help practitioners on the complex path towards MC, academic research has provided some guidelines for MC implementation. Recent reviews in this research sub-stream underlined the lack of MC implementation guidelines (MC-IGs) specifically developed for small and medium enterprises (SMEs) and also indicated the opportunity to use the design science research (DSR) strategy to develop new MC-IGs. The present research answers call for new MC-IGs by developing maturity grid-based MC implementation guidelines for SMEs that comply with the MC-IG building blocks and the MC-IG properties indicated by Suzić, Forza, et al. The development of such guidelines followed a DSR strategy that included short- and long-term observational evaluations in two SMEs over three years

Real-time Data Analytics Edge Computing Application for Industry 4.0: The Mahalanobis-Taguchi Approach

Industry 4.0 and its innovative technologies (e.g., Internet of Things, Cyber-Physical Systems, Cloud Computing, Big Data and Artificial Intelligence) represent great promise. Still, com-panies experience hardship when transforming from reactive to predictive manufacturing systems. The latter, driven by data science development, use predictive models to detect and solve production and maintenance issues before they happen. To eliminate the need for large and varied datasets for development of predictive models, in the present research we propose development of real-time predictive models based on small dataset without faulty data. This is achieved by using Mahalanobis-Taguchi system for fault detection in lack of fault data samples, and by using Edge Computing environment which provides higher re-sponsiveness, better security and decreased costs. Subsequently, two predictive models are developed, tested and compared for the case company from process industry (i.e. the vi-nyl-floor industry sector). Finally, recommendations for the industry are provided