PurposeLean and agile are essential supply chain management (SCM) strategies that enhance companies' performance. Previous studies have reported the capabilities of different SCM strategies to enhance performance; however, the emergence of Industry 4.0 technologies has bred focus on the possibility of attaining more levels of operational performance. Despite being demonstrated helpful at enabling supply chain (SC) strategies, the literature linking Industry 4.0 with SCM strategies is still in its infancy. Thus, this work investigates the degree to which “Industry 4.0 technologies” enable the implementation of lean and agile practices and subsequently assesses the potential performance implications of integrating Industry 4.0 technologies with the SC operations.Design/methodology/approachThe work employs an exploratory case study approach using empirical data from selected organisations drawn from an Estonian manufacturing cluster and digital solution providing companies. The data collected via interviews were used to assign numerical scores and subsequently aggregated across the five cases for the research variables of interest. The work is crowned with a model grounded on the cross-case analysis to depict which technologies impact each of the lean and agile practices.FindingsThe analysis enabled comprehension of the potential impact and level of importance of the main Industry 4.0 technologies on lean and agile practices and ultimately the potential implication on performance. The findings revealed that the technologies have a high impact on the practices. Although the impacts are of varying degrees, the analysis provides means to identify the technologies with the most significant impact on lean and agile SCM and the sets of practices with the greatest likelihood of being enabled by various digital technologies.Practical implicationsThe work presents various lean and agile practices that practitioners can deploy to operations, alongside the technologies that could support the implementation of the practices towards achieving the various performance measures. Also, it provides some guides for the digital solution providing companies towards understanding the SCM practices that can be improved upon by various digital technologies. This enables them to have more saleable proposals for intending companies who might be sceptical about transiting into the digital operation phase.Originality/valueThis is the first attempt to empirically address the connection between Industry 4.0 technologies and the integrated lean and agile strategies despite literature backing of the complementary nature of the two SCM strategies.

Purpose Various networked organizational forms already exist in the market today, however, all of them share the same problem – lack of fast and objective procedures for partners’ selection when addressing a particular project. The purpose of this paper is to solve such problems by offering an alternative to the existing global market layout, dominated by large corporations. Design/methodology/approach This research belongs to the category “fuzzy decision making and multi-attributive decision making,” since it is using methodologies such as the analytical hierarchy process (AHP), the fuzzy-AHP approach, and the Technique for Order of Preference by Similarity to Ideal Solution. Findings The authors defend that appropriate partner selection is a vital success factor in any collaboration. Research limitations/implications The authors have designed a sustainable partner network solution for the field of machinery. Nevertheless, the proposed approach is adaptable to other fields also, whereas the focal player or project owner selects the best partner based on a set of criteria. Practical implications The paper includes a feasibility case study for the approval of findings, where several small and medium enterprises (SMEs) from the field of machining collaborate to achieve a common goal. Social implications The research focuses on production enterprises (especially for SMEs) and is intended to improve their competitiveness. Small enterprises combining into a network makes it possible to compete with large corporations and helps to decrease project initiation time and project realization risks. Originality/value The main idea is to collect information about the available resources of SMEs into a new temporary entity and to utilize those resources for the realization of the tasks of a particularly large project, while meeting customer expectations. In addition, this work also suggests a calculation tool for faster partner evaluation (assessment) based on various criteria for each particular project.

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This research introduces a new approach of using Six Sigma DMAIC (Define, Measure, Analyse, Improve, Control)

In most cases, the complexity of installation work, such as the induction of a collaborative robot at metal-working enterprises exceeds the complexity of machining and significantly exceeds the labour costs for all other types of production. Today the most assembly jobs in the manufacturing domain of Small and Medium-sized Enterprises (SMEs) are still performed by hand due to high mix and low volume orders. The interaction of humans and robots may increase the efficiency in complex assembly processes. The flexibility and variability of assembly processes require close cooperation between the worker and the automated production system. Automation of production is not an easy process for an enterprise, which requires high investment and additional skills, but it is necessary to improve working conditions and product quality. This article provides an efficiency analysis of collaborative robots usage in one of the Estonian enterprise.

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In the current research six dimensions of customer value, namely: quality, cost, time, customization, know-how, and respect for the environment are analyzed in the following industries: automotive, electronics, furniture, food, clothing, and pharmaceutical industry. The research uses inductive approach in which a theory is emerged from the empirical data and observations. The data collection phase benefits from a trade-off based design questionnaire, which was used to collect the comparative data from end customers for each pair of customer value dimension. Due to the pair-wise format of collected data, Friedman test is employed in data analysis phase, in order to prove the validity of dataset in generating meaningful results. Findings are categorized for each dimension of customer value, where the importance of each dimension in comparison with others is discussed. The study results in customer value coefficient for each value dimension in each industry. The proposed coefficient clarifies the priority of value dimensions in different industries based on the dataset. This coefficient enables practitioners to list the corresponding industry customer values in order of importance and support the decision making process in trade-off situation, when improvement of one customer value dimension causes in reduction of the others. The developed coefficient quantitatively states how to sacrifice one and improve another dimension in favour of customer value. In a nut shell, the authors suggest to apply the customer value coefficient for the analysis of customer preferences when trade-off among value dimensions is involved.