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The purpose of this paper is to shortly overview the research in international operations management (OM), to provide background to the papers published in this special section.

As a literature review, the paper investigates the past, present, and future of international OM. It is not a systematic review; the paper just highlights the most important international operational management research networks, streams and concepts in the field.

The paper finds that there is a time lag in the field of international OM compared to other research areas within international business and management. It provides some ideas for the future to be researched.

The paper gives a focused review on international research networks which has not been done before. It also identifies two different streams of researches in international OM: the stream investigating OM differences among geographical areas, and the stream dealing with issues of international manufacturing networks.

This study aims to investigate how measuring the lean practices–performance relationship could add value to businesses.

Case-study methodology was used for the quantitative analysis of self-assessed lean capability scores and key manufacturing performance indicators from a global consumer goods company. Up to 80 sites over 3 years were analysed. Partial least squares structural equation modelling (PLS-SEM) is used to analyse the lean implementation–performance relationship.

Results show that different performance areas may be affected differently and suggest that the measured relationship may depend on the reliability of the self-assessment.

The research is based on the manufacturing sites of a single company, implementing the same lean methodology, producing similar products and using similar process technologies. This helps to control confounding variables but may limit the generalisability of the findings.

Practitioners can use the measurement method to improve lean-based performance-improvement projects. The study suggests that the PLS-SEM technique could improve the calibration of lean-practice self-assessment and could identify further areas for improvement.

Very few papers developed measurement methods for the effectiveness of lean-practice implementation using objective manufacturing performance data within a single multinational company.

In the multilayered capability framework the authors integrate two layers, namely functional level production capabilities and shop floor-level production routines (PRs). The authors examine how these two layers are interlinked, and additionally, they explore how these layers contribute to firm performance.

The authors tested the hypotheses using structural equation modeling (SEM) on a sample of manufacturing firms.

Regarding the capability layers, the authors found that at the functional level, production dynamic capabilities (PDCs) drive the renewal of production ordinary capabilities (POCs), and that at the shop floor level, deployment of Industry 4.0 (I4.0) is influenced by lean production. Regarding the direct links between capability layers, the authors showed that PDCs and POCs have different roles in shaping shop floor PRs: PDCs is linked to I4.0, and lean methods is impacted by POCs. Concerning performance implications, only PDC and POC have significant impact on firm performance (the latter is negative), while PRs do not.

Although, contextual factors (e.g. technology intensity, size) do not influence our findings, the potential country-effect and the dominance of medium-sized firms offer future research directions.

If production managers want to contribute to business performance, they should be more susceptible to resource renewal (PDCs) than to their general (POCs) or specific (PRs) exploitation efforts. As they exploit current resource stocks, they face a trade-off: they must consider that beyond their positive impacts on operational performance, their implications on business performance will be controversial.

Scholars usually examine one layer of capabilities, either capabilities or routines, and associate that with one dimension of performance, either financial and market measures or operational indicators. The authors propose a multilayered capability framework with a complex view on performance implications.

Taking its outset in operations management (OM) contingency research, this paper aims to investigate how firm size, as one of the most powerful explanatory factors, influences the implementation and performance impact of four key manufacturing practices.

Three large-scale surveys from three different points in time, with a total of 1880 observations from varied geographical regions, are used to offer generalizable evidence on how firm size influences the implementation and performance outcome of technology, lean, quality and human resource practices.

The four manufacturing practices positively enhance performance: quality and lean practices produce the most consistent effects, while technology and human resource practices turn more beneficial in the latest sample. Furthermore, the authors offer robust support for the selection and mediation models (larger firms generally invest more in the four practices and, through that, achieve higher performance), while finding no evidence for the moderation model (smaller firms can equally benefit if they possess the resources to invest in these practices).

As manufacturing practices are continuously evolving, their performance impact cannot be guaranteed in any context. Size is a frequently used contingency variable in OM studies, but results are contradictory in terms of its impact on the implementation and performance outcomes of manufacturing practices. This study manages to ease these contradictions.

Many firms today operate international manufacturing networks (IMN) of plants, which may serve different purposes and have different levels of competences. This diversity influences the effectiveness of different manufacturing practices, which has not yet been explored in the literature. The purpose of this paper is to investigate the relationship between plant role and the “goodness” of its manufacturing practices.

Data are used from a sample of 471 plants from the sixth edition of the International Manufacturing Strategy Survey.

The findings show that plants with higher competences use more practices successfully than less competent plants. Furthermore, more competent plants tend to strengthen their differentiation performance, while less competent plants focus on and achieve cost performance improvements instead.

The associations between plant role, manufacturing practices and performance provide important input for the global design of a firm’s IMN as a whole, and the development of local plants within the network.

The “goodness” of manufacturing practices has not been investigated in the plant role literature; the effect of plant role on the “goodness” of manufacturing practices has not been studied in the OM contingency literature. This paper shows that while the role of a plant in a firm’s IMN hardly affects the efforts it puts into implementing different manufacturing practices, it has an important moderating influence on the performance implications of these practices.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

Following the identified need for more explicit contextual studies in servitization research, the purpose of this paper is to investigate the relationships between and among economic context, service provision and service return, including the service paradox.

Firm-level and macroeconomic (country competitiveness) data are combined to operationalize the constructs considered in the study. Structural equation modeling and cluster analysis are used to investigate the direct relationships between economic context, service provision and service return, and the negative association between the development of economic context and the service paradox.

The analyses confirm the general assumption that service provision has a positive direct effect on service return. Economic context seems to have no direct effect on service return and, contrary to what was expected, it has a negative impact on the intensity of service provision. Thus, service provision fully mediates the negative impact of context on service return. Finally, the service paradox occurs more frequently in less-developed economic contexts, where the probability of a relatively low service return coupled with high service provision is significantly higher.

The study identifies five key elements of economic context that have to be incorporated into the strategic decision-making process regarding product-related services offered by manufacturers.

The paper contributes to the contextual research of services offered by manufacturers. Subject to future empirical testing, it is proposed that a more favorable economic context offers more possibilities for manufacturers to cooperate with other business actors to provide services.

The purpose of this paper is to review the literature and offer a more generalizable empirical investigation on the performance impact of implementing Industry 4.0, and the way important contingency factors (plant size, multinational status, country context) affect implementation efforts.

Following a systematic literature review, the empirical research is based on a large-scale survey of 705 manufacturing plants from 22 countries. Structural equation modeling is employed to discover the relationships between the main constructs of interest, complemented with subgroup analyses to offer a more detailed understanding of the main effects.

We provide evidence that technologies enabling Industry 4.0 have a positive impact on operational performance, including cost, quality, delivery and flexibility performance. Results of the analyses further indicate that (1) larger firms invest more in implementing Industry 4.0 technologies, (2) manufacturing firms in less competitive countries, especially in the South-East Asian region invest significantly more effort than competitive countries, while (3) multinational companies have no advantage over local firms.

The survey data employed in this study refers to the early years of companies embracing Industry 4.0 solutions, and thus does not contain the most recent advances in manufacturing technologies.

The paper represents one of the first studies in the literature to assess on a large-scale survey the performance impact of Industry 4.0 technologies, as well as the main contingency factors affecting the implementation of these technologies.

The authors’ main objective is to examine the resource alteration underlying the digital manufacturing transformation. The authors rely on the adaptation aspect of dynamic capabilities (DC) theory and their analysis shows how and why a factory adapts its resources and capabilities during digital transformation.

To grasp the change, the authors apply the longitudinal case study method within a revelatory case setting. The digital transformation is detailed from the perspective of a subsidiary that has played a key role in the division's digital transformation.

Analysing the revealed four stages of the transformation through the lenses of the DC components of adaptation (sensing capability, absorptive capacity, integrative capability, relational capability), this study suggests a sequence with unbalanced characteristics. Each stage starts with sensing capability, each component appears during each stage and each stage is dominated by a different component. Relying on the path dependency concept, the authors also present that the interplay between lean as an old resource stock and digital manufacturing as a new resource stock is rather a necessity, especially at the beginning of the transformation (at a corporation that pursues lean for years).

Digital strategy development is rather an intermediate element of the transformation, since committed personnel (or maybe their network) start bottom-up and coordinate initiatives as they sense the opportunities in the environment. Top managers should rely on their accumulated knowledge and involve them into the transfer coalition in the top-down phase of digitalization. The authors’ case also underlines that starting to experiment with novel technologies requires a solid (and usually expensive) technological and human basis. Finally, process improvement focussed developments at a high-performing factory might be just enough to deal with ever-demanding customer expectations.

This study is among the firsts in operations management that relies on the DC theory to follow up the digital transformation of a factory. A further valuable contribution is that the adaptation process is examined in a longitudinal case study.