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This paper aims to investigate the characteristics and dynamics of the organizational changes needed to facilitate the management of an Industry 4.0 transformation in manufacturing SMEs and propose an approach to manage them.

This research focuses on a single manufacturing SME in North America, and data were collected using a research intervention method. Data were collected through observation and intervention within the SME over 27 months.

The research has shown that organizational changes are required in manufacturing SMEs to better manage their Industry 4.0 transformation projects.

Using the case study method limits the generalization of the results. The organizational changes observed, and their characteristics might be specific to the studied manufacturing. Although results could vary in different contexts, many manufacturing SMEs have similar characteristics to those observed in this study.

This research provides preliminary evidence of an iterative organizational change management approach that manufacturing SMEs must adopt to facilitate the management of their digital transformation.

This research provides a better understanding of how a manufacturing SME can improve its capabilities to manage its digital transformation by introducing iterative organizational changes. From these results, a link to the organizational learning literature can be drawn and developed upon.

The purpose of this paper is to propose a new business process representation adapted to the needs of Industry 4.0 to facilitate the implementation of technological solutions in the construction sector.

This work is based on the Design Research Methodology approach and includes four phases: (1) a literature review on the main business process modeling standards and their ability to take into account the specificities of Industry 4.0; (2) the identification of the attributes to be considered to model Industry 4.0 processes; (3) the development of a mapping model for Industry 4.0; and (4) the validation of the model using a case study from the construction sector.

To the authors’ knowledge, current business process modeling standards do not effectively represent business processes in the context of Industry 4.0.

The proposed model can represent not only the 4.0 solutions that can be implemented in the construction sector, particularly from a technology and data perspective but also measures, with the help of performance indicators, the impacts of successive process changes in terms of skills, cost and time for a true 4.0 transformation.

This paper aims to propose a collaborative approach model developed based on observations of two aerospace manufacturing small and medium-sized enterprises (SMEs) pursuing their digital transformation toward Industry 4.0.

This research focuses on two manufacturing SMEs in North America, and data were collected using longitudinal case study and research intervention method. Data collection was performed through observation and intervention within the collaborative projects over 18 months.

A model of a collaborative approach to digital transformation (CADT) for manufacturing SMEs was produced. Based on the study findings, the collaboration manifests itself at various stages of the transformation projects, such as the business needs alignment, project portfolio creation, technology solution selection and post-mortem phase.

Research using the case study method has a limitation in the generalization of the model. The CADT model generated in this study might be specific to the aerospace manufacturing industry and collaboration patterns between manufacturing SMEs. The results could vary in different contexts.

The proposed CADT model is particularly relevant for manufacturing SMEs' managers and consultants working on digital transformation projects. By adopting this approach, they could better plan and guide their collaboration approach during their Industry 4.0 transformation.

This research provides a new perspective to digital transformation approaches in the aerospace industry. It can be integrated into other research findings to formulate a more integrated and comprehensive CADT model in industries where SMEs are significant players.

The purpose of this paper is to address the difficulties faced by manufacturing enterprises by providing a project portfolio management approach supporting the selection and prioritization of various Industry 4.0 projects where business process analysis is used to ensure the strategic alignment and value of the project portfolio.

The design research methodology, a mixed applied research methodology, was used to develop and test the proposed approach.

Despite the growing interest of the scientific and industrial communities in I4.0, it seems that there is no method by which manufacturing companies can select a large number of improvement projects. Moreover, studies tend to focus on the evaluation and implementation of a single technology, while the transformation of an intelligent plant requires the consolidation and coordination of many initiatives to achieve a global objective.

The proposed project portfolio management model offers support to enterprises during their digital transformation and improves their processes by integrating technology levers through consistent and achievable selection of I4.0 initiatives while meeting strategic goals and objectives.

It is assumed that the production system responds to planned demand at the end of the expected life of each individual piece of equipment and unplanned demand triggered by equipment failures. The difficulty of controlling this type of production system resides in the variable nature of the remanufacturing process. In practice, remanufacturing operations for planned demand can be executed at different rates, referring to different component replacement and repair strategies. A sub‐optimal control policy in which inventory thresholds trigger the use of different execution modes has been formulated in previous research to address this problem when unplanned demands are processed under an exponential time distribution. The aim of this study is to extend this control policy to more realistic unplanned demand arrival and processing times distributions.

The proposed approach is based on a combination of analytical modeling, simulation experimentation and regression analysis. The model was validated by comparing the obtained simulation results with those obtained under an exponential processing time distribution.

The results demonstrate that the structure of optimal control can be approximated by the sub‐optimal multiple hedging point policy with non‐significant cost variations.

The simulation results demonstrate that hedging point control policies could be applicable to a wide variety of complex remanufacturing problems in which analytical solutions are not easily obtained.

The paper extends the concept of hedging point policy to the control of real‐word repair and remanufacturing operations. Once calculated, the sub‐optimal policy parameters can be simply implemented by practitioners through the definition of stock‐level parameters.

The purpose of this study is to propose and model an integrated production‐maintenance strategy for a failure‐prone machine in a just‐in‐time context.

The proposed integrated policy is defined and a simulation model is developed to investigate it.

The paper focuses on finding simultaneously two decision variables: the period (T) at which preventive maintenance actions have to be performed; and the sequence of jobs (S). These values minimize the maintenance costs (M_C) and the expected total earliness and tardiness costs (ET_C) away from a common due‐date D.

The paper attempts to integrate in a single model the two main aspects of any manufacturing and production systems: production and maintenance. It focuses on a stochastic scheduling problem in which n immediately available jobs are to be scheduled jointly with the preventive maintenance. The effect of the period (T) and the sequence of job (S) on the expected total cost are shown through a numerical example.

The paper proposes an integrated model that links production, preventive maintenance and corrective maintenance. It is simultaneously focusing on the period (T) at which preventive maintenance actions have to be performed and the sequence of jobs (S) to reduce production and maintenance‐related costs.

This paper seeks to develop an optimal stochastic control model where interactive feedback consists of the quantity of flawless and defective products. The main objective of this study is to minimize the expected discounted overall cost due to maintenance activities, inventory holding and backlogs.

The model differs from similar research projects in that, instead of age‐dependent machine failure, it considers only defective products as feedback into the optimal model for maintenance and production planning. In this paper a near optimal control policy of the system through numerical techniques is obtained.

In this paper, a new model in which the system's retroaction is the quantity of defective products is presented, considering that defective products are a consequence of global manufacturing system deterioration. Instead of taking into account machine failure and human error separately, it considers a defect in product as being the consequence of a combined failure; this consideration allows one to be more realistic by merging all failure parameters into a single one. A new stochastic control model, which focuses on defective products, inventory, and backlog, has been developed.

This approach extended the concept of hedging point policy to the quantity of defective products combined with preventive and corrective maintenance strategies. The control policy obtained has a bang bang structure and is completely known for given parameters.

The integration of maintenance and production strategies has been mainly focused on the machine. Many research projects have been focusing on the age when dealing with machine failure. It is considered as the main target of the cost reduction in maintenance engineering departments. The originality of this paper is the taking into account of all operational failures into the same optimization model. It brings a value added to high level of maintenance and for operation managers who need to consider all failure parameters before taking decisions related to cost.

It is argued that problem‐based learning (PBL) is a valuable approach to teaching operations management, as it allows learners to apply their knowledge and skills in an environment that is close to real‐life. In fact, many simulations currently exist in the teaching of operations management. However, these simulations lack a connection to real‐life, as they are typically turn‐based and do not use real‐life IT support. The current paper seeks to address this issue by presenting an innovative pedagogical approach designed to provide learners with an authentic problem‐solving experience in operations management within an enterprise resource planning (ERP) system.

The paper proposes a simulation game called ERPsim whereby students must operate an enterprise in a simulated economic environment using in real time a real‐life ERP system, namely SAP. Based on a survey with instructors, it assesses the extent to which this proposed simulation is aligned with the five characteristics of the PBL approach.

Survey respondents confirm that significant improvements in student evaluations, learner motivation, attendance, and engagement, as well as increased learner competence with the technology can be achieved by using the proposed approach.

For more than five years this pedagogical approach has been used by more than 250 professors, lecturers, and professional trainers in over 160 universities worldwide. Between September 2009 and June 2011, more than 3,000 simulations games were played by over 16,000 university student teams.

Results and observations on using the proposed pedagogical approach are presented and compared to the main characteristics of the PBL approach (authenticity, ill structured problems, student‐centered, small group settings and facilitator dimensions).

This paper seeks to address the production control problem of a failure‐prone manufacturing system producing a random fraction of defective items.

A fluid model with perfectly mixed good and defective parts has been proposed. This approach combines the descriptive capacities of continuous/discrete event simulation models with analytical models, experimental design, and regression analysis. The main objective of the paper is to extend the Bielecki and Kumar theory, appearing under the title “Optimality of zero‐inventory policies for unreliable manufacturing systems”, under which the machine considered produced only good quality items, to the case where the items produced are systematically a mixture of good as well as defective items.

The paper first shows that for constant demand rates and exponential failure and repair time distributions of the machine, the Bielecki‐Kumar theory, adequately revisited, provides new and coherent results. For the more complex situation where the machine exhibits non‐exponential failure and repair time distributions, a simulation‐based approach is then considered. The usefulness of the proposed models is illustrated through numerical examples and sensitivity analysis.

Although the decisions taken in response to demands for productivity have a direct impact on product quality, management quality and production management have been traditionally treated as independent research fields. In response to this need, this paper is considered as a preliminary work in the intersection between quality control and production control issues.