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Motivated by recent research indicating that the operational performance of an enterprise can be enhanced by building a supporting data-driven environment in which to operate, this paper presents a simulation framework that enables an examination of the effects of applying smart manufacturing principles to conventional production systems, intending to transition to digital platforms.

To investigate the extent to which conventional production systems can be transformed into novel data-driven environments, the well-known constant work-in-process (CONWIP) production systems and considered production sequencing assignments in flowshops were studied. As a result, a novel data-driven priority heuristic, Net-CONWIP was designed and studied, based on the ability to collect real-time information about customer demand and work-in-process inventory, which was applied as part of a distributed and decentralised production sequencing analysis. Application of heuristics like the Net-CONWIP is only possible through the ability to collect and use real-time data offered by a data-driven system. A four-stage application framework to assist practitioners in applying the proposed model was created.

To assess the robustness of the Net-CONWIP heuristic under the simultaneous effects of different levels of demand, its different levels of variability and the presence of bottlenecks, the performance of Net-CONWIP with conventional CONWIP systems that use first come, first served priority rule was compared. The results show that the Net-CONWIP priority rule significantly reduced customer wait time in all cases relative to FCFS.

Previous research suggests there is considerable value in creating data-driven environments. This study provides a simulation framework that guides the construction of a digital transformation environment. The suggested framework facilitates the inclusion and analysis of relevant smart manufacturing principles in production systems and enables the design and testing of new heuristics that employ real-time data to improve operational performance. An approach that can guide the structuring of data-driven environments in production systems is currently lacking. This paper bridges this gap by proposing a framework to facilitate the design of digital transformation activities, explore their impact on production systems and improve their operational performance.

Shop floor control systems are generally major points of discussion in production planning and control literature. The purpose of this paper is to investigate how lean production control principles can be used in a make-to-order (MTO) job shop, where the volume is typically low and there is high variety. This paper examines the procedures involved in implementing a constant work-in-process (CONWIP)/Kanban hybrid system in the shop floor environment and also provides insights and guidelines on the implementation of a hybrid system in a high-variety/low-volume environment.

The authors review literature on Kanban, CONWIP, and CONWIP/Kanban hybrid systems to analyze how lean production control principles can be used in a MTO job shop. The second part focuses on the process of implementation. Using a case study of a manufacturer of electromechanical components for valve monitoring and controls, the paper describes how the operation is transformed by for more efficient shop floor control systems. Real experiments are used to compare pre- and post-improvement performance.

The study shows that the proposed hybrid Kanban-CONWIP system reduced the cycle time and achieved an increase of 38 percent in inventory turnover. The empirical results from this pilot study provide useful managerial insights for a benchmarking analysis of the actions to be taken into consideration by companies that have similar manufacturing systems.

The statistic generalization of the results is impossible due to the use of a single case method of study.

This paper provides insights and guidelines on the implementation of a hybrid system in a high-variety/low-volume environment. The literature on real applications of hybrid CONWIP/Kanban by case study is limited.

This paper aims to present a model of a multi‐phase multi‐product manufacturing system considering a CONstant work‐in‐process (CONWIP) control mechanism and using continuous‐time Markov chain modelling approach.

The model includes defining a state space then constructing the rate matrix, which contains the transition rates, followed by formulating the transition matrix. The time‐dependent probabilities that a product is in a particular state at a certain time are characterized. Performance measures related to the statistics on the waiting time and average number of work‐in‐process in the production system have been determined. Consequently, a numerical example is presented to illustrate the computations of different model aspects.

The analyses explain a foundation needed for analyzing the steady state behavior of manufacturing systems. Results have shown how production data can be easily modified for what‐if analyses by the use of Excel add‐in tool.

The multi‐level model outlines a framework that provides a practical tool for production engineers seeking to enhance the performance of their production system by selecting the best order release mechanism.

A novel aspect of the work reported in this paper is the application of Chapman‐Kolmogrov mathematics and CONWIP ordering theory, which is developed for evaluating and managing CONWIP controlled production systems.

The purpose of this paper is to propose an adjusted approximate regenerative model (ARM) for a constant‐work‐in‐process (CONWIP)‐based production system that solves the problem of deciding the number of intermediate bulk container in a pharmaceutical company to hold the work in process.

The problem was modeled as a CONWIP system and the ARM was adjusted to estimate the throughput.

By comparing the results from the original ARM and adjusted ARM, a clear superiority of the proposed method is shown for the real case. In addition, the robustness of the adjusted ARM is demonstrated in terms of the violation of rigid assumption and the impacts brought by the limitation of buffer space before the bottleneck station.

The novelty of the proposed model is to take the long transportation time between machines into consideration.

The aims of this paper is to investigate simulation‐based optimisation and stochastic dominance testing while employing kanban‐like production control strategies (PCS) operating dedicated and, where applicable, shared kanban card allocation policies in a multi‐product system with negligible set‐up times and with consideration for robustness to uncertainty.

Discrete event simulation and a genetic algorithm were utilised to optimise the control parameters for dedicated kanban control strategy (KCS), CONWIP and base stock control strategy (BSCS), extended kanban control strategy (EKCS) and generalised kanban control strategy (GKCS) as well as the shared versions of EKCS and GKCS. All‐pairwise comparisons and a ranking and selection technique were employed to compare the performances of the strategies and select the best strategy without consideration of robustness to uncertainty. A latin hypercube sampling experimental design and stochastic dominance testing were utilised to determine the preferred strategy when robustness to uncertainty is considered.

The findings of this work show that shared GKCS outperforms other strategies when robustness is not considered. However, when robustness of the strategies to uncertainty in the production environment is considered, the results of our research show that the dedicated EKCS is preferred. The effect of system bottleneck location on the inventory accumulation behaviour of different strategies is reported and this was also observed to have a relationship to the nature of a PCS's kanban information transmission.

The findings of this study are directly relevant to industry where increasing market pressures for product diversity require operating multi‐product production lines with negligible set‐up times. The optimization and robustness test approaches employed in this work can be extended to the analysis of more complicated system configurations and higher number of product types.

This work involves further investigation into the performance of multi‐product kanban‐like PCS by examining their robustness to common sources of uncertainties after they have been initially optimized for base scenarios. The results of the robustness tests also provide new insights into how dedicated kanban card allocation policies might offer higher flexibility and robustness over shared policies under conditions of uncertainty.

The advent of Industry 4.0 (I4.0) and the requirements imposed on companies still need to be clarified. Companies still strive to understand I4.0 requirements and technological, organizational, operational and management challenges. Current literature on I4.0 underlies the importance of a roadmap with structured steps to achieve the benefits of I4.0, mainly focused on augmenting operational performance. Therefore, this paper proposes a roadmap to implement I4.0 focused on operational management concepts, mainly aiming to augment operational performance and bridge the gap between theory and practice regarding roadmaps focused on the operational management dimension.

This paper follows a research approach divided into the following stages: a literature review to analyze the I4.0 roadmaps and identify the main components of I4.0; development of the proposed I4.0 roadmap presented; field research to test the roadmap by collecting data from a manufacturing company in the automotive industry; validation of the roadmap through modeling and simulation.

The authors presented a production line design with real-time control, fast response, shop floor coordination and predictive capacity. The results prove that the proposed I4.0 roadmap augments operation performance in the investigated automotive company. The main results were work in process reduction, lead time reduction, output increase, real-time control, shop floor coordination and fast response.

The main novelty of the proposed roadmap is to move toward I4.0 implementation with a focus on the operational management dimension. The roadmap has an innovative combination of the two approaches – lean manufacturing and factory physics – a straightforward roadmap with only three steps: (1) requirements, (2) real-time control and (3) predictive capacity, a structured definition of the approaches and operational management concepts fundamental in each step.

With an increasingly fierce competition of the shipbuilding industry, advanced technologies and excellent management philosophies in the manufacturing industry are gradually introduced to domestic shipyards. The purpose of this study is to promote the lean management of Chinese ship outfitting plants by lean production strategy.

To promote the lean implementation of Chinese shipyards, the lean practice of ship-pipe part production is highlighted by lot-sizing optimization and strategic CONWIP (constant work-in-process) control. A nonlinear programming model is formulated to minimize the total cost of ship-pipe part manufacturing and the particle swarm optimization (PSO)-based algorithm is designed to resolve the established model. Besides, the pull-from-the-bottleneck (PFB) strategy is used to control ship-pipe part production, verified by Simulink simulation.

Results show that the proposed lean strategy of the programming model and strategic PFB control could assist Chinese ship outfitting plants to leverage competitive advantage by waste reduction and lean achievement. Specifically, the PFB double-loop control strategy shows better performance when there is high productivity and the PFB single-loop control outperforms at lower productivity scenarios.

To verify the effectiveness of the proposed lean strategy, a case study is performed to validate the formulated model. Also, simulation experiments realized by FlexSim software are conducted to testify results obtained by the constructed programming model.

Lean production management practice of the shipyard building industry is performed by the proposed lean production strategy through lot-sizing optimization and strategic PFB control in terms of ship-pipe part manufacturing.

In a mixed flow production environment, interactions between production planning and scheduling are critical for mixed flow distributed manufacturing management. The purpose of this paper is to assist manufacturers in achieving real-time ordering and obtaining integrated optimization of shop floor production planning and scheduling for mixed flow production systems.

A double decoupling postponement (DDP) approach is presented for production dispatch control, and an integrated model is designed under an assemble to order (ATO) environment. To generate “optimal” lots to fulfil real-time customer requests, constant work in progress (CONWIP) and days of inventory dispatching algorithms are embedded into the proposed DDP model, which can deal with real-time ordering and dynamic scheduling simultaneously. Subsequently, a case study is conducted, and experiments are carried out to verify the presented method.

The proposed DDP model is designed to upgrade a previous CONWIP method in the case study company, and the proposed model demonstrates better performance for the integration of production planning and scheduling in mixed flow manufacturing. As a result, the presented operation mechanism can reflect real-time ordering information to shop floor scheduling and obtain performance metrics in terms of reliability, availability and maintainability.

The presented model can be further proliferated to generic factory manufacturing with the proposed logic and architecture.

The DDP model can integrate real-time customer orders and work in process information, upon which manufacturers can make correct decisions for dispatch strategies and order selection within an integrated system.

The purpose of this paper is to contribute to the lean manufacturing literature by providing a roadmap for pull production control system (PCS) implementation.

Axiomatic Design (AD) methodology is used to develop the proposed pull PCS transformation roadmap.

The proposed design methodology is validated in a real-life manufacturing system. The results show that the proposed methodology significantly reduces the design efforts. The methodology effectively helps to choose the most appropriate pull PCS and determine its operational settings with respect to the manufacturing system characteristics.

This study presents only one case study to test the proposed methodology. In future studies, the validity of the proposed method can be further generalized in different manufacturing sectors by real-life implementations.

In many real-life lean production projects, companies do not know where to start or how to proceed, which leads to repetitive design efforts and inefficient designs. The developed roadmap of this study minimizes incorrect or imperfect design trials and increases the success of pull production transformation projects.

The implementation of pull PCS requires extensive design knowledge and expertise. Therefore, many real-life applications fail due to costly and time-consuming trial-and-error-based design efforts. In the literature, there is no comprehensive guideline or roadmap for pull PCS implementation. To address this issue, this study provides a novel holistic roadmap to transform an existing push PCS to pull. The proposed methodology uses AD principles and combines fragmentary studies of the pull production literature.

The aim of this paper is to develop an efficient analytical procedure to evaluate performance of the most general pull production systems particularly when multiple‐part‐types are involved. The authors consider a kanban controlled production system that can be modelled as a closed queuing network with different product classes. The production line is decomposed into stages which consist of one or several stations and an output buffer. Each stage is associated with a given number of kanbans. The main idea of this analytical algorithm is to analyze each subnetwork individually using a product form approximation technique. The iterative procedure is used to find the unknown parameters.

The authors design a multiclass queuing network that can be used to represent kanban controlled production systems. To solve this model, three procedures are used: decompose the original network into M subnetworks, convergence of unknown parameters in each subnetwork, and convergence of unknown parameters in the original network. The authors now describe these procedures separately.

The main contribution of this paper is the formulation of the problem of kanban controlled production systems with several part‐types. The methodology is based on approximate formula with decomposition and is applicable to more general manufacturing environments. The authors' method can be applied to both limited and unlimited demands. The analytical algorithm designed in this work has demonstrated excellent performance in analyzing kanban controlled production systems.

The methodology of this algorithm is based on approximate formula and is applicable to more general manufacturing environments.