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This paper aims to analyse the current state of research to identify the link between Lean Manufacturing and Industry 4.0 (I4.0) technologies to map out different research themes, to uncover research gaps and propose key recommendations for future research, including lessons to be learnt from the integration of lean and I4.0.

A systematic literature review (SLR) is conducted to thematically analyse and synthesise existing literature on Lean Manufacturing–I4.0 integration. The review analysed 60 papers in peer-reviewed journals.

In total, five main research themes were identified, and a thematic map was created to explore the following: the relationship between Lean Manufacturing and I4.0; Lean Manufacturing and I4.0 implication on performance; Lean Manufacturing and I4.0 framework; Lean Manufacturing and I4.0 integration with other methodologies; and application of I4.0 technologies in Lean Manufacturing. Furthermore, various gaps in the literature were identified, and key recommendations for future directions were proposed.

The integration of Lean Manufacturing and I4.0 will eventually bring many benefits and offers superior and long-term competitive advantages. This research reveals the need for more analysis to thoroughly examine how this can be achieved in real life and promote operational changes that ensure enterprises run more sustainably.

The development of Lean Manufacturing and I4.0 integration is still in its infancy, with most articles in this field published in the past two years. The five main research themes identified through thematic synthesis are provided in the original contribution. This provides scholars better insight into the existing literature related to Lean Manufacturing and I4.0, further contributing to defining clear topics for future research opportunities. It also has important implications for industrialists, who can develop more profound and richer knowledge than Lean and I4.0, which would, in turn, help them develop more effective deployment strategies and have a positive commercial impact.

This study proposes the Smart SME Technology Readiness Assessment (SSTRA) methodology which aims to enable practitioners to assess the SMEs Industry 4.0 technology readiness throughout the end-to-end engineering across the entire value chain; the smart product design phase is the focus in this paper.

The proposed SSTRA utilises the analytic hierarchy process to prioritise smart SME requirements, a graphical interface which tracks technologies' benchmarks under Industry 4.0 Technology Readiness Levels (TRLs); a mathematical model used to determine the technology readiness and visual representation to understand the relative readiness of each smart main area. The validity of the SSTRA is confirmed by testing it in a real industrial environment. In addition, the conceptual model for Smart product design development is proposed and validated.

The proposed SSTRA offers decision-makers the facility to identify requirements and rank them to reflect the current priorities of the enterprise. It allows SMEs to assess their current capabilities in a range of technologies of high relevance to the Industry 4.0 area. The SSTRA assembles a readiness profile allowing decision-makers to not only perceive the overall score of technology readiness but also the distribution of technology readiness across the main smart areas. It helps to visualise strengths and weaknesses; whilst emphasising the fundamental gaps that require serious action to assist the program with a well-balanced effort towards a successful transition to Industry 4.0.

The SSTRA provides a step-by-step approach for decision-making based on data collection, analysis, visualisation and documentation. Hence, it greatly mitigates the risk of further Industry 4.0 technology investment and implementation.

The paper seeks to report on some of the preliminary results of an ongoing scoping study into the shape of the manufacturing enterprise of the future.

This paper evolved through a combination of literature review, focused group discussions, interviews and a questionnaire survey of six aerospace companies in the UK. It is primarily an attempt to provide a broad framework for synthesizing some of the information generally available as a contribution to the current debate regarding the future of manufacturing systems.

The results to date show that the product development process and supply network efficiency are the two most significant domains influencing manufacturing responsiveness. Within those domains, customer driven product development and supply chain design, intelligent and flexible technology, producibility analysis, integrated product and process development and the concurrency of the extended manufacturing enterprise are considered as the most significant elements towards achieving responsiveness. In addition a Responsive Manufacturing Model (RMM) is provided.

The RMM reported in the paper is at an early state of development and the work is ongoing to refine it further. The development of appropriate measures and methods of assessment for the various facets and attributes of manufacturing responsiveness is an important step towards full model development which is still to be addressed.

The process of structuring the various elements influencing manufacturing responsiveness into logical groups in a hierarchical model has proved very useful during model development. It proved a significant aid during the focused group discussions and interviews that preceded completion of the questionnaire. The results to date are very encouraging and provide several interesting insights into the domains and elements of manufacturing responsiveness and the relative importance attached to them in the UK aerospace sector.

The work was funded by EPSRC (IMI) research grant as it was the first attempt in this field over within the UK. The proposed model and the obtained results have led to another research project funded by EPSRC over three years to further investigate the proposed model and the implication of its implementation.

To highlight the differences and common features of taboo search (TS) and genetic algorithms (GA) in solving the problem of board‐type sequencing on the assembly line simultaneously with the combined problem of feeder assignment and component placement sequencing in the printed circuit board (PCB) industry.

Two metaheuristics (search techniques) are used to solve three problems associated with the PCB assembly line: TS and GA. The implemented approach is used to solve the three problems on a single pick‐and‐place sequential machine with a stationary board table and stationary feeders, and with the use of the Euclidean metric.

The achieved results show a satisfactory reduction in assembly time, when TS and GA are compared with a random solution, with a slight superiority of TS over GA. However, the program running time is longer for TS.

The hypothetical case study used shows that in real life the savings could reach an average of 6 per cent when TS is used. Slightly lower savings are possible when GA is used.

This paper provides a clear insight into how some of the problems associated with the production of PCBs can be solved simultaneously using metaheuristics such as TS and GA.

This paper seeks to present the process and results of the application of a decision‐making tool, namely TAPS, which enables translation of knowledge of supply chain uncertainty into business strategy and actions.

The knowledge of supply chain uncertainty is collected from previous research performed under enterprise resource planning (ERP)‐controlled manufacturing environments. The knowledge is used as the input for TAPS and is mapped to investigate and formulate appropriate business strategy and action plan to manage supply chain uncertainty in such environments.

The results of knowledge translation provide a set of guidelines to academics and practitioners, which indicates the underlying causes of supply chain uncertainty in ERP‐controlled manufacturing environments in a priority order, and the suitable business strategy and actions that could potentially be adopted to manage the uncertainty.

Owing to the increasing level of complexity and uncertainty intoday's enterprises, translating knowledge is suggested to be useful in assisting decision making and business strategy formulation.

This research provides a successful case example of knowledge translation process of supply chain uncertainty into business strategy and actions, which enables creation of sustainable strategies to manage uncertainty based on the concept of knowledge management and learning.

This paper discusses the experimental work in modelling uncertainty under a multi‐echelon enterprise resource planning (ERP)‐controlled manufacturing system. A new method known as part tagging (Ptag) is successfully implemented in a material requirements planning (MRP) planning architecture, which is used to generate a planned order release (POR) schedule for controlling purchase and manufacture operations in a batch manufacturing system using simulation. One of the most important findings is that parts tardy delivery (PTD) is a more responsive performance measure compared with finished products tardy delivery (FPTD); therefore it is recommended that PTD should be measured to reveal the unmasked effects of uncertainty. The main conclusion and implication from this experiment are that an ERP‐controlled manufacturing enterprise should diagnose for uncertainty in a way that produces significant effects on delivery tardiness, so that reduction of their levels will significantly minimise tardy delivery.

In this paper manufacturing responsiveness is related to the ability of manufacturing systems to utilise its existing resources to make a rapid and balanced response to the predictable and unpredictable changes. Better understanding of the inherent (hidden) flexibility that exists within a manufacturing system can therefore lead to significant improvement in system performance and responsiveness. In the reported research a conceptual framework for representing the capabilities of machine tools and machining facilities using generic capabilities units termed “resource elements” is presented as well as a mathematical basis of calculating the manufacturing system flexibility using the resource elements. Simulations are used to examine manufacturing system performance and compare resource element‐based scheduling with conventional machine‐based approaches. The results show that significant improvements in system performance and the system’s ability to cope with disturbances can be achieved if manufacturing facilities are represented and scheduled based on the resource elements concept.

To show necessity of risk evaluation during modelling and simulation of production systems. To show an approach to risk evaluation of manufacturing system which has serial reliability structure.

Modelling and simulation of a manufacturing system allows one to conduct verification of different solutions in the area of production planning, before they are started. This is impossible with traditional methods.

Reliable results of simulation research can be obtained only if they are based on an integrated model of the company, that covers all components of the manufacturing process including also its organization and risk in production processes.

The paper describes the stages and results of a project carried out in 2002 in an international company. The proposed method of risk evaluation may be helpful to determine the risk level in the chosen production line and eventually for the whole enterprise manufacturing systems.

The risk concept was treated as a synonym of unreliability. This kind of approach enabled decomposition of the production system into several areas and determination of the reliability structure.

Effective product life cycle management will save costs and resources and improve customer service. Seeks to present a simulation model of the complete life cycle of a batch of products undergoing breakdown and repair and to show the ability of the model to predict future waste arisings and cost savings.

ARENA system simulation software is used for a novel application – a full life cycle of manufacture, use, repair and ultimate disposal. Two batches of products are compared: products with and without features which improve reliability. The number of replications for the Monte Carlo process can be calculated from the statistics of the model data.

The model demonstrates the predicted flows of products through their life cycle. The software has in‐built probability distributions that are not fully suitable for the problem modelled, requiring some artificial treatment, especially when using the delay function. The number of replications should be increased, requiring additional computer time.

The model is potentially valuable for producers wishing to predict the effect on future costs or the risk of modifying designs. The method can also be used to assist waste management using the output graphs of disposed components or products; hence the economics of component remanufacture or reuse can be modelled.

The paper presents the first known application of manufacturing system simulation software for modelling product life cycles.